repo
stringlengths 2
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stringlengths 13
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stringlengths 0
18.3M
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float64 0
1.36M
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int64 0
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|---|---|---|---|---|---|---|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/test_sentiment.py
|
# /usr/bin/env python
# -*- coding:utf-8 -*-
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import nltk
import paddle.v2.dataset.sentiment as st
from nltk.corpus import movie_reviews
class TestSentimentMethods(unittest.TestCase):
def test_get_word_dict(self):
word_dict = st.get_word_dict()[0:10]
test_word_list = [(u',', 0), (u'the', 1), (u'.', 2), (u'a', 3),
(u'and', 4), (u'of', 5), (u'to', 6), (u"'", 7),
(u'is', 8), (u'in', 9)]
for idx, each in enumerate(word_dict):
self.assertEqual(each, test_word_list[idx])
self.assertTrue("/root/.cache/paddle/dataset" in nltk.data.path)
def test_sort_files(self):
last_label = ''
for sample_file in st.sort_files():
current_label = sample_file.split("/")[0]
self.assertNotEqual(current_label, last_label)
last_label = current_label
def test_data_set(self):
data_set = st.load_sentiment_data()
last_label = -1
for each in st.test():
self.assertNotEqual(each[1], last_label)
last_label = each[1]
self.assertEqual(len(data_set), st.NUM_TOTAL_INSTANCES)
self.assertEqual(len(list(st.train())), st.NUM_TRAINING_INSTANCES)
self.assertEqual(
len(list(st.test())),
(st.NUM_TOTAL_INSTANCES - st.NUM_TRAINING_INSTANCES))
if __name__ == '__main__':
unittest.main()
| 2,044 | 35.517857 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/wmt16_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.wmt16
import unittest
class TestWMT16(unittest.TestCase):
def checkout_one_sample(self, sample):
# train data has 3 field: source language word indices,
# target language word indices, and target next word indices.
self.assertEqual(len(sample), 3)
# test start mark and end mark in source word indices.
self.assertEqual(sample[0][0], 0)
self.assertEqual(sample[0][-1], 1)
# test start mask in target word indices
self.assertEqual(sample[1][0], 0)
# test en mask in target next word indices
self.assertEqual(sample[2][-1], 1)
def test_train(self):
for idx, sample in enumerate(
paddle.v2.dataset.wmt16.train(
src_dict_size=100000, trg_dict_size=100000)()):
if idx >= 10: break
self.checkout_one_sample(sample)
def test_test(self):
for idx, sample in enumerate(
paddle.v2.dataset.wmt16.test(
src_dict_size=1000, trg_dict_size=1000)()):
if idx >= 10: break
self.checkout_one_sample(sample)
def test_val(self):
for idx, sample in enumerate(
paddle.v2.dataset.wmt16.validation(
src_dict_size=1000, trg_dict_size=1000)()):
if idx >= 10: break
self.checkout_one_sample(sample)
def test_get_dict(self):
dict_size = 1000
word_dict = paddle.v2.dataset.wmt16.get_dict("en", dict_size, True)
self.assertEqual(len(word_dict), dict_size)
self.assertEqual(word_dict[0], "<s>")
self.assertEqual(word_dict[1], "<e>")
self.assertEqual(word_dict[2], "<unk>")
if __name__ == "__main__":
unittest.main()
| 2,385 | 34.61194 | 75 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/imikolov_test.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.imikolov
import unittest
WORD_DICT = paddle.v2.dataset.imikolov.build_dict()
class TestMikolov(unittest.TestCase):
def check_reader(self, reader, n):
for l in reader():
self.assertEqual(len(l), n)
def test_train(self):
n = 5
self.check_reader(paddle.v2.dataset.imikolov.train(WORD_DICT, n), n)
first_line = 'aer banknote berlitz calloway centrust cluett fromstein '\
'gitano guterman hydro-quebec ipo kia memotec mlx nahb punts '\
'rake regatta rubens sim snack-food ssangyong swapo wachter'
first_line = [
WORD_DICT.get(ch, WORD_DICT['<unk>'])
for ch in first_line.split(' ')
]
for l in paddle.v2.dataset.imikolov.train(
WORD_DICT, n=-1,
data_type=paddle.v2.dataset.imikolov.DataType.SEQ)():
read_line = l[0][1:]
break
self.assertEqual(first_line, read_line)
def test_test(self):
n = 5
self.check_reader(paddle.v2.dataset.imikolov.test(WORD_DICT, n), n)
first_line = 'consumers may want to move their telephones a little '\
'closer to the tv set'
first_line = [
WORD_DICT.get(ch, WORD_DICT['<unk>'])
for ch in first_line.split(' ')
]
for l in paddle.v2.dataset.imikolov.test(
WORD_DICT, n=-1,
data_type=paddle.v2.dataset.imikolov.DataType.SEQ)():
read_line = l[0][1:]
break
self.assertEqual(first_line, read_line)
def test_total(self):
_, idx = zip(*WORD_DICT.items())
self.assertEqual(sorted(idx)[-1], len(WORD_DICT) - 1)
if __name__ == '__main__':
unittest.main()
| 2,383 | 34.058824 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/imdb_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.imdb
import unittest
import re
TRAIN_POS_PATTERN = re.compile("aclImdb/train/pos/.*\.txt$")
TRAIN_NEG_PATTERN = re.compile("aclImdb/train/neg/.*\.txt$")
TRAIN_PATTERN = re.compile("aclImdb/train/.*\.txt$")
TEST_POS_PATTERN = re.compile("aclImdb/test/pos/.*\.txt$")
TEST_NEG_PATTERN = re.compile("aclImdb/test/neg/.*\.txt$")
TEST_PATTERN = re.compile("aclImdb/test/.*\.txt$")
class TestIMDB(unittest.TestCase):
word_idx = None
def test_build_dict(self):
if self.word_idx == None:
self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
150)
self.assertEqual(len(self.word_idx), 7036)
def check_dataset(self, dataset, expected_size):
if self.word_idx == None:
self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
150)
sum = 0
for l in dataset(self.word_idx):
self.assertEqual(l[1], sum % 2)
sum += 1
self.assertEqual(sum, expected_size)
def test_train(self):
self.check_dataset(paddle.v2.dataset.imdb.train, 25000)
def test_test(self):
self.check_dataset(paddle.v2.dataset.imdb.test, 25000)
if __name__ == '__main__':
unittest.main()
| 1,960 | 32.810345 | 76 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/mnist_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.mnist
import unittest
class TestMNIST(unittest.TestCase):
def check_reader(self, reader):
sum = 0
label = 0
for l in reader():
self.assertEqual(l[0].size, 784)
if l[1] > label:
label = l[1]
sum += 1
return sum, label
def test_train(self):
instances, max_label_value = self.check_reader(
paddle.v2.dataset.mnist.train())
self.assertEqual(instances, 60000)
self.assertEqual(max_label_value, 9)
def test_test(self):
instances, max_label_value = self.check_reader(
paddle.v2.dataset.mnist.test())
self.assertEqual(instances, 10000)
self.assertEqual(max_label_value, 9)
if __name__ == '__main__':
unittest.main()
| 1,421 | 30.6 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/common_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.common
import unittest
import tempfile
import glob
class TestCommon(unittest.TestCase):
def test_md5file(self):
_, temp_path = tempfile.mkstemp()
with open(temp_path, 'w') as f:
f.write("Hello\n")
self.assertEqual('09f7e02f1290be211da707a266f153b3',
paddle.v2.dataset.common.md5file(temp_path))
def test_download(self):
yi_avatar = 'https://avatars0.githubusercontent.com/u/1548775?v=3&s=460'
self.assertEqual(
paddle.v2.dataset.common.DATA_HOME + '/test/1548775?v=3&s=460',
paddle.v2.dataset.common.download(
yi_avatar, 'test', 'f75287202d6622414c706c36c16f8e0d'))
def test_split(self):
def test_reader():
def reader():
for x in xrange(10):
yield x
return reader
_, temp_path = tempfile.mkstemp()
paddle.v2.dataset.common.split(
test_reader(), 4, suffix=temp_path + '/test-%05d.pickle')
files = glob.glob(temp_path + '/test-%05d.pickle')
self.assertEqual(len(files), 3)
def test_cluster_file_reader(self):
_, temp_path = tempfile.mkstemp()
for x in xrange(5):
with open(temp_path + '/%05d.test' % x) as f:
f.write('%d\n' % x)
reader = paddle.v2.dataset.common.cluster_files_reader(
temp_path + '/*.test', 5, 0)
for idx, e in enumerate(reader()):
self.assertEqual(e, str("0"))
def test_convert(self):
record_num = 10
num_shards = 4
def test_reader():
def reader():
for x in xrange(record_num):
yield x
return reader
path = tempfile.mkdtemp()
paddle.v2.dataset.common.convert(path,
test_reader(), num_shards,
'random_images')
files = glob.glob(path + '/random_images-*')
self.assertEqual(len(files), num_shards)
recs = []
for i in range(0, num_shards):
n = "%s/random_images-%05d-of-%05d" % (path, i, num_shards - 1)
r = recordio.reader(n)
while True:
d = r.read()
if d is None:
break
recs.append(d)
recs.sort()
self.assertEqual(total, record_num)
if __name__ == '__main__':
unittest.main()
| 3,112 | 31.768421 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/voc2012_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.voc2012
import unittest
class TestVOC(unittest.TestCase):
def check_reader(self, reader):
sum = 0
label = 0
for l in reader():
self.assertEqual(l[0].size, 3 * l[1].size)
sum += 1
return sum
def test_train(self):
count = self.check_reader(paddle.v2.dataset.voc_seg.train())
self.assertEqual(count, 2913)
def test_test(self):
count = self.check_reader(paddle.v2.dataset.voc_seg.test())
self.assertEqual(count, 1464)
def test_val(self):
count = self.check_reader(paddle.v2.dataset.voc_seg.val())
self.assertEqual(count, 1449)
if __name__ == '__main__':
unittest.main()
| 1,332 | 30 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/mq2007_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.mq2007
import unittest
class TestMQ2007(unittest.TestCase):
def test_pairwise(self):
for label, query_left, query_right in paddle.v2.dataset.mq2007.test(
format="pairwise"):
self.assertEqual(query_left.shape(), (46, ))
self.assertEqual(query_right.shape(), (46, ))
def test_listwise(self):
for label_array, query_array in paddle.v2.dataset.mq2007.test(
format="listwise"):
self.assertEqual(len(label_array), len(query_array))
if __name__ == "__main__":
unittest.main()
| 1,205 | 34.470588 | 76 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/dataset/tests/cifar_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.v2.dataset.cifar
import unittest
class TestCIFAR(unittest.TestCase):
def check_reader(self, reader):
sum = 0
label = 0
for l in reader():
self.assertEqual(l[0].size, 3072)
if l[1] > label:
label = l[1]
sum += 1
return sum, label
def test_test10(self):
instances, max_label_value = self.check_reader(
paddle.v2.dataset.cifar.test10())
self.assertEqual(instances, 10000)
self.assertEqual(max_label_value, 9)
def test_train10(self):
instances, max_label_value = self.check_reader(
paddle.v2.dataset.cifar.train10())
self.assertEqual(instances, 50000)
self.assertEqual(max_label_value, 9)
def test_test100(self):
instances, max_label_value = self.check_reader(
paddle.v2.dataset.cifar.test100())
self.assertEqual(instances, 10000)
self.assertEqual(max_label_value, 99)
def test_train100(self):
instances, max_label_value = self.check_reader(
paddle.v2.dataset.cifar.train100())
self.assertEqual(instances, 50000)
self.assertEqual(max_label_value, 99)
if __name__ == '__main__':
unittest.main()
| 1,874 | 31.894737 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/plot/plot.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
class PlotData(object):
def __init__(self):
self.step = []
self.value = []
def append(self, step, value):
self.step.append(step)
self.value.append(value)
def reset(self):
self.step = []
self.value = []
class Ploter(object):
def __init__(self, *args):
self.__args__ = args
self.__plot_data__ = {}
for title in args:
self.__plot_data__[title] = PlotData()
# demo in notebooks will use Ploter to plot figure, but when we convert
# the ipydb to py file for testing, the import of matplotlib will make the
# script crash. So we can use `export DISABLE_PLOT=True` to disable import
# these libs
self.__disable_plot__ = os.environ.get("DISABLE_PLOT")
if not self.__plot_is_disabled__():
import matplotlib.pyplot as plt
from IPython import display
self.plt = plt
self.display = display
def __plot_is_disabled__(self):
return self.__disable_plot__ == "True"
def append(self, title, step, value):
assert isinstance(title, basestring)
assert self.__plot_data__.has_key(title)
data = self.__plot_data__[title]
assert isinstance(data, PlotData)
data.append(step, value)
def plot(self, path=None):
if self.__plot_is_disabled__():
return
titles = []
for title in self.__args__:
data = self.__plot_data__[title]
assert isinstance(data, PlotData)
if len(data.step) > 0:
titles.append(title)
self.plt.plot(data.step, data.value)
self.plt.legend(titles, loc='upper left')
if path is None:
self.display.clear_output(wait=True)
self.display.display(self.plt.gcf())
else:
self.plt.savefig(path)
self.plt.gcf().clear()
def reset(self):
for key in self.__plot_data__:
data = self.__plot_data__[key]
assert isinstance(data, PlotData)
data.reset()
| 2,729 | 31.891566 | 82 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/plot/__init__.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from plot import Ploter
__all__ = ['Ploter']
| 656 | 35.5 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/plot/tests/__init__.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import test_ploter
__all__ = ['test_ploter.py']
| 658 | 37.764706 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/plot/tests/test_ploter.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from paddle.v2.plot import Ploter
class TestCommon(unittest.TestCase):
def test_append(self):
title1 = "title1"
title2 = "title2"
plot_test = Ploter(title1, title2)
plot_test.append(title1, 1, 2)
plot_test.append(title1, 2, 5)
plot_test.append(title2, 3, 4)
self.assertEqual(plot_test.__plot_data__[title1].step, [1, 2])
self.assertEqual(plot_test.__plot_data__[title1].value, [2, 5])
self.assertEqual(plot_test.__plot_data__[title2].step, [3])
self.assertEqual(plot_test.__plot_data__[title2].value, [4])
plot_test.reset()
self.assertEqual(plot_test.__plot_data__[title1].step, [])
self.assertEqual(plot_test.__plot_data__[title1].value, [])
self.assertEqual(plot_test.__plot_data__[title2].step, [])
self.assertEqual(plot_test.__plot_data__[title2].value, [])
if __name__ == '__main__':
unittest.main()
| 1,564 | 37.170732 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_topology.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle.v2.layer as layer
import paddle.v2.topology as topology
import paddle.v2.data_type as data_type
import paddle.trainer_config_helpers as conf_helps
import paddle.trainer.PyDataProvider2 as pydp2
class TestTopology(unittest.TestCase):
def test_data_type(self):
pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
label = layer.data(name='label', type=data_type.integer_value(10))
hidden = layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
cost = layer.classification_cost(input=inference, label=label)
topo = topology.Topology(cost)
data_types = topo.data_type()
self.assertEqual(len(data_types), 2)
pixel_data_type = filter(lambda type: type[0] == "pixel", data_types)
self.assertEqual(len(pixel_data_type), 1)
pixel_data_type = pixel_data_type[0]
self.assertEqual(pixel_data_type[1].type, pydp2.DataType.Dense)
self.assertEqual(pixel_data_type[1].dim, 784)
label_data_type = filter(lambda type: type[0] == "label", data_types)
self.assertEqual(len(label_data_type), 1)
label_data_type = label_data_type[0]
self.assertEqual(label_data_type[1].type, pydp2.DataType.Index)
self.assertEqual(label_data_type[1].dim, 10)
def test_get_layer(self):
pixel = layer.data(name='pixel2', type=data_type.dense_vector(784))
label = layer.data(name='label2', type=data_type.integer_value(10))
hidden = layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
cost = layer.classification_cost(input=inference, label=label)
topo = topology.Topology(cost)
pixel_layer = topo.get_layer("pixel2")
label_layer = topo.get_layer("label2")
self.assertEqual(pixel_layer, pixel)
self.assertEqual(label_layer, label)
def test_parse(self):
pixel = layer.data(name='pixel3', type=data_type.dense_vector(784))
label = layer.data(name='label3', type=data_type.integer_value(10))
hidden = layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
maxid = layer.max_id(input=inference)
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.cross_entropy_cost(input=inference, label=label)
topology.Topology(cost2).proto()
topology.Topology([cost1]).proto()
topology.Topology([cost1, cost2]).proto()
topology.Topology([inference, maxid]).proto()
if __name__ == '__main__':
unittest.main()
| 3,781 | 42.976744 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_data_feeder.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import py_paddle.swig_paddle as api
import numpy as np
from paddle.v2 import data_type
from paddle.v2.data_feeder import DataFeeder
class DataFeederTest(unittest.TestCase):
def dense_reader(self, size):
data = np.random.random(size)
return data
def sparse_binary_reader(self, high, size_limit, non_empty=False):
num = np.random.randint(size_limit) # num could be 0
while non_empty and num == 0:
num = np.random.randint(size_limit)
return np.random.randint(high, size=num).tolist()
def test_dense(self):
def compare(input):
feeder = DataFeeder([('image', data_type.dense_vector(784))],
{'image': 0})
arg = feeder(input)
output = arg.getSlotValue(0).copyToNumpyMat()
input = np.array(input, dtype='float32')
self.assertAlmostEqual(input.all(), output.all())
# test numpy array
batch_size = 32
dim = 784
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(self.dense_reader(dim))
data.append(each_sample)
compare(data)
# each feature is a list
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(self.dense_reader(dim).tolist())
data.append(each_sample)
compare(data)
# test tuple
data = []
for i in xrange(batch_size):
each_sample = (self.dense_reader(dim).tolist(), )
data.append(each_sample)
compare(data)
def test_sparse_binary(self):
dim = 10000
batch_size = 32
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(self.sparse_binary_reader(dim, 50))
data.append(each_sample)
feeder = DataFeeder([('input', data_type.sparse_binary_vector(dim))],
{'input': 0})
arg = feeder(data)
output = arg.getSlotValue(0)
assert isinstance(output, api.Matrix)
for i in xrange(batch_size):
self.assertEqual(output.getSparseRowCols(i), data[i][0])
def test_sparse(self):
dim = 10000
batch_size = 32
v = []
w = []
data = []
for dat in xrange(batch_size):
each_sample = []
a = self.sparse_binary_reader(dim, 40, non_empty=True)
b = self.dense_reader(len(a)).tolist()
v.append(a)
w.append(np.array(b, dtype="float32"))
each_sample.append(zip(a, b))
data.append(each_sample)
feeder = DataFeeder([('input', data_type.sparse_float_vector(dim))],
{'input': 0})
arg = feeder(data)
output = arg.getSlotValue(0)
assert isinstance(output, api.Matrix)
for i in xrange(batch_size):
self.assertEqual(output.getSparseRowCols(i), v[i])
cols_value = output.getSparseRowColsVal(i)
value = [val[1] for val in cols_value]
value = np.array(value, dtype="float32")
self.assertAlmostEqual(value.all(), w[i].all())
def test_integer(self):
value_range = 100
batch_size = 32
index = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(np.random.randint(value_range))
index.append(each_sample)
feeder = DataFeeder([('input', data_type.integer_value(value_range))],
{'input': 0})
arg = feeder(index)
output = arg.getSlotIds(0).copyToNumpyArray()
index = np.array(index, dtype='int')
self.assertEqual(output.all(), index.flatten().all())
def test_integer_sequence(self):
value_range = 10000
batch_size = 32
start = [0]
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(
self.sparse_binary_reader(
value_range, 30, non_empty=True))
data.append(each_sample)
start.append(len(each_sample[0]) + start[-1])
feeder = DataFeeder(
[('input', data_type.integer_value_sequence(value_range))],
{'input': 0})
arg = feeder(data)
output_data = arg.getSlotIds(0).copyToNumpyArray()
output_start = arg.getSlotSequenceStartPositions(0).copyToNumpyArray()
index = []
for dat in data:
index.extend(x for x in dat[0]) # only one feature, so dat[0]
index = np.array(index, dtype='int')
start = np.array(start, dtype='int')
self.assertEqual(output_data.all(), index.all())
self.assertEqual(output_start.all(), start.all())
def test_multiple_features(self):
batch_size = 2
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(np.random.randint(10))
each_sample.append(
self.sparse_binary_reader(
20000, 40, non_empty=True))
each_sample.append(self.dense_reader(100))
data.append(each_sample)
# test multiple features
data_types = [('fea0', data_type.dense_vector(100)),
('fea1', data_type.sparse_binary_vector(20000)),
('fea2', data_type.integer_value(10))]
feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0})
arg = feeder(data)
output_dense = arg.getSlotValue(0).copyToNumpyMat()
output_sparse = arg.getSlotValue(1)
output_index = arg.getSlotIds(2).copyToNumpyArray()
for i in xrange(batch_size):
self.assertEqual(output_dense[i].all(), data[i][2].all())
self.assertEqual(output_sparse.getSparseRowCols(i), data[i][1])
self.assertEqual(output_index[i], data[i][0])
# reader returns 3 features, but only use 2 features
data_types = [('fea0', data_type.dense_vector(100)),
('fea2', data_type.integer_value(10))]
feeder = DataFeeder(data_types, {'fea0': 2, 'fea2': 0})
arg = feeder(data)
output_dense = arg.getSlotValue(0).copyToNumpyMat()
output_index = arg.getSlotIds(1).copyToNumpyArray()
for i in xrange(batch_size):
self.assertEqual(output_dense[i].all(), data[i][2].all())
self.assertEqual(output_index[i], data[i][0])
# reader returns 3 featreus, one is duplicate data
data_types = [('fea0', data_type.dense_vector(100)),
('fea1', data_type.sparse_binary_vector(20000)),
('fea2', data_type.integer_value(10)),
('fea3', data_type.dense_vector(100))]
feeder = DataFeeder(data_types,
{'fea0': 2,
'fea1': 1,
'fea2': 0,
'fea3': 2})
arg = feeder(data)
fea0 = arg.getSlotValue(0).copyToNumpyMat()
fea1 = arg.getSlotValue(1)
fea2 = arg.getSlotIds(2).copyToNumpyArray()
fea3 = arg.getSlotValue(3).copyToNumpyMat()
for i in xrange(batch_size):
self.assertEqual(fea0[i].all(), data[i][2].all())
self.assertEqual(fea1.getSparseRowCols(i), data[i][1])
self.assertEqual(fea2[i], data[i][0])
self.assertEqual(fea3[i].all(), data[i][2].all())
def test_multiple_features_tuple(self):
batch_size = 2
data = []
for i in xrange(batch_size):
a = np.random.randint(10)
b = self.sparse_binary_reader(20000, 40, non_empty=True)
c = self.dense_reader(100)
each_sample = (a, b, c)
data.append(each_sample)
# test multiple features
data_types = [('fea0', data_type.dense_vector(100)),
('fea1', data_type.sparse_binary_vector(20000)),
('fea2', data_type.integer_value(10))]
feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0})
arg = feeder(data)
out_dense = arg.getSlotValue(0).copyToNumpyMat()
out_sparse = arg.getSlotValue(1)
out_index = arg.getSlotIds(2).copyToNumpyArray()
for i in xrange(batch_size):
self.assertEqual(out_dense[i].all(), data[i][2].all())
self.assertEqual(out_sparse.getSparseRowCols(i), data[i][1])
self.assertEqual(out_index[i], data[i][0])
def test_dense_set_shape(self):
# test 2-D data
def gen_data(batch_size, shape):
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(np.random.random(shape))
data.append(each_sample)
return data
feeder = DataFeeder([('image', data_type.dense_array(2352))],
{'image': 0})
arg = feeder(gen_data(32, (3, 28, 28)))
h = arg.getSlotFrameHeight(0)
w = arg.getSlotFrameWidth(0)
self.assertEqual(h, 28)
self.assertEqual(w, 28)
arg = feeder(gen_data(32, (3, 30, 32)))
h = arg.getSlotFrameHeight(0)
w = arg.getSlotFrameWidth(0)
self.assertEqual(h, 30)
self.assertEqual(w, 32)
if __name__ == '__main__':
api.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(DataFeederTest)
unittest.TextTestRunner().run(suite)
if api.isGpuVersion():
api.setUseGpu(True)
unittest.main()
| 10,326 | 37.533582 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_image.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import paddle.v2.image as image
class Image(unittest.TestCase):
def test_resize_flip_chw(self):
# resize
im = image.load_image('cat.jpg')
im = image.resize_short(im, 256)
self.assertEqual(256, min(im.shape[:2]))
self.assertEqual(3, im.shape[2])
# flip
im = image.left_right_flip(im)
im2 = np.flip(im, 1)
self.assertEqual(im.all(), im2.all())
# to_chw
h, w, c = im.shape
im = image.to_chw(im)
self.assertEqual(c, im.shape[0])
self.assertEqual(h, im.shape[1])
self.assertEqual(w, im.shape[2])
if __name__ == '__main__':
unittest.main()
| 1,317 | 28.954545 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle.v2.activation as activation
import paddle.v2.attr as attr
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
import paddle.v2.pooling as pooling
import paddle.v2.networks as networks
import paddle.v2.evaluator as evaluator
pixel = layer.data(name='pixel', type=data_type.dense_vector(128))
label = layer.data(name='label', type=data_type.integer_value(10))
weight = layer.data(name='weight', type=data_type.dense_vector(1))
combine_weight = layer.data(
name='weight_combine', type=data_type.dense_vector(10))
score = layer.data(name='score', type=data_type.dense_vector(1))
hidden = layer.fc(input=pixel,
size=100,
act=activation.Sigmoid(),
param_attr=attr.Param(name='hidden'))
inference = layer.fc(input=hidden, size=10, act=activation.Softmax())
conv = layer.img_conv(
input=pixel,
filter_size=1,
filter_size_y=1,
num_channels=8,
num_filters=16,
act=activation.Linear())
class ImageLayerTest(unittest.TestCase):
def test_conv_layer(self):
conv_shift = layer.conv_shift(a=pixel, b=score)
print layer.parse_network(conv, conv_shift)
def test_pooling_layer(self):
maxpool = layer.img_pool(
input=conv,
pool_size=2,
num_channels=16,
padding=1,
pool_type=pooling.Max())
spp = layer.spp(input=conv,
pyramid_height=2,
num_channels=16,
pool_type=pooling.Max())
maxout = layer.maxout(input=conv, num_channels=16, groups=4)
print layer.parse_network([maxpool, spp, maxout])
def test_norm_layer(self):
norm1 = layer.img_cmrnorm(input=conv, size=5)
norm2 = layer.batch_norm(input=conv)
norm3 = layer.sum_to_one_norm(input=conv)
print layer.parse_network([norm1, norm2, norm3])
class AggregateLayerTest(unittest.TestCase):
def test_aggregate_layer(self):
pool = layer.pooling(
input=pixel,
pooling_type=pooling.Avg(),
agg_level=layer.AggregateLevel.TO_SEQUENCE)
last_seq = layer.last_seq(input=pixel)
first_seq = layer.first_seq(input=pixel)
concat = layer.concat(input=[last_seq, first_seq])
seq_concat = layer.seq_concat(a=last_seq, b=first_seq)
print layer.parse_network(
[pool, last_seq, first_seq, concat, seq_concat])
class MathLayerTest(unittest.TestCase):
def test_math_layer(self):
addto = layer.addto(input=[pixel, pixel])
linear_comb = layer.linear_comb(
weights=combine_weight, vectors=hidden, size=10)
interpolation = layer.interpolation(
input=[hidden, hidden], weight=score)
bilinear = layer.bilinear_interp(input=conv, out_size_x=4, out_size_y=4)
power = layer.power(input=pixel, weight=score)
scaling = layer.scaling(input=pixel, weight=score)
slope = layer.slope_intercept(input=pixel)
tensor = layer.tensor(a=pixel, b=pixel, size=1000)
cos_sim = layer.cos_sim(a=pixel, b=pixel)
trans = layer.trans(input=tensor)
print layer.parse_network([
addto, linear_comb, interpolation, power, scaling, slope, tensor,
cos_sim, trans
])
class ReshapeLayerTest(unittest.TestCase):
def test_reshape_layer(self):
block_expand = layer.block_expand(
input=conv, num_channels=4, stride_x=1, block_x=1)
expand = layer.expand(
input=weight,
expand_as=pixel,
expand_level=layer.ExpandLevel.FROM_NO_SEQUENCE)
repeat = layer.repeat(input=pixel, num_repeats=4)
reshape = layer.seq_reshape(input=pixel, reshape_size=4)
rotate = layer.rotate(input=pixel, height=16, width=49)
print layer.parse_network(
[block_expand, expand, repeat, reshape, rotate])
class RecurrentLayerTest(unittest.TestCase):
def test_recurrent_layer(self):
word = layer.data(name='word', type=data_type.integer_value(12))
recurrent = layer.recurrent(input=word)
lstm = layer.lstmemory(input=word)
gru = layer.grumemory(input=word)
print layer.parse_network([recurrent, lstm, gru])
class CostLayerTest(unittest.TestCase):
def test_cost_layer(self):
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.classification_cost(
input=inference, label=label, weight=weight)
cost3 = layer.cross_entropy_cost(input=inference, label=label)
cost4 = layer.cross_entropy_with_selfnorm_cost(
input=inference, label=label)
cost5 = layer.square_error_cost(input=inference, label=label)
cost6 = layer.square_error_cost(
input=inference, label=label, weight=weight)
cost7 = layer.multi_binary_label_cross_entropy_cost(
input=inference, label=label)
cost8 = layer.rank_cost(left=score, right=score, label=score)
cost9 = layer.lambda_cost(input=inference, score=score)
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_regression_cost(input=score, label=label)
cost12 = layer.huber_classification_cost(input=score, label=label)
print layer.parse_network([cost1, cost2])
print layer.parse_network([cost3, cost4])
print layer.parse_network([cost5, cost6])
print layer.parse_network([cost7, cost8, cost9, cost10, cost11, cost12])
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
ctc = layer.ctc(input=inference, label=label)
warp_ctc = layer.warp_ctc(input=pixel, label=label)
nce = layer.nce(input=inference, label=label, num_classes=3)
hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
print layer.parse_network(
[crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid])
class OtherLayerTest(unittest.TestCase):
def test_sampling_layer(self):
maxid = layer.max_id(input=inference)
sampling_id = layer.sampling_id(input=inference)
eos = layer.eos(input=maxid, eos_id=5)
layer.printer(maxid)
print layer.parse_network([maxid, sampling_id, eos])
def test_slicing_joining_layer(self):
pad = layer.pad(input=conv, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
print layer.parse_network(pad)
class ProjOpTest(unittest.TestCase):
def test_projection(self):
input = layer.data(name='data2', type=data_type.dense_vector(784))
word = layer.data(
name='word2', type=data_type.integer_value_sequence(10000))
fc0 = layer.fc(input=input, size=100, act=activation.Sigmoid())
fc1 = layer.fc(input=input, size=200, act=activation.Sigmoid())
mixed0 = layer.mixed(
size=256,
input=[
layer.full_matrix_projection(input=fc0),
layer.full_matrix_projection(input=fc1)
])
with layer.mixed(size=200) as mixed1:
mixed1 += layer.full_matrix_projection(input=fc0)
mixed1 += layer.identity_projection(input=fc1)
table = layer.table_projection(input=word)
emb0 = layer.mixed(size=512, input=table)
with layer.mixed(size=512) as emb1:
emb1 += table
scale = layer.scaling_projection(input=fc0)
scale0 = layer.mixed(size=100, input=scale)
with layer.mixed(size=100) as scale1:
scale1 += scale
dotmul = layer.dotmul_projection(input=fc0)
dotmul0 = layer.mixed(size=100, input=dotmul)
with layer.mixed(size=100) as dotmul1:
dotmul1 += dotmul
context = layer.context_projection(input=fc0, context_len=5)
context0 = layer.mixed(size=500, input=context)
with layer.mixed(size=500) as context1:
context1 += context
conv = layer.conv_projection(
input=input,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv, bias_attr=True)
with layer.mixed(bias_attr=True) as conv1:
conv1 += conv
print layer.parse_network(mixed0)
print layer.parse_network(mixed1)
print layer.parse_network(emb0)
print layer.parse_network(emb1)
print layer.parse_network(scale0)
print layer.parse_network(scale1)
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
def test_operator(self):
ipt0 = layer.data(name='data1', type=data_type.dense_vector(784))
ipt1 = layer.data(name='word1', type=data_type.dense_vector(128))
fc0 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
fc1 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
dotmul_op = layer.dotmul_operator(a=fc0, b=fc1)
dotmul0 = layer.mixed(input=dotmul_op)
with layer.mixed() as dotmul1:
dotmul1 += dotmul_op
conv = layer.conv_operator(
img=ipt0,
filter=ipt1,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv)
with layer.mixed() as conv1:
conv1 += conv
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
class NetworkTests(unittest.TestCase):
def test_vgg(self):
img = layer.data(name='pixel1', type=data_type.dense_vector(784))
vgg_out = networks.small_vgg(
input_image=img, num_channels=1, num_classes=2)
print layer.parse_network(vgg_out)
class EvaluatorTest(unittest.TestCase):
def test_evaluator(self):
img = layer.data(name='pixel2', type=data_type.dense_vector(784))
output = layer.fc(input=img,
size=10,
act=activation.Softmax(),
name='fc_here')
lbl = layer.data(name='label2', type=data_type.integer_value(10))
cost = layer.cross_entropy_cost(input=output, label=lbl)
evaluator.classification_error(input=output, label=lbl)
print layer.parse_network(cost)
print layer.parse_network(output)
if __name__ == '__main__':
unittest.main()
| 11,278 | 37.75945 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_rnn_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import difflib
import unittest
import paddle.trainer_config_helpers as conf_helps
import paddle.v2.activation as activation
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config as parse_network
from paddle.trainer_config_helpers.config_parser_utils import \
reset_parser
class RNNTest(unittest.TestCase):
def test_simple_rnn(self):
dict_dim = 10
word_dim = 8
hidden_dim = 8
def parse_old_rnn():
reset_parser()
def step(y):
mem = conf_helps.memory(name="rnn_state", size=hidden_dim)
out = conf_helps.fc_layer(
input=[y, mem],
size=hidden_dim,
act=activation.Tanh(),
bias_attr=True,
name="rnn_state")
return out
def test():
data = conf_helps.data_layer(name="word", size=dict_dim)
embd = conf_helps.embedding_layer(input=data, size=word_dim)
conf_helps.recurrent_group(
name="rnn", step=step, input=embd, reverse=True)
return str(parse_network(test))
def parse_new_rnn():
reset_parser()
def new_step(y):
mem = layer.memory(name="rnn_state", size=hidden_dim)
out = layer.fc(input=[y, mem],
size=hidden_dim,
act=activation.Tanh(),
bias_attr=True,
name="rnn_state")
return out
data = layer.data(
name="word", type=data_type.integer_value(dict_dim))
embd = layer.embedding(input=data, size=word_dim)
rnn_layer = layer.recurrent_group(
name="rnn", step=new_step, input=embd, reverse=True)
return str(layer.parse_network(rnn_layer))
diff = difflib.unified_diff(parse_old_rnn().splitlines(1),
parse_new_rnn().splitlines(1))
print ''.join(diff)
def test_sequence_rnn_multi_input(self):
dict_dim = 10
word_dim = 8
hidden_dim = 8
label_dim = 3
def parse_old_rnn():
reset_parser()
def test():
data = conf_helps.data_layer(name="word", size=dict_dim)
label = conf_helps.data_layer(name="label", size=label_dim)
emb = conf_helps.embedding_layer(input=data, size=word_dim)
boot_layer = conf_helps.data_layer(name="boot", size=10)
boot_layer = conf_helps.fc_layer(
name='boot_fc', input=boot_layer, size=10)
def step(y, wid):
z = conf_helps.embedding_layer(input=wid, size=word_dim)
mem = conf_helps.memory(
name="rnn_state",
size=hidden_dim,
boot_layer=boot_layer)
out = conf_helps.fc_layer(
input=[y, z, mem],
size=hidden_dim,
act=conf_helps.TanhActivation(),
bias_attr=True,
name="rnn_state")
return out
out = conf_helps.recurrent_group(
name="rnn", step=step, input=[emb, data])
rep = conf_helps.last_seq(input=out)
prob = conf_helps.fc_layer(
size=label_dim,
input=rep,
act=conf_helps.SoftmaxActivation(),
bias_attr=True)
conf_helps.outputs(
conf_helps.classification_cost(
input=prob, label=label))
return str(parse_network(test))
def parse_new_rnn():
reset_parser()
data = layer.data(
name="word", type=data_type.dense_vector(dict_dim))
label = layer.data(
name="label", type=data_type.dense_vector(label_dim))
emb = layer.embedding(input=data, size=word_dim)
boot_layer = layer.data(
name="boot", type=data_type.dense_vector(10))
boot_layer = layer.fc(name='boot_fc', input=boot_layer, size=10)
def step(y, wid):
z = layer.embedding(input=wid, size=word_dim)
mem = layer.memory(
name="rnn_state", size=hidden_dim, boot_layer=boot_layer)
out = layer.fc(input=[y, z, mem],
size=hidden_dim,
act=activation.Tanh(),
bias_attr=True,
name="rnn_state")
return out
out = layer.recurrent_group(
name="rnn", step=step, input=[emb, data])
rep = layer.last_seq(input=out)
prob = layer.fc(size=label_dim,
input=rep,
act=activation.Softmax(),
bias_attr=True)
cost = layer.classification_cost(input=prob, label=label)
return str(layer.parse_network(cost))
diff = difflib.unified_diff(parse_old_rnn().splitlines(1),
parse_new_rnn().splitlines(1))
print ''.join(diff)
if __name__ == '__main__':
unittest.main()
| 6,213 | 36.209581 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_parameters.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import sys
try:
import py_paddle
del py_paddle
except ImportError:
print >> sys.stderr, "It seems swig of Paddle is not installed, this " \
"unittest will not be run."
sys.exit(0)
import paddle.v2.parameters as parameters
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
from paddle.v2.attr import ParamAttr
from paddle.proto.ParameterConfig_pb2 import ParameterConfig
import random
import cStringIO
import numpy
def __rand_param_config__(name, psize=None):
conf = ParameterConfig()
conf.name = name
size = 1
if psize is None:
for i in xrange(2):
dim = random.randint(1, 1000)
conf.dims.append(dim)
size *= dim
else:
size = psize
conf.size = size
assert conf.IsInitialized()
return conf
class TestParameters(unittest.TestCase):
def test_serialization(self):
params = parameters.Parameters()
params.__append_config__(__rand_param_config__("param_0"))
params.__append_config__(__rand_param_config__("param_1"))
for name in params.names():
param = params.get(name)
param[:] = numpy.random.uniform(
-1.0, 1.0, size=params.get_shape(name))
params.set(name, param)
tmp_file = cStringIO.StringIO()
params.to_tar(tmp_file)
tmp_file.seek(0)
params_dup = parameters.Parameters.from_tar(tmp_file)
self.assertEqual(params_dup.names(), params.names())
for name in params.names():
self.assertEqual(params.get_shape(name), params_dup.get_shape(name))
p0 = params.get(name)
p1 = params_dup.get(name)
self.assertTrue(numpy.isclose(p0, p1).all())
def test_initializer(self):
def initializer(name):
assert name == "fc.w"
mat = numpy.ones((3, 2), dtype=numpy.float32)
mat[1, 1] = 2
return mat
x = layer.data(name="x", type=data_type.dense_vector(3))
y = layer.fc(x,
size=2,
bias_attr=False,
param_attr=ParamAttr(
name="fc.w", initializer=initializer))
params = parameters.create(y)
val = params["fc.w"]
assert val.shape == (3, 2)
expected = numpy.array([[1, 1], [1, 2], [1, 1]], numpy.float32)
assert numpy.logical_and.reduce(numpy.reshape(val == expected, 6))
def test_init_from_tar(self):
def get_param(names, size):
p = parameters.Parameters()
for k, v in zip(names, size):
p.__append_config__(__rand_param_config__(k, v))
for name in p.names():
param = p.get(name)
param[:] = numpy.random.uniform(
-1.0, 1.0, size=p.get_shape(name))
p.set(name, param)
return p
def get_parames():
name1 = ['param_0', 'param_1']
size1 = [128, 256]
p1 = get_param(name1, size1)
file1 = cStringIO.StringIO()
p1.to_tar(file1)
file1.seek(0)
name2 = ['param_0', 'param_1', 'param_2']
size2 = [128, 256, 288]
p2 = get_param(name2, size2)
file2 = cStringIO.StringIO()
p2.to_tar(file2)
file2.seek(0)
return p1, file1, p2, file2
p1, file1, p2, file2 = get_parames()
p2.init_from_tar(file1)
for name in p1.names():
self.assertEqual(p1.get_shape(name), p2.get_shape(name))
v1 = p1.get(name)
v2 = p2.get(name)
self.assertTrue(numpy.isclose(v1, v2).all())
p1, file1, p2, file2 = get_parames()
p1.init_from_tar(file2)
for name in p1.names():
self.assertEqual(p1.get_shape(name), p2.get_shape(name))
v1 = p1.get(name)
v2 = p2.get(name)
self.assertTrue(numpy.isclose(v1, v2).all())
if __name__ == '__main__':
unittest.main()
| 4,716 | 31.756944 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_paramconf_order.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright PaddlePaddle contributors. All Rights Reservedd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import math
import paddle.v2 as paddle
def wordemb(inlayer):
wordemb = paddle.layer.table_projection(
input=inlayer,
size=5,
param_attr=paddle.attr.Param(
name="_proj", initial_std=0.001, learning_rate=1, l2_rate=0))
return wordemb
def train():
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
# Every layer takes integer value of range [0, dict_size)
firstword = paddle.layer.data(
name="firstw", type=paddle.data_type.integer_value(dict_size))
secondword = paddle.layer.data(
name="secondw", type=paddle.data_type.integer_value(dict_size))
thirdword = paddle.layer.data(
name="thirdw", type=paddle.data_type.integer_value(dict_size))
fourthword = paddle.layer.data(
name="fourthw", type=paddle.data_type.integer_value(dict_size))
nextword = paddle.layer.data(
name="fifthw", type=paddle.data_type.integer_value(dict_size))
Efirst = wordemb(firstword)
Esecond = wordemb(secondword)
Ethird = wordemb(thirdword)
Efourth = wordemb(fourthword)
contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
hidden1 = paddle.layer.fc(name="fc1",
input=contextemb,
size=128,
act=paddle.activation.Sigmoid(),
layer_attr=paddle.attr.Extra(drop_rate=0.5),
bias_attr=paddle.attr.Param(learning_rate=2),
param_attr=paddle.attr.Param(
initial_std=1. / math.sqrt(5 * 8),
learning_rate=1,
l2_rate=6e-4))
predictword = paddle.layer.fc(input=hidden1,
size=dict_size,
bias_attr=paddle.attr.Param(learning_rate=2),
act=paddle.activation.Softmax())
return paddle.layer.classification_cost(input=predictword, label=nextword)
class TestParamConfOrder(unittest.TestCase):
def test_param_conf_order(self):
paddle.init()
cost = train()
parameters = paddle.parameters.create(cost)
adagrad = paddle.optimizer.AdaGrad(
learning_rate=3e-3,
regularization=paddle.optimizer.L2Regularization(rate=8e-4))
trainer = paddle.trainer.SGD(cost, parameters, adagrad)
for p in trainer.get_topology_proto().parameters:
if p.name == "_fc1.w0":
self.assertEqual(p.decay_rate, 6e-4)
else:
self.assertEqual(p.decay_rate, 8e-4)
if __name__ == '__main__':
unittest.main()
| 3,981 | 38.82 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/tests/test_op.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
import paddle.v2.op as op
class OpTest(unittest.TestCase):
def test_op(self):
x = layer.data(name='data', type=data_type.dense_vector(128))
x = op.exp(x)
x = op.sqrt(x)
x = op.reciprocal(x)
x = op.log(x)
x = op.abs(x)
x = op.sigmoid(x)
x = op.tanh(x)
x = op.square(x)
x = op.relu(x)
y = 1 + x
y = y + 1
y = x + y
y = y - x
y = y - 2
y = 2 - y
y = 2 * y
y = y * 3
z = layer.data(name='data_2', type=data_type.dense_vector(1))
y = y * z
y = z * y
y = y + z
y = z + y
print layer.parse_network(y)
if __name__ == '__main__':
unittest.main()
| 1,443 | 26.769231 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/reader/decorator.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = [
'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
'ComposeNotAligned', 'firstn', 'xmap_readers', 'PipeReader'
]
from threading import Thread
import subprocess
from Queue import Queue
import itertools
import random
import zlib
def map_readers(func, *readers):
"""
Creates a data reader that outputs return value of function using
output of each data readers as arguments.
:param func: function to use. The type of func should be (Sample) => Sample
:type: callable
:param readers: readers whose outputs will be used as arguments of func.
:return: the created data reader.
:rtype: callable
"""
def reader():
rs = []
for r in readers:
rs.append(r())
for e in itertools.imap(func, *rs):
yield e
return reader
def shuffle(reader, buf_size):
"""
Creates a data reader whose data output is shuffled.
Output from the iterator that created by original reader will be
buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
is determined by argument buf_size.
:param reader: the original reader whose output will be shuffled.
:type reader: callable
:param buf_size: shuffle buffer size.
:type buf_size: int
:return: the new reader whose output is shuffled.
:rtype: callable
"""
def data_reader():
buf = []
for e in reader():
buf.append(e)
if len(buf) >= buf_size:
random.shuffle(buf)
for b in buf:
yield b
buf = []
if len(buf) > 0:
random.shuffle(buf)
for b in buf:
yield b
return data_reader
def chain(*readers):
"""
Creates a data reader whose output is the outputs of input data
readers chained together.
If input readers output following data entries:
[0, 0, 0]
[1, 1, 1]
[2, 2, 2]
The chained reader will output:
[0, 0, 0, 1, 1, 1, 2, 2, 2]
:param readers: input readers.
:return: the new data reader.
:rtype: callable
"""
def reader():
rs = []
for r in readers:
rs.append(r())
for e in itertools.chain(*rs):
yield e
return reader
class ComposeNotAligned(ValueError):
pass
def compose(*readers, **kwargs):
"""
Creates a data reader whose output is the combination of input readers.
If input readers output following data entries:
(1, 2) 3 (4, 5)
The composed reader will output:
(1, 2, 3, 4, 5)
:param readers: readers that will be composed together.
:param check_alignment: if True, will check if input readers are aligned
correctly. If False, will not check alignment and trailing outputs
will be discarded. Defaults to True.
:type check_alignment: bool
:return: the new data reader.
:raises ComposeNotAligned: outputs of readers are not aligned.
Will not raise when check_alignment is set to False.
"""
check_alignment = kwargs.pop('check_alignment', True)
def make_tuple(x):
if isinstance(x, tuple):
return x
else:
return (x, )
def reader():
rs = []
for r in readers:
rs.append(r())
if not check_alignment:
for outputs in itertools.izip(*rs):
yield sum(map(make_tuple, outputs), ())
else:
for outputs in itertools.izip_longest(*rs):
for o in outputs:
if o is None:
# None will be not be present if compose is aligned
raise ComposeNotAligned(
"outputs of readers are not aligned.")
yield sum(map(make_tuple, outputs), ())
return reader
def buffered(reader, size):
"""
Creates a buffered data reader.
The buffered data reader will read and save data entries into a
buffer. Reading from the buffered data reader will proceed as long
as the buffer is not empty.
:param reader: the data reader to read from.
:type reader: callable
:param size: max buffer size.
:type size: int
:returns: the buffered data reader.
"""
class EndSignal():
pass
end = EndSignal()
def read_worker(r, q):
for d in r:
q.put(d)
q.put(end)
def data_reader():
r = reader()
q = Queue(maxsize=size)
t = Thread(
target=read_worker, args=(
r,
q, ))
t.daemon = True
t.start()
e = q.get()
while e != end:
yield e
e = q.get()
return data_reader
def firstn(reader, n):
"""
Limit the max number of samples that reader could return.
:param reader: the data reader to read from.
:type reader: callable
:param n: the max number of samples that return.
:type n: int
:return: the decorated reader.
:rtype: callable
"""
# TODO(yuyang18): Check if just drop the reader, could clean the opened
# resource or not?
def firstn_reader():
for i, item in enumerate(reader()):
if i == n:
break
yield item
return firstn_reader
class XmapEndSignal():
pass
def xmap_readers(mapper, reader, process_num, buffer_size, order=False):
"""
Use multiprocess to map samples from reader by a mapper defined by user.
And this function contains a buffered decorator.
:param mapper: a function to map sample.
:type mapper: callable
:param reader: the data reader to read from
:type reader: callable
:param process_num: process number to handle original sample
:type process_num: int
:param buffer_size: max buffer size
:type buffer_size: int
:param order: keep the order of reader
:type order: bool
:return: the decarated reader
:rtype: callable
"""
end = XmapEndSignal()
# define a worker to read samples from reader to in_queue
def read_worker(reader, in_queue):
for i in reader():
in_queue.put(i)
in_queue.put(end)
# define a worker to read samples from reader to in_queue with order flag
def order_read_worker(reader, in_queue):
in_order = 0
for i in reader():
in_queue.put((in_order, i))
in_order += 1
in_queue.put(end)
# define a worker to handle samples from in_queue by mapper
# and put mapped samples into out_queue
def handle_worker(in_queue, out_queue, mapper):
sample = in_queue.get()
while not isinstance(sample, XmapEndSignal):
r = mapper(sample)
out_queue.put(r)
sample = in_queue.get()
in_queue.put(end)
out_queue.put(end)
# define a worker to handle samples from in_queue by mapper
# and put mapped samples into out_queue by order
def order_handle_worker(in_queue, out_queue, mapper, out_order):
ins = in_queue.get()
while not isinstance(ins, XmapEndSignal):
order, sample = ins
r = mapper(sample)
while order != out_order[0]:
pass
out_queue.put(r)
out_order[0] += 1
ins = in_queue.get()
in_queue.put(end)
out_queue.put(end)
def xreader():
in_queue = Queue(buffer_size)
out_queue = Queue(buffer_size)
out_order = [0]
# start a read worker in a thread
target = order_read_worker if order else read_worker
t = Thread(target=target, args=(reader, in_queue))
t.daemon = True
t.start()
# start several handle_workers
target = order_handle_worker if order else handle_worker
args = (in_queue, out_queue, mapper, out_order) if order else (
in_queue, out_queue, mapper)
workers = []
for i in xrange(process_num):
worker = Thread(target=target, args=args)
worker.daemon = True
workers.append(worker)
for w in workers:
w.start()
sample = out_queue.get()
while not isinstance(sample, XmapEndSignal):
yield sample
sample = out_queue.get()
finish = 1
while finish < process_num:
sample = out_queue.get()
if isinstance(sample, XmapEndSignal):
finish += 1
else:
yield sample
return xreader
def _buf2lines(buf, line_break="\n"):
# FIXME: line_break should be automatically configured.
lines = buf.split(line_break)
return lines[:-1], lines[-1]
class PipeReader:
"""
PipeReader read data by stream from a command, take it's
stdout into a pipe buffer and redirect it to the parser to
parse, then yield data as your desired format.
You can using standard linux command or call another program
to read data, from HDFS, Ceph, URL, AWS S3 etc:
.. code-block:: python
cmd = "hadoop fs -cat /path/to/some/file"
cmd = "cat sample_file.tar.gz"
cmd = "curl http://someurl"
cmd = "python print_s3_bucket.py"
An example:
.. code-block:: python
def example_reader():
for f in myfiles:
pr = PipeReader("cat %s"%f)
for l in pr.get_line():
sample = l.split(" ")
yield sample
"""
def __init__(self, command, bufsize=8192, file_type="plain"):
if not isinstance(command, str):
raise TypeError("left_cmd must be a string")
if file_type == "gzip":
self.dec = zlib.decompressobj(
32 + zlib.MAX_WBITS) # offset 32 to skip the header
self.file_type = file_type
self.bufsize = bufsize
self.process = subprocess.Popen(
command.split(" "), bufsize=bufsize, stdout=subprocess.PIPE)
def get_line(self, cut_lines=True, line_break="\n"):
"""
:param cut_lines: cut buffer to lines
:type cut_lines: bool
:param line_break: line break of the file, like \n or \r
:type line_break: string
:return: one line or a buffer of bytes
:rtype: string
"""
remained = ""
while True:
buff = self.process.stdout.read(self.bufsize)
if buff:
if self.file_type == "gzip":
decomp_buff = self.dec.decompress(buff)
elif self.file_type == "plain":
decomp_buff = buff
else:
raise TypeError("file_type %s is not allowed" %
self.file_type)
if cut_lines:
lines, remained = _buf2lines(''.join(
[remained, decomp_buff]), line_break)
for line in lines:
yield line
else:
yield decomp_buff
else:
break
| 11,823 | 28.123153 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/reader/__init__.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
At training and testing time, PaddlePaddle programs need to read data. To ease
the users' work to write data reading code, we define that
- A *reader* is a function that reads data (from file, network, random number
generator, etc) and yields data items.
- A *reader creator* is a function that returns a reader function.
- A *reader decorator* is a function, which accepts one or more readers, and
returns a reader.
- A *batch reader* is a function that reads data (from *reader*, file, network,
random number generator, etc) and yields a batch of data items.
#####################
Data Reader Interface
#####################
Indeed, *data reader* doesn't have to be a function that reads and yields data
items. It can be any function with no parameter that creates a iterable
(anything can be used in :code:`for x in iterable`)\:
.. code-block:: python
iterable = data_reader()
Element produced from the iterable should be a **single** entry of data,
**not** a mini batch. That entry of data could be a single item, or a tuple of
items.
Item should be of `supported type <http://www.paddlepaddle.org/doc/ui/data_provider
/pydataprovider2.html?highlight=dense_vector#input-types>`_ (e.g., numpy 1d
array of float32, int, list of int)
An example implementation for single item data reader creator:
.. code-block:: python
def reader_creator_random_image(width, height):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
An example implementation for multiple item data reader creator:
.. code-block:: python
def reader_creator_random_image_and_label(width, height, label):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height), label
return reader
TODO(yuyang18): Should we add whole design doc here?
"""
import decorator
from decorator import *
import creator
__all__ = decorator.__all__ + ['creator']
| 2,609 | 33.8 | 83 |
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|
Paddle
|
Paddle-master/python/paddle/v2/reader/creator.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Creator package contains some simple reader creator, which could
be used in user program.
"""
__all__ = ['np_array', 'text_file', 'recordio', 'cloud_reader']
def np_array(x):
"""
Creates a reader that yields elements of x, if it is a
numpy vector. Or rows of x, if it is a numpy matrix.
Or any sub-hyperplane indexed by the highest dimension.
:param x: the numpy array to create reader from.
:returns: data reader created from x.
"""
def reader():
if x.ndim < 1:
yield x
for e in x:
yield e
return reader
def text_file(path):
"""
Creates a data reader that outputs text line by line from given text file.
Trailing new line ('\\\\n') of each line will be removed.
:path: path of the text file.
:returns: data reader of text file
"""
def reader():
f = open(path, "r")
for l in f:
yield l.rstrip('\n')
f.close()
return reader
def recordio(paths, buf_size=100):
"""
Creates a data reader from given RecordIO file paths separated by ",",
glob pattern is supported.
:path: path of recordio files, can be a string or a string list.
:returns: data reader of recordio files.
"""
import recordio as rec
import paddle.v2.reader.decorator as dec
import cPickle as pickle
def reader():
if isinstance(paths, basestring):
path = paths
else:
path = ",".join(paths)
f = rec.reader(path)
while True:
r = f.read()
if r is None:
break
yield pickle.loads(r)
f.close()
return dec.buffered(reader, buf_size)
pass_num = 0
def cloud_reader(paths, etcd_endpoints, timeout_sec=5, buf_size=64):
"""
Create a data reader that yield a record one by one from
the paths:
:paths: path of recordio files, can be a string or a string list.
:etcd_endpoints: the endpoints for etcd cluster
:returns: data reader of recordio files.
.. code-block:: python
from paddle.v2.reader.creator import cloud_reader
etcd_endpoints = "http://127.0.0.1:2379"
trainer.train.(
reader=cloud_reader(["/work/dataset/uci_housing/uci_housing*"], etcd_endpoints),
)
"""
import os
import cPickle as pickle
import paddle.v2.master as master
c = master.client(etcd_endpoints, timeout_sec, buf_size)
if isinstance(paths, basestring):
path = [paths]
else:
path = paths
c.set_dataset(path)
def reader():
global pass_num
c.paddle_start_get_records(pass_num)
pass_num += 1
while True:
r, e = c.next_record()
if not r:
if e != -2:
print "get record error: ", e
break
yield pickle.loads(r)
return reader
| 3,535 | 25.992366 | 92 |
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|
Paddle
|
Paddle-master/python/paddle/v2/reader/tests/creator_test.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright PaddlePaddle contributors. All Rights Reservedd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
import numpy as np
import paddle.v2.reader.creator
class TestNumpyArray(unittest.TestCase):
def test_numpy_array(self):
l = [[1, 2, 3], [4, 5, 6]]
x = np.array(l, np.int32)
reader = paddle.v2.reader.creator.np_array(x)
for idx, e in enumerate(reader()):
self.assertItemsEqual(e, l[idx])
class TestTextFile(unittest.TestCase):
def test_text_file(self):
path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt")
reader = paddle.v2.reader.creator.text_file(path)
for idx, e in enumerate(reader()):
self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
class TestRecordIO(unittest.TestCase):
def do_test(self, path):
reader = paddle.v2.reader.creator.recordio(path)
idx = 0
for e in reader():
if idx == 0:
self.assertEqual(e, (1, 2, 3))
elif idx == 1:
self.assertEqual(e, (4, 5, 6))
idx += 1
self.assertEqual(idx, 2)
def test_recordIO(self):
self.do_test(
os.path.join(
os.path.dirname(__file__), "test_reader_recordio.dat"))
self.do_test([
os.path.join(
os.path.dirname(__file__), "test_reader_recordio.dat")
])
if __name__ == '__main__':
unittest.main()
| 2,605 | 33.746667 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/reader/tests/decorator_test.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import unittest
import paddle.v2.reader
def reader_creator_10(dur):
def reader():
for i in range(10):
# this invocation helps testing paddle.reader.buffer
time.sleep(dur)
yield i
return reader
class TestMap(unittest.TestCase):
def test_map(self):
d = {"h": 0, "i": 1}
def tokenize(x):
return d[x]
def read():
yield "h"
yield "i"
r = paddle.v2.reader.map_readers(tokenize, read)
for i, e in enumerate(r()):
self.assertEqual(e, i)
class TestBuffered(unittest.TestCase):
def test_read(self):
for size in range(20):
b = paddle.v2.reader.buffered(reader_creator_10(0), size)
c = 0
for i in b():
self.assertEqual(i, c)
c += 1
self.assertEqual(c, 10)
def test_buffering(self):
# read have 30ms delay.
b = paddle.v2.reader.buffered(reader_creator_10(0.03), 10)
last_time = time.time()
for idx, i in enumerate(b()):
elapsed_time = time.time() - last_time
if i == 0:
time.sleep(0.3)
else:
# read time should be short, meaning already buffered.
self.assertLess(elapsed_time, 0.05)
last_time = time.time()
class TestCompose(unittest.TestCase):
def test_compse(self):
reader = paddle.v2.reader.compose(
reader_creator_10(0), reader_creator_10(0))
for idx, e in enumerate(reader()):
self.assertEqual(e, (idx, idx))
def test_compose_not_aligned(self):
total = 0
reader = paddle.v2.reader.compose(
paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader_creator_10(0))
with self.assertRaises(paddle.v2.reader.ComposeNotAligned):
for e in reader():
total += 1
# expecting 10, not 20
self.assertEqual(total, 10)
def test_compose_not_aligned_no_check(self):
total = 0
reader = paddle.v2.reader.compose(
paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader_creator_10(0),
check_alignment=False)
for e in reader():
total += 1
# expecting 10, not 20
self.assertEqual(total, 10)
class TestChain(unittest.TestCase):
def test_chain(self):
c = paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0))
idx = 0
for e in c():
self.assertEqual(e, idx % 10)
idx += 1
self.assertEqual(idx, 20)
class TestShuffle(unittest.TestCase):
def test_shuffle(self):
case = [(0, True), (1, True), (10, False), (100, False)]
a = reader_creator_10(0)
for size, checkEq in case:
s = paddle.v2.reader.shuffle(a, size)
total = 0
for idx, e in enumerate(s()):
if checkEq:
self.assertEqual(idx, e)
total += 1
self.assertEqual(total, 10)
class TestXmap(unittest.TestCase):
def test_xmap(self):
def mapper(x):
return (x + 1)
orders = (True, False)
thread_nums = (1, 2, 4, 8, 16)
buffered_size = (1, 2, 4, 8, 16)
for order in orders:
for tNum in thread_nums:
for size in buffered_size:
reader = paddle.v2.reader.xmap_readers(mapper,
reader_creator_10(0),
tNum, size, order)
for n in xrange(3):
result = []
for i in reader():
result.append(i)
if not order:
result.sort()
for idx, e in enumerate(result):
self.assertEqual(e, mapper(idx))
class TestPipeReader(unittest.TestCase):
def test_pipe_reader(self):
def example_reader(myfiles):
for f in myfiles:
pr = paddle.v2.reader.PipeReader("cat %s" % f, bufsize=128)
for l in pr.get_line():
yield l
import tempfile
records = [str(i) for i in xrange(5)]
temp = tempfile.NamedTemporaryFile()
try:
with open(temp.name, 'w') as f:
for r in records:
f.write('%s\n' % r)
result = []
for r in example_reader([temp.name]):
result.append(r)
for idx, e in enumerate(records):
self.assertEqual(e, result[idx])
finally:
# delete the temporary file
temp.close()
if __name__ == '__main__':
unittest.main()
| 5,561 | 30.072626 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/reader/tests/__init__.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| 612 | 42.785714 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/master/client.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ctypes
import os
__lib__ = None
def get_c_lib():
global __lib__
if __lib__ is None:
path = os.path.join(os.path.dirname(__file__), "libpaddle_master.so")
__lib__ = ctypes.cdll.LoadLibrary(path)
return __lib__
class client(object):
"""
client is a client to the master server.
"""
def __init__(self, etcd_endpoints, timeout_sec, buf_size=0):
self.c = get_c_lib().paddle_new_etcd_master_client(
etcd_endpoints, timeout_sec, buf_size)
def request_save_model(self, trainer_id, block_ms):
"""request to save model
Conventionally the 0-th trainer will save model. But in
distributed training, any trainer could be killed. This
function asks the master server if the trainer should proceed
with saving model.
:param trainer_id: trainer id.
:param block_ms: number of millisecond that other save model
will be blocked if this save model request succeeded.
Returns:
int: 1 if the save the model request is approved, 0 if
does the request is rejected because other trainer is
saving the model, -1 if error happened.
"""
return get_c_lib().paddle_request_save_model(self.c, trainer_id,
block_ms)
def release(self):
get_c_lib().paddle_release_master_client(self.c)
self.c = None
def set_dataset(self, paths):
holder_type = ctypes.c_char_p * len(paths)
holder = holder_type()
for idx, path in enumerate(paths):
c_ptr = ctypes.c_char_p(path)
holder[idx] = c_ptr
get_c_lib().paddle_set_dataset(self.c, holder, len(paths))
def next_record(self):
"""gets next record for training
Returns:
string: the record.
int: error code, 0 if successful, < 0 otherwise.
"""
p = ctypes.c_char_p()
ret = ctypes.pointer(p)
size = get_c_lib().paddle_next_record(self.c, ret)
if size < 0:
# Error
return None, size
if size == 0:
# Empty record
return "", 0
record = ret.contents.value[:size]
# Memory created from C should be freed.
get_c_lib().mem_free(ret.contents)
return record, 0
def paddle_start_get_records(self, pass_id):
get_c_lib().paddle_start_get_records(self.c, pass_id)
| 3,097 | 31.270833 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/v2/master/__init__.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from client import *
__all__ = ['client']
| 656 | 35.5 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/reader/decorator.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = [
'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
'ComposeNotAligned', 'firstn', 'xmap_readers', 'PipeReader'
]
from threading import Thread
import subprocess
from Queue import Queue
import itertools
import random
import zlib
def map_readers(func, *readers):
"""
Creates a data reader that outputs return value of function using
output of each data readers as arguments.
:param func: function to use. The type of func should be (Sample) => Sample
:type: callable
:param readers: readers whose outputs will be used as arguments of func.
:return: the created data reader.
:rtype: callable
"""
def reader():
rs = []
for r in readers:
rs.append(r())
for e in itertools.imap(func, *rs):
yield e
return reader
def shuffle(reader, buf_size):
"""
Creates a data reader whose data output is shuffled.
Output from the iterator that created by original reader will be
buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
is determined by argument buf_size.
:param reader: the original reader whose output will be shuffled.
:type reader: callable
:param buf_size: shuffle buffer size.
:type buf_size: int
:return: the new reader whose output is shuffled.
:rtype: callable
"""
def data_reader():
buf = []
for e in reader():
buf.append(e)
if len(buf) >= buf_size:
random.shuffle(buf)
for b in buf:
yield b
buf = []
if len(buf) > 0:
random.shuffle(buf)
for b in buf:
yield b
return data_reader
def chain(*readers):
"""
Creates a data reader whose output is the outputs of input data
readers chained together.
If input readers output following data entries:
[0, 0, 0]
[1, 1, 1]
[2, 2, 2]
The chained reader will output:
[0, 0, 0, 1, 1, 1, 2, 2, 2]
:param readers: input readers.
:return: the new data reader.
:rtype: callable
"""
def reader():
rs = []
for r in readers:
rs.append(r())
for e in itertools.chain(*rs):
yield e
return reader
class ComposeNotAligned(ValueError):
pass
def compose(*readers, **kwargs):
"""
Creates a data reader whose output is the combination of input readers.
If input readers output following data entries:
(1, 2) 3 (4, 5)
The composed reader will output:
(1, 2, 3, 4, 5)
:param readers: readers that will be composed together.
:param check_alignment: if True, will check if input readers are aligned
correctly. If False, will not check alignment and trailing outputs
will be discarded. Defaults to True.
:type check_alignment: bool
:return: the new data reader.
:raises ComposeNotAligned: outputs of readers are not aligned.
Will not raise when check_alignment is set to False.
"""
check_alignment = kwargs.pop('check_alignment', True)
def make_tuple(x):
if isinstance(x, tuple):
return x
else:
return (x, )
def reader():
rs = []
for r in readers:
rs.append(r())
if not check_alignment:
for outputs in itertools.izip(*rs):
yield sum(map(make_tuple, outputs), ())
else:
for outputs in itertools.izip_longest(*rs):
for o in outputs:
if o is None:
# None will be not be present if compose is aligned
raise ComposeNotAligned(
"outputs of readers are not aligned.")
yield sum(map(make_tuple, outputs), ())
return reader
def buffered(reader, size):
"""
Creates a buffered data reader.
The buffered data reader will read and save data entries into a
buffer. Reading from the buffered data reader will proceed as long
as the buffer is not empty.
:param reader: the data reader to read from.
:type reader: callable
:param size: max buffer size.
:type size: int
:returns: the buffered data reader.
"""
class EndSignal():
pass
end = EndSignal()
def read_worker(r, q):
for d in r:
q.put(d)
q.put(end)
def data_reader():
r = reader()
q = Queue(maxsize=size)
t = Thread(
target=read_worker, args=(
r,
q, ))
t.daemon = True
t.start()
e = q.get()
while e != end:
yield e
e = q.get()
return data_reader
def firstn(reader, n):
"""
Limit the max number of samples that reader could return.
:param reader: the data reader to read from.
:type reader: callable
:param n: the max number of samples that return.
:type n: int
:return: the decorated reader.
:rtype: callable
"""
# TODO(yuyang18): Check if just drop the reader, could clean the opened
# resource or not?
def firstn_reader():
for i, item in enumerate(reader()):
if i == n:
break
yield item
return firstn_reader
class XmapEndSignal():
pass
def xmap_readers(mapper, reader, process_num, buffer_size, order=False):
"""
Use multiprocess to map samples from reader by a mapper defined by user.
And this function contains a buffered decorator.
:param mapper: a function to map sample.
:type mapper: callable
:param reader: the data reader to read from
:type reader: callable
:param process_num: process number to handle original sample
:type process_num: int
:param buffer_size: max buffer size
:type buffer_size: int
:param order: keep the order of reader
:type order: bool
:return: the decarated reader
:rtype: callable
"""
end = XmapEndSignal()
# define a worker to read samples from reader to in_queue
def read_worker(reader, in_queue):
for i in reader():
in_queue.put(i)
in_queue.put(end)
# define a worker to read samples from reader to in_queue with order flag
def order_read_worker(reader, in_queue):
in_order = 0
for i in reader():
in_queue.put((in_order, i))
in_order += 1
in_queue.put(end)
# define a worker to handle samples from in_queue by mapper
# and put mapped samples into out_queue
def handle_worker(in_queue, out_queue, mapper):
sample = in_queue.get()
while not isinstance(sample, XmapEndSignal):
r = mapper(sample)
out_queue.put(r)
sample = in_queue.get()
in_queue.put(end)
out_queue.put(end)
# define a worker to handle samples from in_queue by mapper
# and put mapped samples into out_queue by order
def order_handle_worker(in_queue, out_queue, mapper, out_order):
ins = in_queue.get()
while not isinstance(ins, XmapEndSignal):
order, sample = ins
r = mapper(sample)
while order != out_order[0]:
pass
out_queue.put(r)
out_order[0] += 1
ins = in_queue.get()
in_queue.put(end)
out_queue.put(end)
def xreader():
in_queue = Queue(buffer_size)
out_queue = Queue(buffer_size)
out_order = [0]
# start a read worker in a thread
target = order_read_worker if order else read_worker
t = Thread(target=target, args=(reader, in_queue))
t.daemon = True
t.start()
# start several handle_workers
target = order_handle_worker if order else handle_worker
args = (in_queue, out_queue, mapper, out_order) if order else (
in_queue, out_queue, mapper)
workers = []
for i in xrange(process_num):
worker = Thread(target=target, args=args)
worker.daemon = True
workers.append(worker)
for w in workers:
w.start()
sample = out_queue.get()
while not isinstance(sample, XmapEndSignal):
yield sample
sample = out_queue.get()
finish = 1
while finish < process_num:
sample = out_queue.get()
if isinstance(sample, XmapEndSignal):
finish += 1
else:
yield sample
return xreader
def _buf2lines(buf, line_break="\n"):
# FIXME: line_break should be automatically configured.
lines = buf.split(line_break)
return lines[:-1], lines[-1]
class PipeReader:
"""
PipeReader read data by stream from a command, take it's
stdout into a pipe buffer and redirect it to the parser to
parse, then yield data as your desired format.
You can using standard linux command or call another program
to read data, from HDFS, Ceph, URL, AWS S3 etc:
.. code-block:: python
cmd = "hadoop fs -cat /path/to/some/file"
cmd = "cat sample_file.tar.gz"
cmd = "curl http://someurl"
cmd = "python print_s3_bucket.py"
An example:
.. code-block:: python
def example_reader():
for f in myfiles:
pr = PipeReader("cat %s"%f)
for l in pr.get_line():
sample = l.split(" ")
yield sample
"""
def __init__(self, command, bufsize=8192, file_type="plain"):
if not isinstance(command, str):
raise TypeError("left_cmd must be a string")
if file_type == "gzip":
self.dec = zlib.decompressobj(
32 + zlib.MAX_WBITS) # offset 32 to skip the header
self.file_type = file_type
self.bufsize = bufsize
self.process = subprocess.Popen(
command.split(" "), bufsize=bufsize, stdout=subprocess.PIPE)
def get_line(self, cut_lines=True, line_break="\n"):
"""
:param cut_lines: cut buffer to lines
:type cut_lines: bool
:param line_break: line break of the file, like \n or \r
:type line_break: string
:return: one line or a buffer of bytes
:rtype: string
"""
remained = ""
while True:
buff = self.process.stdout.read(self.bufsize)
if buff:
if self.file_type == "gzip":
decomp_buff = self.dec.decompress(buff)
elif self.file_type == "plain":
decomp_buff = buff
else:
raise TypeError("file_type %s is not allowed" %
self.file_type)
if cut_lines:
lines, remained = _buf2lines(''.join(
[remained, decomp_buff]), line_break)
for line in lines:
yield line
else:
yield decomp_buff
else:
break
| 11,823 | 28.123153 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/reader/__init__.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
At training and testing time, PaddlePaddle programs need to read data. To ease
the users' work to write data reading code, we define that
- A *reader* is a function that reads data (from file, network, random number
generator, etc) and yields data items.
- A *reader creator* is a function that returns a reader function.
- A *reader decorator* is a function, which accepts one or more readers, and
returns a reader.
- A *batch reader* is a function that reads data (from *reader*, file, network,
random number generator, etc) and yields a batch of data items.
#####################
Data Reader Interface
#####################
Indeed, *data reader* doesn't have to be a function that reads and yields data
items. It can be any function with no parameter that creates a iterable
(anything can be used in :code:`for x in iterable`)\:
.. code-block:: python
iterable = data_reader()
Element produced from the iterable should be a **single** entry of data,
**not** a mini batch. That entry of data could be a single item, or a tuple of
items.
Item should be of `supported type <http://www.paddlepaddle.org/doc/ui/data_provider
/pydataprovider2.html?highlight=dense_vector#input-types>`_ (e.g., numpy 1d
array of float32, int, list of int)
An example implementation for single item data reader creator:
.. code-block:: python
def reader_creator_random_image(width, height):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height)
return reader
An example implementation for multiple item data reader creator:
.. code-block:: python
def reader_creator_random_image_and_label(width, height, label):
def reader():
while True:
yield numpy.random.uniform(-1, 1, size=width*height), label
return reader
TODO(yuyang18): Should we add whole design doc here?
"""
import decorator
from decorator import *
import creator
__all__ = decorator.__all__ + ['creator']
| 2,601 | 33.693333 | 83 |
py
|
Paddle
|
Paddle-master/python/paddle/reader/creator.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Creator package contains some simple reader creator, which could
be used in user program.
"""
__all__ = ['np_array', 'text_file', 'recordio']
def np_array(x):
"""
Creates a reader that yields elements of x, if it is a
numpy vector. Or rows of x, if it is a numpy matrix.
Or any sub-hyperplane indexed by the highest dimension.
:param x: the numpy array to create reader from.
:returns: data reader created from x.
"""
def reader():
if x.ndim < 1:
yield x
for e in x:
yield e
return reader
def text_file(path):
"""
Creates a data reader that outputs text line by line from given text file.
Trailing new line ('\\\\n') of each line will be removed.
:path: path of the text file.
:returns: data reader of text file
"""
def reader():
f = open(path, "r")
for l in f:
yield l.rstrip('\n')
f.close()
return reader
def recordio(paths, buf_size=100):
"""
Creates a data reader from given RecordIO file paths separated by ",",
glob pattern is supported.
:path: path of recordio files, can be a string or a string list.
:returns: data reader of recordio files.
"""
import recordio as rec
import paddle.reader.decorator as dec
import cPickle as pickle
def reader():
if isinstance(paths, basestring):
path = paths
else:
path = ",".join(paths)
f = rec.reader(path)
while True:
r = f.read()
if r is None:
break
yield pickle.loads(r)
f.close()
return dec.buffered(reader, buf_size)
| 2,304 | 25.802326 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/reader/tests/creator_test.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright PaddlePaddle contributors. All Rights Reservedd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
import numpy as np
import paddle.reader.creator
class TestNumpyArray(unittest.TestCase):
def test_numpy_array(self):
l = [[1, 2, 3], [4, 5, 6]]
x = np.array(l, np.int32)
reader = paddle.reader.creator.np_array(x)
for idx, e in enumerate(reader()):
self.assertItemsEqual(e, l[idx])
class TestTextFile(unittest.TestCase):
def test_text_file(self):
path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt")
reader = paddle.reader.creator.text_file(path)
for idx, e in enumerate(reader()):
self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
class TestRecordIO(unittest.TestCase):
def do_test(self, path):
reader = paddle.reader.creator.recordio(path)
idx = 0
for e in reader():
if idx == 0:
self.assertEqual(e, (1, 2, 3))
elif idx == 1:
self.assertEqual(e, (4, 5, 6))
idx += 1
self.assertEqual(idx, 2)
def test_recordIO(self):
self.do_test(
os.path.join(
os.path.dirname(__file__), "test_reader_recordio.dat"))
self.do_test([
os.path.join(
os.path.dirname(__file__), "test_reader_recordio.dat")
])
if __name__ == '__main__':
unittest.main()
| 2,593 | 33.586667 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/reader/tests/decorator_test.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import unittest
import paddle.reader
def reader_creator_10(dur):
def reader():
for i in range(10):
# this invocation helps testing paddle.reader.buffer
time.sleep(dur)
yield i
return reader
class TestMap(unittest.TestCase):
def test_map(self):
d = {"h": 0, "i": 1}
def tokenize(x):
return d[x]
def read():
yield "h"
yield "i"
r = paddle.reader.map_readers(tokenize, read)
for i, e in enumerate(r()):
self.assertEqual(e, i)
class TestBuffered(unittest.TestCase):
def test_read(self):
for size in range(20):
b = paddle.reader.buffered(reader_creator_10(0), size)
c = 0
for i in b():
self.assertEqual(i, c)
c += 1
self.assertEqual(c, 10)
def test_buffering(self):
# read have 30ms delay.
b = paddle.reader.buffered(reader_creator_10(0.03), 10)
last_time = time.time()
for idx, i in enumerate(b()):
elapsed_time = time.time() - last_time
if i == 0:
time.sleep(0.3)
else:
# read time should be short, meaning already buffered.
self.assertLess(elapsed_time, 0.05)
last_time = time.time()
class TestCompose(unittest.TestCase):
def test_compse(self):
reader = paddle.reader.compose(
reader_creator_10(0), reader_creator_10(0))
for idx, e in enumerate(reader()):
self.assertEqual(e, (idx, idx))
def test_compose_not_aligned(self):
total = 0
reader = paddle.reader.compose(
paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader_creator_10(0))
with self.assertRaises(paddle.reader.ComposeNotAligned):
for e in reader():
total += 1
# expecting 10, not 20
self.assertEqual(total, 10)
def test_compose_not_aligned_no_check(self):
total = 0
reader = paddle.reader.compose(
paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader_creator_10(0),
check_alignment=False)
for e in reader():
total += 1
# expecting 10, not 20
self.assertEqual(total, 10)
class TestChain(unittest.TestCase):
def test_chain(self):
c = paddle.reader.chain(reader_creator_10(0), reader_creator_10(0))
idx = 0
for e in c():
self.assertEqual(e, idx % 10)
idx += 1
self.assertEqual(idx, 20)
class TestShuffle(unittest.TestCase):
def test_shuffle(self):
case = [(0, True), (1, True), (10, False), (100, False)]
a = reader_creator_10(0)
for size, checkEq in case:
s = paddle.reader.shuffle(a, size)
total = 0
for idx, e in enumerate(s()):
if checkEq:
self.assertEqual(idx, e)
total += 1
self.assertEqual(total, 10)
class TestXmap(unittest.TestCase):
def test_xmap(self):
def mapper(x):
return (x + 1)
orders = (True, False)
thread_nums = (1, 2, 4, 8, 16)
buffered_size = (1, 2, 4, 8, 16)
for order in orders:
for tNum in thread_nums:
for size in buffered_size:
reader = paddle.reader.xmap_readers(mapper,
reader_creator_10(0),
tNum, size, order)
for n in xrange(3):
result = []
for i in reader():
result.append(i)
if not order:
result.sort()
for idx, e in enumerate(result):
self.assertEqual(e, mapper(idx))
class TestPipeReader(unittest.TestCase):
def test_pipe_reader(self):
def example_reader(myfiles):
for f in myfiles:
pr = paddle.reader.PipeReader("cat %s" % f, bufsize=128)
for l in pr.get_line():
yield l
import tempfile
records = [str(i) for i in xrange(5)]
temp = tempfile.NamedTemporaryFile()
try:
with open(temp.name, 'w') as f:
for r in records:
f.write('%s\n' % r)
result = []
for r in example_reader([temp.name]):
result.append(r)
for idx, e in enumerate(records):
self.assertEqual(e, result[idx])
finally:
# delete the temporary file
temp.close()
if __name__ == '__main__':
unittest.main()
| 5,513 | 29.804469 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/reader/tests/__init__.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| 612 | 42.785714 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/predefined_net.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import os
from paddle.trainer.config_parser import *
from paddle.utils.preprocess_img import \
ImageClassificationDatasetCreater
from paddle.trainer_config_helpers import *
def image_data(data_dir,
processed_image_size,
overwrite=False,
color=True,
train_list="batches/train.list",
test_list="batches/test.list",
meta_file="batches/batches.meta",
use_jpeg=1):
"""
Predefined image data provider for image classification.
train_list: a text file containing a list of training batches.
test_list: a text file containing a list of test batches.
processed_image_size: all the input images will be resized into this size.
If the image is not square. Then the shorter edge will be resized into
this size, and the aspect ratio is kept the same.
color: whether the images are color or gray.
meta_path: the path of the meta file that stores the mean image file and
other dataset information, such as the size of images,
the size of the mean image, the number of classes.
async_load_data: whether to load image data asynchronuously.
"""
data_creator = ImageClassificationDatasetCreater(
data_dir, processed_image_size, color)
batch_data_dir = data_dir
train_list = os.path.join(batch_data_dir, train_list)
test_list = os.path.join(batch_data_dir, test_list)
meta_path = os.path.join(batch_data_dir, meta_file)
image_size = processed_image_size
conf = np.load(meta_path)
mean_image_size = conf["mean_image_size"]
is_color = conf["color"]
num_classes = conf["num_classes"]
color_string = "color" if is_color else "gray"
args = {
'meta': meta_path,
'mean_img_size': mean_image_size,
'img_size': image_size,
'num_classes': num_classes,
'use_jpeg': use_jpeg != 0,
'color': color_string
}
define_py_data_sources2(
train_list,
test_list,
module='image_provider',
obj='processData',
args=args)
return {
"image_size": image_size,
"num_classes": num_classes,
"is_color": is_color
}
def get_extra_layer_attr(drop_rate):
if drop_rate == 0:
return None
else:
return ExtraLayerAttribute(drop_rate=drop_rate)
def image_data_layers(image_size, num_classes, is_color=False,
is_predict=False):
"""
Data layers for image classification.
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
is_predict: whether the network is used for prediction.
"""
num_image_channels = 3 if is_color else 1
data_input = data_layer("input",
image_size * image_size * num_image_channels)
if is_predict:
return data_input, None, num_image_channels
else:
label_input = data_layer("label", 1)
return data_input, label_input, num_image_channels
def simple_conv_net(data_conf, is_color=False):
"""
A Wrapper for a simple network for MNIST digit recognition.
It contains two convolutional layers, one fully conencted layer, and
one softmax layer.
data_conf is a dictionary with the following keys:
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
"""
for k, v in data_conf.iteritems():
globals()[k] = v
data_input, label_input, num_image_channels = \
image_data_layers(image_size, num_classes, is_color, is_predict)
filter_sizes = [5, 5]
num_channels = [32, 64]
strides = [1, 1]
fc_dims = [500]
conv_bn_pool1 = img_conv_bn_pool(
name="g1",
input=data_input,
filter_size=filter_sizes[0],
num_channel=num_image_channels,
num_filters=num_channels[0],
conv_stride=1,
conv_padding=0,
pool_size=3,
pool_stride=2,
act=ReluActivation())
conv_bn_pool2 = img_conv_bn_pool(
name="g2",
input=conv_bn_pool1,
filter_size=filter_sizes[1],
num_channel=num_channels[0],
num_filters=num_channels[1],
conv_stride=1,
conv_padding=0,
pool_size=3,
pool_stride=2,
act=ReluActivation())
fc3 = fc_layer(
name="fc3", input=conv_bn_pool2, dim=fc_dims[0], act=ReluActivation())
fc3_dropped = dropout_layer(name="fc3_dropped", input=fc3, dropout_rate=0.5)
output = fc_layer(
name="output",
input=fc3_dropped,
dim=fc_dims[0],
act=SoftmaxActivation())
if is_predict:
end_of_network(output)
else:
cost = classify(name="cost", input=output, label=label_input)
end_of_network(cost)
def conv_layer_group(prefix_num,
num_layers,
input,
input_channels,
output_channels,
drop_rates=[],
strides=[],
with_bn=[]):
"""
A set of convolution layers, and batch normalization layers,
followed by one pooling layer.
It is utilized in VGG network for image classifcation.
prefix_num: the prefix number of the layer names.
For example, if prefix_num = 1, the first convolutioal layer's
name will be conv_1_1.
num_layers: number of the convolutional layers.
input: the name of the input layer.
input_channels: the number of channels of the input feature map.
output_channels: the number of channels of the output feature map.
drop_rates: the drop rates of the BN layers. It will be all zero by default.
strides: the stride of the convolution for the layers.
It will be all 1 by default.
with_bn: whether to use Batch Normalization for Conv layers.
By default, it is all false.
"""
if len(drop_rates) == 0: drop_rates = [0] * num_layers
if len(strides) == 0: strides = [1] * num_layers
if len(with_bn) == 0: with_bn = [False] * num_layers
assert (len(drop_rates) == num_layers)
assert (len(strides) == num_layers)
for i in range(1, num_layers + 1):
if i == 1:
i_conv_in = input
else:
i_conv_in = group_output
i_channels_conv = input_channels if i == 1 else output_channels
conv_act = LinearActivation() if with_bn[i - 1] else ReluActivation()
conv_output = img_conv_layer(
name="conv%d_%d" % (prefix_num, i),
input=i_conv_in,
filter_size=3,
num_channels=i_channels_conv,
num_filters=output_channels,
stride=strides[i - 1],
padding=1,
act=conv_act)
if with_bn[i - 1]:
bn = batch_norm_layer(
name="conv%d_%d_bn" % (prefix_num, i),
input=conv_output,
num_channels=output_channels,
act=ReluActivation(),
layer_attr=get_extra_layer_attr(drop_rate=drop_rates[i - 1]))
group_output = bn
else:
group_output = conv_output
pool = img_pool_layer(
name="pool%d" % prefix_num,
input=group_output,
pool_size=2,
num_channels=output_channels,
stride=2)
return pool
def vgg_conv_net(image_size,
num_classes,
num_layers,
channels,
strides,
with_bn,
fc_dims,
drop_rates,
drop_rates_fc=[],
is_color=True,
is_predict=False):
"""
A Wrapper for a VGG network for image classification.
It is a set of convolutional groups followed by several fully
connected layers, and a cross-entropy classifiation loss.
The detailed architecture of the paper can be found here:
Very Deep Convolutional Networks for Large-Scale Visual Recognition
http://www.robots.ox.ac.uk/~vgg/research/very_deep/
image_size: image size.
num_classes: num of classes.
num_layers: the number of layers for all the convolution groups.
channels: the number of output filters for all the convolution groups.
with_bn: whether each layer of a convolution group is followed by a
batch normalization.
drop_rates: the dropout rates for all the convolutional layers.
fc_dims: the dimension for all the fully connected layers.
is_color: whether the input images are color.
"""
data_input, label_input, num_image_channels = \
image_data_layers(image_size, num_classes, is_color, is_predict)
assert (len(num_layers) == len(channels))
assert (len(num_layers) == len(strides))
assert (len(num_layers) == len(with_bn))
num_fc_layers = len(fc_dims)
assert (num_fc_layers + 1 == len(drop_rates_fc))
for i in range(len(num_layers)):
input_layer = data_input if i == 0 else group_output
input_channels = 3 if i == 0 else channels[i - 1]
group_output = conv_layer_group(
prefix_num=i + 1,
num_layers=num_layers[i],
input=input_layer,
input_channels=input_channels,
output_channels=channels[i],
drop_rates=drop_rates[i],
strides=strides[i],
with_bn=with_bn[i])
conv_output_name = group_output
if drop_rates_fc[0] != 0.0:
dropped_pool_name = "pool_dropped"
conv_output_name = dropout_layer(
name=dropped_pool_name,
input=conv_output_name,
dropout_rate=drop_rates_fc[0])
for i in range(len(fc_dims)):
input_layer_name = conv_output_name if i == 0 else fc_output
active_type = LinearActivation() if i == len(
fc_dims) - 1 else ReluActivation()
drop_rate = 0.0 if i == len(fc_dims) - 1 else drop_rates_fc[i + 1]
fc_output = fc_layer(
name="fc%d" % (i + 1),
input=input_layer_name,
size=fc_dims[i],
act=active_type,
layer_attr=get_extra_layer_attr(drop_rate))
bn = batch_norm_layer(
name="fc_bn",
input=fc_output,
num_channels=fc_dims[len(fc_dims) - 1],
act=ReluActivation(),
layer_attr=get_extra_layer_attr(drop_rate=drop_rates_fc[-1]))
output = fc_layer(
name="output", input=bn, size=num_classes, act=SoftmaxActivation())
if is_predict:
outputs(output)
else:
cost = classification_cost(name="cost", input=output, label=label_input)
outputs(cost)
def vgg16_conv_net(image_size, num_classes, is_color=True, is_predict=False):
"""
A Wrapper for a 16 layers VGG network for image classification.
The detailed architecture of the paper can be found here:
Very Deep Convolutional Networks for Large-Scale Visual Recognition
http://www.robots.ox.ac.uk/~vgg/research/very_deep/
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
"""
vgg_conv_net(image_size, num_classes,
num_layers=[2, 2, 3, 3, 3],
channels=[64, 128, 256, 512, 512],
strides=[[], [], [], [], []],
with_bn=[[False, True], [False, True], [False, False, True], \
[False, False, True], [False, False, True]],
drop_rates=[[]] * 5,
drop_rates_fc=[0.0, 0.5, 0.5],
fc_dims=[4096, 4096],
is_predict=is_predict)
def small_vgg(data_conf, is_predict=False):
"""
A Wrapper for a small VGG network for CIFAR-10 image classification.
The detailed architecture of the paper can be found here:
92.45% on CIFAR-10 in Torch
http://torch.ch/blog/2015/07/30/cifar.html
Due to the constraints of CuDNN, it only has four convolutional groups
rather than five.
Thus, it only achieves 91.2% test accuracy and 98.1% training accuracy.
data_conf is a dictionary with the following keys:
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
"""
for k, v in data_conf.iteritems():
globals()[k] = v
vgg_conv_net(image_size, num_classes,
num_layers=[2, 2, 3, 3],
channels=[64, 128, 256, 512],
strides=[[], [], [], []],
with_bn=[[True, True], [True, True], [True, True, True], \
[True, True, True]],
drop_rates=[[0.3, 0.0], [0.4, 0.0],
[0.4, 0.4, 0.0], [0.4, 0.4, 0.0]],
drop_rates_fc=[0.5, 0.5],
fc_dims=[512],
is_predict=is_predict)
def training_settings(learning_rate=0.1,
batch_size=128,
algorithm="sgd",
momentum=0.9,
decay_rate=0.001):
"""
Training settings.
learning_rate: learning rate of the training.
batch_size: the size of each training batch.
algorithm: training algorithm, can be
- sgd
- adagrad
- adadelta
- rmsprop
momentum: momentum of the training algorithm.
decay_rate: weight decay rate.
"""
Settings(
algorithm=algorithm,
batch_size=batch_size,
learning_rate=learning_rate / float(batch_size))
default_momentum(momentum)
default_decay_rate(decay_rate * batch_size)
| 14,269 | 36.454068 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/plotcurve.py
|
#!/usr/bin/python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Plot training and testing curve from paddle log.
It takes input from a file or stdin, and output to a file or stdout.
Note: must have numpy and matplotlib installed in order to use this tool.
usage: Plot training and testing curves from paddle log file.
[-h] [-i INPUT] [-o OUTPUT] [--format FORMAT] [key [key ...]]
positional arguments:
key keys of scores to plot, the default will be AvgCost
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
input filename of paddle log, default will be standard
input
-o OUTPUT, --output OUTPUT
output filename of figure, default will be standard
output
--format FORMAT figure format(png|pdf|ps|eps|svg)
The keys must be in the order of paddle output(!!!).
For example, paddle.INFO contrains the following log
I0406 21:26:21.325584 3832 Trainer.cpp:601] Pass=0 Batch=7771 AvgCost=0.624935 Eval: error=0.260972
To use this script to generate plot for AvgCost, error:
python plotcurve.py -i paddle.INFO -o figure.png AvgCost error
"""
import sys
import matplotlib
# the following line is added immediately after import matplotlib
# and before import pylot. The purpose is to ensure the plotting
# works even under remote login (i.e. headless display)
matplotlib.use('Agg')
from matplotlib import cm
import matplotlib.pyplot as pyplot
import numpy
import argparse
import re
import os
def plot_paddle_curve(keys, inputfile, outputfile, format='png',
show_fig=False):
"""Plot curves from paddle log and save to outputfile.
:param keys: a list of strings to be plotted, e.g. AvgCost
:param inputfile: a file object for input
:param outputfile: a file object for output
:return: None
"""
pass_pattern = r"Pass=([0-9]*)"
test_pattern = r"Test samples=([0-9]*)"
if not keys:
keys = ['AvgCost']
for k in keys:
pass_pattern += r".*?%s=([0-9e\-\.]*)" % k
test_pattern += r".*?%s=([0-9e\-\.]*)" % k
data = []
test_data = []
compiled_pattern = re.compile(pass_pattern)
compiled_test_pattern = re.compile(test_pattern)
for line in inputfile:
found = compiled_pattern.search(line)
found_test = compiled_test_pattern.search(line)
if found:
data.append([float(x) for x in found.groups()])
if found_test:
test_data.append([float(x) for x in found_test.groups()])
x = numpy.array(data)
x_test = numpy.array(test_data)
if x.shape[0] <= 0:
sys.stderr.write("No data to plot. Exiting!\n")
return
m = len(keys) + 1
for i in xrange(1, m):
pyplot.plot(
x[:, 0],
x[:, i],
color=cm.jet(1.0 * (i - 1) / (2 * m)),
label=keys[i - 1])
if (x_test.shape[0] > 0):
pyplot.plot(
x[:, 0],
x_test[:, i],
color=cm.jet(1.0 - 1.0 * (i - 1) / (2 * m)),
label="Test " + keys[i - 1])
pyplot.xlabel('number of epoch')
pyplot.legend(loc='best')
if show_fig:
pyplot.show()
pyplot.savefig(outputfile, bbox_inches='tight')
pyplot.clf()
def main(argv):
"""
main method of plotting curves.
"""
cmdparser = argparse.ArgumentParser(
"Plot training and testing curves from paddle log file.")
cmdparser.add_argument(
'key', nargs='*', help='keys of scores to plot, the default is AvgCost')
cmdparser.add_argument(
'-i',
'--input',
help='input filename of paddle log, '
'default will be standard input')
cmdparser.add_argument(
'-o',
'--output',
help='output filename of figure, '
'default will be standard output')
cmdparser.add_argument('--format', help='figure format(png|pdf|ps|eps|svg)')
args = cmdparser.parse_args(argv)
keys = args.key
if args.input:
inputfile = open(args.input)
else:
inputfile = sys.stdin
format = args.format
if args.output:
outputfile = open(args.output, 'wb')
if not format:
format = os.path.splitext(args.output)[1]
if not format:
format = 'png'
else:
outputfile = sys.stdout
plot_paddle_curve(keys, inputfile, outputfile, format)
inputfile.close()
outputfile.close()
if __name__ == "__main__":
main(sys.argv[1:])
| 5,166 | 32.335484 | 104 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/make_model_diagram.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Generate dot diagram file for the given paddle model config
# The generated file can be viewed using Graphviz (http://graphviz.org)
import sys
import traceback
from paddle.trainer.config_parser import parse_config
def make_layer_label(layer_config):
label = '%s type=%s' % (layer_config.name, layer_config.type)
if layer_config.reversed:
label += ' <=='
label2 = ''
if layer_config.active_type:
label2 += 'act=%s ' % layer_config.active_type
if layer_config.bias_parameter_name:
label2 += 'bias=%s ' % layer_config.bias_parameter_name
if label2:
label += '\l' + label2
return label
def make_diagram(config_file, dot_file, config_arg_str):
config = parse_config(config_file, config_arg_str)
make_diagram_from_proto(config.model_config, dot_file)
def make_diagram_from_proto(model_config, dot_file):
# print >> sys.stderr, config
name2id = {}
f = open(dot_file, 'w')
submodel_layers = set()
def make_link(link):
return 'l%s -> l%s;' % (name2id[link.layer_name],
name2id[link.link_name])
def make_mem(mem):
s = ''
if mem.boot_layer_name:
s += 'l%s -> l%s;\n' % (name2id[mem.boot_layer_name],
name2id[mem.layer_name])
s += 'l%s -> l%s [style=dashed];' % (name2id[mem.layer_name],
name2id[mem.link_name])
return s
print >> f, 'digraph graphname {'
print >> f, 'node [width=0.375,height=0.25];'
for i in xrange(len(model_config.layers)):
l = model_config.layers[i]
name2id[l.name] = i
i = 0
for sub_model in model_config.sub_models:
if sub_model.name == 'root':
continue
print >> f, 'subgraph cluster_%s {' % i
print >> f, 'style=dashed;'
label = '%s ' % sub_model.name
if sub_model.reversed:
label += '<=='
print >> f, 'label = "%s";' % label
i += 1
submodel_layers.add(sub_model.name)
for layer_name in sub_model.layer_names:
submodel_layers.add(layer_name)
lid = name2id[layer_name]
layer_config = model_config.layers[lid]
label = make_layer_label(layer_config)
print >> f, 'l%s [label="%s", shape=box];' % (lid, label)
print >> f, '}'
for i in xrange(len(model_config.layers)):
l = model_config.layers[i]
if l.name not in submodel_layers:
label = make_layer_label(l)
print >> f, 'l%s [label="%s", shape=box];' % (i, label)
for sub_model in model_config.sub_models:
if sub_model.name == 'root':
continue
for link in sub_model.in_links:
print >> f, make_link(link)
for link in sub_model.out_links:
print >> f, make_link(link)
for mem in sub_model.memories:
print >> f, make_mem(mem)
for i in xrange(len(model_config.layers)):
for l in model_config.layers[i].inputs:
print >> f, 'l%s -> l%s [label="%s"];' % (
name2id[l.input_layer_name], i, l.input_parameter_name)
print >> f, '}'
f.close()
def usage():
print >> sys.stderr, ("Usage: python show_model_diagram.py" +
" CONFIG_FILE DOT_FILE [config_str]")
exit(1)
if __name__ == '__main__':
if len(sys.argv) < 3 or len(sys.argv) > 4:
usage()
config_file = sys.argv[1]
dot_file = sys.argv[2]
config_arg_str = sys.argv[3] if len(sys.argv) == 4 else ''
try:
make_diagram(config_file, dot_file, config_arg_str)
except:
traceback.print_exc()
raise
| 4,346 | 31.440299 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/dump_v2_config.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
from paddle.trainer_config_helpers.layers import LayerOutput
from paddle.v2.layer import parse_network
from paddle.proto import TrainerConfig_pb2
__all__ = ["dump_v2_config"]
def dump_v2_config(topology, save_path, binary=False):
""" Dump the network topology to a specified file.
This function is only used to dump network defined by using PaddlePaddle V2
APIs. This function will NOT dump configurations related to PaddlePaddle
optimizer.
:param topology: The output layers (can be more than one layers given in a
Python List or Tuple) of the entire network. Using the
specified layers (if more than one layer is given) as root,
traversing back to the data layer(s), all the layers
connected to the specified output layers will be dumped.
Layers not connceted to the specified will not be dumped.
:type topology: LayerOutput|List|Tuple
:param save_path: The path to save the dumped network topology.
:type save_path: str
:param binary: Whether to dump the serialized network topology or not.
The default value is false. NOTE that, if you call this
function to generate network topology for PaddlePaddle C-API,
a serialized version of network topology is required. When
using PaddlePaddle C-API, this flag MUST be set to True.
:type binary: bool
"""
if isinstance(topology, LayerOutput):
topology = [topology]
elif isinstance(topology, collections.Sequence):
for out_layer in topology:
assert isinstance(out_layer, LayerOutput), (
"The type of each element in the parameter topology "
"should be LayerOutput.")
else:
raise RuntimeError("Error input type for parameter topology.")
model_str = parse_network(topology)
with open(save_path, "w") as fout:
if binary:
fout.write(model_str.SerializeToString())
else:
fout.write(str(model_str))
| 2,727 | 42.301587 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/merge_model.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gzip
import struct
import os
from paddle.trainer_config_helpers.layers import LayerOutput
from paddle.v2.parameters import Parameters
from paddle.proto import ModelConfig_pb2
from paddle.v2.topology import Topology
def merge_v2_model(net, param_file, output_file):
'''Merge the model config and parameters into one file.
The model configuration file describes the model structure which
ends with .py. The parameters file stores the parameters of the model
which ends with .tar.gz.
@param net The output layer of the network for inference.
@param param_file Path of the parameters (.tar.gz) which is stored by
v2 api.
@param output_file Path of the merged file which will be generated.
Usage:
from paddle.utils.merge_model import merge_v2_model
# import your network configuration
from example_net import net_conf
net = net_conf(is_predict=True)
param_file = './param_pass_00000.tar.gz'
output_file = './output.paddle'
merge_v2_model(net, param_file, output_file)
'''
assert isinstance(net, LayerOutput), \
"The net should be the output of the network for inference"
assert os.path.exists(param_file), \
"The model parameters file %s does not exists " % (param_file)
model_proto = Topology(net).proto()
assert isinstance(model_proto, ModelConfig_pb2.ModelConfig)
with gzip.open(param_file) as f:
params = Parameters.from_tar(f)
if os.path.exists(output_file):
os.remove(output_file)
with open(output_file, 'w') as f:
param_names = [param.name for param in model_proto.parameters]
conf_str = model_proto.SerializeToString()
f.write(struct.pack('q', len(conf_str)))
f.write(conf_str)
for pname in param_names:
params.serialize(pname, f)
print 'Generate %s success!' % (output_file)
| 2,578 | 33.851351 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/preprocess_util.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import math
import cPickle as pickle
import random
import collections
def save_file(data, filename):
"""
Save data into pickle format.
data: the data to save.
filename: the output filename.
"""
pickle.dump(data, open(filename, 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
def save_list(l, outfile):
"""
Save a list of string into a text file. There is one line for each string.
l: the list of string to save
outfile: the output file
"""
open(outfile, "w").write("\n".join(l))
def exclude_pattern(f):
"""
Return whether f is in the exlucde pattern.
Exclude the files that starts with . or ends with ~.
"""
return f.startswith(".") or f.endswith("~")
def list_dirs(path):
"""
Return a list of directories in path. Exclude all the directories that
start with '.'.
path: the base directory to search over.
"""
return [
os.path.join(path, d) for d in next(os.walk(path))[1]
if not exclude_pattern(d)
]
def list_images(path, exts=set(["jpg", "png", "bmp", "jpeg"])):
"""
Return a list of images in path.
path: the base directory to search over.
exts: the extensions of the images to find.
"""
return [os.path.join(path, d) for d in os.listdir(path) \
if os.path.isfile(os.path.join(path, d)) and not exclude_pattern(d)\
and os.path.splitext(d)[-1][1:] in exts]
def list_files(path):
"""
Return a list of files in path.
path: the base directory to search over.
exts: the extensions of the images to find.
"""
return [os.path.join(path, d) for d in os.listdir(path) \
if os.path.isfile(os.path.join(path, d)) and not exclude_pattern(d)]
def get_label_set_from_dir(path):
"""
Return a dictionary of the labels and label ids from a path.
Assume each direcotry in the path corresponds to a unique label.
The keys of the dictionary is the label name.
The values of the dictionary is the label id.
"""
dirs = list_dirs(path)
return dict([(os.path.basename(d), i) for i, d in enumerate(sorted(dirs))])
class Label:
"""
A class of label data.
"""
def __init__(self, label, name):
"""
label: the id of the label.
name: the name of the label.
"""
self.label = label
self.name = name
def convert_to_paddle_format(self):
"""
convert the image into the paddle batch format.
"""
return int(self.label)
def __hash__(self):
return hash((self.label))
class Dataset:
"""
A class to represent a dataset. A dataset contains a set of items.
Each item contains multiple slots of data.
For example: in image classification dataset, each item contains two slot,
The first slot is an image, and the second slot is a label.
"""
def __init__(self, data, keys):
"""
data: a list of data.
Each data is a tuple containing multiple slots of data.
Each slot is an object with convert_to_paddle_format function.
keys: contains a list of keys for all the slots.
"""
self.data = data
self.keys = keys
def check_valid(self):
for d in self.data:
assert (len(d) == len(self.keys))
def permute(self, key_id, num_per_batch):
"""
Permuate data for batching. It supports two types now:
1. if key_id == None, the batching process is completely random.
2. if key_id is not None. The batching process Permuate the data so that the key specified by key_id are
uniformly distributed in batches. See the comments of permute_by_key for details.
"""
if key_id is None:
self.uniform_permute()
else:
self.permute_by_key(key_id, num_per_batch)
def uniform_permute(self):
"""
Permuate the data randomly.
"""
random.shuffle(self.data)
def permute_by_key(self, key_id, num_per_batch):
"""
Permuate the data so that the key specified by key_id are
uniformly distributed in batches.
For example: if we have three labels, and the number of data
for each label are 100, 200, and 300, respectively. The number of batches is 4.
Then, the number of data for these labels is 25, 50, and 75.
"""
# Store the indices of the data that has the key value
# specified by key_id.
keyvalue_indices = collections.defaultdict(list)
for idx in range(len(self.data)):
keyvalue_indices[self.data[idx][key_id].label].append(idx)
for k in keyvalue_indices:
random.shuffle(keyvalue_indices[k])
num_data_per_key_batch = \
math.ceil(num_per_batch / float(len(keyvalue_indices.keys())))
if num_data_per_key_batch < 2:
raise Exception("The number of data in a batch is too small")
permuted_data = []
keyvalue_readpointer = collections.defaultdict(int)
while len(permuted_data) < len(self.data):
for k in keyvalue_indices:
begin_idx = keyvalue_readpointer[k]
end_idx = int(
min(begin_idx + num_data_per_key_batch,
len(keyvalue_indices[k])))
print "begin_idx, end_idx"
print begin_idx, end_idx
for idx in range(begin_idx, end_idx):
permuted_data.append(self.data[keyvalue_indices[k][idx]])
keyvalue_readpointer[k] = end_idx
self.data = permuted_data
class DataBatcher:
"""
A class that is used to create batches for both training and testing
datasets.
"""
def __init__(self, train_data, test_data, label_set):
"""
train_data, test_data: Each one is a dataset object repesenting
training and testing data, respectively.
label_set: a dictionary storing the mapping from label name to label id.
"""
self.train_data = train_data
self.test_data = test_data
self.label_set = label_set
self.num_per_batch = 5000
assert (self.train_data.keys == self.test_data.keys)
def create_batches_and_list(self, output_path, train_list_name,
test_list_name, label_set_name):
"""
Create batches for both training and testing objects.
It also create train.list and test.list to indicate the list
of the batch files for training and testing data, respectively.
"""
train_list = self.create_batches(self.train_data, output_path, "train_",
self.num_per_batch)
test_list = self.create_batches(self.test_data, output_path, "test_",
self.num_per_batch)
save_list(train_list, os.path.join(output_path, train_list_name))
save_list(test_list, os.path.join(output_path, test_list_name))
save_file(self.label_set, os.path.join(output_path, label_set_name))
def create_batches(self,
data,
output_path,
prefix="",
num_data_per_batch=5000):
"""
Create batches for a Dataset object.
data: the Dataset object to process.
output_path: the output path of the batches.
prefix: the prefix of each batch.
num_data_per_batch: number of data in each batch.
"""
num_batches = int(math.ceil(len(data.data) / float(num_data_per_batch)))
batch_names = []
data.check_valid()
num_slots = len(data.keys)
for i in range(num_batches):
batch_name = os.path.join(output_path, prefix + "batch_%03d" % i)
out_data = dict([(k, []) for k in data.keys])
begin_idx = i * num_data_per_batch
end_idx = min((i + 1) * num_data_per_batch, len(data.data))
for j in range(begin_idx, end_idx):
for slot_id in range(num_slots):
out_data[data.keys[slot_id]].\
append(data.data[j][slot_id].convert_to_paddle_format())
save_file(out_data, batch_name)
batch_names.append(batch_name)
return batch_names
class DatasetCreater(object):
"""
A virtual class for creating datasets.
The derived clasas needs to implemnt the following methods:
- create_dataset()
- create_meta_file()
"""
def __init__(self, data_path):
"""
data_path: the path to store the training data and batches.
train_dir_name: relative training data directory.
test_dir_name: relative testing data directory.
batch_dir_name: relative batch directory.
num_per_batch: the number of data in a batch.
meta_filename: the filename of the meta file.
train_list_name: training batch list name.
test_list_name: testing batch list name.
label_set: label set name.
overwrite: whether to overwrite the files if the batches are already in
the given path.
"""
self.data_path = data_path
self.train_dir_name = 'train'
self.test_dir_name = 'test'
self.batch_dir_name = 'batches'
self.num_per_batch = 50000
self.meta_filename = "batches.meta"
self.train_list_name = "train.list"
self.test_list_name = "test.list"
self.label_set_name = "labels.pkl"
self.output_path = os.path.join(self.data_path, self.batch_dir_name)
self.overwrite = False
self.permutate_key = "labels"
self.from_list = False
def create_meta_file(self, data):
"""
Create a meta file from training data.
data: training data given in a Dataset format.
"""
raise NotImplementedError
def create_dataset(self, path):
"""
Create a data set object from a path.
It will use directory structure or a file list to determine dataset if
self.from_list is True. Otherwise, it will uses a file list to
determine the datset.
path: the path of the dataset.
return a tuple of Dataset object, and a mapping from lable set
to label id.
"""
if self.from_list:
return self.create_dataset_from_list(path)
else:
return self.create_dataset_from_dir(path)
def create_dataset_from_list(self, path):
"""
Create a data set object from a path.
It will uses a file list to determine the datset.
path: the path of the dataset.
return a tuple of Dataset object, and a mapping from lable set
to label id
"""
raise NotImplementedError
def create_dataset_from_dir(self, path):
"""
Create a data set object from a path.
It will use directory structure or a file list to determine dataset if
self.from_list is True.
path: the path of the dataset.
return a tuple of Dataset object, and a mapping from lable set
to label id
"""
raise NotImplementedError
def create_batches(self):
"""
create batches and meta file.
"""
train_path = os.path.join(self.data_path, self.train_dir_name)
test_path = os.path.join(self.data_path, self.test_dir_name)
out_path = os.path.join(self.data_path, self.batch_dir_name)
if not os.path.exists(out_path):
os.makedirs(out_path)
if (self.overwrite or not os.path.exists(
os.path.join(out_path, self.train_list_name))):
train_data, train_label_set = \
self.create_dataset(train_path)
test_data, test_label_set = \
self.create_dataset(test_path)
train_data.permute(
self.keys.index(self.permutate_key), self.num_per_batch)
assert (train_label_set == test_label_set)
data_batcher = DataBatcher(train_data, test_data, train_label_set)
data_batcher.num_per_batch = self.num_per_batch
data_batcher.create_batches_and_list(
self.output_path, self.train_list_name, self.test_list_name,
self.label_set_name)
self.num_classes = len(train_label_set.keys())
self.create_meta_file(train_data)
return out_path
| 13,149 | 35.225895 | 112 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/preprocess_img.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import os
import random
import numpy as np
import PIL.Image as Image
import StringIO
import preprocess_util
from image_util import crop_img
def resize_image(img, target_size):
"""
Resize an image so that the shorter edge has length target_size.
img: the input image to be resized.
target_size: the target resized image size.
"""
percent = (target_size / float(min(img.size[0], img.size[1])))
resized_size = int(round(img.size[0] * percent)),\
int(round(img.size[1] * percent))
img = img.resize(resized_size, Image.ANTIALIAS)
return img
class DiskImage:
"""
A class of image data on disk.
"""
def __init__(self, path, target_size):
"""
path: path of the image.
target_size: target resize size.
"""
self.path = path
self.target_size = target_size
self.img = None
pass
def read_image(self):
if self.img is None:
print "reading: " + self.path
image = resize_image(Image.open(self.path), self.target_size)
self.img = image
def convert_to_array(self):
self.read_image()
np_array = np.array(self.img)
if len(np_array.shape) == 3:
np_array = np.swapaxes(np_array, 1, 2)
np_array = np.swapaxes(np_array, 1, 0)
return np_array
def convert_to_paddle_format(self):
"""
convert the image into the paddle batch format.
"""
self.read_image()
output = StringIO.StringIO()
self.img.save(output, "jpeg")
contents = output.getvalue()
return contents
class ImageClassificationDatasetCreater(preprocess_util.DatasetCreater):
"""
A class to process data for image classification.
"""
def __init__(self, data_path, target_size, color=True):
"""
data_path: the path to store the training data and batches.
target_size: processed image size in a batch.
color: whether to use color images.
"""
preprocess_util.DatasetCreater.__init__(self, data_path)
self.target_size = target_size
self.color = color
self.keys = ["images", "labels"]
self.permute_key = "labels"
def create_meta_file(self, data):
"""
Create a meta file for image classification.
The meta file contains the meam image, as well as some configs.
data: the training Dataaet.
"""
output_path = os.path.join(self.data_path, self.batch_dir_name,
self.meta_filename)
if self.color:
mean_img = np.zeros((3, self.target_size, self.target_size))
else:
mean_img = np.zeros((self.target_size, self.target_size))
for d in data.data:
img = d[0].convert_to_array()
cropped_img = crop_img(img, self.target_size, self.color)
mean_img += cropped_img
mean_img /= len(data.data)
mean_img = mean_img.astype('int32').flatten()
preprocess_util.save_file({
"data_mean": mean_img,
"image_size": self.target_size,
"mean_image_size": self.target_size,
"num_classes": self.num_classes,
"color": self.color
}, output_path)
pass
def create_dataset_from_list(self, path):
data = []
label_set = []
for line in open(file_list):
items = line.rstrip.split()
image_path = items[0]
label_name = items[1]
if not label_name in label_set:
label_set[label_name] = len(label_set.keys())
img = DiskImage(path=image_path, target_size=self.target_size)
label = preprocess_util.Lablel(
label=label_set[label_name], name=label_name)
return preprocess_util.Dataset(data, self.keys), label_set
def create_dataset_from_dir(self, path):
"""
Create a Dataset object for image classfication.
Each folder in the path directory corresponds to a set of images of
this label, and the name of the folder is the name of the
path: the path of the image dataset.
"""
if self.from_list:
return create_dataset_from_list(path)
label_set = preprocess_util.get_label_set_from_dir(path)
data = []
for l_name in label_set.keys():
image_paths = preprocess_util.list_images(
os.path.join(path, l_name))
for p in image_paths:
img = DiskImage(path=p, target_size=self.target_size)
label = preprocess_util.Label(
label=label_set[l_name], name=l_name)
data.append((img, label))
random.shuffle(data)
return preprocess_util.Dataset(data, self.keys), label_set
| 5,496 | 34.012739 | 75 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/__init__.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['dump_config']
| 636 | 38.8125 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/show_pb.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Show the content of proto buffer data file of PADDLE
"""
import os
import sys
from google.protobuf.internal.decoder import _DecodeVarint
import paddle.proto.DataFormat_pb2 as DataFormat
def read_proto(file, message):
"""
read a protobuffer struct from file, the length of the struct is stored as
a varint, then followed by the actual struct data.
@return True success, False for end of file
"""
buf = file.read(8)
if not buf:
return False
result, pos = _DecodeVarint(buf, 0)
buf = buf[pos:] + file.read(result - len(buf) + pos)
message.ParseFromString(buf)
return True
def usage():
print >> sys.stderr, "Usage: python show_pb.py PROTO_DATA_FILE"
exit(1)
if __name__ == '__main__':
if len(sys.argv) < 2:
usage()
f = open(sys.argv[1])
header = DataFormat.DataHeader()
read_proto(f, header)
print header
sample = DataFormat.DataSample()
while read_proto(f, sample):
print sample
| 1,604 | 26.672414 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/image_util.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from PIL import Image
from cStringIO import StringIO
def resize_image(img, target_size):
"""
Resize an image so that the shorter edge has length target_size.
img: the input image to be resized.
target_size: the target resized image size.
"""
percent = (target_size / float(min(img.size[0], img.size[1])))
resized_size = int(round(img.size[0] * percent)), int(
round(img.size[1] * percent))
img = img.resize(resized_size, Image.ANTIALIAS)
return img
def flip(im):
"""
Return the flipped image.
Flip an image along the horizontal direction.
im: input image, (H x W x K) ndarrays
"""
if len(im.shape) == 3:
return im[:, :, ::-1]
else:
return im[:, ::-1]
def crop_img(im, inner_size, color=True, test=True):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
im: (K x H x W) ndarrays
inner_size: the cropped image size.
color: whether it is color image.
test: whether in test mode.
If False, does random cropping and flipping.
If True, crop the center of images.
"""
if color:
height, width = max(inner_size, im.shape[1]), max(inner_size,
im.shape[2])
padded_im = np.zeros((3, height, width))
startY = (height - im.shape[1]) / 2
startX = (width - im.shape[2]) / 2
endY, endX = startY + im.shape[1], startX + im.shape[2]
padded_im[:, startY:endY, startX:endX] = im
else:
im = im.astype('float32')
height, width = max(inner_size, im.shape[0]), max(inner_size,
im.shape[1])
padded_im = np.zeros((height, width))
startY = (height - im.shape[0]) / 2
startX = (width - im.shape[1]) / 2
endY, endX = startY + im.shape[0], startX + im.shape[1]
padded_im[startY:endY, startX:endX] = im
if test:
startY = (height - inner_size) / 2
startX = (width - inner_size) / 2
else:
startY = np.random.randint(0, height - inner_size + 1)
startX = np.random.randint(0, width - inner_size + 1)
endY, endX = startY + inner_size, startX + inner_size
if color:
pic = padded_im[:, startY:endY, startX:endX]
else:
pic = padded_im[startY:endY, startX:endX]
if (not test) and (np.random.randint(2) == 0):
pic = flip(pic)
return pic
def decode_jpeg(jpeg_string):
np_array = np.array(Image.open(StringIO(jpeg_string)))
if len(np_array.shape) == 3:
np_array = np.transpose(np_array, (2, 0, 1))
return np_array
def preprocess_img(im, img_mean, crop_size, is_train, color=True):
"""
Does data augmentation for images.
If is_train is false, cropping the center region from the image.
If is_train is true, randomly crop a region from the image,
and randomy does flipping.
im: (K x H x W) ndarrays
"""
im = im.astype('float32')
test = not is_train
pic = crop_img(im, crop_size, color, test)
pic -= img_mean
return pic.flatten()
def load_meta(meta_path, mean_img_size, crop_size, color=True):
"""
Return the loaded meta file.
Load the meta image, which is the mean of the images in the dataset.
The mean image is subtracted from every input image so that the expected mean
of each input image is zero.
"""
mean = np.load(meta_path)['data_mean']
border = (mean_img_size - crop_size) / 2
if color:
assert (mean_img_size * mean_img_size * 3 == mean.shape[0])
mean = mean.reshape(3, mean_img_size, mean_img_size)
mean = mean[:, border:border + crop_size, border:border +
crop_size].astype('float32')
else:
assert (mean_img_size * mean_img_size == mean.shape[0])
mean = mean.reshape(mean_img_size, mean_img_size)
mean = mean[border:border + crop_size, border:border +
crop_size].astype('float32')
return mean
def load_image(img_path, is_color=True):
"""
Load image and return.
img_path: image path.
is_color: is color image or not.
"""
img = Image.open(img_path)
img.load()
return img
def oversample(img, crop_dims):
"""
image : iterable of (H x W x K) ndarrays
crop_dims: (height, width) tuple for the crops.
Returned data contains ten crops of input image, namely,
four corner patches and the center patch as well as their
horizontal reflections.
"""
# Dimensions and center.
im_shape = np.array(img[0].shape)
crop_dims = np.array(crop_dims)
im_center = im_shape[:2] / 2.0
# Make crop coordinates
h_indices = (0, im_shape[0] - crop_dims[0])
w_indices = (0, im_shape[1] - crop_dims[1])
crops_ix = np.empty((5, 4), dtype=int)
curr = 0
for i in h_indices:
for j in w_indices:
crops_ix[curr] = (i, j, i + crop_dims[0], j + crop_dims[1])
curr += 1
crops_ix[4] = np.tile(im_center, (1, 2)) + np.concatenate(
[-crop_dims / 2.0, crop_dims / 2.0])
crops_ix = np.tile(crops_ix, (2, 1))
# Extract crops
crops = np.empty(
(10 * len(img), crop_dims[0], crop_dims[1], im_shape[-1]),
dtype=np.float32)
ix = 0
for im in img:
for crop in crops_ix:
crops[ix] = im[crop[0]:crop[2], crop[1]:crop[3], :]
ix += 1
crops[ix - 5:ix] = crops[ix - 5:ix, :, ::-1, :] # flip for mirrors
return crops
class ImageTransformer:
def __init__(self,
transpose=None,
channel_swap=None,
mean=None,
is_color=True):
self.is_color = is_color
self.set_transpose(transpose)
self.set_channel_swap(channel_swap)
self.set_mean(mean)
def set_transpose(self, order):
if order is not None:
if self.is_color:
assert 3 == len(order)
self.transpose = order
def set_channel_swap(self, order):
if order is not None:
if self.is_color:
assert 3 == len(order)
self.channel_swap = order
def set_mean(self, mean):
if mean is not None:
# mean value, may be one value per channel
if mean.ndim == 1:
mean = mean[:, np.newaxis, np.newaxis]
else:
# elementwise mean
if self.is_color:
assert len(mean.shape) == 3
self.mean = mean
def transformer(self, data):
if self.transpose is not None:
data = data.transpose(self.transpose)
if self.channel_swap is not None:
data = data[self.channel_swap, :, :]
if self.mean is not None:
data -= self.mean
return data
| 7,497 | 32.324444 | 81 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/dump_config.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.config_parser import parse_config
from paddle.proto import TrainerConfig_pb2
import sys
__all__ = []
if __name__ == '__main__':
whole_conf = False
binary = False
if len(sys.argv) == 2:
conf = parse_config(sys.argv[1], '')
elif len(sys.argv) == 3:
conf = parse_config(sys.argv[1], sys.argv[2])
elif len(sys.argv) == 4:
conf = parse_config(sys.argv[1], sys.argv[2])
if sys.argv[3] == '--whole':
whole_conf = True
elif sys.argv[3] == '--binary':
binary = True
else:
raise RuntimeError()
assert isinstance(conf, TrainerConfig_pb2.TrainerConfig)
if whole_conf:
print conf
else:
if binary:
sys.stdout.write(conf.model_config.SerializeToString())
else:
print conf.model_config
| 1,460 | 30.76087 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/image_multiproc.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os, sys
import numpy as np
from PIL import Image
from cStringIO import StringIO
import multiprocessing
import functools
import itertools
from paddle.utils.image_util import *
from paddle.trainer.config_parser import logger
try:
import cv2
except ImportError:
logger.warning("OpenCV2 is not installed, using PIL to process")
cv2 = None
__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]
class CvTransformer(ImageTransformer):
"""
CvTransformer used python-opencv to process image.
"""
def __init__(
self,
min_size=None,
crop_size=None,
transpose=(2, 0, 1), # transpose to C * H * W
channel_swap=None,
mean=None,
is_train=True,
is_color=True):
ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
self.min_size = min_size
self.crop_size = crop_size
self.is_train = is_train
def resize(self, im, min_size):
row, col = im.shape[:2]
new_row, new_col = min_size, min_size
if row > col:
new_row = min_size * row / col
else:
new_col = min_size * col / row
im = cv2.resize(im, (new_row, new_col), interpolation=cv2.INTER_CUBIC)
return im
def crop_and_flip(self, im):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
im: (H x W x K) ndarrays
"""
row, col = im.shape[:2]
start_h, start_w = 0, 0
if self.is_train:
start_h = np.random.randint(0, row - self.crop_size + 1)
start_w = np.random.randint(0, col - self.crop_size + 1)
else:
start_h = (row - self.crop_size) / 2
start_w = (col - self.crop_size) / 2
end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
if self.is_color:
im = im[start_h:end_h, start_w:end_w, :]
else:
im = im[start_h:end_h, start_w:end_w]
if (self.is_train) and (np.random.randint(2) == 0):
if self.is_color:
im = im[:, ::-1, :]
else:
im = im[:, ::-1]
return im
def transform(self, im):
im = self.resize(im, self.min_size)
im = self.crop_and_flip(im)
# transpose, swap channel, sub mean
im = im.astype('float32')
ImageTransformer.transformer(self, im)
return im
def load_image_from_string(self, data):
flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
im = cv2.imdecode(np.fromstring(data, np.uint8), flag)
return im
def transform_from_string(self, data):
im = self.load_image_from_string(data)
return self.transform(im)
def load_image_from_file(self, file):
flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
im = cv2.imread(file, flag)
return im
def transform_from_file(self, file):
im = self.load_image_from_file(file)
return self.transform(im)
class PILTransformer(ImageTransformer):
"""
PILTransformer used PIL to process image.
"""
def __init__(
self,
min_size=None,
crop_size=None,
transpose=(2, 0, 1), # transpose to C * H * W
channel_swap=None,
mean=None,
is_train=True,
is_color=True):
ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
self.min_size = min_size
self.crop_size = crop_size
self.is_train = is_train
def resize(self, im, min_size):
row, col = im.size[:2]
new_row, new_col = min_size, min_size
if row > col:
new_row = min_size * row / col
else:
new_col = min_size * col / row
im = im.resize((new_row, new_col), Image.ANTIALIAS)
return im
def crop_and_flip(self, im):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
"""
row, col = im.size[:2]
start_h, start_w = 0, 0
if self.is_train:
start_h = np.random.randint(0, row - self.crop_size + 1)
start_w = np.random.randint(0, col - self.crop_size + 1)
else:
start_h = (row - self.crop_size) / 2
start_w = (col - self.crop_size) / 2
end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
im = im.crop((start_h, start_w, end_h, end_w))
if (self.is_train) and (np.random.randint(2) == 0):
im = im.transpose(Image.FLIP_LEFT_RIGHT)
return im
def transform(self, im):
im = self.resize(im, self.min_size)
im = self.crop_and_flip(im)
im = np.array(im, dtype=np.float32) # convert to numpy.array
# transpose, swap channel, sub mean
ImageTransformer.transformer(self, im)
return im
def load_image_from_string(self, data):
im = Image.open(StringIO(data))
return im
def transform_from_string(self, data):
im = self.load_image_from_string(data)
return self.transform(im)
def load_image_from_file(self, file):
im = Image.open(file)
return im
def transform_from_file(self, file):
im = self.load_image_from_file(file)
return self.transform(im)
def job(is_img_string, transformer, (data, label)):
if is_img_string:
return transformer.transform_from_string(data), label
else:
return transformer.transform_from_file(data), label
class MultiProcessImageTransformer(object):
def __init__(self,
procnum=10,
resize_size=None,
crop_size=None,
transpose=(2, 0, 1),
channel_swap=None,
mean=None,
is_train=True,
is_color=True,
is_img_string=True):
"""
Processing image with multi-process. If it is used in PyDataProvider,
the simple usage for CNN is as follows:
.. code-block:: python
def hool(settings, is_train, **kwargs):
settings.is_train = is_train
settings.mean_value = np.array([103.939,116.779,123.68], dtype=np.float32)
settings.input_types = [
dense_vector(3 * 224 * 224),
integer_value(1)]
settings.transformer = MultiProcessImageTransformer(
procnum=10,
resize_size=256,
crop_size=224,
transpose=(2, 0, 1),
mean=settings.mean_values,
is_train=settings.is_train)
@provider(init_hook=hook, pool_size=20480)
def process(settings, file_list):
with open(file_list, 'r') as fdata:
for line in fdata:
data_dic = np.load(line.strip()) # load the data batch pickled by Pickle.
data = data_dic['data']
labels = data_dic['label']
labels = np.array(labels, dtype=np.float32)
for im, lab in settings.dp.run(data, labels):
yield [im.astype('float32'), int(lab)]
:param procnum: processor number.
:type procnum: int
:param resize_size: the shorter edge size of image after resizing.
:type resize_size: int
:param crop_size: the croping size.
:type crop_size: int
:param transpose: the transpose order, Paddle only allow C * H * W order.
:type transpose: tuple or list
:param channel_swap: the channel swap order, RGB or BRG.
:type channel_swap: tuple or list
:param mean: the mean values of image, per-channel mean or element-wise mean.
:type mean: array, The dimension is 1 for per-channel mean.
The dimension is 3 for element-wise mean.
:param is_train: training peroid or testing peroid.
:type is_train: bool.
:param is_color: the image is color or gray.
:type is_color: bool.
:param is_img_string: The input can be the file name of image or image string.
:type is_img_string: bool.
"""
self.procnum = procnum
self.pool = multiprocessing.Pool(procnum)
self.is_img_string = is_img_string
if cv2 is not None:
self.transformer = CvTransformer(resize_size, crop_size, transpose,
channel_swap, mean, is_train,
is_color)
else:
self.transformer = PILTransformer(resize_size, crop_size, transpose,
channel_swap, mean, is_train,
is_color)
def run(self, data, label):
fun = functools.partial(job, self.is_img_string, self.transformer)
return self.pool.imap_unordered(
fun, itertools.izip(data, label), chunksize=100 * self.procnum)
| 9,913 | 34.790614 | 97 |
py
|
Paddle
|
Paddle-master/python/paddle/utils/torch2paddle.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Convert torch parameter file to paddle model files.
Note: must have torchfile installed in order to use this tool.
Usage: python torch2paddle.py -i torchfile.t7 -l layers.txt -o path/to/paddle_model
"""
import os
import sys
import struct
import numpy as np
import torchfile
import cPickle as pickle
import argparse
# save parameters
def save_layer_parameters(outfile, feats):
version = 0
value_size = 4
ret = ""
for feat in feats:
ret += feat.tostring()
size = len(ret) / 4
fo = open(outfile, 'wb')
fo.write(struct.pack('iIQ', version, value_size, size))
fo.write(ret)
fo.close()
def save_net_parameters(layers, params, output_path):
for i in range(len(layers)):
weight = params[i * 2]
biases = params[i * 2 + 1]
weight_file = os.path.join(output_path, '_%s.w0' % layers[i])
biases_file = os.path.join(output_path, '_%s.wbias' % layers[i])
print "Saving for layer %s." % layers[i]
save_layer_parameters(weight_file, [weight])
save_layer_parameters(biases_file, biases)
def load_layer_parameters(filename):
fn = open(filename, 'rb')
version, = struct.unpack('i', fn.read(4))
value_length, = struct.unpack("I", fn.read(4))
dtype = 'float32' if value_length == 4 else 'float64'
param_size, = struct.unpack("L", fn.read(8))
value = np.fromfile(fn, dtype)
return value
def main(argv):
"""
main method of converting torch to paddle files.
:param argv:
:return:
"""
cmdparser = argparse.ArgumentParser(
"Convert torch parameter file to paddle model files.")
cmdparser.add_argument(
'-i', '--input', help='input filename of torch parameters')
cmdparser.add_argument('-l', '--layers', help='list of layer names')
cmdparser.add_argument(
'-o', '--output', help='output file path of paddle model')
args = cmdparser.parse_args(argv)
if args.input and args.layers and args.output:
params = torchfile.load(args.input)
layers = [line.strip() for line in open(args.layers, 'r')]
save_net_parameters(layers, params, args.output)
else:
print(
'Usage: python torch2paddle.py -i torchfile.t7 -l layers.txt -o path/to/paddle_model'
)
if __name__ == "__main__":
main(sys.argv[1:])
| 2,946 | 30.688172 | 97 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/evaluators.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.config_parser import *
from default_decorators import *
__all__ = [
"evaluator_base",
"classification_error_evaluator",
"auc_evaluator",
"pnpair_evaluator",
"precision_recall_evaluator",
"ctc_error_evaluator",
"chunk_evaluator",
"sum_evaluator",
"column_sum_evaluator",
"value_printer_evaluator",
"gradient_printer_evaluator",
"maxid_printer_evaluator",
"maxframe_printer_evaluator",
"seqtext_printer_evaluator",
"classification_error_printer_evaluator",
"detection_map_evaluator",
]
class EvaluatorAttribute(object):
FOR_CLASSIFICATION = 1
FOR_REGRESSION = 1 << 1
FOR_RANK = 1 << 2
FOR_PRINT = 1 << 3
FOR_UTILS = 1 << 4
FOR_DETECTION = 1 << 5
KEYS = [
"for_classification", "for_regression", "for_rank", "for_print",
"for_utils", "for_detection"
]
@staticmethod
def to_key(idx):
tmp = 1
for i in xrange(0, len(EvaluatorAttribute.KEYS)):
if idx == tmp:
return EvaluatorAttribute.KEYS[i]
else:
tmp = (tmp << 1)
def evaluator(*attrs):
def impl(method):
for attr in attrs:
setattr(method, EvaluatorAttribute.to_key(attr), True)
method.is_evaluator = True
return method
return impl
def evaluator_base(input,
type,
label=None,
weight=None,
name=None,
chunk_scheme=None,
num_chunk_types=None,
classification_threshold=None,
positive_label=None,
dict_file=None,
result_file=None,
num_results=None,
delimited=None,
top_k=None,
excluded_chunk_types=None,
overlap_threshold=None,
background_id=None,
evaluate_difficult=None,
ap_type=None):
"""
Evaluator will evaluate the network status while training/testing.
User can use evaluator by classify/regression job. For example.
.. code-block:: python
classify(prediction, output, evaluator=classification_error_evaluator)
And user could define evaluator separately as follow.
.. code-block:: python
classification_error_evaluator("ErrorRate", prediction, label)
The evaluator often contains a name parameter. It will also be printed when
evaluating network. The printed information may look like the following.
.. code-block:: text
Batch=200 samples=20000 AvgCost=0.679655 CurrentCost=0.662179 Eval:
classification_error_evaluator=0.4486
CurrentEval: ErrorRate=0.3964
:param input: Input layers, a object of LayerOutput or a list of
LayerOutput.
:type input: list|LayerOutput
:param label: An input layer containing the ground truth label.
:type label: LayerOutput|None
:param weight: An input layer which is a weight for each sample.
Each evaluator may calculate differently to use this weight.
:type weight: LayerOutput.
:param top_k: number k in top-k error rate
:type top_k: int
:param overlap_threshold: In detection tasks to filter detection results
:type overlap_threshold: float
:param background_id: Identifier of background class
:type background_id: int
:param evaluate_difficult: Whether to evaluate difficult objects
:type evaluate_difficult: bool
:param ap_type: How to calculate average persicion
:type ap_type: str
"""
# inputs type assertions.
assert classification_threshold is None or isinstance(
classification_threshold, float)
assert positive_label is None or isinstance(positive_label, int)
assert num_results is None or isinstance(num_results, int)
assert top_k is None or isinstance(top_k, int)
if not isinstance(input, list):
input = [input]
if label:
input.append(label)
if weight:
input.append(weight)
Evaluator(
name=name,
type=type,
inputs=[i.name for i in input],
chunk_scheme=chunk_scheme,
num_chunk_types=num_chunk_types,
classification_threshold=classification_threshold,
positive_label=positive_label,
dict_file=dict_file,
result_file=result_file,
delimited=delimited,
num_results=num_results,
top_k=top_k,
excluded_chunk_types=excluded_chunk_types,
overlap_threshold=overlap_threshold,
background_id=background_id,
evaluate_difficult=evaluate_difficult,
ap_type=ap_type)
@evaluator(EvaluatorAttribute.FOR_DETECTION)
@wrap_name_default()
def detection_map_evaluator(input,
label,
overlap_threshold=0.5,
background_id=0,
evaluate_difficult=False,
ap_type="11point",
name=None):
"""
Detection mAP Evaluator. It will print mean Average Precision (mAP) for detection.
The detection mAP Evaluator based on the output of detection_output layer counts
the true positive and the false positive bbox and integral them to get the
mAP.
The simple usage is:
.. code-block:: python
eval = detection_map_evaluator(input=det_output,label=lbl)
:param input: Input layer.
:type input: LayerOutput
:param label: Label layer.
:type label: LayerOutput
:param overlap_threshold: The bbox overlap threshold of a true positive.
:type overlap_threshold: float
:param background_id: The background class index.
:type background_id: int
:param evaluate_difficult: Whether evaluate a difficult ground truth.
:type evaluate_difficult: bool
"""
if not isinstance(input, list):
input = [input]
if label:
input.append(label)
evaluator_base(
name=name,
type="detection_map",
input=input,
label=label,
overlap_threshold=overlap_threshold,
background_id=background_id,
evaluate_difficult=evaluate_difficult,
ap_type=ap_type)
@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def classification_error_evaluator(input,
label,
name=None,
weight=None,
top_k=None,
threshold=None):
"""
Classification Error Evaluator. It will print error rate for classification.
The classification error is:
.. math::
classification\\_error = \\frac{NumOfWrongPredicts}{NumOfAllSamples}
The simple usage is:
.. code-block:: python
eval = classification_error_evaluator(input=prob,label=lbl)
:param name: Evaluator name.
:type name: basestring
:param input: Input Layer name. The output prediction of network.
:type input: LayerOutput
:param label: Label layer name.
:type label: basestring
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1]. And will just multiply to NumOfWrongPredicts
and NumOfAllSamples. So, the elements of weight are all one,
then means not set weight. The larger weight it is, the more
important this sample is.
:type weight: LayerOutput
:param top_k: number k in top-k error rate
:type top_k: int
:param threshold: The classification threshold.
:type threshold: float
:return: None.
"""
evaluator_base(
name=name,
type="classification_error",
input=input,
label=label,
weight=weight,
top_k=top_k,
classification_threshold=threshold, )
@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def auc_evaluator(
input,
label,
name=None,
weight=None, ):
"""
Auc Evaluator which adapts to binary classification.
The simple usage:
.. code-block:: python
eval = auc_evaluator(input, label)
:param name: Evaluator name.
:type name: None|basestring
:param input: Input Layer name. The output prediction of network.
:type input: LayerOutput
:param label: Label layer name.
:type label: None|basestring
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1].
:type weight: LayerOutput
"""
evaluator_base(
name=name,
type="last-column-auc",
input=input,
label=label,
weight=weight)
@evaluator(EvaluatorAttribute.FOR_RANK)
@wrap_name_default()
def pnpair_evaluator(
input,
label,
query_id,
weight=None,
name=None, ):
"""
Positive-negative pair rate Evaluator which adapts to rank task like
learning to rank. This evaluator must contain at least three layers.
The simple usage:
.. code-block:: python
eval = pnpair_evaluator(input, label, query_id)
:param input: Input Layer name. The output prediction of network.
:type input: LayerOutput
:param label: Label layer name.
:type label: LayerOutput
:param query_id: Query_id layer name. Query_id indicates that which query
each sample belongs to. Its shape should be
the same as output of Label layer.
:type query_id: LayerOutput
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1] which indicates the weight of each sample.
The default weight of sample is 1 if the weight layer is None.
And the pair weight is the mean of the two samples' weight.
:type weight: LayerOutput
:param name: Evaluator name.
:type name: None|basestring
"""
if not isinstance(input, list):
input = [input]
if label:
input.append(label)
if query_id:
input.append(query_id)
evaluator_base(
input=input,
type="pnpair",
weight=weight,
name=name, )
@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def precision_recall_evaluator(
input,
label,
positive_label=None,
weight=None,
name=None, ):
"""
An Evaluator to calculate precision and recall, F1-score.
It is adapt to the task with multiple labels.
- If positive_label=-1, it will print the average precision, recall,
F1-score of all labels.
- If use specify positive_label, it will print the precision, recall,
F1-score of this label.
The simple usage:
.. code-block:: python
eval = precision_recall_evaluator(input, label)
:param name: Evaluator name.
:type name: None|basestring
:param input: Input Layer name. The output prediction of network.
:type input: LayerOutput
:param label: Label layer name.
:type label: LayerOutput
:param positive_label: The input label layer.
:type positive_label: LayerOutput.
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1]. (TODO, explaination)
:type weight: LayerOutput
"""
evaluator_base(
name=name,
type="precision_recall",
input=input,
label=label,
positive_label=positive_label,
weight=weight)
@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def ctc_error_evaluator(
input,
label,
name=None, ):
"""
This evaluator is to calculate sequence-to-sequence edit distance.
The simple usage is :
.. code-block:: python
eval = ctc_error_evaluator(input=input, label=lbl)
:param name: Evaluator name.
:type name: None|basestring
:param input: Input Layer. Should be the same as the input for ctc_layer.
:type input: LayerOutput
:param label: input label, which is a data_layer. Should be the same as the
label for ctc_layer
:type label: LayerOutput
"""
evaluator_base(
name=name, type="ctc_edit_distance", input=input, label=label)
@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@wrap_name_default()
def chunk_evaluator(
input,
label,
chunk_scheme,
num_chunk_types,
name=None,
excluded_chunk_types=None, ):
"""
Chunk evaluator is used to evaluate segment labelling accuracy for a
sequence. It calculates precision, recall and F1 scores for the chunk detection.
To use chunk evaluator, several concepts need to be clarified firstly.
* **Chunk type** is the type of the whole chunk and a chunk consists of one or several words. (For example in NER, ORG for organization name, PER for person name etc.)
* **Tag type** indicates the position of a word in a chunk. (B for begin, I for inside, E for end, S for single)
We can name a label by combining tag type and chunk type. (ie. B-ORG for begining of an organization name)
The construction of label dictionary should obey the following rules:
- Use one of the listed labelling schemes. These schemes differ in ways indicating chunk boundry.
.. code-block:: text
Scheme Description
plain Use the same label for the whole chunk.
IOB Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
IOE Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
IOBES Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
To make it clear, let's illustrate by an NER example.
Assuming that there are three named entity types including ORG, PER and LOC which are called 'chunk type' here,
if 'IOB' scheme were used, the label set will be extended to a set including B-ORG, I-ORG, B-PER, I-PER, B-LOC, I-LOC and O,
in which B-ORG for begining of ORG and I-ORG for inside of ORG.
Prefixes which are called 'tag type' here are added to chunk types and there are two tag types including B and I.
Of course, the training data should be labeled accordingly.
- Mapping is done correctly by the listed equations and assigning protocol.
The following table are equations to extract tag type and chunk type from a label.
.. code-block:: text
tagType = label % numTagType
chunkType = label / numTagType
otherChunkType = numChunkTypes
The following table shows the mapping rule between tagType and tag type in each scheme.
.. code-block:: text
Scheme Begin Inside End Single
plain 0 - - -
IOB 0 1 - -
IOE - 0 1 -
IOBES 0 1 2 3
Continue the NER example, and the label dict should look like this to satify above equations:
.. code-block:: text
B-ORG 0
I-ORG 1
B-PER 2
I-PER 3
B-LOC 4
I-LOC 5
O 6
In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
"IOB" so tagType has two values: 0 for B and 1 for I.
Here we will use I-LOC to explain the above mapping rules in detail.
For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
and the tag is I.
The simple usage is:
.. code-block:: python
eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
:param input: The input layers.
:type input: LayerOutput
:param label: An input layer containing the ground truth label.
:type label: LayerOutput
:param chunk_scheme: The labelling schemes support 4 types. It is one of
"IOB", "IOE", "IOBES", "plain". It is required.
:type chunk_scheme: basestring
:param num_chunk_types: number of chunk types other than "other"
:param name: The Evaluator name, it is optional.
:type name: basename|None
:param excluded_chunk_types: chunks of these types are not considered
:type excluded_chunk_types: list of integer|None
"""
evaluator_base(
name=name,
type="chunk",
input=input,
label=label,
chunk_scheme=chunk_scheme,
num_chunk_types=num_chunk_types,
excluded_chunk_types=excluded_chunk_types, )
@evaluator(EvaluatorAttribute.FOR_UTILS)
@wrap_name_default()
def sum_evaluator(
input,
name=None,
weight=None, ):
"""
An Evaluator to sum the result of input.
The simple usage:
.. code-block:: python
eval = sum_evaluator(input)
:param name: Evaluator name.
:type name: None|basestring
:param input: Input Layer name.
:type input: LayerOutput
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1]. (TODO, explaination)
:type weight: LayerOutput
"""
evaluator_base(name=name, type="sum", input=input, weight=weight)
@evaluator(EvaluatorAttribute.FOR_UTILS)
@wrap_name_default()
def column_sum_evaluator(
input,
name=None,
weight=None, ):
"""
This Evaluator is used to sum the last column of input.
The simple usage is:
.. code-block:: python
eval = column_sum_evaluator(input, label)
:param name: Evaluator name.
:type name: None|basestring
:param input: Input Layer name.
:type input: LayerOutput
"""
evaluator_base(
name=name, type="last-column-sum", input=input, weight=weight)
"""
The following are printer Evaluators which are usually used to
print the result, like value or gradient of input layers, the
results generated in machine translation, the classification error etc.
"""
@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def value_printer_evaluator(
input,
name=None, ):
"""
This Evaluator is used to print the values of input layers. It contains
one or more input layers.
The simple usage is:
.. code-block:: python
eval = value_printer_evaluator(input)
:param input: One or more input layers.
:type input: LayerOutput|list
:param name: Evaluator name.
:type name: None|basestring
"""
evaluator_base(name=name, type="value_printer", input=input)
@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def gradient_printer_evaluator(
input,
name=None, ):
"""
This Evaluator is used to print the gradient of input layers. It contains
one or more input layers.
The simple usage is:
.. code-block:: python
eval = gradient_printer_evaluator(input)
:param input: One or more input layers.
:type input: LayerOutput|list
:param name: Evaluator name.
:type name: None|basestring
"""
evaluator_base(name=name, type="gradient_printer", input=input)
@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def maxid_printer_evaluator(
input,
num_results=None,
name=None, ):
"""
This Evaluator is used to print maximum top k values and their indexes
of each row of input layers. It contains one or more input layers.
k is specified by num_results.
The simple usage is:
.. code-block:: python
eval = maxid_printer_evaluator(input)
:param input: Input Layer name.
:type input: LayerOutput|list
:param num_results: This number is used to specify the top k numbers.
It is 1 by default.
:type num_results: int.
:param name: Evaluator name.
:type name: None|basestring
"""
evaluator_base(
name=name, type="max_id_printer", input=input, num_results=num_results)
@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def maxframe_printer_evaluator(
input,
num_results=None,
name=None, ):
"""
This Evaluator is used to print the top k frames of each input layers.
The input layers should contain sequences info or sequences type.
k is specified by num_results.
It contains one or more input layers.
Note:
The width of each frame is 1.
The simple usage is:
.. code-block:: python
eval = maxframe_printer_evaluator(input)
:param input: Input Layer name.
:type input: LayerOutput|list
:param name: Evaluator name.
:type name: None|basestring
"""
evaluator_base(
name=name,
type="max_frame_printer",
input=input,
num_results=num_results)
@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def seqtext_printer_evaluator(
input,
result_file,
id_input=None,
dict_file=None,
delimited=None,
name=None, ):
"""
Sequence text printer will print text according to index matrix and a
dictionary. There can be multiple input to this layer:
1. If there is no id_input, the input must be a matrix containing
the sequence of indices;
2. If there is id_input, it should be ids, and interpreted as sample ids.
The output format will be:
1. sequence without sub-sequence, and there is probability.
.. code-block:: python
id \t prob space_seperated_tokens_from_dictionary_according_to_seq
2. sequence without sub-sequence, and there is not probability.
.. code-block:: python
id \t space_seperated_tokens_from_dictionary_according_to_seq
3. sequence with sub-sequence, and there is not probability.
.. code-block:: python
id \t space_seperated_tokens_from_dictionary_according_to_sub_seq
\t \t space_seperated_tokens_from_dictionary_according_to_sub_seq
...
Typically SequenceTextPrinter layer takes output of maxid or RecurrentGroup
with maxid (when generating) as an input.
The simple usage is:
.. code-block:: python
eval = seqtext_printer_evaluator(input=maxid_layer,
id_input=sample_id,
dict_file=dict_file,
result_file=result_file)
:param input: Input Layer name.
:type input: LayerOutput|list
:param result_file: Path of the file to store the generated results.
:type result_file: basestring
:param id_input: Index of the input sequence, and the specified index will
be prited in the gereated results. This an optional
parameter.
:type id_input: LayerOutput
:param dict_file: Path of dictionary. This is an optional parameter.
Every line is a word in the dictionary with
(line number - 1) as the word index.
If this parameter is set to None, or to an empty string,
only word index are printed in the generated results.
:type dict_file: basestring
:param delimited: Whether to use space to separate output tokens.
Default is True. No space is added if set to False.
:type delimited: bool
:param name: Evaluator name.
:type name: None|basestring
:return: The seq_text_printer that prints the generated sequence to a file.
:rtype: evaluator
"""
assert isinstance(result_file, basestring)
if id_input is None:
inputs = [input]
else:
inputs = [id_input, input]
input.parents.append(id_input)
evaluator_base(
name=name,
type="seq_text_printer",
input=inputs,
dict_file=dict_file,
result_file=result_file,
delimited=delimited)
@evaluator(EvaluatorAttribute.FOR_PRINT)
@wrap_name_default()
def classification_error_printer_evaluator(
input,
label,
threshold=0.5,
name=None, ):
"""
This Evaluator is used to print the classification error of each sample.
The simple usage is:
.. code-block:: python
eval = classification_error_printer_evaluator(input)
:param input: Input layer.
:type input: LayerOutput
:param label: Input label layer.
:type label: LayerOutput
:param name: Evaluator name.
:type name: None|basestring
"""
evaluator_base(
name=name,
type="classification_error_printer",
input=input,
label=label,
classification_threshold=threshold)
| 25,289 | 30.068796 | 172 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/optimizers.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.config_parser import Settings, default_decay_rate, \
default_gradient_clipping_threshold, default_momentum
from .default_decorators import wrap_param_default
__all__ = [
'Optimizer', 'BaseSGDOptimizer', 'MomentumOptimizer', 'AdamaxOptimizer',
'AdamOptimizer', 'AdaGradOptimizer', 'RMSPropOptimizer',
'DecayedAdaGradOptimizer', 'AdaDeltaOptimizer', 'BaseRegularization',
'L2Regularization', 'settings', 'ModelAverage'
]
class Optimizer(object):
def to_setting_kwargs(self):
raise NotImplementedError()
def extra_settings(self):
pass
@property
def is_support_sparse(self):
return True
class BaseSGDOptimizer(Optimizer):
"""
SGD Optimizer.
SGD is an optimization method, trying to find a neural network that
minimize the "cost/error" of it by iteration. In paddle's implementation
SGD Optimizer is synchronized, which means all gradients will be wait to
calculate and reduced into one gradient, then do optimize operation.
The neural network consider the learning problem of minimizing an objective
function, that has the form of a sum
.. math::
Q(w) = \\sum_{i}^{n} Q_i(w)
The value of function Q sometimes is the cost of neural network (Mean
Square Error between prediction and label for example). The function Q is
parametrised by w, the weight/bias of neural network. And weights is what to
be learned. The i is the i-th observation in (trainning) data.
So, the SGD method will optimize the weight by
.. math::
w = w - \\eta \\nabla Q(w) = w - \\eta \\sum_{i}^{n} \\nabla Q_i(w)
where :math:`\\eta` is learning rate. And :math:`n` is batch size.
"""
def to_setting_kwargs(self):
raise NotImplementedError()
class MomentumOptimizer(BaseSGDOptimizer):
"""
MomentumOptimizer.
When sparse=True, the update scheme:
.. math::
\\alpha_t &= \\alpha_{t-1} / k \\\\
\\beta_t &= \\beta_{t-1} / (1 + \\lambda \\gamma_t) \\\\
u_t &= u_{t-1} - \\alpha_t \\gamma_t g_t \\\\
v_t &= v_{t-1} + \\tau_{t-1} \\alpha_t \\gamma_t g_t \\\\
\\tau_t &= \\tau_{t-1} + \\beta_t / \\alpha_t
where :math:`k` is momentum, :math:`\\lambda` is decay rate,
:math:`\\gamma_t` is learning rate at the t'th step.
:param sparse: with sparse support or not.
:type sparse: bool
"""
def extra_settings(self):
default_momentum(self.momentum)
def to_setting_kwargs(self):
if self.sparse:
return {'learning_method': 'sparse_momentum'}
else:
return {'learning_method': 'momentum'}
def __init__(self, momentum=None, sparse=False):
self.momentum = momentum
self.sparse = sparse
class AdamOptimizer(BaseSGDOptimizer):
"""
Adam optimizer.
The details of please refer `Adam: A Method for Stochastic Optimization
<https://arxiv.org/abs/1412.6980>`_
.. math::
m(w, t) & = \\beta_1 m(w, t-1) + (1 - \\beta_1) \\nabla Q_i(w) \\\\
v(w, t) & = \\beta_2 v(w, t-1) + (1 - \\beta_2)(\\nabla Q_i(w)) ^2 \\\\
w & = w - \\frac{\\eta m(w, t)}{\\sqrt{v(w,t) + \\epsilon}}
:param beta1: the :math:`\\beta_1` in equation.
:type beta1: float
:param beta2: the :math:`\\beta_2` in equation.
:type beta2: float
:param epsilon: the :math:`\\epsilon` in equation. It is used to prevent
divided by zero.
:type epsilon: float
"""
@property
def is_support_sparse(self):
return False
def __init__(self, beta1=0.9, beta2=0.999, epsilon=1e-8):
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
def to_setting_kwargs(self):
return {
'learning_method': 'adam',
'adam_beta1': self.beta1,
'adam_beta2': self.beta2,
'adam_epsilon': self.epsilon
}
class AdamaxOptimizer(BaseSGDOptimizer):
"""
Adamax optimizer.
The details of please refer this `Adam: A Method for Stochastic Optimization
<https://arxiv.org/abs/1412.6980>`_
.. math::
m_t & = \\beta_1 * m_{t-1} + (1-\\beta_1)* \\nabla Q_i(w) \\\\
u_t & = max(\\beta_2*u_{t-1}, abs(\\nabla Q_i(w))) \\\\
w_t & = w_{t-1} - (\\eta/(1-\\beta_1^t))*m_t/u_t
:param beta1: the :math:`\\beta_1` in the equation.
:type beta1: float
:param beta2: the :math:`\\beta_2` in the equation.
:type beta2: float
"""
def __init__(self, beta1, beta2):
self.beta1 = beta1
self.beta2 = beta2
def to_setting_kwargs(self):
return {
'learning_method': 'adamax',
'adam_beta1': self.beta1,
'adam_beta2': self.beta2
}
@property
def is_support_sparse(self):
return False
class AdaGradOptimizer(BaseSGDOptimizer):
"""
Adagrad(for ADAptive GRAdient algorithm) optimizer.
For details please refer this `Adaptive Subgradient Methods for
Online Learning and Stochastic Optimization
<http://www.magicbroom.info/Papers/DuchiHaSi10.pdf>`_.
.. math::
G &= \\sum_{\\tau=1}^{t} g_{\\tau} g_{\\tau}^T \\\\
w & = w - \\eta diag(G)^{-\\frac{1}{2}} \\circ g
"""
def to_setting_kwargs(self):
return {'learning_method': 'adagrad'}
def __init__(self):
pass
class RMSPropOptimizer(BaseSGDOptimizer):
"""
RMSProp(for Root Mean Square Propagation) optimizer. For details please
refer this `slide <http://www.cs.toronto.edu/~tijmen/csc321/slides/
lecture_slides_lec6.pdf>`_.
The equations of this method as follows:
.. math::
v(w, t) & = \\rho v(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 \\\\
w & = w - \\frac{\\eta} {\\sqrt{v(w,t) + \\epsilon}} \\nabla Q_{i}(w)
:param rho: the :math:`\\rho` in the equation. The forgetting factor.
:type rho: float
:param epsilon: the :math:`\\epsilon` in the equation.
:type epsilon: float
"""
def to_setting_kwargs(self):
return {
'learning_method': 'rmsprop',
'ada_rou': self.rho,
'ada_epsilon': self.epsilon
}
def __init__(self, rho=0.95, epsilon=1e-6):
self.rho = rho
self.epsilon = epsilon
class DecayedAdaGradOptimizer(BaseSGDOptimizer):
"""
AdaGrad method with decayed sum gradients. The equations of this method
show as follow.
.. math::
E(g_t^2) &= \\rho * E(g_{t-1}^2) + (1-\\rho) * g^2 \\\\
learning\\_rate &= 1/sqrt( ( E(g_t^2) + \\epsilon )
:param rho: The :math:`\\rho` parameter in that equation
:type rho: float
:param epsilon: The :math:`\\epsilon` parameter in that equation.
:type epsilon: float
"""
def to_setting_kwargs(self):
return {
'learning_method': 'decayed_adagrad',
'ada_rou': self.rho,
'ada_epsilon': self.epsilon
}
def __init__(self, rho=0.95, epsilon=1e-6):
self.rho = rho
self.epsilon = epsilon
class AdaDeltaOptimizer(BaseSGDOptimizer):
"""
AdaDelta method. The details of adadelta please refer to this
`ADADELTA: AN ADAPTIVE LEARNING RATE METHOD
<http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf>`_.
.. math::
E(g_t^2) &= \\rho * E(g_{t-1}^2) + (1-\\rho) * g^2 \\\\
learning\\_rate &= sqrt( ( E(dx_{t-1}^2) + \\epsilon ) / ( \\
E(g_t^2) + \\epsilon ) ) \\\\
E(dx_t^2) &= \\rho * E(dx_{t-1}^2) + (1-\\rho) * (-g*learning\\_rate)^2
:param rho: :math:`\\rho` in equation
:type rho: float
:param epsilon: :math:`\\rho` in equation
:type epsilon: float
"""
def to_setting_kwargs(self):
return {
'learning_method': 'adadelta',
'ada_rou': self.rho,
'ada_epsilon': self.epsilon
}
def __init__(self, rho=0.95, epsilon=1e-6):
self.rho = rho
self.epsilon = epsilon
class BaseRegularization(Optimizer):
def __init__(self):
self.algorithm = ""
self.learning_method = ""
def to_setting_kwargs(self):
return {}
class L2Regularization(BaseRegularization):
def __init__(self, rate):
super(L2Regularization, self).__init__()
self.decay_rate = rate
def to_setting_kwargs(self):
if self.algorithm == 'owlqn':
return {'l2weight': self.decay_rate}
else:
return dict()
def extra_settings(self):
if self.algorithm == 'sgd' or self.algorithm == 'async_sgd':
default_decay_rate(self.decay_rate)
class ModelAverage(Optimizer):
def to_setting_kwargs(self):
return {
'average_window': self.average_window,
'max_average_window': self.max_average_window,
'do_average_in_cpu': self.do_average_in_cpu
}
def __init__(self,
average_window,
max_average_window=None,
do_average_in_cpu=False):
self.average_window = average_window
self.max_average_window = max_average_window
self.do_average_in_cpu = do_average_in_cpu
class GradientClippingThreshold(Optimizer):
def extra_settings(self):
default_gradient_clipping_threshold(self.threshold)
def __init__(self, threshold):
self.threshold = threshold
def to_setting_kwargs(self):
return dict()
def __extends__(dict1, dict2):
for key in dict2:
assert key not in dict1
dict1[key] = dict2[key]
return dict1
@wrap_param_default(
['learning_method'], default_factory=lambda _: MomentumOptimizer())
@wrap_param_default(
['regularization'], default_factory=lambda _: BaseRegularization())
def settings(batch_size,
learning_rate=1e-3,
learning_rate_decay_a=0.,
learning_rate_decay_b=0.,
learning_rate_schedule='poly',
learning_rate_args='',
async_lagged_grad_discard_ratio=1.5,
learning_method=None,
regularization=None,
is_async=False,
model_average=None,
gradient_clipping_threshold=None):
"""
Set the optimization method, learning rate, batch size, and other training
settings. The currently supported algorithms are SGD and Async-SGD.
.. warning::
Note that the 'batch_size' in PaddlePaddle is not equal to global
training batch size. It represents the single training process's batch
size. If you use N processes to train one model, for example use three
GPU machines, the global batch size is N*'batch_size'.
:param batch_size: batch size for one training process.
:type batch_size: int
:param learning_rate: learning rate for SGD
:type learning_rate: float
:param learning_method: The extension optimization algorithms of gradient
descent, such as momentum, adagrad, rmsprop, etc.
Note that it should be instance with base type
BaseSGDOptimizer.
:type learning_method: BaseSGDOptimizer
:param regularization: The regularization method.
:type regularization: BaseRegularization
:param is_async: Is Async-SGD or not. Default value is False.
:type is_async: bool
:param model_average: Model Average Settings.
:type model_average: ModelAverage
:param gradient_clipping_threshold: gradient clipping threshold. If gradient
value larger than some value, will be
clipped.
:type gradient_clipping_threshold: float
:param async_lagged_grad_discard_ratio: async SGD gradient commit control,
when async_lagged_grad_discard_ratio * num_gradient_servers commit passed,
the current async SGD gradient is discarded.
:type async_lagged_grad_discard_ratio: float
"""
if isinstance(regularization, BaseRegularization):
regularization = [regularization]
assert isinstance(learning_method, Optimizer)
if isinstance(learning_method, BaseSGDOptimizer):
algorithm = 'async_sgd' if is_async else 'sgd'
else:
algorithm = 'owlqn'
args = [
'batch_size', 'learning_rate', 'learning_rate_decay_a',
'learning_rate_decay_b', 'learning_rate_schedule', 'learning_rate_args',
'gradient_clipping_threshold', 'async_lagged_grad_discard_ratio'
]
kwargs = dict()
kwargs['algorithm'] = algorithm
for arg in args:
kwargs[arg] = locals()[arg]
kwargs = __extends__(kwargs, learning_method.to_setting_kwargs())
learning_method.extra_settings()
for regular in regularization:
assert isinstance(regular, BaseRegularization)
regular.algorithm = algorithm
regular.learning_method = kwargs['learning_method']
kwargs = __extends__(kwargs, regular.to_setting_kwargs())
regular.extra_settings()
if gradient_clipping_threshold is not None:
gradient_clipping_threshold = GradientClippingThreshold(
threshold=gradient_clipping_threshold)
for each in [model_average, gradient_clipping_threshold]:
if each is not None:
assert isinstance(each, Optimizer)
each.algorithm = algorithm
each.learning_method = kwargs['learning_method']
kwargs = __extends__(kwargs, each.to_setting_kwargs())
each.extra_settings()
# Do Check?
Settings(**kwargs)
| 14,237 | 30.78125 | 85 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/utils.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.config_parser import logger
import functools
__all__ = ['deprecated']
def deprecated(instead):
def __impl__(func):
@functools.wraps(func)
def __wrapper__(*args, **kwargs):
logger.warning("The interface %s is deprecated, "
"will be removed soon. Please use %s instead." %
(func.__name__, instead))
return func(*args, **kwargs)
return __wrapper__
return __impl__
| 1,107 | 31.588235 | 75 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/networks.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from activations import LinearActivation, ReluActivation, SoftmaxActivation, \
IdentityActivation, TanhActivation, SequenceSoftmaxActivation
from attrs import ExtraAttr
from default_decorators import wrap_name_default, wrap_act_default, \
wrap_param_default, wrap_bias_attr_default, wrap_param_attr_default
from layers import * # There are too many layers used in network, so import *
from poolings import MaxPooling, SumPooling
from paddle.trainer.config_parser import *
__all__ = [
'sequence_conv_pool', 'simple_lstm', "simple_img_conv_pool",
"img_conv_bn_pool", 'lstmemory_group', 'lstmemory_unit', 'small_vgg',
'img_conv_group', 'img_separable_conv', 'vgg_16_network', 'gru_unit',
'gru_group', 'simple_gru', 'simple_attention', 'dot_product_attention',
'multi_head_attention', 'simple_gru2', 'bidirectional_gru',
'text_conv_pool', 'bidirectional_lstm', 'inputs', 'outputs'
]
######################################################
# Text CNN #
######################################################
@wrap_name_default("sequence_conv_pooling")
def sequence_conv_pool(input,
context_len,
hidden_size,
name=None,
context_start=None,
pool_type=None,
context_proj_layer_name=None,
context_proj_param_attr=False,
fc_layer_name=None,
fc_param_attr=None,
fc_bias_attr=None,
fc_act=None,
pool_bias_attr=None,
fc_attr=None,
context_attr=None,
pool_attr=None):
"""
Text convolution pooling group.
Text input => Context Projection => FC Layer => Pooling => Output.
:param name: group name.
:type name: basestring
:param input: input layer.
:type input: LayerOutput
:param context_len: context projection length. See
context_projection's document.
:type context_len: int
:param hidden_size: FC Layer size.
:type hidden_size: int
:param context_start: context start position. See
context_projection's context_start.
:type context_start: int|None
:param pool_type: pooling layer type. See pooling_layer's document.
:type pool_type: BasePoolingType
:param context_proj_layer_name: context projection layer name.
None if user don't care.
:type context_proj_layer_name: basestring
:param context_proj_param_attr: padding parameter attribute of context projection layer.
If false, it means padding always be zero.
:type context_proj_param_attr: ParameterAttribute|None
:param fc_layer_name: fc layer name. None if user don't care.
:type fc_layer_name: basestring
:param fc_param_attr: fc layer parameter attribute. None if user don't care.
:type fc_param_attr: ParameterAttribute|None
:param fc_bias_attr: fc bias parameter attribute. False if no bias,
None if user don't care.
:type fc_bias_attr: ParameterAttribute|False|None
:param fc_act: fc layer activation type. None means tanh.
:type fc_act: BaseActivation
:param pool_bias_attr: pooling layer bias attr. False if no bias.
None if user don't care.
:type pool_bias_attr: ParameterAttribute|False|None
:param fc_attr: fc layer extra attribute.
:type fc_attr: ExtraLayerAttribute
:param context_attr: context projection layer extra attribute.
:type context_attr: ExtraLayerAttribute
:param pool_attr: pooling layer extra attribute.
:type pool_attr: ExtraLayerAttribute
:return: layer's output.
:rtype: LayerOutput
"""
# Set Default Value to param
context_proj_layer_name = "%s_conv_proj" % name \
if context_proj_layer_name is None else context_proj_layer_name
with mixed_layer(
name=context_proj_layer_name,
size=input.size * context_len,
act=LinearActivation(),
layer_attr=context_attr) as m:
m += context_projection(
input,
context_len=context_len,
context_start=context_start,
padding_attr=context_proj_param_attr)
fc_layer_name = "%s_conv_fc" % name \
if fc_layer_name is None else fc_layer_name
fl = fc_layer(
name=fc_layer_name,
input=m,
size=hidden_size,
act=fc_act,
layer_attr=fc_attr,
param_attr=fc_param_attr,
bias_attr=fc_bias_attr)
return pooling_layer(
name=name,
input=fl,
pooling_type=pool_type,
bias_attr=pool_bias_attr,
layer_attr=pool_attr)
text_conv_pool = sequence_conv_pool
############################################################################
# Images #
############################################################################
@wrap_name_default("conv_pool")
def simple_img_conv_pool(input,
filter_size,
num_filters,
pool_size,
name=None,
pool_type=None,
act=None,
groups=1,
conv_stride=1,
conv_padding=0,
bias_attr=None,
num_channel=None,
param_attr=None,
shared_bias=True,
conv_layer_attr=None,
pool_stride=1,
pool_padding=0,
pool_layer_attr=None):
"""
Simple image convolution and pooling group.
Img input => Conv => Pooling => Output.
:param name: group name.
:type name: basestring
:param input: input layer.
:type input: LayerOutput
:param filter_size: see img_conv_layer for details.
:type filter_size: int
:param num_filters: see img_conv_layer for details.
:type num_filters: int
:param pool_size: see img_pool_layer for details.
:type pool_size: int
:param pool_type: see img_pool_layer for details.
:type pool_type: BasePoolingType
:param act: see img_conv_layer for details.
:type act: BaseActivation
:param groups: see img_conv_layer for details.
:type groups: int
:param conv_stride: see img_conv_layer for details.
:type conv_stride: int
:param conv_padding: see img_conv_layer for details.
:type conv_padding: int
:param bias_attr: see img_conv_layer for details.
:type bias_attr: ParameterAttribute
:param num_channel: see img_conv_layer for details.
:type num_channel: int
:param param_attr: see img_conv_layer for details.
:type param_attr: ParameterAttribute
:param shared_bias: see img_conv_layer for details.
:type shared_bias: bool
:param conv_layer_attr: see img_conv_layer for details.
:type conv_layer_attr: ExtraLayerAttribute
:param pool_stride: see img_pool_layer for details.
:type pool_stride: int
:param pool_padding: see img_pool_layer for details.
:type pool_padding: int
:param pool_layer_attr: see img_pool_layer for details.
:type pool_layer_attr: ExtraLayerAttribute
:return: layer's output
:rtype: LayerOutput
"""
_conv_ = img_conv_layer(
name="%s_conv" % name,
input=input,
filter_size=filter_size,
num_filters=num_filters,
num_channels=num_channel,
act=act,
groups=groups,
stride=conv_stride,
padding=conv_padding,
bias_attr=bias_attr,
param_attr=param_attr,
shared_biases=shared_bias,
layer_attr=conv_layer_attr)
return img_pool_layer(
name="%s_pool" % name,
input=_conv_,
pool_size=pool_size,
pool_type=pool_type,
stride=pool_stride,
padding=pool_padding,
layer_attr=pool_layer_attr)
@wrap_name_default("conv_bn_pool")
def img_conv_bn_pool(input,
filter_size,
num_filters,
pool_size,
name=None,
pool_type=None,
act=None,
groups=1,
conv_stride=1,
conv_padding=0,
conv_bias_attr=None,
num_channel=None,
conv_param_attr=None,
shared_bias=True,
conv_layer_attr=None,
bn_param_attr=None,
bn_bias_attr=None,
bn_layer_attr=None,
pool_stride=1,
pool_padding=0,
pool_layer_attr=None):
"""
Convolution, batch normalization, pooling group.
Img input => Conv => BN => Pooling => Output.
:param name: group name.
:type name: basestring
:param input: input layer.
:type input: LayerOutput
:param filter_size: see img_conv_layer for details.
:type filter_size: int
:param num_filters: see img_conv_layer for details.
:type num_filters: int
:param pool_size: see img_pool_layer for details.
:type pool_size: int
:param pool_type: see img_pool_layer for details.
:type pool_type: BasePoolingType
:param act: see batch_norm_layer for details.
:type act: BaseActivation
:param groups: see img_conv_layer for details.
:type groups: int
:param conv_stride: see img_conv_layer for details.
:type conv_stride: int
:param conv_padding: see img_conv_layer for details.
:type conv_padding: int
:param conv_bias_attr: see img_conv_layer for details.
:type conv_bias_attr: ParameterAttribute
:param num_channel: see img_conv_layer for details.
:type num_channel: int
:param conv_param_attr: see img_conv_layer for details.
:type conv_param_attr: ParameterAttribute
:param shared_bias: see img_conv_layer for details.
:type shared_bias: bool
:param conv_layer_attr: see img_conv_layer for details.
:type conv_layer_attr: ExtraLayerOutput
:param bn_param_attr: see batch_norm_layer for details.
:type bn_param_attr: ParameterAttribute
:param bn_bias_attr: see batch_norm_layer for details.
:type bn_bias_attr: ParameterAttribute
:param bn_layer_attr: see batch_norm_layer for details.
:type bn_layer_attr: ExtraLayerAttribute
:param pool_stride: see img_pool_layer for details.
:type pool_stride: int
:param pool_padding: see img_pool_layer for details.
:type pool_padding: int
:param pool_layer_attr: see img_pool_layer for details.
:type pool_layer_attr: ExtraLayerAttribute
:return: layer's output
:rtype: LayerOutput
"""
__conv__ = img_conv_layer(
name="%s_conv" % name,
input=input,
filter_size=filter_size,
num_filters=num_filters,
num_channels=num_channel,
act=LinearActivation(),
groups=groups,
stride=conv_stride,
padding=conv_padding,
bias_attr=conv_bias_attr,
param_attr=conv_param_attr,
shared_biases=shared_bias,
layer_attr=conv_layer_attr)
__bn__ = batch_norm_layer(
name="%s_bn" % name,
input=__conv__,
act=act,
bias_attr=bn_bias_attr,
param_attr=bn_param_attr,
layer_attr=bn_layer_attr)
return img_pool_layer(
name="%s_pool" % name,
input=__bn__,
pool_type=pool_type,
pool_size=pool_size,
stride=pool_stride,
padding=pool_padding,
layer_attr=pool_layer_attr)
@wrap_act_default(param_names=['conv_act'], act=ReluActivation())
@wrap_param_default(
param_names=['pool_type'], default_factory=lambda _: MaxPooling())
def img_conv_group(input,
conv_num_filter,
pool_size,
num_channels=None,
conv_padding=1,
conv_filter_size=3,
conv_act=None,
conv_with_batchnorm=False,
conv_batchnorm_drop_rate=0,
pool_stride=1,
pool_type=None,
param_attr=None):
"""
Image Convolution Group, Used for vgg net.
:param conv_batchnorm_drop_rate: if conv_with_batchnorm[i] is true,
conv_batchnorm_drop_rate[i] represents the drop rate of each batch norm.
:type conv_batchnorm_drop_rate: list
:param input: input layer.
:type input: LayerOutput
:param conv_num_filter: list of output channels num.
:type conv_num_filter: list|tuple
:param pool_size: pooling filter size.
:type pool_size: int
:param num_channels: input channels num.
:type num_channels: int
:param conv_padding: convolution padding size.
:type conv_padding: int
:param conv_filter_size: convolution filter size.
:type conv_filter_size: int
:param conv_act: activation funciton after convolution.
:type conv_act: BaseActivation
:param conv_with_batchnorm: if conv_with_batchnorm[i] is true,
there is a batch normalization operation after each convolution.
:type conv_with_batchnorm: list
:param pool_stride: pooling stride size.
:type pool_stride: int
:param pool_type: pooling type.
:type pool_type: BasePoolingType
:param param_attr: param attribute of convolution layer,
None means default attribute.
:type param_attr: ParameterAttribute
:return: layer's output
:rtype: LayerOutput
"""
tmp = input
# Type checks
assert isinstance(tmp, LayerOutput)
assert isinstance(conv_num_filter, list) or isinstance(conv_num_filter,
tuple)
for each_num_filter in conv_num_filter:
assert isinstance(each_num_filter, int)
assert isinstance(pool_size, int)
def __extend_list__(obj):
if not hasattr(obj, '__len__'):
return [obj] * len(conv_num_filter)
else:
return obj
conv_padding = __extend_list__(conv_padding)
conv_filter_size = __extend_list__(conv_filter_size)
conv_act = __extend_list__(conv_act)
conv_with_batchnorm = __extend_list__(conv_with_batchnorm)
conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate)
for i in xrange(len(conv_num_filter)):
extra_kwargs = dict()
if num_channels is not None:
extra_kwargs['num_channels'] = num_channels
num_channels = None
if conv_with_batchnorm[i]:
extra_kwargs['act'] = LinearActivation()
else:
extra_kwargs['act'] = conv_act[i]
tmp = img_conv_layer(
input=tmp,
padding=conv_padding[i],
filter_size=conv_filter_size[i],
num_filters=conv_num_filter[i],
param_attr=param_attr,
**extra_kwargs)
# logger.debug("tmp.num_filters = %d" % tmp.num_filters)
if conv_with_batchnorm[i]:
dropout = conv_batchnorm_drop_rate[i]
if dropout == 0 or abs(dropout) < 1e-5: # dropout not set
tmp = batch_norm_layer(input=tmp, act=conv_act[i])
else:
tmp = batch_norm_layer(
input=tmp,
act=conv_act[i],
layer_attr=ExtraAttr(drop_rate=dropout))
return img_pool_layer(
input=tmp, stride=pool_stride, pool_size=pool_size, pool_type=pool_type)
@wrap_name_default("separable_conv")
def img_separable_conv(input,
num_channels,
num_out_channels,
filter_size,
stride=1,
padding=0,
depth_multiplier=1,
act=None,
bias_attr=None,
param_attr=None,
shared_bias=True,
layer_type='exconv',
name=None):
"""
Separable Convolution.
The separable convolution module is consisted of a depthwise convolution
that acts separately on input channels, followed by a pointwise convolution
with 1*1 kernels that mixes channels. It is used for Xception:
https://arxiv.org/pdf/1610.02357.pdf
:param input: input layer.
:type input: LayerOutput
:param num_channels: the number of input channels.
:type num_channels: int
:param num_out_channels: the number of output channels.
:type num_out_channels: int
:param filter_size: the filter size for the depthwise convolution.
:type filter_size: int|tuple
:param stride: the stride size for the depthwise convolution.
:type stride: int|tuple
:param padding: the padding size for the depthwise convolution.
:type padding: int|tuple
:param depth_multiplier: the number of filter for one channel in the
depthwize convolution.
:type depth_multiplier: int
:param act: the activation function for the output.
:type act: BaseActivation
:param bias_attr: see img_conv_layer for details.
:type bias_attr: ParameterAttribute
:param param_attr: see img_conv_layer for details.
:type param_attr: ParameterAttribute
:param shared_bias: see img_conv_layer for details.
:type shared_bias: bool
:param layer_type: see img_conv_layer for details.
:type layer_type: bool
:return: layer's output
:rtype: LayerOutput
"""
__depthwise_conv__ = img_conv_layer(
name="%s_depthwise_conv" % name,
input=input,
num_channels=num_channels,
num_filters=num_channels * depth_multiplier,
groups=num_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
act=LinearActivation(),
bias_attr=bias_attr,
param_attr=param_attr,
shared_biases=shared_bias,
layer_type=layer_type)
__pointwise_conv__ = img_conv_layer(
name="%s_pointwise_conv" % name,
input=__depthwise_conv__,
num_channels=num_channels * depth_multiplier,
num_filters=num_out_channels,
filter_size=1,
stride=1,
padding=0,
act=act,
bias_attr=bias_attr,
param_attr=param_attr,
shared_biases=shared_bias)
return __pointwise_conv__
def small_vgg(input_image, num_channels, num_classes):
def __vgg__(ipt, num_filter, times, dropouts, num_channels_=None):
return img_conv_group(
input=ipt,
num_channels=num_channels_,
pool_size=2,
pool_stride=2,
conv_num_filter=[num_filter] * times,
conv_filter_size=3,
conv_act=ReluActivation(),
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type=MaxPooling())
tmp = __vgg__(input_image, 64, 2, [0.3, 0], num_channels)
tmp = __vgg__(tmp, 128, 2, [0.4, 0])
tmp = __vgg__(tmp, 256, 3, [0.4, 0.4, 0])
tmp = __vgg__(tmp, 512, 3, [0.4, 0.4, 0])
tmp = img_pool_layer(
input=tmp, stride=2, pool_size=2, pool_type=MaxPooling())
tmp = dropout_layer(input=tmp, dropout_rate=0.5)
tmp = fc_layer(
input=tmp,
size=512,
layer_attr=ExtraAttr(drop_rate=0.5),
act=LinearActivation())
tmp = batch_norm_layer(input=tmp, act=ReluActivation())
return fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
def vgg_16_network(input_image, num_channels, num_classes=1000):
"""
Same model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8
:param num_classes: number of class.
:type num_classes: int
:param input_image: input layer.
:type input_image: LayerOutput
:param num_channels: input channels num.
:type num_channels: int
:return: layer's output
:rtype: LayerOutput
"""
tmp = img_conv_group(
input=input_image,
num_channels=num_channels,
conv_padding=1,
conv_num_filter=[64, 64],
conv_filter_size=3,
conv_act=ReluActivation(),
pool_size=2,
pool_stride=2,
pool_type=MaxPooling())
tmp = img_conv_group(
input=tmp,
conv_num_filter=[128, 128],
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = img_conv_group(
input=tmp,
conv_num_filter=[256, 256, 256],
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = img_conv_group(
input=tmp,
conv_num_filter=[512, 512, 512],
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = img_conv_group(
input=tmp,
conv_num_filter=[512, 512, 512],
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
return fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
############################################################################
# Recurrent #
############################################################################
@wrap_name_default("lstm")
def simple_lstm(input,
size,
name=None,
reverse=False,
mat_param_attr=None,
bias_param_attr=None,
inner_param_attr=None,
act=None,
gate_act=None,
state_act=None,
mixed_layer_attr=None,
lstm_cell_attr=None):
"""
Simple LSTM Cell.
It just combines a mixed layer with fully_matrix_projection and a lstmemory
layer. The simple lstm cell was implemented with follow equations.
.. math::
i_t & = \\sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)
f_t & = \\sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)
o_t & = \\sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)
h_t & = o_t tanh(c_t)
Please refer to **Generating Sequences With Recurrent Neural Networks** for more
details about lstm. Link_ is here.
.. _Link: http://arxiv.org/abs/1308.0850
:param name: lstm layer name.
:type name: basestring
:param input: layer's input.
:type input: LayerOutput
:param size: lstm layer size.
:type size: int
:param reverse: process the input in a reverse order or not.
:type reverse: bool
:param mat_param_attr: parameter attribute of matrix projection in mixed layer.
:type mat_param_attr: ParameterAttribute
:param bias_param_attr: bias parameter attribute. False means no bias, None
means default bias.
:type bias_param_attr: ParameterAttribute|False
:param inner_param_attr: parameter attribute of lstm cell.
:type inner_param_attr: ParameterAttribute
:param act: last activiation type of lstm.
:type act: BaseActivation
:param gate_act: gate activiation type of lstm.
:type gate_act: BaseActivation
:param state_act: state activiation type of lstm.
:type state_act: BaseActivation
:param mixed_layer_attr: extra attribute of mixed layer.
:type mixed_layer_attr: ExtraLayerAttribute
:param lstm_cell_attr: extra attribute of lstm.
:type lstm_cell_attr: ExtraLayerAttribute
:return: layer's output.
:rtype: LayerOutput
"""
fc_name = 'lstm_transform_%s' % name
with mixed_layer(
name=fc_name,
size=size * 4,
act=IdentityActivation(),
layer_attr=mixed_layer_attr,
bias_attr=False) as m:
m += full_matrix_projection(input, param_attr=mat_param_attr)
return lstmemory(
name=name,
input=m,
reverse=reverse,
bias_attr=bias_param_attr,
param_attr=inner_param_attr,
act=act,
gate_act=gate_act,
state_act=state_act,
layer_attr=lstm_cell_attr)
@wrap_name_default('lstm_unit')
def lstmemory_unit(input,
out_memory=None,
name=None,
size=None,
param_attr=None,
act=None,
gate_act=None,
state_act=None,
input_proj_bias_attr=None,
input_proj_layer_attr=None,
lstm_bias_attr=None,
lstm_layer_attr=None):
"""
lstmemory_unit defines the caculation process of a LSTM unit during a
single time step. This function is not a recurrent layer, so it can not be
directly used to process sequence input. This function is always used in
recurrent_group (see layers.py for more details) to implement attention
mechanism.
Please refer to **Generating Sequences With Recurrent Neural Networks**
for more details about LSTM. The link goes as follows:
.. _Link: https://arxiv.org/abs/1308.0850
.. math::
i_t & = \\sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)
f_t & = \\sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)
o_t & = \\sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)
h_t & = o_t tanh(c_t)
The example usage is:
.. code-block:: python
lstm_step = lstmemory_unit(input=[layer1],
size=256,
act=TanhActivation(),
gate_act=SigmoidActivation(),
state_act=TanhActivation())
:param input: Input layer.
:type input: LayerOutput
:param out_memory: The output of previous time step.
:type out_memory: LayerOutput | None
:param name: The lstmemory unit name.
:type name: basestring
:param size: The lstmemory unit size.
:type size: int
:param param_attr: The parameter attribute for the weights in
input to hidden projection.
None means default attribute.
:type param_attr: ParameterAttribute
:param act: The last activiation type of lstm.
:type act: BaseActivation
:param gate_act: The gate activiation type of lstm.
:type gate_act: BaseActivation
:param state_act: The state activiation type of lstm.
:type state_act: BaseActivation
:param input_proj_bias_attr: The parameter attribute for the bias in
input to hidden projection.
False or None means no bias.
If this parameter is set to True,
the bias is initialized to zero.
:type input_proj_bias_attr: ParameterAttribute|bool|None
:param input_proj_layer_attr: The extra layer attribute for
input to hidden projection of the LSTM unit,
such as dropout, error clipping.
:type input_proj_layer_attr: ExtraLayerAttribute
:param lstm_bias_attr: The parameter attribute for the bias in lstm layer.
False or None means no bias.
If this parameter is set to True,
the bias is initialized to zero.
:type lstm_bias_attr: ParameterAttribute|True|None
:param lstm_layer_attr: The extra attribute of lstm layer.
:type lstm_layer_attr: ExtraLayerAttribute
:return: The lstmemory unit name.
:rtype: LayerOutput
"""
if size is None:
assert input.size % 4 == 0
size = input.size / 4
if out_memory is None:
out_mem = memory(name=name, size=size)
else:
out_mem = out_memory
state_mem = memory(name="%s_state" % name, size=size)
with mixed_layer(
name="%s_input_recurrent" % name,
size=size * 4,
bias_attr=input_proj_bias_attr,
layer_attr=input_proj_layer_attr,
act=IdentityActivation()) as m:
m += identity_projection(input=input)
m += full_matrix_projection(input=out_mem, param_attr=param_attr)
lstm_out = lstm_step_layer(
name=name,
input=m,
state=state_mem,
size=size,
bias_attr=lstm_bias_attr,
act=act,
gate_act=gate_act,
state_act=state_act,
layer_attr=lstm_layer_attr)
get_output_layer(name='%s_state' % name, input=lstm_out, arg_name='state')
return lstm_out
@wrap_name_default('lstm_group')
def lstmemory_group(input,
size=None,
name=None,
out_memory=None,
reverse=False,
param_attr=None,
act=None,
gate_act=None,
state_act=None,
input_proj_bias_attr=None,
input_proj_layer_attr=None,
lstm_bias_attr=None,
lstm_layer_attr=None):
"""
lstm_group is a recurrent_group version of Long Short Term Memory. It
does exactly the same calculation as the lstmemory layer (see lstmemory in
layers.py for the maths) does. A promising benefit is that LSTM memory
cell states(or hidden states) in every time step are accessible to the
user. This is especially useful in attention model. If you do not need to
access the internal states of the lstm and merely use its outputs,
it is recommended to use the lstmemory, which is relatively faster than
lstmemory_group.
NOTE: In PaddlePaddle's implementation, the following input-to-hidden
multiplications:
:math:`W_{x_i}x_{t}` , :math:`W_{x_f}x_{t}`,
:math:`W_{x_c}x_t`, :math:`W_{x_o}x_{t}` are not done in lstmemory_unit to
speed up the calculations. Consequently, an additional mixed_layer with
full_matrix_projection must be included before lstmemory_unit is called.
The example usage is:
.. code-block:: python
lstm_step = lstmemory_group(input=[layer1],
size=256,
act=TanhActivation(),
gate_act=SigmoidActivation(),
state_act=TanhActivation())
:param input: Input layer.
:type input: LayerOutput
:param size: The lstmemory group size.
:type size: int
:param name: The name of lstmemory group.
:type name: basestring
:param out_memory: The output of previous time step.
:type out_memory: LayerOutput | None
:param reverse: Process the input in a reverse order or not.
:type reverse: bool
:param param_attr: The parameter attribute for the weights in
input to hidden projection.
None means default attribute.
:type param_attr: ParameterAttribute
:param act: The last activiation type of lstm.
:type act: BaseActivation
:param gate_act: The gate activiation type of lstm.
:type gate_act: BaseActivation
:param state_act: The state activiation type of lstm.
:type state_act: BaseActivation
:param input_proj_bias_attr: The parameter attribute for the bias in
input to hidden projection.
False or None means no bias.
If this parameter is set to True,
the bias is initialized to zero.
:type input_proj_bias_attr: ParameterAttribute|bool|None
:param input_proj_layer_attr: The extra layer attribute for
input to hidden projection of the LSTM unit,
such as dropout, error clipping.
:type input_proj_layer_attr: ExtraLayerAttribute
:param lstm_bias_attr: The parameter attribute for the bias in lstm layer.
False or None means no bias.
If this parameter is set to True,
the bias is initialized to zero.
:type lstm_bias_attr: ParameterAttribute|True|None
:param lstm_layer_attr: The extra attribute of lstm layer.
:type lstm_layer_attr: ExtraLayerAttribute
:return: the lstmemory group.
:rtype: LayerOutput
"""
def __lstm_step__(ipt):
return lstmemory_unit(
input=ipt,
name=name,
size=size,
act=act,
gate_act=gate_act,
state_act=state_act,
out_memory=out_memory,
input_proj_bias_attr=input_proj_bias_attr,
input_proj_layer_attr=input_proj_layer_attr,
param_attr=param_attr,
lstm_layer_attr=lstm_layer_attr,
lstm_bias_attr=lstm_bias_attr)
return recurrent_group(
name='%s_recurrent_group' % name,
step=__lstm_step__,
reverse=reverse,
input=input)
@wrap_name_default('gru_unit')
def gru_unit(input,
memory_boot=None,
size=None,
name=None,
gru_bias_attr=None,
gru_param_attr=None,
act=None,
gate_act=None,
gru_layer_attr=None,
naive=False):
"""
gru_unit defines the calculation process of a gated recurrent unit during a single
time step. This function is not a recurrent layer, so it can not be
directly used to process sequence input. This function is always used in
the recurrent_group (see layers.py for more details) to implement attention
mechanism.
Please see grumemory in layers.py for the details about the maths.
:param input: input layer.
:type input: LayerOutput
:param memory_boot: the initialization state of the LSTM cell.
:type memory_boot: LayerOutput | None
:param name: name of the gru group.
:type name: basestring
:param size: hidden size of the gru.
:type size: int
:param act: activation type of gru
:type act: BaseActivation
:param gate_act: gate activation type or gru
:type gate_act: BaseActivation
:param gru_layer_attr: Extra attribute of the gru layer.
:type gru_layer_attr: ExtraLayerAttribute
:return: the gru output layer.
:rtype: LayerOutput
"""
assert input.size % 3 == 0
if size is None:
size = input.size / 3
out_mem = memory(name=name, size=size, boot_layer=memory_boot)
if naive:
__step__ = gru_step_naive_layer
else:
__step__ = gru_step_layer
gru_out = __step__(
name=name,
input=input,
output_mem=out_mem,
size=size,
bias_attr=gru_bias_attr,
param_attr=gru_param_attr,
act=act,
gate_act=gate_act,
layer_attr=gru_layer_attr)
return gru_out
@wrap_name_default('gru_group')
def gru_group(input,
memory_boot=None,
size=None,
name=None,
reverse=False,
gru_bias_attr=None,
gru_param_attr=None,
act=None,
gate_act=None,
gru_layer_attr=None,
naive=False):
"""
gru_group is a recurrent_group version of Gated Recurrent Unit. It
does exactly the same calculation as the grumemory layer does. A promising
benefit is that gru hidden states are accessible to the user. This is
especially useful in attention model. If you do not need to access
any internal state and merely use the outputs of a GRU, it is recommended
to use the grumemory, which is relatively faster.
Please see grumemory in layers.py for more detail about the maths.
The example usage is:
.. code-block:: python
gru = gru_group(input=[layer1],
size=256,
act=TanhActivation(),
gate_act=SigmoidActivation())
:param input: input layer.
:type input: LayerOutput
:param memory_boot: the initialization state of the LSTM cell.
:type memory_boot: LayerOutput | None
:param name: name of the gru group.
:type name: basestring
:param size: hidden size of the gru.
:type size: int
:param reverse: process the input in a reverse order or not.
:type reverse: bool
:param act: activiation type of gru
:type act: BaseActivation
:param gate_act: gate activiation type of gru
:type gate_act: BaseActivation
:param gru_bias_attr: bias parameter attribute of gru layer,
False means no bias, None means default bias.
:type gru_bias_attr: ParameterAttribute|False|None
:param gru_layer_attr: Extra attribute of the gru layer.
:type gru_layer_attr: ExtraLayerAttribute
:return: the gru group.
:rtype: LayerOutput
"""
def __gru_step__(ipt):
return gru_unit(
input=ipt,
memory_boot=memory_boot,
name=name,
size=size,
gru_bias_attr=gru_bias_attr,
gru_param_attr=gru_param_attr,
act=act,
gate_act=gate_act,
gru_layer_attr=gru_layer_attr,
naive=naive)
return recurrent_group(
name='%s_recurrent_group' % name,
step=__gru_step__,
reverse=reverse,
input=input)
@wrap_name_default('simple_gru')
def simple_gru(input,
size,
name=None,
reverse=False,
mixed_param_attr=None,
mixed_bias_param_attr=None,
mixed_layer_attr=None,
gru_bias_attr=None,
gru_param_attr=None,
act=None,
gate_act=None,
gru_layer_attr=None,
naive=False):
"""
You may see gru_step_layer, grumemory in layers.py, gru_unit, gru_group,
simple_gru in network.py. The reason why there are so many interfaces is
that we have two ways to implement recurrent neural network. One way is to
use one complete layer to implement rnn (including simple rnn, gru and lstm)
with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But
the multiplication operation :math:`W x_t` is not computed in these layers.
See details in their interfaces in layers.py.
The other implementation is to use an recurrent group which can ensemble a
series of layers to compute rnn step by step. This way is flexible for
attenion mechanism or other complex connections.
- gru_step_layer: only compute rnn by one step. It needs an memory as input
and can be used in recurrent group.
- gru_unit: a wrapper of gru_step_layer with memory.
- gru_group: a GRU cell implemented by a combination of multiple layers in
recurrent group.
But :math:`W x_t` is not done in group.
- gru_memory: a GRU cell implemented by one layer, which does same calculation
with gru_group and is faster than gru_group.
- simple_gru: a complete GRU implementation inlcuding :math:`W x_t` and
gru_group. :math:`W` contains :math:`W_r`, :math:`W_z` and :math:`W`, see
formula in grumemory.
The computational speed is that, grumemory is relatively better than
gru_group, and gru_group is relatively better than simple_gru.
The example usage is:
.. code-block:: python
gru = simple_gru(input=[layer1], size=256)
:param input: input layer.
:type input: LayerOutput
:param name: name of the gru group.
:type name: basestring
:param size: hidden size of the gru.
:type size: int
:param reverse: process the input in a reverse order or not.
:type reverse: bool
:param act: activiation type of gru
:type act: BaseActivation
:param gate_act: gate activiation type of gru
:type gate_act: BaseActivation
:param gru_bias_attr: bias parameter attribute of gru layer,
False means no bias, None means default bias.
:type gru_bias_attr: ParameterAttribute|False|None
:param gru_layer_attr: Extra attribute of the gru layer.
:type gru_layer_attr: ExtraLayerAttribute
:return: the gru group.
:rtype: LayerOutput
"""
with mixed_layer(
name='%s_transform' % name,
size=size * 3,
bias_attr=mixed_bias_param_attr,
layer_attr=mixed_layer_attr) as m:
m += full_matrix_projection(input=input, param_attr=mixed_param_attr)
return gru_group(
name=name,
size=size,
input=m,
reverse=reverse,
gru_bias_attr=gru_bias_attr,
gru_param_attr=gru_param_attr,
act=act,
gate_act=gate_act,
gru_layer_attr=gru_layer_attr,
naive=naive)
@wrap_name_default('simple_gru2')
def simple_gru2(input,
size,
name=None,
reverse=False,
mixed_param_attr=None,
mixed_bias_attr=None,
gru_param_attr=None,
gru_bias_attr=None,
act=None,
gate_act=None,
mixed_layer_attr=None,
gru_cell_attr=None):
"""
simple_gru2 is the same with simple_gru, but using grumemory instead.
Please refer to grumemory in layers.py for more detail about the math.
simple_gru2 is faster than simple_gru.
The example usage is:
.. code-block:: python
gru = simple_gru2(input=[layer1], size=256)
:param input: input layer.
:type input: LayerOutput
:param name: name of the gru group.
:type name: basestring
:param size: hidden size of the gru.
:type size: int
:param reverse: process the input in a reverse order or not.
:type reverse: bool
:param act: activiation type of gru
:type act: BaseActivation
:param gate_act: gate activiation type of gru
:type gate_act: BaseActivation
:param gru_bias_attr: bias parameter attribute of gru layer,
False means no bias, None means default bias.
:type gru_bias_attr: ParameterAttribute|False|None
:param gru_param_attr: param parameter attribute of gru layer,
None means default param.
:type gru_param_attr: ParameterAttribute|None
:return: the gru group.
:rtype: LayerOutput
"""
with mixed_layer(
name='%s_transform' % name,
size=size * 3,
bias_attr=mixed_bias_attr,
layer_attr=mixed_layer_attr) as m:
m += full_matrix_projection(input=input, param_attr=mixed_param_attr)
return grumemory(
name=name,
input=m,
reverse=reverse,
bias_attr=gru_bias_attr,
param_attr=gru_param_attr,
act=act,
gate_act=gate_act,
layer_attr=gru_cell_attr)
@wrap_name_default("bidirectional_gru")
def bidirectional_gru(input,
size,
name=None,
return_seq=False,
fwd_mixed_param_attr=None,
fwd_mixed_bias_attr=None,
fwd_gru_param_attr=None,
fwd_gru_bias_attr=None,
fwd_act=None,
fwd_gate_act=None,
fwd_mixed_layer_attr=None,
fwd_gru_cell_attr=None,
bwd_mixed_param_attr=None,
bwd_mixed_bias_attr=None,
bwd_gru_param_attr=None,
bwd_gru_bias_attr=None,
bwd_act=None,
bwd_gate_act=None,
bwd_mixed_layer_attr=None,
bwd_gru_cell_attr=None,
last_seq_attr=None,
first_seq_attr=None,
concat_attr=None,
concat_act=None):
"""
A bidirectional_gru is a recurrent unit that iterates over the input
sequence both in forward and backward orders, and then concatenate two
outputs to form a final output. However, concatenation of two outputs
is not the only way to form the final output, you can also, for example,
just add them together.
The example usage is:
.. code-block:: python
bi_gru = bidirectional_gru(input=[input1], size=512)
:param name: bidirectional gru layer name.
:type name: basestring
:param input: input layer.
:type input: LayerOutput
:param size: gru layer size.
:type size: int
:param return_seq: If set False, the last time step of output are
concatenated and returned.
If set True, the entire output sequences in forward
and backward directions are concatenated and returned.
:type return_seq: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
args = locals()
fw = simple_gru2(
name='%s_fw' % name,
input=input,
size=size,
**dict((k[len('fwd_'):], v) for k, v in args.iteritems()
if k.startswith('fwd_')))
bw = simple_gru2(
name="%s_bw" % name,
input=input,
size=size,
reverse=True,
**dict((k[len('bwd_'):], v) for k, v in args.iteritems()
if k.startswith('bwd_')))
if return_seq:
return concat_layer(
name=name, input=[fw, bw], layer_attr=concat_attr, act=concat_act)
else:
fw_seq = last_seq(
name="%s_fw_last" % name, input=fw, layer_attr=last_seq_attr)
bw_seq = first_seq(
name="%s_bw_last" % name, input=bw, layer_attr=first_seq_attr)
return concat_layer(
name=name,
input=[fw_seq, bw_seq],
layer_attr=concat_attr,
act=concat_act)
@wrap_name_default("bidirectional_lstm")
def bidirectional_lstm(input,
size,
name=None,
return_seq=False,
fwd_mat_param_attr=None,
fwd_bias_param_attr=None,
fwd_inner_param_attr=None,
fwd_act=None,
fwd_gate_act=None,
fwd_state_act=None,
fwd_mixed_layer_attr=None,
fwd_lstm_cell_attr=None,
bwd_mat_param_attr=None,
bwd_bias_param_attr=None,
bwd_inner_param_attr=None,
bwd_act=None,
bwd_gate_act=None,
bwd_state_act=None,
bwd_mixed_layer_attr=None,
bwd_lstm_cell_attr=None,
last_seq_attr=None,
first_seq_attr=None,
concat_attr=None,
concat_act=None):
"""
A bidirectional_lstm is a recurrent unit that iterates over the input
sequence both in forward and backward orders, and then concatenate two
outputs to form a final output. However, concatenation of two outputs
is not the only way to form the final output, you can also, for example,
just add them together.
Please refer to **Neural Machine Translation by Jointly Learning to Align
and Translate** for more details about the bidirectional lstm.
The link goes as follows:
.. _Link: https://arxiv.org/pdf/1409.0473v3.pdf
The example usage is:
.. code-block:: python
bi_lstm = bidirectional_lstm(input=[input1], size=512)
:param name: bidirectional lstm layer name.
:type name: basestring
:param input: input layer.
:type input: LayerOutput
:param size: lstm layer size.
:type size: int
:param return_seq: If set False, the last time step of output are
concatenated and returned.
If set True, the entire output sequences in forward
and backward directions are concatenated and returned.
:type return_seq: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
args = locals()
fw = simple_lstm(
name='%s_fw' % name,
input=input,
size=size,
**dict((k[len('fwd_'):], v) for k, v in args.iteritems()
if k.startswith('fwd_')))
bw = simple_lstm(
name="%s_bw" % name,
input=input,
size=size,
reverse=True,
**dict((k[len('bwd_'):], v) for k, v in args.iteritems()
if k.startswith('bwd_')))
if return_seq:
return concat_layer(
name=name, input=[fw, bw], layer_attr=concat_attr, act=concat_act)
else:
fw_seq = last_seq(
name="%s_fw_last" % name, input=fw, layer_attr=last_seq_attr)
bw_seq = first_seq(
name="%s_bw_last" % name, input=bw, layer_attr=first_seq_attr)
return concat_layer(
name=name,
input=[fw_seq, bw_seq],
layer_attr=concat_attr,
act=concat_act)
@wrap_name_default()
@wrap_act_default(param_names=['weight_act'], act=TanhActivation())
def simple_attention(encoded_sequence,
encoded_proj,
decoder_state,
transform_param_attr=None,
softmax_param_attr=None,
weight_act=None,
name=None):
"""
Calculate and return a context vector with attention mechanism.
Size of the context vector equals to size of the encoded_sequence.
.. math::
a(s_{i-1},h_{j}) & = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})
e_{i,j} & = a(s_{i-1}, h_{j})
a_{i,j} & = \\frac{exp(e_{i,j})}{\\sum_{k=1}^{T_x}{exp(e_{i,k})}}
c_{i} & = \\sum_{j=1}^{T_{x}}a_{i,j}h_{j}
where :math:`h_{j}` is the jth element of encoded_sequence,
:math:`U_{a}h_{j}` is the jth element of encoded_proj
:math:`s_{i-1}` is decoder_state
:math:`f` is weight_act, and is set to tanh by default.
Please refer to **Neural Machine Translation by Jointly Learning to
Align and Translate** for more details. The link is as follows:
https://arxiv.org/abs/1409.0473.
The example usage is:
.. code-block:: python
context = simple_attention(encoded_sequence=enc_seq,
encoded_proj=enc_proj,
decoder_state=decoder_prev,)
:param name: name of the attention model.
:type name: basestring
:param softmax_param_attr: parameter attribute of sequence softmax
that is used to produce attention weight.
:type softmax_param_attr: ParameterAttribute
:param weight_act: activation of the attention model.
:type weight_act: BaseActivation
:param encoded_sequence: output of the encoder
:type encoded_sequence: LayerOutput
:param encoded_proj: attention weight is computed by a feed forward neural
network which has two inputs : decoder's hidden state
of previous time step and encoder's output.
encoded_proj is output of the feed-forward network for
encoder's output. Here we pre-compute it outside
simple_attention for speed consideration.
:type encoded_proj: LayerOutput
:param decoder_state: hidden state of decoder in previous time step
:type decoder_state: LayerOutput
:param transform_param_attr: parameter attribute of the feed-forward
network that takes decoder_state as inputs to
compute attention weight.
:type transform_param_attr: ParameterAttribute
:return: a context vector
:rtype: LayerOutput
"""
assert encoded_proj.size == decoder_state.size
proj_size = encoded_proj.size
with mixed_layer(size=proj_size, name="%s_transform" % name) as m:
m += full_matrix_projection(
decoder_state, param_attr=transform_param_attr)
expanded = expand_layer(
input=m, expand_as=encoded_sequence, name='%s_expand' % name)
with mixed_layer(
size=proj_size, act=weight_act, name="%s_combine" % name) as m:
m += identity_projection(expanded)
m += identity_projection(encoded_proj)
# sequence softmax is used to normalize similarities between decoder state
# and encoder outputs into a distribution
attention_weight = fc_layer(
input=m,
size=1,
act=SequenceSoftmaxActivation(),
param_attr=softmax_param_attr,
name="%s_softmax" % name,
bias_attr=False)
scaled = scaling_layer(
weight=attention_weight,
input=encoded_sequence,
name='%s_scaling' % name)
return pooling_layer(
input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name)
@wrap_name_default()
def dot_product_attention(encoded_sequence,
attended_sequence,
transformed_state,
softmax_param_attr=None,
name=None):
"""
Calculate and return a context vector with dot-product attention mechanism.
The dimension of the context vector equals to that of the attended_sequence.
.. math::
a(s_{i-1},h_{j}) & = s_{i-1}^\mathrm{T} h_{j}
e_{i,j} & = a(s_{i-1}, h_{j})
a_{i,j} & = \\frac{exp(e_{i,j})}{\\sum_{k=1}^{T_x}{exp(e_{i,k})}}
c_{i} & = \\sum_{j=1}^{T_{x}}a_{i,j}z_{j}
where :math:`h_{j}` is the jth element of encoded_sequence,
:math:`z_{j}` is the jth element of attended_sequence,
:math:`s_{i-1}` is transformed_state.
The example usage is:
.. code-block:: python
context = dot_product_attention(encoded_sequence=enc_seq,
attended_sequence=att_seq,
transformed_state=state,)
:param name: A prefix attached to the name of each layer that defined inside
the dot_product_attention.
:type name: basestring
:param softmax_param_attr: The parameter attribute of sequence softmax
that is used to produce attention weight.
:type softmax_param_attr: ParameterAttribute
:param encoded_sequence: The output hidden vectors of the encoder.
:type encoded_sequence: LayerOutput
:param attended_sequence: The attention weight is computed by a feed forward neural
network which has two inputs : decoder's transformed hidden
state of previous time step and encoder's output.
attended_sequence is the sequence to be attended.
:type attended_sequence: LayerOutput
:param transformed_state: The transformed hidden state of decoder in previous time step.
Since the dot-product operation will be performed on it and the
encoded_sequence, their dimensions must be equal. For flexibility,
we suppose transformations of the decoder's hidden state have been
done outside dot_product_attention and no more will be performed
inside. Then users can use either the original or transformed one.
:type transformed_state: LayerOutput
:return: The context vector.
:rtype: LayerOutput
"""
assert transformed_state.size == encoded_sequence.size
expanded = expand_layer(
input=transformed_state,
expand_as=encoded_sequence,
name='%s_expand' % name)
m = dot_prod_layer(
input1=expanded, input2=encoded_sequence, name='%s_dot-product' % name)
attention_weight = fc_layer(
input=m,
size=1,
act=SequenceSoftmaxActivation(),
param_attr=softmax_param_attr,
name="%s_softmax" % name,
bias_attr=False)
scaled = scaling_layer(
weight=attention_weight,
input=attended_sequence,
name='%s_scaling' % name)
return pooling_layer(
input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name)
@wrap_name_default()
def multi_head_attention(query,
key,
value,
key_proj_size,
value_proj_size,
head_num,
attention_type,
softmax_param_attr=None,
name=None):
"""
Calculate and return a context vector with dot-product attention mechanism.
The dimension of the context vector equals to value_proj_size * head_num.
Please refer to **Attention Is All You Need** for more details. The link is
as follows:
https://arxiv.org/abs/1706.03762.
The example usage is:
.. code-block:: python
context = multi_head_attention(query=decoder_state,
key=enc_seq,
value=enc_seq,
key_proj_size=64,
value_pro_size=64,
head_num=8,
attention_type='dot-product attention')
:param name: A prefix attached to the name of each layer that defined inside
the multi_head_attention.
:type name: basestring
:param softmax_param_attr: The parameter attribute of sequence softmax
that is used to produce attention weight.
:type softmax_param_attr: ParameterAttribute
:param query: query is used to calculate attention weights over values at current step.
:type query: LayerOutput
:param key: key is used to calculate the attention weight of the corresponding value.
:type key: LayerOutput
:param value: value is the sequence to be attended.
:type value: LayerOutput
:param key_proj_size: The dimension of the linear projection performed on key and query.
:type key_proj_size: int
:param value_proj_size: The dimension of the linear projection performed on value.
:type value_proj_size: int
:param head_num: The number of attention heads.
:type head_num: int
:param attention_type: The type of the attention mechanism used in each attention
heads. Now, we only support scaled dot-product attention and
additive attention.
:type attention_type: basestring
:return: The context vector.
:rtype: LayerOutput
"""
assert attention_type in ['dot-product attention', 'additive attention']
with mixed_layer(
size=key_proj_size * head_num,
name='%s_query_proj' % name) as query_proj:
query_proj += full_matrix_projection(query)
query_proj = expand_layer(input=query_proj, expand_as=key)
with mixed_layer(
size=key_proj_size * head_num,
name='%s_key_proj' % name) as key_proj:
key_proj += full_matrix_projection(key)
with mixed_layer(
size=value_proj_size * head_num,
name='%s_value_proj' % name) as value_proj:
value_proj += full_matrix_projection(value)
head_list = []
for i in range(head_num):
with mixed_layer(size=key_proj_size) as sub_query_proj:
sub_query_proj += identity_projection(
query_proj, offset=key_proj_size * i, size=key_proj_size)
with mixed_layer(size=key_proj_size) as sub_key_proj:
sub_key_proj += identity_projection(
key_proj, offset=key_proj_size * i, size=key_proj_size)
with mixed_layer(size=value_proj_size) as sub_value_proj:
sub_value_proj += identity_projection(
value_proj, offset=value_proj_size * i, size=value_proj_size)
if attention_type == 'dot-product attention':
m = dot_prod_layer(
input1=sub_query_proj,
input2=sub_key_proj,
name='%s_dot-product_%d' % (name, i))
m = slope_intercept_layer(
input=m,
slope=math.sqrt(1.0 / key_proj_size),
name='%s_dot-product_scaling_%d' % (name, i))
else:
with mixed_layer(
size=key_proj_size,
act=TanhActivation(),
name='%s_combine_%d' % (name, i)) as m:
m += identity_projection(sub_query_proj)
m += identity_projection(sub_key_proj)
attention_weight = fc_layer(
input=m,
size=1,
act=SequenceSoftmaxActivation(),
param_attr=softmax_param_attr,
name="%s_softmax_%d" % (name, i),
bias_attr=False)
scaled = scaling_layer(
weight=attention_weight,
input=sub_value_proj,
name='%s_scaling_%d' % (name, i))
head = pooling_layer(
input=scaled,
pooling_type=SumPooling(),
name="%s_pooling_%d" % (name, i))
head_list.append(head)
attended = concat_layer(head_list)
return attended
def inputs(layers, *args):
"""
Declare the inputs of network. The order of input should be as same as
the data provider's return order.
:param layers: Input Layers.
:type layers: list|tuple|LayerOutput.
:return:
"""
if isinstance(layers, LayerOutput) or isinstance(layers, basestring):
layers = [layers]
if len(args) != 0:
layers.extend(args)
Inputs(*[l.name for l in layers])
def outputs(layers, *args):
"""
Declare the outputs of network. If user has not defined the inputs of
network, this method will calculate the input order by dfs travel.
:param layers: Output layers.
:type layers: list|tuple|LayerOutput
:return:
"""
traveled = set()
def __dfs_travel__(layer,
predicate=lambda x: x.layer_type == LayerType.DATA):
"""
DFS LRV Travel for output layer.
The return order is define order for data_layer in this leaf node.
:param layer:
:type layer: LayerOutput
:return:
"""
if layer in traveled:
return []
else:
traveled.add(layer)
assert isinstance(layer, LayerOutput), "layer is %s" % (layer)
retv = []
if layer.parents is not None:
for p in layer.parents:
retv.extend(__dfs_travel__(p, predicate))
if predicate(layer):
retv.append(layer)
return retv
if isinstance(layers, LayerOutput):
layers = [layers]
if len(args) != 0:
layers.extend(args)
assert len(layers) > 0
if HasInputsSet(): # input already set
Outputs(*[l.name for l in layers])
return # just return outputs.
if len(layers) != 1:
logger.warning("`outputs` routine try to calculate network's"
" inputs and outputs order. It might not work well."
"Please see follow log carefully.")
inputs = []
outputs_ = []
for each_layer in layers:
assert isinstance(each_layer, LayerOutput)
inputs.extend(__dfs_travel__(each_layer))
outputs_.extend(
__dfs_travel__(each_layer,
lambda x: x.layer_type == LayerType.COST))
# Currently, we got each leaf node's inputs order, output order.
# We merge them together.
final_inputs = []
final_outputs = []
for each_input in inputs:
assert isinstance(each_input, LayerOutput)
if each_input.name not in final_inputs:
final_inputs.append(each_input.name)
for each_output in outputs_:
assert isinstance(each_output, LayerOutput)
if each_output.name not in final_outputs:
final_outputs.append(each_output.name)
logger.info("".join(["The input order is [", ", ".join(final_inputs), "]"]))
if len(final_outputs) == 0:
final_outputs = map(lambda x: x.name, layers)
logger.info("".join(
["The output order is [", ", ".join(final_outputs), "]"]))
Inputs(*final_inputs)
Outputs(*final_outputs)
| 65,836 | 35.293826 | 96 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/layers.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import collections
import inspect
import paddle.trainer.config_parser as cp
from paddle.trainer.config_parser import *
from .activations import LinearActivation, SigmoidActivation, TanhActivation, \
ReluActivation, IdentityActivation, SoftmaxActivation, BaseActivation
from .evaluators import *
from .poolings import MaxPooling, AvgPooling, MaxWithMaskPooling, BasePoolingType, \
CudnnAvgPooling, CudnnAvgInclPadPooling, CudnnMaxPooling
from .attrs import *
from .default_decorators import *
try:
import cPickle as pickle
except ImportError:
import pickle
import copy
__all__ = [
'full_matrix_projection',
'AggregateLevel',
'ExpandLevel',
'identity_projection',
'dotmul_projection',
'dotmul_operator',
'repeat_layer',
'seq_reshape_layer',
'table_projection',
'mixed_layer',
'data_layer',
'embedding_layer',
'fc_layer',
'grumemory',
'pooling_layer',
'lstmemory',
'last_seq',
'first_seq',
'cos_sim',
'l2_distance_layer',
'hsigmoid',
'conv_projection',
'square_error_cost',
'regression_cost',
'classification_cost',
'LayerOutput',
'img_conv_layer',
'img_pool_layer',
'batch_norm_layer',
'img_cmrnorm_layer',
'addto_layer',
'concat_layer',
'seq_concat_layer',
'lstm_step_layer',
'recurrent_group',
'memory',
'StaticInput',
'expand_layer',
'scaling_layer',
'scaling_projection',
'power_layer',
'interpolation_layer',
'bilinear_interp_layer',
'trans_layer',
'rotate_layer',
'sum_to_one_norm_layer',
'row_l2_norm_layer',
'get_output_layer',
'LayerType',
'context_projection',
'beam_search',
'maxid_layer',
'GeneratedInput',
'SubsequenceInput',
'gru_step_layer',
'gru_step_naive_layer',
'recurrent_layer',
'BaseGeneratedInput',
'conv_operator',
'conv_shift_layer',
'tensor_layer',
'selective_fc_layer',
'sampling_id_layer',
'slope_intercept_layer',
'trans_full_matrix_projection',
'linear_comb_layer',
'convex_comb_layer',
'ctc_layer',
'warp_ctc_layer',
'crf_layer',
'crf_decoding_layer',
'nce_layer',
'cross_entropy_with_selfnorm',
'cross_entropy',
'BeamInput',
'cross_entropy_over_beam',
'multi_binary_label_cross_entropy',
'sum_cost',
'rank_cost',
'lambda_cost',
'huber_regression_cost',
'huber_classification_cost',
'block_expand_layer',
'maxout_layer',
'dot_prod_layer',
'out_prod_layer',
'printer_layer',
'print_layer',
'priorbox_layer',
'cross_channel_norm_layer',
'multibox_loss_layer',
'detection_output_layer',
'roi_pool_layer',
'spp_layer',
'pad_layer',
'eos_layer',
'smooth_l1_cost',
'layer_support',
'multiplex_layer',
'row_conv_layer',
'dropout_layer',
'prelu_layer',
'switch_order_layer',
'gated_unit_layer',
'crop_layer',
'sub_nested_seq_layer',
'clip_layer',
'slice_projection',
'seq_slice_layer',
'kmax_seq_score_layer',
'img_pool3d_layer',
'scale_shift_layer',
'img_conv3d_layer',
'resize_layer',
'sub_seq_layer',
'scale_sub_region_layer',
'upsample_layer',
'factorization_machine',
]
class LayerType(object):
"""
Layer type enumerations.
"""
DATA = 'data'
MIXED_LAYER = 'mixed'
LSTMEMORY = 'lstmemory'
GRUMEMORY = 'gated_recurrent'
SEQUENCE_LAST_INSTANCE = 'seqlastins'
SEQUENCE_FIRST_INSTANCE = 'seqfirstins'
SEQUENCE_RESHAPE = 'seqreshape'
POOLING_MAX = 'max'
POOLING_AVG = 'average'
UPSAMPLE_LAYER = 'upsample'
FC_LAYER = 'fc'
COST = 'cost'
COSINE_SIM_VEC = 'cos_vm'
COSINE_SIM = 'cos'
L2_DISTANCE = 'l2_distance'
HSIGMOID = 'hsigmoid'
CONV_LAYER = 'conv'
CONVTRANS_LAYER = 'convt'
EXCONV_LAYER = 'exconv'
EXCONVTRANS_LAYER = 'exconvt'
CUDNNCONV_LAYER = 'cudnn_conv'
CUDNNCONVTRANS_LAYER = 'cudnn_convt'
POOL_LAYER = 'pool'
POOL3D_LAYER = 'pool3d'
BATCH_NORM_LAYER = 'batch_norm'
NORM_LAYER = 'norm'
SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm'
ROW_L2_NORM_LAYER = 'row_l2_norm'
ADDTO_LAYER = 'addto'
CONCAT_LAYER = 'concat'
CONCAT_PROJ_LAYER = 'concat2'
SEQUENCE_CONCAT_LAYER = 'seqconcat'
LSTM_STEP_LAYER = 'lstm_step'
GRU_STEP_LAYER = 'gru_step'
GET_OUTPUT_LAYER = 'get_output'
EXPAND_LAYER = 'expand'
INTERPOLATION_LAYER = 'interpolation'
BILINEAR_INTERP_LAYER = 'bilinear_interp'
POWER_LAYER = 'power'
SCALING_LAYER = 'scaling'
TRANS_LAYER = 'trans'
ROTATE_LAYER = 'rotate'
DOT_PROD_LAYER = 'dot_prod'
OUT_PROD_LAYER = 'out_prod'
FEATURE_MAP_EXPAND_LAYER = 'featmap_expand'
MEMORY = 'memory'
MAXID_LAYER = 'maxid'
EOSID_LAYER = 'eos_id'
RECURRENT_LAYER = 'recurrent'
CONV_SHIFT_LAYER = "conv_shift"
TENSOR_LAYER = "tensor"
SEL_FC_LAYER = "selective_fc"
SAMPLING_ID_LAYER = "sampling_id"
SLOPE_INTERCEPT_LAYER = "slope_intercept"
LINEAR_COMBINATION_LAYER = "convex_comb"
BLOCK_EXPAND = "blockexpand"
MAXOUT = "maxout"
SPP_LAYER = "spp"
PAD_LAYER = "pad"
MULTIPLEX_LAYER = "multiplex"
ROW_CONV_LAYER = "row_conv"
PRINT_LAYER = 'print'
PRIORBOX_LAYER = 'priorbox'
MULTIBOX_LOSS_LAYER = 'multibox_loss'
DETECTION_OUTPUT_LAYER = 'detection_output'
ROI_POOL_LAYER = 'roi_pool'
CTC_LAYER = 'ctc'
WARP_CTC_LAYER = 'warp_ctc'
CRF_LAYER = 'crf'
CRF_DECODING_LAYER = 'crf_decoding'
NCE_LAYER = 'nce'
CONV3D_LAYER = 'conv3d'
DECONV3D_LAYER = 'deconv3d'
RANK_COST = 'rank-cost'
LAMBDA_COST = 'lambda_cost'
HUBER_REGRESSION = 'huber_regression'
HUBER_CLASSIFICATION = 'huber_classification'
CROSS_ENTROPY = 'multi-class-cross-entropy'
CROSS_ENTROPY_WITH_SELFNORM = 'multi_class_cross_entropy_with_selfnorm'
CROSS_ENTROPY_OVER_BEAM = 'cross_entropy_over_beam'
SOFT_BIN_CLASS_CROSS_ENTROPY = 'soft_binary_class_cross_entropy'
MULTI_BIN_LABEL_CROSS_ENTROPY = 'multi_binary_label_cross_entropy'
SUM_COST = 'sum_cost'
SMOOTH_L1 = 'smooth_l1'
PRELU = 'prelu'
SWITCH_ORDER_LAYER = 'switch_order'
CROP_LAYER = 'crop'
SUB_NESTED_SEQ = 'sub_nested_seq'
CLIP_LAYER = 'clip'
SEQ_SLICE = 'seq_slice'
KMAX_SEQ_SCORE = 'kmax_seq_score'
SCALE_SHIFT_LAYER = 'scale_shift'
RESIZE = 'resize'
SUB_SEQ_LAYER = 'subseq'
SCALE_SUB_REGION_LAYER = 'scale_sub_region'
FACTORIZATION_MACHINE = 'factorization_machine'
@staticmethod
def is_layer_type(type_name):
"""
Whether type_name is a layer type.
:param type_name: layer type name. Because layer type enumerations are
strings.
:type type_name: basestring
:return: True if is a layer_type
:rtype: bool
"""
for key in dir(LayerType):
if key.isupper():
att = getattr(LayerType, key)
if isinstance(att, basestring) and type_name == att:
return True
return False
class AggregateLevel(object):
"""
PaddlePaddle supports three sequence types:
- :code:`SequenceType.NO_SEQUENCE` means the sample is not a sequence.
- :code:`SequenceType.SEQUENCE` means the sample is a sequence.
- :code:`SequenceType.SUB_SEQUENCE` means the sample is a nested sequence,
each timestep of which is also a sequence.
Accordingly, AggregateLevel supports two modes:
- :code:`AggregateLevel.TO_NO_SEQUENCE` means the aggregation acts on each
timestep of a sequence, both :code:`SUB_SEQUENCE` and :code:`SEQUENCE` will
be aggregated to :code:`NO_SEQUENCE`.
- :code:`AggregateLevel.TO_SEQUENCE` means the aggregation acts on each
sequence of a nested sequence, :code:`SUB_SEQUENCE` will be aggregated to
:code:`SEQUENCE`.
"""
TO_NO_SEQUENCE = 'non-seq'
TO_SEQUENCE = 'seq'
# compatible with previous configuration
EACH_TIMESTEP = TO_NO_SEQUENCE
EACH_SEQUENCE = TO_SEQUENCE
class LayerOutput(object):
"""
LayerOutput is output for layer function. It is used internally by several
reasons.
- Check layer connection make sense.
- FC(Softmax) => Cost(MSE Error) is not good for example.
- Tracking layer connection.
- Pass to layer methods as input.
:param name: Layer output name.
:type name: basestring
:param layer_type: Current Layer Type. One of LayerType enumeration.
:type layer_type: basestring
:param activation: Layer Activation.
:type activation: BaseActivation.
:param parents: Layer's parents.
:type parents: list | tuple | collections.Sequence
"""
def __init__(self,
name,
layer_type,
parents=None,
activation=None,
num_filters=None,
img_norm_type=None,
size=None,
outputs=None,
reverse=None):
assert isinstance(name, basestring)
assert isinstance(layer_type, basestring)
assert size is not None
assert LayerType.is_layer_type(layer_type)
self.name = name
self.full_name = MakeLayerNameInSubmodel(name)
self.layer_type = layer_type
if parents is not None and type(parents) != list:
parents = [parents]
self.parents = [] if parents is None else parents
self.activation = activation
self.num_filters = num_filters
self.img_norm_type = img_norm_type
self.size = size
if outputs is None:
outputs = ['default']
self.outputs = outputs
self.reverse = reverse
@property
def width(self):
return cp.g_layer_map[self.full_name].width
@property
def height(self):
return cp.g_layer_map[self.full_name].height
@property
def depth(self):
return cp.g_layer_map[self.full_name].depth
def set_input(self, input):
"""
Set the input for a memory layer. Can only be used for memory layer
"""
assert isinstance(input, LayerOutput)
assert self.layer_type == LayerType.MEMORY
SetMemoryInput(self.name, input.name)
ERROR_CLIPPING = 'error_clipping_threshold'
DROPOUT = 'drop_rate'
DEVICE = 'device'
def layer_support(*attrs):
attrs_list = list(attrs)
attrs_list.append(DEVICE)
def decorator(method):
@functools.wraps(method)
def wrapper(*args, **kwargs):
for attr in attrs_list:
for each in args:
if isinstance(each, ExtraLayerAttribute):
setattr(each, '_'.join(['can', attr]), True)
for key in kwargs:
val = kwargs[key]
if isinstance(val, ExtraLayerAttribute):
setattr(val, '_'.join(['can', attr]), True)
for each in args:
if isinstance(each, ExtraLayerAttribute):
each.check(method.__name__)
for key in kwargs:
val = kwargs[key]
if isinstance(val, ExtraLayerAttribute):
val.check(method.__name__)
return method(*args, **kwargs)
if hasattr(method, 'argspec'):
wrapper.argspec = method.argspec
else:
wrapper.argspec = inspect.getargspec(method)
return wrapper
return decorator
@wrap_param_attr_default()
def full_matrix_projection(input, size=0, param_attr=None):
"""
Full Matrix Projection. It performs full matrix multiplication.
.. math::
out.row[i] += in.row[i] * weight
There are two styles of usage.
1. When used in mixed_layer like this, you can only set the input:
.. code-block:: python
with mixed_layer(size=100) as m:
m += full_matrix_projection(input=layer)
2. When used as an independent object like this, you must set the size:
.. code-block:: python
proj = full_matrix_projection(input=layer,
size=100,
param_attr=ParamAttr(name='_proj'))
:param input: The input of this layer.
:type input: LayerOutput
:param size: The dimension of this layer.
:type size: int
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:return: FullMatrixProjection Object.
:rtype: FullMatrixProjection
"""
proj = FullMatrixProjection(
input_layer_name=input.name, size=size, **param_attr.attr)
proj.origin = input
return proj
@wrap_param_attr_default()
def trans_full_matrix_projection(input, size=0, param_attr=None):
"""
Different from full_matrix_projection, this projection performs matrix
multiplication, using the transpose of weight.
.. math::
out.row[i] += in.row[i] * w^\mathrm{T}
:math:`w^\mathrm{T}` means the transpose of weight.
The simply usage is:
.. code-block:: python
proj = trans_full_matrix_projection(input=layer,
size=100,
param_attr=ParamAttr(
name='_proj',
initial_mean=0.0,
initial_std=0.01))
:param input: The input of this layer.
:type input: LayerOutput
:param size: The parameter size. Means the width of parameter.
:type size: int
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:return: TransposedFullMatrixProjection Object.
:rtype: TransposedFullMatrixProjection
"""
proj = TransposedFullMatrixProjection(
input_layer_name=input.name, size=size, **param_attr.attr)
proj.origin = input
return proj
@wrap_param_attr_default()
def table_projection(input, size=0, param_attr=None):
"""
Table Projection. It selects rows from parameter where row\_id
is in input\_ids.
.. math::
out.row[i] += table.row[ids[i]]
where :math:`out` is output, :math:`table` is parameter, :math:`ids` is input\_ids,
and :math:`i` is row\_id.
There are two styles of usage.
1. When used in mixed_layer like this, you can only set the input:
.. code-block:: python
with mixed_layer(size=100) as m:
m += table_projection(input=layer)
2. When used as an independent object like this, you must set the size:
.. code-block:: python
proj = table_projection(input=layer,
size=100,
param_attr=ParamAttr(name='_proj'))
:param input: The input of this layer, which must contains id fields.
:type input: LayerOutput
:param size: The dimension of the output.
:type size: int
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:return: TableProjection Object.
:rtype: TableProjection
"""
proj = TableProjection(
input_layer_name=input.name, size=size, **param_attr.attr)
proj.origin = input
return proj
def identity_projection(input, offset=None, size=None):
"""
1. If offset=None, it performs IdentityProjection as follows:
.. math::
out.row[i] += in.row[i]
The example usage is:
.. code-block:: python
proj = identity_projection(input=layer)
2. If offset!=None, It executes IdentityOffsetProjection and takes the
elements of the input in the range [offset, offset+size) as output.
.. math::
out.row[i] += in.row[i + \\textrm{offset}]
The example usage is:
.. code-block:: python
proj = identity_projection(input=layer,
offset=10)
Note that neither of the projections have trainable parameter.
:param input: The input of this layer.
:type input: LayerOutput
:param offset: The offset from the start of the input. The input's
elements in the range [offset, offset+size) will be
taken as output. If this parameter is not set or set
to None, the output will be the same as the input.
:type offset: int
:param size: The dimension of this layer. It will be neglected
when offset is None or not set.
:type size: int
:return: IdentityProjection or IdentityOffsetProjection object
:rtype: IdentityProjection | IdentityOffsetProjection
"""
if offset is None:
proj = IdentityProjection(input_layer_name=input.name)
proj.origin = input
else:
if size is None:
size = input.size - offset
proj = IdentityOffsetProjection(
input_layer_name=input.name, offset=offset, size=size)
proj.origin = input
return proj
def slice_projection(input, slices):
"""
slice_projection slices the input value into multiple parts,
then selects and merges some of them into a new output.
.. math::
output = [input.slices()]
The example usage is:
.. code-block:: python
proj = slice_projection(input=layer, slices=[(0, 10), (20, 30)])
Note that slice_projection has no trainable parameter.
:param input: The input of this layer.
:type input: LayerOutput
:param slices: A list of start and end offsets of each slice.
:type slices: list of tuple
:return: SliceProjection object.
:rtype: SliceProjection
"""
assert len(slices) >= 1
start = 0
for i in xrange(len(slices)):
assert len(slices[i]) == 2
# The start position of the next slice needs to be greater than
# or equal to the end position of the previous slice.
assert slices[i][0] >= start
assert slices[i][1] >= slices[i][0]
start = slices[i][1]
proj = SliceProjection(input_layer_name=input.name, slices=slices)
proj.origin = input
return proj
@wrap_param_attr_default()
def scaling_projection(input, param_attr=None):
"""
scaling_projection multiplies the input with a scalar parameter.
.. math::
out += w * in
The example usage is:
.. code-block:: python
proj = scaling_projection(input=layer)
:param input: The input of this layer.
:type input: LayerOutput
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:return: ScalingProjection object.
:rtype: ScalingProjection
"""
proj = ScalingProjection(input_layer_name=input.name, **param_attr.attr)
proj.origin = input
return proj
@wrap_param_attr_default()
def dotmul_projection(input, param_attr=None):
"""
DotMulProjection takes a layer as input and performs
element-wise multiplication with weight.
.. math::
out.row[i] += in.row[i] .* weight
where :math:`.*` means element-wise multiplication.
The example usage is:
.. code-block:: python
proj = dotmul_projection(input=layer)
:param input: The input of this layer.
:type input: LayerOutput
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:return: DotMulProjection object.
:rtype: DotMulProjection
"""
proj = DotMulProjection(
input_layer_name=input.name, size=input.size, **param_attr.attr)
proj.origin = input
return proj
def dotmul_operator(a=None, b=None, scale=1, **kwargs):
"""
DotMulOperator takes two inputs and performs element-wise multiplication:
.. math::
out.row[i] += scale * (a.row[i] .* b.row[i])
where :math:`.*` means element-wise multiplication, and
scale is a config scalar, its default value is 1.
The example usage is:
.. code-block:: python
op = dotmul_operator(a=layer1, b=layer2, scale=0.5)
:param a: The first input of this layer.
:type a: LayerOutput
:param b: The second input of this layer.
:type b: LayerOutput
:param scale: A scalar to scale the product. Its default value is 1.
:type scale: float
:return: DotMulOperator object.
:rtype: DotMulOperator
"""
if 'x' in kwargs or 'y' in kwargs:
logger.warning('x and y arguments for dotmul_operator is deprecated. '
'Please use a and b as parameter.')
a = kwargs.get('x', a) # For Backward capacity.
b = kwargs.get('y', b)
assert isinstance(a, LayerOutput)
assert isinstance(b, LayerOutput)
if a.size is not None and b.size is not None:
assert a.size == b.size
op = DotMulOperator(input_layer_names=[a.name, b.name], scale=scale)
op.origin = [a, b]
return op
@wrap_bias_attr_default(['padding_attr'])
def context_projection(input,
context_len,
context_start=None,
padding_attr=False):
"""
Context Projection.
It just reorganizes input sequence, combines "context_len" elements of the
sequence to one context from context_start. "context_start" will be set to
-(context_len - 1) / 2 by default. When context position is out of sequence
length, padding will be filled as zero if padding_attr = False, otherwise
it is trainable.
For example, origin sequence is [A B C D E F G], context len is 3, padding_attr
is not set, then after context projection, sequence will
be [ 0AB ABC BCD CDE DEF EFG FG0 ].
:param input: The input of this layer, which should be a sequence.
:type input: LayerOutput
:param context_len: The length of the context.
:type context_len: int
:param context_start: The start position of the context. The default value is
-(context_len - 1)/2
:type context_start: int
:param padding_attr: Parameter attribute of the padding. If the parameter is
set to False, padding will be zero. In other cases, the
padding is trainable, and its parameter attribute is set
by this parameter.
:type padding_attr: bool | ParameterAttribute
:return: Projection object.
:rtype: Projection
"""
context_start = -(
context_len - 1) / 2 if context_start is None else context_start
extra_dict = dict()
trainable = isinstance(padding_attr, ParameterAttribute)
if trainable:
extra_dict = padding_attr.attr
proj = ContextProjection(
input_layer_name=input.name,
context_length=context_len,
context_start=context_start,
trainable_padding=trainable,
**extra_dict)
proj.origin = input
return proj
class MixedLayerType(LayerOutput):
"""
The internal object for trainer_helpers.
"""
class AddToSealedMixedLayerException(Exception):
def __init__(self):
Exception.__init__(self)
def __init__(self, name, size, act, bias_attr, layer_attr, parents=None):
"""
:param name: The name of this layer.
:type name: basestring
:param size: The dimension of this layer.
:type size: int
:param act: Activation type.
:type act: BaseActivation
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
"""
LayerOutput.__init__(
self,
name,
LayerType.MIXED_LAYER,
parents,
size=size,
activation=act)
self.bias_attr = bias_attr
self.layer_attr = layer_attr
self.inputs = []
self.finalized = False
def __iadd__(self, other):
"""
+ += operator
:param other: Other projection.
:type other: Projection
:return: self.
:rtype: MixedLayerType
"""
if not self.finalized:
assert isinstance(other, Projection) or isinstance(other, Operator)
self.inputs.append(other)
if isinstance(other, Projection):
self.parents.append(other.origin)
else:
self.parents.extend(other.origin)
return self
else:
raise MixedLayerType.AddToSealedMixedLayerException()
def __enter__(self):
assert len(self.inputs) == 0
return self
def __exit__(self, exc_type, exc_value, tb):
if exc_value is not None:
raise exc_value
assert len(self.inputs) != 0
ml = MixedLayer(
name=self.name,
size=self.size,
active_type=self.activation.name,
bias=ParamAttr.to_bias(self.bias_attr),
inputs=self.inputs,
**ExtraLayerAttribute.to_kwargs(self.layer_attr))
# update the size which might be computed inside MixedLayer
# according to the operator's output size
self.size = ml.config.size
self.finalized = True
@wrap_name_default("mixed")
@wrap_act_default(act=LinearActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(ERROR_CLIPPING, DROPOUT)
def mixed_layer(size=0,
input=None,
name=None,
act=None,
bias_attr=False,
layer_attr=None):
"""
Mixed Layer. A mixed layer will add all inputs together, then activate the sum.
Each input is a projection or operator.
There are two styles of usages.
1. When the parameter input is not set, use mixed_layer like this:
.. code-block:: python
with mixed_layer(size=256) as m:
m += full_matrix_projection(input=layer1)
m += identity_projection(input=layer2)
2. You can also set all inputs when invoke mixed_layer as follows:
.. code-block:: python
m = mixed_layer(size=256,
input=[full_matrix_projection(input=layer1),
full_matrix_projection(input=layer2)])
:param name: The name of this layer. It is optional.
:type name: basestring
:param size: The dimension of this layer.
:type size: int
:param input: The input of this layer. It is an optional parameter.
:param act: Activation Type. LinearActivation is the default activation.
:type act: BaseActivation
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: MixedLayerType object.
:rtype: MixedLayerType
"""
if input is None:
return MixedLayerType(name, size, act, bias_attr, layer_attr)
else:
with mixed_layer(
name=name,
size=size,
act=act,
bias_attr=bias_attr,
layer_attr=layer_attr) as m:
if isinstance(input, collections.Sequence):
for each in input:
m += each
else:
m += input
return m
@layer_support()
def data_layer(name, size, depth=None, height=None, width=None,
layer_attr=None):
"""
Define DataLayer For NeuralNetwork.
The example usage is:
.. code-block:: python
data = data_layer(name="input", size=1000)
:param name: The name of this layer.
:type name: basestring
:param size: The dimension of this data layer.
:type size: int
:param height: The height of the input image data.
:type height: int | None
:param width: The width of the input image data.
:type width: int | None
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
type=LayerType.DATA,
name=name,
size=size,
depth=depth,
height=height,
width=width,
**ExtraLayerAttribute.to_kwargs(layer_attr))
if depth is None:
depth = 1
num_filters = None
if height is not None and width is not None:
num_filters = size / (width * height * depth)
assert num_filters * width * height * depth == size, \
"size=%s width=%s height=%s depth=%s" % (size, width, height, depth)
return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters)
@wrap_name_default("embedding")
@wrap_param_attr_default()
@layer_support(ERROR_CLIPPING, DROPOUT)
def embedding_layer(input, size, name=None, param_attr=None, layer_attr=None):
"""
Define a embedding Layer.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer, whose type must be Index Data.
:type input: LayerOutput
:param size: The dimension of the embedding vector.
:type size: int
:param param_attr: The embedding parameter attribute. See ParameterAttribute
for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
with mixed_layer(
name=name,
size=size,
act=LinearActivation(),
bias_attr=False,
layer_attr=layer_attr) as mix:
mix += table_projection(input=input, size=size, param_attr=param_attr)
return mix
@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default()
@layer_support(ERROR_CLIPPING, DROPOUT)
def fc_layer(input,
size,
act=None,
name=None,
param_attr=None,
bias_attr=None,
layer_attr=None):
"""
The fully connected layer.
The example usage is:
.. code-block:: python
fc = fc_layer(input=layer,
size=1024,
act=LinearActivation(),
bias_attr=False)
which is equal to:
.. code-block:: python
with mixed_layer(size=1024) as fc:
fc += full_matrix_projection(input=layer)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput | list | tuple
:param size: The dimension of this layer.
:type size: int
:param act: Activation Type. TanhActivation is the default activation.
:type act: BaseActivation
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
assert not isinstance(param_attr, collections.Sequence)
param_attr = [param_attr]
else:
if isinstance(param_attr, collections.Sequence):
assert len(input) == len(param_attr)
else:
if "parameter_name" in param_attr.attr and len(input) > 1:
logger.fatal(
"When the name field of param_attr is manually specified "
"and the input is a list, the param_attr should also be a "
"list with each item being the param_attr for each input "
"item. If only one named param_attr is provided, all the "
"input items would share this parameter.")
param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]
assert isinstance(input, collections.Sequence)
Layer(
inputs=[
Input(ipt.name, **attr.attr) for ipt, attr in zip(input, param_attr)
],
name=name,
type=LayerType.FC_LAYER,
size=size,
bias=ParamAttr.to_bias(bias_attr),
active_type=act.name,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.FC_LAYER, input, activation=act, size=size)
@wrap_name_default("print")
def printer_layer(input, format=None, name=None):
"""
Print the output value of the layers specified by the parameter input.
This layer is useful for debugging.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput | list | tuple
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
assert isinstance(input, collections.Sequence) # list or tuple
for each in input:
assert isinstance(each, LayerOutput)
Layer(
name=name,
format=format,
type=LayerType.PRINT_LAYER,
inputs=[l.name for l in input], )
# this layer don't return anything, can not be input of other layer.
# Keep print_layer for compatibility with V1 API.
# 'print_layer' does not work for V2 API because it will be changed to
# 'print' for V2 API. But 'print' is a reserved key word in python.
print_layer = printer_layer
@wrap_name_default("priorbox")
def priorbox_layer(input,
image,
aspect_ratio,
variance,
min_size,
max_size=[],
name=None):
"""
Compute the priorbox and set the variance. This layer is necessary for ssd.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param image: The network input image.
:type image: LayerOutput
:param aspect_ratio: The aspect ratio.
:type aspect_ratio: list
:param variance: The bounding box variance.
:type min_size: The minimum size of the priorbox width/height.
:param min_size: list
:type max_size: The maximum size of the priorbox width/height. It could be NULL.
:param max_size: list
:return: LayerOutput object.
:rtype: LayerOutput
"""
# plus one for ratio 1.
num_filters = (len(aspect_ratio) * 2 + 1 + len(max_size)) * 4
size = (input.size / input.num_filters) * num_filters * 2
Layer(
name=name,
type=LayerType.PRIORBOX_LAYER,
inputs=[input.name, image.name],
size=size,
min_size=min_size,
max_size=max_size,
aspect_ratio=aspect_ratio,
variance=variance)
return LayerOutput(
name,
LayerType.PRIORBOX_LAYER,
parents=[input, image],
num_filters=num_filters,
size=size)
@wrap_name_default("multibox_loss")
def multibox_loss_layer(input_loc,
input_conf,
priorbox,
label,
num_classes,
overlap_threshold=0.5,
neg_pos_ratio=3.0,
neg_overlap=0.5,
background_id=0,
name=None):
"""
Compute the location loss and the confidence loss for ssd.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input_loc: The input predicted locations.
:type input_loc: LayerOutput | List of LayerOutput
:param input_conf: The input priorbox confidence.
:type input_conf: LayerOutput | List of LayerOutput
:param priorbox: The input priorbox location and the variance.
:type priorbox: LayerOutput
:param label: The input label.
:type label: LayerOutput
:param num_classes: The number of the classification.
:type num_classes: int
:param overlap_threshold: The threshold of the overlap.
:type overlap_threshold: float
:param neg_pos_ratio: The ratio of the negative bounding box to
the positive bounding box.
:type neg_pos_ratio: float
:param neg_overlap: The negative bounding box overlap threshold.
:type neg_overlap: float
:param background_id: The background class index.
:type background_id: int
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input_loc, LayerOutput):
input_loc = [input_loc]
assert isinstance(input_loc, collections.Sequence) # list or tuple
for each in input_loc:
assert isinstance(each, LayerOutput)
input_loc_num = len(input_loc)
if isinstance(input_conf, LayerOutput):
input_conf = [input_conf]
assert isinstance(input_conf, collections.Sequence) # list or tuple
for each in input_conf:
assert isinstance(each, LayerOutput)
input_conf_num = len(input_conf)
# Check the input layer number.
assert input_loc_num == input_conf_num
inputs = [priorbox.name, label.name]
inputs.extend([l.name for l in input_loc])
inputs.extend([l.name for l in input_conf])
parents = [priorbox, label]
parents.extend(input_loc)
parents.extend(input_conf)
Layer(
name=name,
type=LayerType.MULTIBOX_LOSS_LAYER,
inputs=inputs,
input_num=input_loc_num,
num_classes=num_classes,
overlap_threshold=overlap_threshold,
neg_pos_ratio=neg_pos_ratio,
neg_overlap=neg_overlap,
background_id=background_id)
return LayerOutput(
name, LayerType.MULTIBOX_LOSS_LAYER, parents=parents, size=1)
@wrap_name_default("detection_output")
def detection_output_layer(input_loc,
input_conf,
priorbox,
num_classes,
nms_threshold=0.45,
nms_top_k=400,
keep_top_k=200,
confidence_threshold=0.01,
background_id=0,
name=None):
"""
Apply the NMS to the output of network and compute the predict bounding
box location. The output's shape of this layer could be zero if there is
no valid bounding box.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input_loc: The input predict locations.
:type input_loc: LayerOutput | List of LayerOutput.
:param input_conf: The input priorbox confidence.
:type input_conf: LayerOutput | List of LayerOutput.
:param priorbox: The input priorbox location and the variance.
:type priorbox: LayerOutput
:param num_classes: The number of the classes.
:type num_classes: int
:param nms_threshold: The Non-maximum suppression threshold.
:type nms_threshold: float
:param nms_top_k: The bounding boxes number kept of the NMS's output.
:type nms_top_k: int
:param keep_top_k: The bounding boxes number kept of the layer's output.
:type keep_top_k: int
:param confidence_threshold: The classification confidence threshold.
:type confidence_threshold: float
:param background_id: The background class index.
:type background_id: int
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input_loc, LayerOutput):
input_loc = [input_loc]
assert isinstance(input_loc, collections.Sequence) # list or tuple
for each in input_loc:
assert isinstance(each, LayerOutput)
input_loc_num = len(input_loc)
if isinstance(input_conf, LayerOutput):
input_conf = [input_conf]
assert isinstance(input_conf, collections.Sequence) # list or tuple
for each in input_conf:
assert isinstance(each, LayerOutput)
input_conf_num = len(input_conf)
# Check the input layer number.
assert input_loc_num == input_conf_num
inputs = [priorbox.name]
inputs.extend([l.name for l in input_loc])
inputs.extend([l.name for l in input_conf])
parents = [priorbox]
parents.extend(input_loc)
parents.extend(input_conf)
size = keep_top_k * 7
Layer(
name=name,
type=LayerType.DETECTION_OUTPUT_LAYER,
inputs=inputs,
size=size,
input_num=input_loc_num,
num_classes=num_classes,
nms_threshold=nms_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
confidence_threshold=confidence_threshold,
background_id=background_id)
return LayerOutput(
name, LayerType.DETECTION_OUTPUT_LAYER, parents=parents, size=size)
@wrap_name_default("roi_pool")
def roi_pool_layer(input,
rois,
pooled_width,
pooled_height,
spatial_scale,
num_channels=None,
name=None):
"""
A layer used by Fast R-CNN to extract feature maps of ROIs from the last
feature map.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput.
:param rois: The input ROIs' data.
:type rois: LayerOutput.
:param pooled_width: The width after pooling.
:type pooled_width: int
:param pooled_height: The height after pooling.
:type pooled_height: int
:param spatial_scale: The spatial scale between the image and feature map.
:type spatial_scale: float
:param num_channels: The number of the input channels.
:type num_channels: int
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
size = num_channels * pooled_width * pooled_height
Layer(
name=name,
type=LayerType.ROI_POOL_LAYER,
inputs=[input.name, rois.name],
pooled_width=pooled_width,
pooled_height=pooled_height,
spatial_scale=spatial_scale,
num_channels=num_channels)
return LayerOutput(
name, LayerType.ROI_POOL_LAYER, parents=[input, rois], size=size)
@wrap_name_default("cross_channel_norm")
def cross_channel_norm_layer(input, name=None, param_attr=None):
"""
Normalize a layer's output. This layer is necessary for ssd. This
layer applys normalization across the channels of each sample to
a convolutional layer's output and scales the output by a group of
trainable factors whose dimensions equal to the channel's number.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert input.num_filters is not None
Layer(
name=name,
type=LayerType.NORM_LAYER,
inputs=[
Input(
input.name,
norm=Norm(
norm_type="cross-channel-norm",
channels=input.num_filters,
size=input.size,
scale=0,
pow=0,
blocked=0),
**param_attr.attr)
])
return LayerOutput(
name,
LayerType.NORM_LAYER,
parents=input,
num_filters=input.num_filters,
size=input.size)
@wrap_name_default("seq_pooling")
@wrap_bias_attr_default(has_bias=False)
@wrap_param_default(['pooling_type'], default_factory=lambda _: MaxPooling())
@layer_support()
def pooling_layer(input,
pooling_type=None,
name=None,
bias_attr=None,
agg_level=AggregateLevel.TO_NO_SEQUENCE,
stride=-1,
layer_attr=None):
"""
Pooling layer for sequence inputs, not used for Image.
If stride > 0, this layer slides a window whose size is determined by stride,
and returns the pooling value of the sequence in the window as the output. Thus,
a long sequence will be shortened. Note that for sequence with sub-sequence, the
default value of stride is -1.
The example usage is:
.. code-block:: python
seq_pool = pooling_layer(input=layer,
pooling_type=AvgPooling(),
agg_level=AggregateLevel.TO_NO_SEQUENCE)
:param agg_level: AggregateLevel.TO_NO_SEQUENCE or
AggregateLevel.TO_SEQUENCE
:type agg_level: AggregateLevel
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param pooling_type: Type of pooling. MaxPooling is the default pooling.
:type pooling_type: BasePoolingType | None
:param stride: The step size between successive pooling regions.
:type stride: int
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
extra_dict = dict()
# noinspection PyUnresolvedReferences
if isinstance(pooling_type, AvgPooling):
extra_dict['average_strategy'] = pooling_type.strategy
elif isinstance(pooling_type, MaxPooling) and \
pooling_type.output_max_index is not None:
assert isinstance(pooling_type.output_max_index, bool)
extra_dict['output_max_index'] = pooling_type.output_max_index
extra_dict.update(ExtraLayerAttribute.to_kwargs(layer_attr))
if agg_level == AggregateLevel.TO_SEQUENCE:
assert stride == -1
Layer(
name=name,
type=pooling_type.name,
inputs=[Input(input.name)],
bias=ParamAttr.to_bias(bias_attr),
trans_type=agg_level,
stride=stride,
**extra_dict)
return LayerOutput(
name, pooling_type.name, parents=[input], size=input.size)
@wrap_bias_attr_default()
@wrap_param_attr_default()
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
@wrap_act_default(param_names=["act", 'state_act'], act=TanhActivation())
@wrap_name_default("lstmemory")
@layer_support()
def lstmemory(input,
name=None,
size=None,
reverse=False,
act=None,
gate_act=None,
state_act=None,
bias_attr=None,
param_attr=None,
layer_attr=None):
"""
Long Short-term Memory Cell.
The memory cell was implemented as follow equations.
.. math::
i_t & = \\sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)
f_t & = \\sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)
o_t & = \\sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)
h_t & = o_t tanh(c_t)
NOTE: In PaddlePaddle's implementation, the multiplications
:math:`W_{xi}x_{t}` , :math:`W_{xf}x_{t}`,
:math:`W_{xc}x_t`, :math:`W_{xo}x_{t}` are not done in the lstmemory layer,
so an additional mixed_layer with full_matrix_projection or a fc_layer must
be included in the configuration file to complete the input-to-hidden
mappings before lstmemory is called.
NOTE: This is a low level user interface. You can use network.simple_lstm
to config a simple plain lstm layer.
Reference:
`Generating Sequences With Recurrent Neural Networks
<https://arxiv.org/pdf/1308.0850.pdf>`_
:param name: The name of this layer. It is optional.
:type name: basestring
:param size: DEPRECATED. The dimension of the lstm cell.
:type size: int
:param input: The input of this layer.
:type input: LayerOutput
:param reverse: Whether the input sequence is processed in a reverse order.
:type reverse: bool
:param act: Activation type. TanhActivation is the default activation.
:type act: BaseActivation
:param gate_act: Activation type of this layer's gates. SigmoidActivation is the
default activation.
:type gate_act: BaseActivation
:param state_act: Activation type of the state. TanhActivation is the default activation.
:type state_act: BaseActivation
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert gate_act.support_hppl
assert state_act.support_hppl
assert act.support_hppl
assert input.size is not None and input.size % 4 == 0
if size is not None:
if input.size / 4 == size:
plog = logger.warning
else:
plog = logger.fatal
plog("size of lstmemory layer: %s is automatically set to "
"size of input layer / 4. The parameter size passing to "
"this layer is ignored." % (name))
Layer(
name=name,
type=LayerType.LSTMEMORY,
active_type=act.name,
active_state_type=state_act.name,
active_gate_type=gate_act.name,
reversed=reverse,
bias=ParamAttr.to_bias(bias_attr),
inputs=[Input(input.name, **param_attr.attr)],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.LSTMEMORY, [input],
size=input.size / 4,
reverse=reverse)
@wrap_bias_attr_default()
@wrap_param_attr_default()
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
@wrap_act_default(param_names=["act"], act=TanhActivation())
@wrap_name_default("gru")
@layer_support()
def grumemory(input,
size=None,
name=None,
reverse=False,
act=None,
gate_act=None,
bias_attr=None,
param_attr=None,
layer_attr=None):
"""
Gate Recurrent Unit Layer.
The memory cell was implemented as follow equations.
1. update gate :math:`z`: defines how much of the previous memory to
keep around or the unit updates its activations. The update gate
is computed by:
.. math::
z_t = \\sigma(W_{z}x_{t} + U_{z}h_{t-1} + b_z)
2. reset gate :math:`r`: determines how to combine the new input with the
previous memory. The reset gate is computed similarly to the update gate:
.. math::
r_t = \\sigma(W_{r}x_{t} + U_{r}h_{t-1} + b_r)
3. The candidate activation :math:`\\tilde{h_t}` is computed similarly to
that of the traditional recurrent unit:
.. math::
{\\tilde{h_t}} = tanh(W x_{t} + U (r_{t} \odot h_{t-1}) + b)
4. The hidden activation :math:`h_t` of the GRU at time t is a linear
interpolation between the previous activation :math:`h_{t-1}` and the
candidate activation :math:`\\tilde{h_t}`:
.. math::
h_t = (1 - z_t) h_{t-1} + z_t {\\tilde{h_t}}
NOTE: In PaddlePaddle's implementation, the multiplication operations
:math:`W_{r}x_{t}`, :math:`W_{z}x_{t}` and :math:`W x_t` are not performed
in gate_recurrent layer. Consequently, an additional mixed_layer with
full_matrix_projection or a fc_layer must be included before grumemory
is called.
Reference:
`Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
<https://arxiv.org/abs/1412.3555>`_
The simple usage is:
.. code-block:: python
gru = grumemory(input)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput.
:param size: DEPRECATED. The dimension of the gru cell.
:type size: int
:param reverse: Whether the input sequence is processed in a reverse order.
:type reverse: bool
:param act: Activation type, TanhActivation is the default. This activation
affects the :math:`{\\tilde{h_t}}`.
:type act: BaseActivation
:param gate_act: Activation type of this layer's two gates. SigmoidActivation is
the default activation. This activation affects the :math:`z_t`
and :math:`r_t`. It is the :math:`\\sigma` in the above formula.
:type gate_act: BaseActivation
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert act.support_hppl
assert gate_act.support_hppl
assert input.size is not None and input.size % 3 == 0
if size is not None:
if input.size / 3 == size:
plog = logger.warning
else:
plog = logger.fatal
plog("size of grumemory layer: %s is automatically set to "
"size of input layer / 3. The parameter size passing to this "
"layer is ignored." % (name))
Layer(
name=name,
type=LayerType.GRUMEMORY,
active_type=act.name,
active_gate_type=gate_act.name,
reversed=reverse,
bias=ParamAttr.to_bias(bias_attr),
inputs=[Input(input.name, **param_attr.attr)],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.GRUMEMORY, [input],
size=input.size / 3,
reverse=reverse)
@wrap_name_default()
@layer_support()
def last_seq(input,
name=None,
agg_level=AggregateLevel.TO_NO_SEQUENCE,
stride=-1,
layer_attr=None):
"""
Get Last Timestamp Activation of a sequence.
If stride > 0, this layer will slide a window whose size is determined by stride,
and return the last value of the sequence in the window as the output. Thus, a
long sequence will be shortened. Note that for sequence with sub-sequence, the
default value of stride is -1.
The simple usage is:
.. code-block:: python
seq = last_seq(input=layer)
:param agg_level: Aggregated level
:type agg_level: AggregateLevel
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param stride: The step size between successive pooling regions.
:type stride: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if input.reverse is not None and input.reverse:
logger.warning("You are getting the last instance of a sequence that"
" is a output of a REVERSED layer. There is no time"
" series information at all. Maybe you want to use"
" first_seq instead.")
if agg_level == AggregateLevel.TO_SEQUENCE:
assert stride == -1
Layer(
name=name,
type=LayerType.SEQUENCE_LAST_INSTANCE,
inputs=[input.name],
trans_type=agg_level,
stride=stride,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.SEQUENCE_LAST_INSTANCE,
parents=[input],
size=input.size)
@wrap_name_default()
@layer_support()
def first_seq(input,
name=None,
agg_level=AggregateLevel.TO_NO_SEQUENCE,
stride=-1,
layer_attr=None):
"""
Get First Timestamp Activation of a sequence.
If stride > 0, this layer will slide a window whose size is determined by stride,
and return the first value of the sequence in the window as the output. Thus, a
long sequence will be shortened. Note that for sequence with sub-sequence, the
default value of stride is -1.
The simple usage is:
.. code-block:: python
seq = first_seq(input=layer)
:param agg_level: aggregation level
:type agg_level: AggregateLevel
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param stride: The step size between successive pooling regions.
:type stride: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
if input.reverse is not None and not input.reverse:
logger.warning('You are getting the first instance for a time series,'
' and it is a normal recurrent layer output. There is no'
' time series information at all. Maybe you want to use'
' last_seq instead.')
if agg_level == AggregateLevel.TO_SEQUENCE:
assert stride == -1
Layer(
name=name,
type=LayerType.SEQUENCE_FIRST_INSTANCE,
inputs=[input.name],
trans_type=agg_level,
stride=stride,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.SEQUENCE_FIRST_INSTANCE,
parents=[input],
size=input.size)
class ExpandLevel(object):
"""
Please refer to AggregateLevel first.
ExpandLevel supports two modes:
- :code:`ExpandLevel.FROM_NO_SEQUENCE` means the expansion acts on
:code:`NO_SEQUENCE`, which will be expanded to
:code:`SEQUENCE` or :code:`SUB_SEQUENCE`.
- :code:`ExpandLevel.FROM_SEQUENCE` means the expansion acts on
:code:`SEQUENCE`, which will be expanded to
:code:`SUB_SEQUENCE`.
"""
FROM_NO_SEQUENCE = AggregateLevel.TO_NO_SEQUENCE
FROM_SEQUENCE = AggregateLevel.TO_SEQUENCE
# compatible with previous configuration
FROM_TIMESTEP = FROM_NO_SEQUENCE
@wrap_name_default()
@layer_support()
def expand_layer(input,
expand_as,
name=None,
bias_attr=False,
expand_level=ExpandLevel.FROM_NO_SEQUENCE,
layer_attr=None):
"""
A layer for expanding dense data or (sequence data where the length of each
sequence is one) to sequence data.
The example usage is:
.. code-block:: python
expand = expand_layer(input=layer1,
expand_as=layer2,
expand_level=ExpandLevel.FROM_NO_SEQUENCE)
:param input: The input of this layer.
:type input: LayerOutput
:param expand_as: Expand the input according to this layer's sequence infomation. And
after the operation, the input expanded will have the same number of
elememts as this layer.
:type expand_as: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param expand_level: Whether the input layer is a sequence or the element of a sequence.
:type expand_level: ExpandLevel
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
inputs=[input.name, expand_as.name],
name=name,
bias=ParamAttr.to_bias(bias_attr=bias_attr),
type=LayerType.EXPAND_LAYER,
trans_type=expand_level,
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name=name,
size=input.size,
layer_type=LayerType.EXPAND_LAYER,
parents=[input, expand_as])
@wrap_name_default()
@wrap_act_default(act=IdentityActivation())
@layer_support()
def repeat_layer(input,
num_repeats,
as_row_vector=True,
act=None,
name=None,
layer_attr=None):
"""
A layer for repeating the input for num_repeats times.
If as_row_vector:
.. math::
y = [x_1,\cdots, x_n, \cdots, x_1, \cdots, x_n]
If not as_row_vector:
.. math::
y = [x_1,\cdots, x_1, \cdots, x_n, \cdots, x_n]
The example usage is:
.. code-block:: python
expand = repeat_layer(input=layer, num_repeats=4)
:param input: The input of this layer.
:type input: LayerOutput
:param num_repeats: The times of repeating the input.
:type num_repeats: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param as_row_vector: Whether to treat the input as row vectors or not. If
the parameter is set to True, the repeating operation
will be performed in the column direction. Otherwise,
it will be performed in the row direction.
:type as_row_vector: bool
:param act: Activation type. IdentityActivation is the default activation.
:type act: BaseActivation
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
l = Layer(
inputs=[input.name],
name=name,
active_type=act.name,
num_filters=num_repeats,
as_row_vector=as_row_vector,
type=LayerType.FEATURE_MAP_EXPAND_LAYER,
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name=name,
size=l.config.size,
layer_type=LayerType.FEATURE_MAP_EXPAND_LAYER,
activation=act,
parents=[input])
@wrap_name_default("seqreshape")
@wrap_act_default(act=IdentityActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(ERROR_CLIPPING, DROPOUT)
def seq_reshape_layer(input,
reshape_size,
act=None,
name=None,
layer_attr=None,
bias_attr=None):
"""
A layer for reshaping the sequence. Assume the input sequence has T instances,
the dimension of each instance is M, and the input reshape_size is N, then the
output sequence has T*M/N instances, the dimension of each instance is N.
Note that T*M/N must be an integer.
The example usage is:
.. code-block:: python
reshape = seq_reshape_layer(input=layer, reshape_size=4)
:param input: The input of this layer.
:type input: LayerOutput
:param reshape_size: The dimension of the reshaped sequence.
:type reshape_size: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param act: Activation type. IdentityActivation is the default activation.
:type act: BaseActivation
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
inputs=[input.name],
name=name,
size=reshape_size,
type=LayerType.SEQUENCE_RESHAPE,
bias=ParamAttr.to_bias(bias_attr),
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name=name,
size=reshape_size,
layer_type=LayerType.SEQUENCE_RESHAPE,
parents=[input])
@wrap_name_default()
@layer_support()
def interpolation_layer(input, weight, name=None, layer_attr=None):
"""
This layer performs linear interpolation on two inputs,
which is used in NEURAL TURING MACHINE.
.. math::
y.row[i] = w[i] * x_1.row[i] + (1 - w[i]) * x_2.row[i]
where :math:`x_1` and :math:`x_2` are two (batchSize x dataDim) inputs,
:math:`w` is (batchSize x 1) weight vector, and :math:`y` is
(batchSize x dataDim) output.
The example usage is:
.. code-block:: python
interpolation = interpolation_layer(input=[layer1, layer2], weight=layer3)
:param input: The input of this layer.
:type input: list | tuple
:param weight: Weight layer.
:type weight: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, collections.Sequence)
assert len(input) == 2
assert isinstance(input[0], LayerOutput) and isinstance(input[1],
LayerOutput)
if input[0].size is not None and input[1].size is not None:
assert input[0].size == input[1].size
assert isinstance(weight, LayerOutput)
if weight.size is not None:
assert weight.size == 1
Layer(
name=name,
type=LayerType.INTERPOLATION_LAYER,
inputs=[weight.name, input[0].name, input[1].name],
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.INTERPOLATION_LAYER,
parents=[weight, input[0], input[1]],
size=input[0].size)
@wrap_name_default()
@layer_support()
def bilinear_interp_layer(input,
out_size_x=None,
out_size_y=None,
name=None,
layer_attr=None):
"""
This layer implements bilinear interpolation on convolutional layer's output.
Please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation
The simple usage is:
.. code-block:: python
bilinear = bilinear_interp_layer(input=layer1, out_size_x=64, out_size_y=64)
:param input: The input of this layer.
:type input: LayerOutput.
:param out_size_x: The width of the output.
:type out_size_x: int
:param out_size_y: The height of the output.
:type out_size_y: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert input.layer_type == LayerType.CONV_LAYER
assert isinstance(input.activation, LinearActivation)
assert out_size_x > 0 and out_size_y > 0
assert input.num_filters is not None
num_channels = input.num_filters
l = Layer(
name=name,
inputs=Input(
input.name,
bilinear_interp=BilinearInterp(
out_size_x=out_size_x,
out_size_y=out_size_y,
channels=num_channels)),
type=LayerType.BILINEAR_INTERP_LAYER,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.BILINEAR_INTERP_LAYER,
parents=[input],
num_filters=num_channels,
size=l.config.size)
@wrap_name_default()
@layer_support()
def power_layer(input, weight, name=None, layer_attr=None):
"""
This layer applies a power function to a vector element-wise,
which is used in NEURAL TURING MACHINE.
.. math::
y = x^w
where :math:`x` is an input vector, :math:`w` is a scalar exponent,
and :math:`y` is an output vector.
The example usage is:
.. code-block:: python
power = power_layer(input=layer1, weight=layer2)
:param input: The input of this layer.
:type input: LayerOutput
:param weight: The exponent of the power.
:type weight: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput) and isinstance(weight, LayerOutput)
if weight.size is not None:
assert weight.size == 1
Layer(
name=name,
type=LayerType.POWER_LAYER,
inputs=[weight.name, input.name],
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.POWER_LAYER, parents=[input, weight], size=input.size)
@wrap_name_default()
@layer_support()
def scaling_layer(input, weight, name=None, layer_attr=None):
"""
A layer for multiplying input vector by weight scalar.
.. math::
y = w x
where :math:`x` is size=dataDim input, :math:`w` is size=1 weight,
and :math:`y` is size=dataDim output.
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The example usage is:
.. code-block:: python
scale = scaling_layer(input=layer1, weight=layer2)
:param input: The input of this layer.
:type input: LayerOutput
:param weight: The weight of each sample.
:type weight: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(weight, LayerOutput) and isinstance(input, LayerOutput)
if weight.size is not None:
assert weight.size == 1
Layer(
name=name,
type=LayerType.SCALING_LAYER,
inputs=[weight.name, input.name],
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.SCALING_LAYER, parents=[weight, input], size=input.size)
@wrap_name_default()
@layer_support()
def trans_layer(input, name=None, layer_attr=None):
"""
A layer for transposing a minibatch matrix.
.. math::
y = x^\mathrm{T}
where :math:`x` is (M x N) input, and :math:`y` is (N x M) output.
The example usage is:
.. code-block:: python
trans = trans_layer(input=layer)
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.TRANS_LAYER,
inputs=[input.name],
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.TRANS_LAYER, parents=[input], size=input.size)
@wrap_name_default()
@layer_support()
def rotate_layer(input, height, width, name=None, layer_attr=None):
"""
A layer for rotating 90 degrees (clock-wise) for each feature channel,
usually used when the input sample is some image or feature map.
.. math::
y(j,i,:) = x(M-i-1,j,:)
where :math:`x` is (M x N x C) input, and :math:`y` is (N x M x C) output.
The example usage is:
.. code-block:: python
rot = rotate_layer(input=layer,
height=100,
width=100)
:param input: The input of this layer.
:type input: LayerOutput
:param height: The height of the sample matrix.
:type height: int
:param width: The width of the sample matrix.
:type width: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
l = Layer(
name=name,
height=height,
width=width,
type=LayerType.ROTATE_LAYER,
inputs=[input.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.ROTATE_LAYER,
parents=[input],
size=l.config.size)
@wrap_name_default()
@layer_support()
def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
"""
Cosine Similarity Layer. The cosine similarity equation is here.
.. math::
similarity = cos(\\theta) = {\\mathbf{a} \\cdot \\mathbf{b}
\\over \\|\\mathbf{a}\\| \\|\\mathbf{b}\\|}
The size of a is M, size of b is M*N,
Similarity will be calculated N times by step M. The output size is
N. The scale will be multiplied to similarity.
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The example usage is:
.. code-block:: python
cos = cos_sim(a=layer1, b=layer2, size=3)
:param name: The name of this layer. It is optional.
:type name: basestring
:param a: The first input of this layer.
:type a: LayerOutput
:param b: The second input of this layer.
:type b: LayerOutput
:param scale: The scale of the cosine similarity. 1 is the default value.
:type scale: float
:param size: The dimension of this layer. NOTE size_a * size should equal size_b.
:type size: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
if size == 1:
Layer(
name=name,
type=LayerType.COSINE_SIM,
cos_scale=scale,
inputs=[a.name, b.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
else:
if a.size is not None and b.size is not None:
assert size == b.size / a.size
Layer(
name=name,
type=LayerType.COSINE_SIM_VEC,
size=size,
cos_scale=scale,
inputs=[a.name, b.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.COSINE_SIM, parents=[a, b], size=size)
@wrap_name_default()
@layer_support()
def l2_distance_layer(x, y, name=None, layer_attr=None):
"""
This layer calculates and returns the Euclidean distance between two input
vectors x and y. The equation is as follows:
.. math::
l2_distance(\\mathbf{x}, \\mathbf{y}) = \\sqrt{\\sum_{i=1}^D(x_i - y_i)}
The output size of this layer is fixed to be 1. Note that the above
computation is for one sample. Multiple samples are processed in one batch.
The example usage is:
.. code-block:: python
l2_sim = l2_distance(x=layer1, y=layer2)
:param name: The name of this layer. It is optional.
:type name: basestring
:param x: The first input x for this layer, whose output is a matrix with
dimensionality N x D. N is the sample number in a mini-batch.
D is the dimensionality of x's output.
:type x: LayerOutput
:param y: The second input y for this layer, whose output is a matrix with
dimensionality N x D. N is the sample number in a mini-batch.
D is the dimensionality of y's output.
:type y: LayerOutput
:param layer_attr: The extra layer attributes, for example, drop rate.
See ExtraLayerAttribute for more details.
:type layer_attr: ExtraLayerAttribute
:return: The returned LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(x, LayerOutput) and isinstance(y, LayerOutput)
Layer(
name=name,
type=LayerType.L2_DISTANCE,
inputs=[x.name, y.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.L2_DISTANCE, parents=[x, y], size=1)
@wrap_name_default()
@wrap_bias_attr_default(has_bias=True)
@wrap_param_attr_default()
@layer_support()
def hsigmoid(input,
label,
num_classes=None,
name=None,
bias_attr=None,
param_attr=None,
layer_attr=None):
"""
Organize the classes into a binary tree. At each node, a sigmoid function
is used to calculate the probability of belonging to the right branch.
Reference:
`Hierarchical Probabilistic Neural Network Language Model
<http://www.gatsby.ucl.ac.uk/aistats/fullpapers/208.pdf>`_
The example usage is:
.. code-block:: python
cost = hsigmoid(input=[layer1, layer2],
label=data_layer)
:param input: The input of this layer.
:type input: LayerOutput | list | tuple
:param label: The input label.
:type label: LayerOutput
:param num_classes: The number of classes. And it should be larger than 2. If the parameter
is not set or set to None, its actual value will be automatically set to
the number of labels.
:type num_classes: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
if not isinstance(param_attr, collections.Sequence):
param_attr = [param_attr]
else:
if not isinstance(param_attr, collections.Sequence):
param_attr = [param_attr] * len(input)
else:
assert len(param_attr) == len(input)
assert isinstance(input, collections.Sequence)
assert isinstance(label, LayerOutput)
assert label.layer_type == LayerType.DATA
if num_classes is None:
num_classes = label.size
if num_classes is None or num_classes <= 2:
raise ValueError("hsigmoid label size must larger than 2.")
ipts_for_layer = []
parents = []
for each_input, each_param_attr in zip(input, param_attr):
assert isinstance(each_input, LayerOutput)
ipts_for_layer.append(Input(each_input.name, **each_param_attr.attr))
parents.append(each_input)
ipts_for_layer.append(label.name)
parents.append(label)
l = Layer(
name=name,
type=LayerType.HSIGMOID,
num_classes=num_classes,
bias=ParamAttr.to_bias(bias_attr),
inputs=ipts_for_layer,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.HSIGMOID, parents=parents, size=l.config.size)
@wrap_name_default("conv")
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default(act=ReluActivation())
@layer_support(DROPOUT)
def img_conv_layer(input,
filter_size,
num_filters,
name=None,
num_channels=None,
act=None,
groups=1,
stride=1,
padding=0,
dilation=1,
bias_attr=None,
param_attr=None,
shared_biases=True,
layer_attr=None,
filter_size_y=None,
stride_y=None,
padding_y=None,
dilation_y=None,
trans=False,
layer_type=None):
"""
Convolution layer for image. Paddle can support both square and non-square
input currently.
The details of convolution layer, please refer UFLDL's `convolution
<http://ufldl.stanford.edu/tutorial/supervised/
FeatureExtractionUsingConvolution/>`_ .
Convolution Transpose (deconv) layer for image. Paddle can support both square
and non-square input currently.
The details of convolution transpose layer,
please refer to the following explanation and references therein
<http://datascience.stackexchange.com/questions/6107/
what-are-deconvolutional-layers/>`_ .
The num_channel means input image's channel number. It may be 1 or 3 when
input is raw pixels of image(mono or RGB), or it may be the previous layer's
num_filters.
There are several groups of filters in PaddlePaddle implementation.
If the groups attribute is greater than 1, for example groups=2,
the input will be splitted into 2 parts along the channel axis, and
the filters will also be splitted into 2 parts. The first half of the filters
is only connected to the first half of the input channels, while the second
half of the filters is only connected to the second half of the input. After
the computation of convolution for each part of input,
the output will be obtained by concatenating the two results.
The details of grouped convolution, please refer to:
`ImageNet Classification with Deep Convolutional Neural Networks
<http://www.cs.toronto.edu/~kriz/imagenet_classification_with_deep_convolutional.pdf>`_
The example usage is:
.. code-block:: python
conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,
act=ReluActivation())
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param filter_size: The dimensions of the filter kernel. If the parameter is
set to one integer, the two dimensions on x and y axises
will be same when filter_size_y is not set. If it is set
to a list, the first element indicates the dimension on
the x axis, and the second is used to specify the dimension
on the y axis when filter_size_y is not provided.
:type filter_size: int | tuple | list
:param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
is not set, it will be set automatically according to filter_size.
:type filter_size_y: int
:param num_filters: The number of filters. It is as same as the output image channel.
:type num_filters: int
:param act: Activation type. ReluActivation is the default activation.
:type act: BaseActivation
:param groups: The group number. 1 is the default group number.
:type groups: int
:param stride: The strides. If the parameter is set to one integer, the strides
on x and y axises will be same when stride_y is not set. If it is
set to a list, the first element indicates the stride on the x axis,
and the second is used to specify the stride on the y axis when
stride_y is not provided. 1 is the default value.
:type stride: int | tuple | list
:param stride_y: The stride on the y axis.
:type stride_y: int
:param padding: The padding sizes. If the parameter is set to one integer, the padding
sizes on x and y axises will be same when padding_y is not set. If it
is set to a list, the first element indicates the padding size on the
x axis, and the second is used to specify the padding size on the y axis
when padding_y is not provided. 0 is the default padding size.
:type padding: int | tuple | list
:param padding_y: The padding size on the y axis.
:type padding_y: int
:param dilation: The dimensions of the dilation. If the parameter is set to one integer,
the two dimensions on x and y axises will be same when dilation_y is not
set. If it is set to a list, the first element indicates the dimension
on the x axis, and the second is used to specify the dimension on the y
axis when dilation_y is not provided. 1 is the default dimension.
:type dilation: int | tuple | list
:param dilation_y: The dimension of the dilation on the y axis.
:type dilation_y: int
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channel number of the input.
:type num_channels: int
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param shared_biases: Whether biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr: The extra layer attributes. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param trans: True if it is a convTransLayer, False if it is a convLayer
:type trans: bool
:param layer_type: Specify the layer type. If the dilation's dimension on one axis is
larger than 1, layer_type has to be "cudnn_conv" or "cudnn_convt".
If trans=True, layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or "cudnn_conv".
:type layer_type: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if filter_size_y is None:
if isinstance(filter_size, collections.Sequence):
assert len(filter_size) == 2
filter_size, filter_size_y = filter_size
else:
filter_size_y = filter_size
if stride_y is None:
if isinstance(stride, collections.Sequence):
assert len(stride) == 2
stride, stride_y = stride
else:
stride_y = stride
if padding_y is None:
if isinstance(padding, collections.Sequence):
assert len(padding) == 2
padding, padding_y = padding
else:
padding_y = padding
if dilation_y is None:
if isinstance(dilation, collections.Sequence):
assert len(dilation) == 2
dilation, dilation_y = dilation
else:
dilation_y = dilation
if param_attr.attr.get('initial_smart'):
# special initial for conv layers.
init_w = (2.0 / (filter_size**2 * num_channels))**0.5
param_attr.attr["initial_mean"] = 0.0
param_attr.attr["initial_std"] = init_w
param_attr.attr["initial_strategy"] = 0
param_attr.attr["initial_smart"] = False
if layer_type:
if dilation > 1 or dilation_y > 1:
assert layer_type in [
"cudnn_conv", "cudnn_convt", "exconv", "exconvt"
]
if trans:
assert layer_type in ["exconvt", "cudnn_convt"]
else:
assert layer_type in ["exconv", "cudnn_conv"]
lt = layer_type
else:
lt = LayerType.CONVTRANS_LAYER if trans else LayerType.CONV_LAYER
l = Layer(
name=name,
inputs=Input(
input.name,
conv=Conv(
filter_size=filter_size,
padding=padding,
dilation=dilation,
stride=stride,
channels=num_channels,
groups=groups,
filter_size_y=filter_size_y,
padding_y=padding_y,
dilation_y=dilation_y,
stride_y=stride_y),
**param_attr.attr),
active_type=act.name,
num_filters=num_filters,
bias=ParamAttr.to_bias(bias_attr),
shared_biases=shared_biases,
type=lt,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
lt,
parents=[input],
activation=act,
num_filters=num_filters,
size=l.config.size)
@wrap_name_default("pool")
@layer_support()
def img_pool_layer(input,
pool_size,
name=None,
num_channels=None,
pool_type=None,
stride=1,
padding=0,
layer_attr=None,
pool_size_y=None,
stride_y=None,
padding_y=None,
ceil_mode=True,
exclude_mode=None):
"""
Image pooling Layer.
The details of pooling layer, please refer to ufldl's pooling_ .
.. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
- ceil_mode=True:
.. math::
w & = 1 + ceil(\\frac{input\_width + 2 * padding - pool\_size}{stride})
h & = 1 + ceil(\\frac{input\_height + 2 * padding\_y - pool\_size\_y}{stride\_y})
- ceil_mode=False:
.. math::
w & = 1 + floor(\\frac{input\_width + 2 * padding - pool\_size}{stride})
h & = 1 + floor(\\frac{input\_height + 2 * padding\_y - pool\_size\_y}{stride\_y})
The example usage is:
.. code-block:: python
maxpool = img_pool_layer(input=conv,
pool_size=3,
pool_size_y=5,
num_channels=8,
stride=1,
stride_y=2,
padding=1,
padding_y=2,
pool_type=MaxPooling())
:param padding: The padding size on the x axis. 0 is the default padding size.
:type padding: int
:param padding_y: The padding size on the y axis. If the parameter is not set
or set to None, it will be set to 'padding' automatically.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param pool_size: The pooling window length on the x axis.
:type pool_size: int
:param pool_size_y: The pooling window length on the y axis. If the parameter is
not set or set to None, its actual value will be automatically
set to pool_size.
:type pool_size_y: int
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param pool_type: Pooling type. MaxPooling is the default pooling.
:type pool_type: BasePoolingType
:param stride: The stride on the x axis. 1 is the default value.
:type stride: int
:param stride_y: The stride on the y axis. If the parameter is not set or set to
None, its actual value will be automatically set to 'stride'.
:type stride_y: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param ceil_mode: Whether to use the ceil function to calculate output height and width.
True is the default. If it is set to False, the floor function will
be used.
:type ceil_mode: bool
:param exclude_mode: Whether to exclude the padding cells when calculating, but only
work when pool_type is AvgPooling. If None, also exclude the padding
cells. If use cudnn, use CudnnAvgPooling or CudnnAvgInclPadPooling
as pool_type to identify the mode.
:type exclude_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if pool_type is None:
pool_type = MaxPooling()
elif isinstance(pool_type, AvgPooling):
pool_type.name = 'avg'
assert type(pool_type) in [AvgPooling, MaxPooling, MaxWithMaskPooling, CudnnAvgPooling,
CudnnMaxPooling, CudnnAvgInclPadPooling], \
"only (Cudnn)AvgPooling, (Cudnn)MaxPooling, MaxWithMaskPooling are supported"
type_name = pool_type.name + '-projection' \
if (
isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
else pool_type.name
pool_size_y = pool_size if pool_size_y is None else pool_size_y
stride_y = stride if stride_y is None else stride_y
padding_y = padding if padding_y is None else padding_y
l = Layer(
name=name,
type=LayerType.POOL_LAYER,
inputs=[
Input(
input.name,
pool=Pool(
pool_type=type_name,
channels=num_channels,
size_x=pool_size,
start=None,
stride=stride,
padding=padding,
size_y=pool_size_y,
stride_y=stride_y,
padding_y=padding_y))
],
ceil_mode=ceil_mode,
exclude_mode=exclude_mode,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.POOL_LAYER,
parents=[input],
num_filters=num_channels,
size=l.config.size)
@wrap_name_default("pool3d")
@layer_support()
def img_pool3d_layer(input,
pool_size,
name=None,
num_channels=None,
pool_type=None,
stride=1,
padding=0,
layer_attr=None,
pool_size_y=None,
stride_y=None,
padding_y=None,
pool_size_z=None,
stride_z=None,
padding_z=None,
ceil_mode=True):
"""
Image pooling Layer.
The details of pooling layer, please refer ufldl's pooling_ .
.. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
- ceil_mode=True:
.. math::
w & = 1 + \\frac{ceil(input\_width + 2 * padding - pool\_size)}{stride}
h & = 1 + \\frac{ceil(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y}
d & = 1 + \\frac{ceil(input\_depth + 2 * padding\_z - pool\_size\_z)}{stride\_z}
- ceil_mode=False:
.. math::
w & = 1 + \\frac{floor(input\_width + 2 * padding - pool\_size)}{stride}
h & = 1 + \\frac{floor(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y}
d & = 1 + \\frac{floor(input\_depth + 2 * padding\_z - pool\_size\_z)}{stride\_z}
The example usage is:
.. code-block:: python
maxpool = img_pool3d_layer(input=conv,
pool_size=3,
num_channels=8,
stride=1,
padding=1,
pool_type=MaxPooling())
:param padding: pooling padding width.
:type padding: int | tuple | list
:param name: The name of this layer. It is optional.
:type name: basestring.
:param input: The input of this layer.
:type input: LayerOutput
:param pool_size: The pooling window lengths along three axises. If the parameter
is set to one integer, the three lengths will be same.
:type pool_size: int | tuple | list
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param pool_type: Pooling type. MaxPooling is the default pooling.
:type pool_type: BasePoolingType
:param stride: The strides of the pooling along three axises. If the parameter
is set to one integer, the three strides will be same. 1 is the
default value.
:type stride: int | tuple | list
:param padding: The sizes of padding along three axises. If the parameter is set to
one integer, they will be same. 0 is the default padding size.
:type padding: int | tuple | list
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use the ceil function to calculate output height and width.
True is the default. If it is set to False, the floor function will
be used.
:type ceil_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if pool_type is None:
pool_type = MaxPooling()
elif isinstance(pool_type, AvgPooling):
pool_type.name = 'avg'
type_name = pool_type.name + '-projection' \
if (
isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
else pool_type.name
if isinstance(pool_size, collections.Sequence):
assert len(pool_size) == 3
pool_size, pool_size_y, pool_size_z = pool_size
else:
pool_size_y = pool_size
pool_size_z = pool_size
if isinstance(stride, collections.Sequence):
assert len(stride) == 3
stride, stride_y, stride_z = stride
else:
stride_y = stride
stride_z = stride
if isinstance(padding, collections.Sequence):
assert len(padding) == 3
padding, padding_y, padding_y = padding
else:
padding_y = padding
padding_z = padding
l = Layer(
name=name,
type=LayerType.POOL3D_LAYER,
inputs=[
Input(
input.name,
pool=Pool3d(
pool_type=type_name,
channels=num_channels,
size_x=pool_size,
start=None,
stride=stride,
padding=padding,
size_y=pool_size_y,
stride_y=stride_y,
padding_y=padding_y,
size_z=pool_size_z,
stride_z=stride_z,
padding_z=padding_z))
],
ceil_mode=ceil_mode,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.POOL_LAYER,
parents=[input],
num_filters=num_channels,
size=l.config.size)
@wrap_name_default("upsample")
@layer_support()
def upsample_layer(input,
name=None,
scale=None,
scale_y=None,
upsample_size=None,
upsample_size_y=None,
pad_out_x=False,
pad_out_y=False,
layer_attr=None):
"""
The DePooling process.
Inputs should be a list of length 2. The first input is a layer,
and the second input should be the MaxWithMaskPoolingLayer
The example usage is:
.. code-block:: python
pool1 = paddle.v2.layer.img_pool(input=input, pool_size=2, stride=2,
pool_type=paddle.pooling.MaxWithMask())
upsample = paddle.v2.layer.upsample(input=[layer1, pool1])
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: contains an input layer and a MaxWithMaskPoolingLayer
:type input: list | tuple | collections.Sequence
:param scale: outputSize = scale * inputSize
:type scale: int | list | tuple | .
:param scale_y: scale_y will be equal to scale, if it's value is None,
:type scale: int | None.
:param upsample_size: specify the outputSize.
:type upsample_size: int | list | tuple.
:param upsample_size_y: specify the y dimension outputSize.
:type upsample_size_y: int.
:param pad_out_x: specify exact x dimension size. This parameter only works when scale is 2
:type pad_out_x: bool.
:param pad_out_y: specify exact y dimension size. This parameter only works when scale is 2
:type pad_out_y: bool.
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert (scale is not None) or (upsample_size is not None), \
'scale or upsample_size, there must be one to be designated'
assert len(input) == 2, 'layer input size must be 2'
assert input[1].layer_type == LayerType.POOL_LAYER, \
'the second input should be the MaxPoolWithMaskLayer'
scale_y = scale \
if scale is not None else scale_y
upsample_size_y = upsample_size \
if upsample_size is not None else upsample_size_y
layer_type = LayerType.UPSAMPLE_LAYER
layer = Layer(
name=name,
type=layer_type,
inputs=[
Input(
input[0].name,
upsample=Upsample(scale, scale_y, pad_out_x, pad_out_y,
upsample_size, upsample_size_y)),
Input(input[1].name)
],
**ExtraLayerAttribute.to_kwargs(layer_attr))
sz = layer.config.size
return LayerOutput(name, layer_type=layer_type, parents=input, size=sz)
@wrap_name_default("spp")
@layer_support()
def spp_layer(input,
name=None,
num_channels=None,
pool_type=None,
pyramid_height=None,
layer_attr=None):
"""
A layer performs spatial pyramid pooling.
Reference:
`Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
<https://arxiv.org/abs/1406.4729>`_
The example usage is:
.. code-block:: python
spp = spp_layer(input=data,
pyramid_height=2,
num_channels=16,
pool_type=MaxPooling())
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param pool_type: Pooling type. MaxPooling is the default pooling.
:type scale: BasePoolingType
:param pyramid_height: The pyramid height of this pooling.
:type pyramid_height: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if pool_type is None:
pool_type = MaxPooling()
elif isinstance(pool_type, AvgPooling):
pool_type.name = 'avg'
type_name = pool_type.name
if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)):
type_name += '-projection'
l = Layer(
name=name,
type=LayerType.SPP_LAYER,
inputs=Input(
input.name,
spp=SpatialPyramidPool(
pool_type=type_name,
channels=num_channels,
pyramid_height=pyramid_height)),
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
layer_type=LayerType.SPP_LAYER,
parents=[input],
num_filters=num_channels,
size=l.config.size)
def __img_norm_layer__(name, input, size, norm_type, scale, power, num_channels,
blocked, layer_attr):
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
l = Layer(
name=name,
type=LayerType.NORM_LAYER,
inputs=Input(
input.name,
norm=Norm(
norm_type=norm_type,
channels=num_channels,
size=size,
scale=scale,
pow=power,
blocked=blocked)),
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
layer_type=LayerType.NORM_LAYER,
parents=[input],
num_filters=num_channels,
img_norm_type=norm_type,
size=l.config.size)
@wrap_name_default("crmnorm")
@layer_support()
def img_cmrnorm_layer(input,
size,
scale=0.0128,
power=0.75,
name=None,
num_channels=None,
layer_attr=None):
"""
Response normalization across feature maps.
Reference:
`ImageNet Classification with Deep Convolutional Neural Networks
<http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_
The example usage is:
.. code-block:: python
norm = img_cmrnorm_layer(input=net, size=5)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param size: Normalize in number of :math:`size` feature maps.
:type size: int
:param scale: The hyper-parameter.
:type scale: float
:param power: The hyper-parameter.
:type power: float
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:param layer_attr: The extra layer attributes. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
return __img_norm_layer__(name, input, size, "cmrnorm-projection", scale,
power, num_channels, 0, layer_attr)
@wrap_bias_attr_default()
@wrap_param_attr_default(
default_factory=lambda _: ParamAttr(initial_mean=1.0, initial_std=0.))
@wrap_act_default(act=ReluActivation())
@wrap_name_default("batch_norm")
@layer_support(DROPOUT, ERROR_CLIPPING)
def batch_norm_layer(input,
act=None,
name=None,
img3D=False,
num_channels=None,
bias_attr=None,
param_attr=None,
layer_attr=None,
batch_norm_type=None,
epsilon=1e-5,
moving_average_fraction=0.9,
use_global_stats=None,
mean_var_names=None):
"""
Batch Normalization Layer. The notation of this layer is as follows.
:math:`x` is the input features over a mini-batch.
.. math::
\\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\
\ mini-batch\ mean \\\\
\\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
\\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
\\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
\\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
Reference:
`Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift
<http://arxiv.org/abs/1502.03167>`_
The example usage is:
.. code-block:: python
norm = batch_norm_layer(input=net, act=ReluActivation())
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: This layer's input which is to be performed batch normalization on.
:type input: LayerOutput
:param batch_norm_type: We have batch_norm, mkldnn_batch_norm and cudnn_batch_norm.
batch_norm supports CPU, MKLDNN and GPU. cudnn_batch_norm
requires cuDNN version greater or equal to v4 (>=v4).
But cudnn_batch_norm is faster and needs less
memory than batch_norm. mkldnn_batch_norm requires
use_mkldnn is enabled. By default (None), we will
automatically select cudnn_batch_norm for GPU,
mkldnn_batch_norm for MKLDNN and batch_norm for CPU.
Users can specify the batch norm type. If you use
cudnn_batch_norm, we suggested you use latest version,
such as v5.1.
:type batch_norm_type: None | string, None or "batch_norm" or "cudnn_batch_norm"
or "mkldnn_batch_norm"
:param act: Activation type. ReluActivation is the default activation.
:type act: BaseActivation
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param bias_attr: :math:`\\beta`. The bias attribute. If the parameter is set to
False or an object whose type is not ParameterAttribute, no
bias is defined. If the parameter is set to True, the bias is
initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: :math:`\\gamma`. The parameter attribute. See ParameterAttribute
for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param use_global_stats: Whether use moving mean/variance statistics during
testing peroid. If the parameter is set to None or
True, it will use moving mean/variance statistics
during testing. If the parameter is set to False, it
will use the mean and variance of the current batch
of test data.
:type use_global_stats: bool | None.
:param epsilon: The small constant added to the variance to improve numeric stability.
:type epsilon: float.
:param moving_average_fraction: Factor used in the moving average computation.
:math:`runningMean = newMean*(1-factor) + runningMean*factor`
:type moving_average_fraction: float.
:param mean_var_names: [mean name, variance name]
:type mean_var_names: string list
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
if input.num_filters is not None:
num_channels = input.num_filters
else:
num_channels = input.size
assert (batch_norm_type is None) or (batch_norm_type == "batch_norm") or \
(batch_norm_type == "mkldnn_batch_norm") or \
(batch_norm_type == "cudnn_batch_norm")
l = Layer(
name=name,
img3D=img3D,
inputs=Input(
input.name, image=Image(channels=num_channels), **param_attr.attr),
active_type=act.name,
type=LayerType.BATCH_NORM_LAYER,
batch_norm_type=batch_norm_type,
bias=ParamAttr.to_bias(bias_attr),
epsilon=epsilon,
moving_average_fraction=moving_average_fraction,
use_global_stats=use_global_stats,
mean_var_names=mean_var_names,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.BATCH_NORM_LAYER,
parents=[input],
activation=act,
num_filters=num_channels,
size=l.config.size)
@wrap_name_default()
@layer_support()
def sum_to_one_norm_layer(input, name=None, layer_attr=None):
"""
A layer for sum-to-one normalization,
which is used in NEURAL TURING MACHINE.
.. math::
out[i] = \\frac {in[i]} {\sum_{k=1}^N in[k]}
where :math:`in` is a (batchSize x dataDim) input vector,
and :math:`out` is a (batchSize x dataDim) output vector.
The example usage is:
.. code-block:: python
sum_to_one_norm = sum_to_one_norm_layer(input=layer)
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute
for details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.SUM_TO_ONE_NORM_LAYER,
inputs=[input.name],
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.SUM_TO_ONE_NORM_LAYER, parents=[input], size=input.size)
@wrap_name_default()
@layer_support()
def row_l2_norm_layer(input, name=None, layer_attr=None):
"""
A layer for L2-normalization in each row.
.. math::
out[i] = \\frac{in[i]} {\\sqrt{\\sum_{k=1}^N in[k]^{2}}}
where the size of :math:`in` is (batchSize x dataDim) ,
and the size of :math:`out` is a (batchSize x dataDim) .
The example usage is:
.. code-block:: python
row_l2_norm_layer = row_l2_norm_layer(input=layer)
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute
for details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.ROW_L2_NORM_LAYER,
inputs=[input.name],
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.ROW_L2_NORM_LAYER, parents=[input], size=input.size)
@wrap_name_default("addto")
@wrap_act_default(act=LinearActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(DROPOUT, ERROR_CLIPPING)
def addto_layer(input, act=None, name=None, bias_attr=None, layer_attr=None):
"""
AddtoLayer.
.. math::
y = f(\\sum_{i} x_i + b)
where :math:`y` is output, :math:`x` is input, :math:`b` is bias,
and :math:`f` is activation function.
The example usage is:
.. code-block:: python
addto = addto_layer(input=[layer1, layer2],
act=ReluActivation(),
bias_attr=False)
This layer just simply adds all input layers together, then activates the
sum. All inputs should share the same dimension, which is also the dimension
of this layer's output.
There is no weight matrix for each input, because it just a simple add
operation. If you want a complicated operation before add, please use
mixed_layer.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input layers. It could be a LayerOutput or list/tuple of
LayerOutput.
:type input: LayerOutput | list | tuple
:param act: Activation Type. LinearActivation is the default activation.
:type act: BaseActivation
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
num_filters = None
if isinstance(input, LayerOutput):
input = [input]
assert isinstance(input, collections.Sequence)
ipts_for_layer = []
for each_input in input:
assert isinstance(each_input, LayerOutput)
ipts_for_layer.append(Input(each_input.name))
if each_input.num_filters is not None:
num_filters = each_input.num_filters
l = Layer(
name=name,
type=LayerType.ADDTO_LAYER,
inputs=ipts_for_layer,
bias=ParamAttr.to_bias(bias_attr),
active_type=act.name,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.ADDTO_LAYER,
parents=input,
activation=act,
num_filters=num_filters,
size=l.config.size)
@wrap_act_default(act=IdentityActivation())
@wrap_name_default("concat")
@layer_support(DROPOUT, ERROR_CLIPPING)
def concat_layer(input, act=None, name=None, layer_attr=None, bias_attr=None):
"""
Concatenate all input vectors to one vector.
Inputs can be a list of LayerOutput or a list of projection.
The example usage is:
.. code-block:: python
concat = concat_layer(input=[layer1, layer2])
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input layers or projections
:type input: list | tuple | collections.Sequence
:param act: Activation type. IdentityActivation is the default activation.
:type act: BaseActivation
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
elif isinstance(input, Projection):
input = [input]
else:
assert isinstance(input, collections.Sequence)
def __is_type__(o, tp):
if not isinstance(o, collections.Sequence):
if o == tp:
return True
elif len(o.__bases__) == 0:
return False
else:
for bs in o.__bases__:
if __is_type__(bs, tp):
return True
return False
else:
tmp = map(lambda _x: __is_type__(_x, tp), o)
a = tmp[0]
for b in tmp[1:]:
assert a == b
return a
def __reduce_concat_type__(a, b):
assert __is_type__([a, b], Projection) or __is_type__([a, b],
LayerOutput)
return a
is_concat_layer = __is_type__(
reduce(__reduce_concat_type__, map(type, input)), LayerOutput)
layer_type = (LayerType.CONCAT_LAYER
if is_concat_layer else LayerType.CONCAT_PROJ_LAYER)
if layer_type == LayerType.CONCAT_LAYER:
assert not bias_attr
layer = Layer(
name=name,
type=layer_type,
inputs=[x.name for x in input] if is_concat_layer else input,
active_type=act.name,
bias=ParamAttr.to_bias(bias_attr),
**ExtraLayerAttribute.to_kwargs(layer_attr))
sz = layer.config.size
return LayerOutput(
name,
layer_type=layer_type,
parents=input if is_concat_layer else [x.origin for x in input],
activation=act,
size=sz)
@wrap_name_default("seqconcat")
@wrap_act_default(act=IdentityActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(DROPOUT, ERROR_CLIPPING)
def seq_concat_layer(a, b, act=None, name=None, layer_attr=None,
bias_attr=None):
"""
Concatenate sequence a and sequence b.
Inputs:
- a = [a1, a2, ..., am]
- b = [b1, b2, ..., bn]
Output: [a1, ..., am, b1, ..., bn]
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The example usage is:
.. code-block:: python
concat = seq_concat_layer(a=layer1, b=layer2)
:param name: The name of this layer. It is optional.
:type name: basestring
:param a: The first input sequence layer
:type a: LayerOutput
:param b: The second input sequence layer
:type b: LayerOutput
:param act: Activation type. IdentityActivation is the default activation.
:type act: BaseActivation
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
assert a.size == b.size
Layer(
name=name,
type=LayerType.SEQUENCE_CONCAT_LAYER,
inputs=[a.name, b.name],
active_type=act.name,
bias=ParamAttr.to_bias(bias_attr),
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
layer_type=LayerType.SEQUENCE_CONCAT_LAYER,
parents=[a, b],
activation=act,
size=a.size)
@wrap_name_default("memory", "memory_name")
def memory(name,
size,
memory_name=None,
is_seq=False,
boot_layer=None,
boot_bias=None,
boot_bias_active_type=None,
boot_with_const_id=None):
"""
The memory takes a layer's output at previous time step as its own output.
If boot_bias, the activation of the bias is the initial value of the memory.
If boot_with_const_id is set, then the memory's output at the first time step
is a IndexSlot, the Arguments.ids()[0] is this :code:`cost_id`.
If boot_layer is specified, the memory's output at the first time step will
be the boot_layer's output.
In other case, the default memory's output at the first time step is zero.
.. code-block:: python
mem = memory(size=256, name='state')
state = fc_layer(input=mem, size=256, name='state')
If you do not want to specify the name, you can also use set_input()
to specify the layer to be remembered as the following:
.. code-block:: python
mem = memory(size=256)
state = fc_layer(input=mem, size=256)
mem.set_input(mem)
:param name: The name of the layer which this memory remembers.
If name is None, user should call set_input() to specify the
name of the layer which this memory remembers.
:type name: basestring
:param size: The dimensionality of memory.
:type size: int
:param memory_name: The name of the memory. It is ignored when name is provided.
:type memory_name: basestring
:param is_seq: DEPRECATED. is sequence for boot_layer
:type is_seq: bool
:param boot_layer: This parameter specifies memory's output at the first time
step and the output is boot_layer's output.
:type boot_layer: LayerOutput | None
:param boot_bias: The bias attribute of memory's output at the first time step.
If the parameter is set to False or an object whose type is not
ParameterAttribute, no bias is defined. If the parameter is set
to True, the bias is initialized to zero.
:type boot_bias: ParameterAttribute | None
:param boot_bias_active_type: Activation type for memory's bias at the first time
step. LinearActivation is the default activation.
:type boot_bias_active_type: BaseActivation
:param boot_with_const_id: This parameter specifies memory's output at the first
time step and the output is an index.
:type boot_with_const_id: int
:return: LayerOutput object.
:rtype: LayerOutput
"""
if boot_bias_active_type is None:
boot_bias_active_type = LinearActivation()
assert boot_bias is None or isinstance(boot_bias, ParameterAttribute)
if isinstance(boot_bias, ParameterAttribute):
boot_bias = ParamAttr.to_bias(boot_bias)
assert boot_layer is None or isinstance(boot_layer, LayerOutput)
if name is not None:
memory_name = None
memory_name = Memory(
name,
size,
boot_layer=boot_layer.name if boot_layer is not None else None,
boot_bias=boot_bias,
boot_bias_active_type=boot_bias_active_type.name,
boot_with_const_id=boot_with_const_id,
memory_name=memory_name)
lout = LayerOutput(
name=memory_name,
size=size,
layer_type=LayerType.MEMORY,
parents=[boot_layer] if boot_layer is not None else None)
return lout
@wrap_bias_attr_default()
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
@wrap_act_default(param_names=['state_act'], act=TanhActivation())
@wrap_act_default(act=TanhActivation())
@wrap_name_default('lstm_step')
@layer_support()
def lstm_step_layer(input,
state,
size=None,
act=None,
name=None,
gate_act=None,
state_act=None,
bias_attr=None,
layer_attr=None):
"""
LSTM Step Layer. This function is used only in recurrent_group.
The lstm equations are shown as follows.
.. math::
i_t & = \\sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)
f_t & = \\sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)
c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)
o_t & = \\sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)
h_t & = o_t tanh(c_t)
The input of lstm step is :math:`Wx_t + Wh_{t-1}`, and user should use
:code:`mixed_layer` and :code:`full_matrix_projection` to calculate these
input vectors.
The state of lstm step is :math:`c_{t-1}`. And lstm step layer will do
.. math::
i_t = \\sigma(input + W_{ci}c_{t-1} + b_i)
...
This layer has two outputs. The default output is :math:`h_t`. The other
output is :math:`o_t`, whose name is 'state' and users can use
:code:`get_output_layer` to extract this output.
:param name: The name of this layer. It is optional.
:type name: basestring
:param size: The dimension of this layer's output, which must be
equal to the dimension of the state.
:type size: int
:param input: The input of this layer.
:type input: LayerOutput
:param state: The state of the LSTM unit.
:type state: LayerOutput
:param act: Activation type. TanhActivation is the default activation.
:type act: BaseActivation
:param gate_act: Activation type of the gate. SigmoidActivation is the
default activation.
:type gate_act: BaseActivation
:param state_act: Activation type of the state. TanhActivation is the
default activation.
:type state_act: BaseActivation
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert size is None or state.size == size
size = state.size
Layer(
name=name,
type=LayerType.LSTM_STEP_LAYER,
active_type=act.name,
active_gate_type=gate_act.name,
active_state_type=state_act.name,
bias=ParamAttr.to_bias(bias_attr),
size=state.size,
inputs=[input.name, state.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.LSTM_STEP_LAYER,
parents=[input, state],
activation=act,
size=size,
outputs=['default', 'state'])
@wrap_bias_attr_default()
@wrap_param_attr_default()
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
@wrap_act_default(act=TanhActivation())
@wrap_name_default('gru_step')
@layer_support()
def gru_step_layer(input,
output_mem,
size=None,
act=None,
name=None,
gate_act=None,
bias_attr=None,
param_attr=None,
layer_attr=None):
"""
:param input: The input of this layer, whose dimension can be divided by 3.
:type input: LayerOutput
:param output_mem: A memory which memorizes the output of this layer at previous
time step.
:type output_mem: LayerOutput
:param size: The dimension of this layer's output. If it is not set or set to None,
it will be set to one-third of the dimension of the input automatically.
:type size: int
:param act: Activation type of this layer's output. TanhActivation
is the default activation.
:type act: BaseActivation
:param name: The name of this layer. It is optional.
:type name: basestring
:param gate_act: Activation type of this layer's two gates. SigmoidActivation is
the default activation.
:type gate_act: BaseActivation
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute, no bias
is defined. If this parameter is set to True,
the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert input.size % 3 == 0
if size is None:
size = input.size / 3
Layer(
name=name,
type=LayerType.GRU_STEP_LAYER,
# The parameter here is for transforming the output_mem. The input has
# already been transformed outside this module so it does not need
# parameter associated with it.
# The parameter here is instead grouped with input is due to
# backward model compatibility.
inputs=[Input(input.name, **param_attr.attr), output_mem.name],
bias=ParamAttr.to_bias(bias_attr),
size=size,
active_type=act.name,
active_gate_type=gate_act.name,
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.GRU_STEP_LAYER,
parents=[input, output_mem],
size=size,
activation=act)
@wrap_bias_attr_default()
@wrap_param_attr_default()
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
@wrap_act_default(act=TanhActivation())
@wrap_name_default('gru_step_naive')
@layer_support(ERROR_CLIPPING, DROPOUT)
def gru_step_naive_layer(input,
output_mem,
size=None,
name=None,
act=None,
gate_act=None,
bias_attr=None,
param_attr=None,
layer_attr=None):
"""
GRU Step Layer, which is realized using PaddlePaddle API. It supports ERROR_CLIPPING
and DROPOUT.
:param input: The input of this layer, whose dimensionality can be divided by 3.
:param output_mem: A memory which memorizes the output of this layer at previous
time step.
:type output_mem: LayerOutput
:param size: The dimension of this layer's output. If it is not set or set to None,
it will be set to one-third of the dimension of the input automatically.
:type size: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param act: Activation type of this layer's output. TanhActivation
is the default activation.
:type act: BaseActivation
:param gate_act: Activation type of this layer's two gates. SigmoidActivation
is the default activation.
:type gate_act: BaseActivation
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute, no bias
is defined. If this parameter is set to True,
the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if input.size % 3 != 0:
raise ValueError("GruStep input size must be divided by 3")
if size is None:
size = input.size / 3
if bias_attr and bias_attr.attr.get("parameter_name", None) is not None:
raise ValueError("You should not specify the field `name` in bias_attr."
" Otherwise, the three biases, which correponding to "
" the two gates and the mixed layer for computing Wx+b"
", will share the same parameter matrix unexpectedly.")
def __gate__(gate_name, offset):
with mixed_layer(
name=name + "_" + gate_name,
size=size,
layer_attr=layer_attr,
bias_attr=bias_attr,
act=gate_act) as gate:
gate += identity_projection(input=input, offset=offset)
gate += full_matrix_projection(
input=output_mem, param_attr=param_attr)
return gate
update_gate = __gate__("update", 0)
reset_gate = __gate__("reset", size)
with mixed_layer(
name=name + "_reset_output", bias_attr=False) as reset_output:
reset_output += dotmul_operator(a=output_mem, b=reset_gate)
with mixed_layer(
name=name + "_output_candidate",
size=size,
layer_attr=layer_attr,
bias_attr=bias_attr,
act=act) as output_candidate:
output_candidate += identity_projection(input=input, offset=2 * size)
output_candidate += full_matrix_projection(
input=reset_output, param_attr=param_attr)
with mixed_layer(name=name) as output:
output += identity_projection(output_mem)
output += dotmul_operator(a=output_mem, b=update_gate, scale=-1.0)
output += dotmul_operator(a=output_candidate, b=update_gate)
return output
@wrap_name_default()
@layer_support()
def get_output_layer(input, arg_name, name=None, layer_attr=None):
"""
Get layer's output by name. In PaddlePaddle, a layer might return multiple
values, but returns one layer's output. If the user wants to use another
output besides the default one, please use get_output_layer first to get
the output from input.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input layer. And this layer should contain
multiple outputs.
:type input: LayerOutput
:param arg_name: The name of the output to be extracted from the input layer.
:type arg_name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:return: LayerOutput object.
:rtype: LayerOutput
"""
# GetOutputLayer
assert arg_name in input.outputs, 'Get Output From an not existed input.' \
' The get output name is %s, which not' \
' in %s' % (
arg_name, ",".join(input.outputs))
Layer(
name=name,
type=LayerType.GET_OUTPUT_LAYER,
inputs=[Input(
input.name, input_layer_argument=arg_name)],
size=input.size,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.GET_OUTPUT_LAYER,
parents=[input],
size=input.size)
@wrap_name_default()
@wrap_act_default()
@wrap_bias_attr_default()
@wrap_param_attr_default()
@layer_support()
def recurrent_layer(input,
act=None,
bias_attr=None,
param_attr=None,
name=None,
reverse=False,
layer_attr=None):
"""
Simple recurrent unit layer. It is just a fully connect layer through both
time and neural network.
For each sequence [start, end] it performs the following computation\:
.. math::
out_{i} = act(in_{i}) \\ \\ \\text{for} \\ i = start \\\\
out_{i} = act(in_{i} + out_{i-1} * W) \\ \\ \\text{for} \\ start < i <= end
If reversed is true, the order is reversed\:
.. math::
out_{i} = act(in_{i}) \\ \\ \\text{for} \\ i = end \\\\
out_{i} = act(in_{i} + out_{i+1} * W) \\ \\ \\text{for} \\ start <= i < end
:param input: The input of this layer.
:type input: LayerOutput
:param act: Activation type. TanhActivation is the default activation.
:type act: BaseActivation
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute,
no bias is defined. If the parameter is set to True,
the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.RECURRENT_LAYER,
inputs=Input(input.name, **param_attr.attr),
active_type=act.name,
bias=ParamAttr.to_bias(bias_attr),
reversed=reverse,
**ExtraAttr.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.RECURRENT_LAYER,
parents=[input],
size=input.size,
activation=act,
reverse=reverse)
class StaticInput(object):
"""
StaticInput is only used in recurrent_group which defines a read-only memory
and can be a sequence or non-sequence.
:param size: DEPRECATED
:param is_seq: DEPRECATED
"""
def __init__(self, input, is_seq=False, size=None):
assert isinstance(input, LayerOutput)
self.input = input
assert input.size is not None
if size is not None:
assert input.size == size
def SubsequenceInput(input):
"""
DEPRECATED.
Input sequence has sub-sequence, used in recurrent_group.
The example usage is:
.. code-block:: python
input = SubsequenceInput(layer)
"""
return input
@wrap_name_default("recurrent_group")
def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
"""
Recurrent layer group is an extremely flexible recurrent unit in
PaddlePaddle. As long as the user defines the calculation done within a
time step, PaddlePaddle will iterate such a recurrent calculation over
sequence input. This is useful for attention-based models, or Neural
Turning Machine like models.
The basic usage (time steps) is:
.. code-block:: python
def step(input):
output = fc_layer(input=layer,
size=1024,
act=LinearActivation(),
bias_attr=False)
return output
group = recurrent_group(input=layer,
step=step)
You can see following configs for further usages:
- time steps: lstmemory_group, paddle/gserver/tests/sequence_layer_group.conf, \
demo/seqToseq/seqToseq_net.py
- sequence steps: paddle/gserver/tests/sequence_nest_layer_group.conf
:param step: A step function which takes the input of recurrent_group as its own
input and returns values as recurrent_group's output every time step.
The recurrent group scatters a sequence into time steps. And
for each time step, it will invoke step function, and return
a time step result. Then gather outputs of each time step into
layer group's output.
:type step: callable
:param name: The recurrent_group's name. It is optional.
:type name: basestring
:param input: Input links array.
LayerOutput will be scattered into time steps.
SubsequenceInput will be scattered into sequence steps.
StaticInput will be imported to each time step, and doesn't change
over time. It's a mechanism to access layer outside step function.
:type input: LayerOutput | StaticInput | SubsequenceInput | list | tuple
:param reverse: If reverse is set to True, the recurrent unit will process the
input sequence in a reverse order.
:type reverse: bool
:param targetInlink: DEPRECATED.
The input layer which share info with layer group's output
Param input specifies multiple input layers. For
SubsequenceInput inputs, config should assign one input
layer that share info(the number of sentences and the number
of words in each sentence) with all layer group's outputs.
targetInlink should be one of the layer group's input.
:type targetInlink: LayerOutput | SubsequenceInput
:return: LayerOutput object.
:rtype: LayerOutput
"""
model_type('recurrent_nn')
if isinstance(input, LayerOutput) or isinstance(input, StaticInput):
input = [input]
assert isinstance(input, collections.Sequence)
def is_in_links(x):
return isinstance(x, LayerOutput)
in_links = filter(is_in_links, input)
RecurrentLayerGroupWithoutOutLinksBegin(
name=name,
in_links=map(lambda x: x.name, in_links),
seq_reversed=reverse)
in_args = []
for each_input in input:
if isinstance(each_input, StaticInput): # StaticInput
mem_name = "__%s_memory__" % each_input.input.name
mem = memory(
name=None,
size=each_input.input.size,
boot_layer=each_input.input)
mem.set_input(mem)
in_args.append(mem)
else:
in_args.append(each_input)
layer_outs = step(*in_args)
if isinstance(layer_outs, LayerOutput):
layer_outs = [layer_outs]
for layer_out in layer_outs:
assert isinstance(
layer_out, LayerOutput
), "Type of step function's return value must be LayerOutput."
layer_out.reverse = reverse
RecurrentLayerGroupSetOutLink(layer_out.name)
RecurrentLayerGroupEnd(name=name)
for layer_out in layer_outs:
# The previous full_name is the name inside the recurrent group.
# We need a full_name outside the recurrent group.
layer_out.full_name = MakeLayerNameInSubmodel(layer_out.name)
if len(layer_outs) == 1:
return layer_outs[0]
else:
return layer_outs
class BaseGeneratedInput(object):
def __init__(self):
self.bos_id = None
self.eos_id = None
def before_real_step(self):
raise NotImplementedError()
def after_real_step(self, *args):
raise NotImplementedError()
class GeneratedInput(BaseGeneratedInput):
def after_real_step(self, input):
if isinstance(input, LayerOutput):
input = [input]
elif isinstance(input, collections.Sequence):
input = list(input)
if len(input) > 1:
logger.info(
("More than one layers inside the recurrent_group "
"are returned as outputs of the entire recurrent_group "
"PLEASE garantee the first output is probability of "
"the predicted next word."))
return [maxid_layer(
input=input[0], name='__beam_search_predict__')] + (
input[1:] if len(input) > 1 else [])
def before_real_step(self):
predict_id = memory(
name='__beam_search_predict__',
size=self.size,
boot_with_const_id=self.bos_id)
trg_emb = embedding_layer(
input=predict_id,
size=self.embedding_size,
param_attr=ParamAttr(name=self.embedding_name))
return trg_emb
def __init__(self, size, embedding_name, embedding_size):
super(GeneratedInput, self).__init__()
self.size = size
self.embedding_name = embedding_name
self.embedding_size = embedding_size
@wrap_name_default()
def maxid_layer(input, name=None, layer_attr=None):
"""
A layer for finding the id which has the maximal value for each sample.
The result is stored in output.ids.
The example usage is:
.. code-block:: python
maxid = maxid_layer(input=layer)
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
l = Layer(
name=name,
type='maxid',
inputs=[input.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.MAXID_LAYER,
parents=[input],
size=l.config.size)
@wrap_name_default()
def dot_prod_layer(input1, input2, name=None, layer_attr=None):
"""
A layer for computing the dot product of two vectors.
The example usage is:
.. code-block:: python
dot_prod = dot_prod_layer(input1=vec1, input2=vec2)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input1: The first input layer.
:type input1: LayerOutput
:param input2: The second input layer.
:type input2: LayerOutput
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input1, LayerOutput)
assert isinstance(input2, LayerOutput)
assert input1.size == input2.size, ("Two inputs should have the same size.")
l = Layer(
name=name,
type=LayerType.DOT_PROD_LAYER,
inputs=[input1.name, input2.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.DOT_PROD_LAYER,
parents=[input1, input2],
size=l.config.size)
@wrap_name_default()
def out_prod_layer(input1, input2, name=None, layer_attr=None):
"""
A layer for computing the outer product of two vectors
The result is a matrix of size(input1) x size(input2)
The example usage is:
.. code-block:: python
out_prod = out_prod_layer(input1=vec1, input2=vec2)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input1: The first input layer.
:type input: LayerOutput
:param input2: The second input layer.
:type input2: LayerOutput
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input1, LayerOutput)
assert isinstance(input2, LayerOutput)
l = Layer(
name=name,
type=LayerType.OUT_PROD_LAYER,
inputs=[input1.name, input2.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.OUT_PROD_LAYER,
parents=[input1, input2],
size=l.config.size)
@wrap_name_default()
def eos_layer(input, eos_id, name=None, layer_attr=None):
"""
A layer for checking EOS for each sample:
- output_id = (input_id == conf.eos_id)
The result is stored in output\_.ids.
It is used by recurrent layer group.
The example usage is:
.. code-block:: python
eos = eos_layer(input=layer, eos_id=id)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param eos_id: End id of sequence
:type eos_id: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
l = Layer(
name=name,
type=LayerType.EOSID_LAYER,
eos_id=eos_id,
inputs=[input.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.EOSID_LAYER,
parents=[input],
size=l.config.size)
@wrap_name_default()
def beam_search(step,
input,
bos_id,
eos_id,
beam_size,
max_length=500,
name=None,
num_results_per_sample=None):
"""
Beam search is a heuristic search algorithm used in sequence generation.
It explores a graph by expanding the most promising nodes in a limited set
to maintain tractability.
The example usage is:
.. code-block:: python
def rnn_step(input):
last_time_step_output = memory(name='rnn', size=512)
with mixed_layer(size=512, name='rnn') as simple_rnn:
simple_rnn += full_matrix_projection(input)
simple_rnn += last_time_step_output
return simple_rnn
generated_word_embedding = GeneratedInput(
size=target_dictionary_dim,
embedding_name="target_language_embedding",
embedding_size=word_vector_dim)
beam_gen = beam_search(name="decoder",
step=rnn_step,
input=[StaticInput(encoder_last),
generated_word_embedding],
bos_id=0,
eos_id=1,
beam_size=5)
Please see the following demo for more details:
- machine translation : demo/seqToseq/translation/gen.conf \
demo/seqToseq/seqToseq_net.py
:param name: The name of the recurrent unit that is responsible for
generating sequences. It is optional.
:type name: basestring
:param step: A callable function that defines the calculation in a time
step, and it is applied to sequences with arbitrary length by
sharing a same set of weights.
You can refer to the first parameter of recurrent_group, or
demo/seqToseq/seqToseq_net.py for more details.
:type step: callable
:param input: Input data for the recurrent unit, which should include the
previously generated words as a GeneratedInput object.
In beam_search, none of the input's type should be LayerOutput.
:type input: list
:param bos_id: Index of the start symbol in the dictionary. The start symbol
is a special token for NLP task, which indicates the
beginning of a sequence. In the generation task, the start
symbol is essential, since it is used to initialize the RNN
internal state.
:type bos_id: int
:param eos_id: Index of the end symbol in the dictionary. The end symbol is
a special token for NLP task, which indicates the end of a
sequence. The generation process will stop once the end
symbol is generated, or a pre-defined max iteration number
is exceeded.
:type eos_id: int
:param max_length: Max generated sequence length.
:type max_length: int
:param beam_size: Beam search for sequence generation is an iterative search
algorithm. To maintain tractability, every iteration only
only stores a predetermined number, called the beam_size,
of the most promising next words. The greater the beam
size, the fewer candidate words are pruned.
:type beam_size: int
:param num_results_per_sample: Number of the generated results per input
sequence. This number must always be less than
beam size.
:type num_results_per_sample: int
:return: The generated word index.
:rtype: LayerOutput
"""
if num_results_per_sample is None:
num_results_per_sample = beam_size
if num_results_per_sample > beam_size:
logger.warning("num_results_per_sample should be less than beam_size")
if isinstance(input, StaticInput) or isinstance(input, BaseGeneratedInput):
input = [input]
generated_input_index = -1
real_input = []
for i, each_input in enumerate(input):
assert not isinstance(each_input, LayerOutput), (
"in beam_search, "
"none of the input should has a type of LayerOutput.")
if isinstance(each_input, BaseGeneratedInput):
assert generated_input_index == -1, ("recurrent_group accepts "
"only one GeneratedInput.")
generated_input_index = i
else:
real_input.append(each_input)
assert generated_input_index != -1, "No GeneratedInput is given."
gipt = input[generated_input_index]
gipt.bos_id = bos_id
gipt.eos_id = eos_id
def __real_step__(*args):
eos_name = "__%s_eos_layer__" % name
RecurrentLayerGroupSetGenerator(
Generator(
eos_layer_name=eos_name,
max_num_frames=max_length,
beam_size=beam_size,
num_results_per_sample=num_results_per_sample))
args = list(args)
args.insert(generated_input_index, gipt.before_real_step())
predict = gipt.after_real_step(step(*args))
eos_layer(input=predict[0], eos_id=eos_id, name=eos_name)
return predict
return recurrent_group(
step=__real_step__, input=real_input, reverse=False, name=name)
def __cost_input__(input, label, weight=None):
"""
inputs and parents for cost layers.
"""
if isinstance(input, LayerOutput):
input = [input]
if isinstance(label, LayerOutput):
label = [label]
ipts = [Input(ipt.name) for ipt in (input + label)]
parents = [ipt for ipt in (input + label)]
if weight is not None:
assert weight.size == 1
ipts.append(Input(weight.name))
parents.append(weight)
return ipts, parents
@wrap_name_default()
@layer_support()
def square_error_cost(input,
label,
weight=None,
name=None,
coeff=1.0,
layer_attr=None):
"""
sum of square error cost:
.. math::
cost = \\sum_{i=1}^N(t_i-y_i)^2
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The first input layer.
:type input: LayerOutput
:param label: The input label.
:type label: LayerOutput
:param weight: The weight layer defines a weight for each sample in the
mini-batch. It is optional.
:type weight: LayerOutput
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
ipts, parents = __cost_input__(input, label, weight)
Layer(
inputs=ipts,
type="square_error",
name=name,
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.COST, parents=parents, size=1)
regression_cost = square_error_cost
@wrap_name_default("cost")
@layer_support()
def classification_cost(input,
label,
weight=None,
name=None,
evaluator=classification_error_evaluator,
layer_attr=None,
coeff=1.):
"""
classification cost Layer.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The first input layer.
:type input: LayerOutput
:param label: The input label.
:type label: LayerOutput
:param weight: The weight layer defines a weight for each sample in the
mini-batch. It is optional.
:type weight: LayerOutput
:param evaluator: Evaluator method. classification_error_evaluator is the default.
:type evaluator: Evaluator method
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert input.layer_type != LayerType.DATA
assert isinstance(input.activation, SoftmaxActivation)
assert label.layer_type == LayerType.DATA
ipts, parents = __cost_input__(input, label, weight)
Layer(
name=name,
type="multi-class-cross-entropy",
inputs=ipts,
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
def __add_evaluator__(e):
assert callable(e)
assert hasattr(e, 'is_evaluator')
assert isinstance(e.is_evaluator, bool)
assert e.is_evaluator
assert hasattr(e, "for_classification")
assert isinstance(e.for_classification, bool)
assert e.for_classification
e(name=e.__name__, input=input, label=label, weight=weight)
if not isinstance(evaluator, collections.Sequence):
evaluator = [evaluator]
for each_evaluator in evaluator:
__add_evaluator__(each_evaluator)
return LayerOutput(name, LayerType.COST, parents=parents, size=1)
def conv_operator(img,
filter,
filter_size,
num_filters,
num_channels=None,
stride=1,
padding=0,
filter_size_y=None,
stride_y=None,
padding_y=None,
trans=False):
"""
Different from img_conv_layer, conv_op is an Operator, which can be used
in mixed_layer. And conv_op takes two inputs to perform convolution.
The first input is the image and the second is filter kernel. It only
supports GPU mode.
The example usage is:
.. code-block:: python
op = conv_operator(img=input1,
filter=input2,
filter_size=3,
num_filters=64,
num_channels=64)
:param img: The input image.
:type img: LayerOutput
:param filter: The input filter.
:type filter: LayerOutput
:param filter_size: The dimension of the filter kernel on the x axis.
:type filter_size: int
:param filter_size_y: The dimension of the filter kernel on the y axis.
If the parameter is not set or set to None, it will
set to 'filter_size' automatically.
:type filter_size_y: int
:param num_filters: The number of the output channels.
:type num_filters: int
:param num_channels: The number of the input channels. If the parameter is not set
or set to None, it will be automatically set to the channel
number of the 'img'.
:type num_channels: int
:param stride: The stride on the x axis.
:type stride: int
:param stride_y: The stride on the y axis. If the parameter is not set or
set to None, it will be set to 'stride' automatically.
:type stride_y: int
:param padding: The padding size on the x axis.
:type padding: int
:param padding_y: The padding size on the y axis. If the parameter is not set
or set to None, it will be set to 'padding' automatically.
:type padding_y: int
:return: A ConvOperator Object.
:rtype: ConvOperator
"""
if filter_size_y is None:
filter_size_y = filter_size
if stride_y is None:
stride_y = stride
if padding_y is None:
padding_y = padding
if num_channels is None:
num_channels = img.num_filters
assert isinstance(filter, LayerOutput)
assert filter.size is not None
opCls = ConvTransOperator if trans else ConvOperator
op = opCls(
input_layer_names=[img.name, filter.name],
num_filters=num_filters,
conv_conf=Conv(
filter_size=filter_size,
padding=padding,
stride=stride,
channels=num_channels,
filter_size_y=filter_size_y,
padding_y=padding_y,
stride_y=stride_y,
groups=1))
op.origin = [img, filter]
return op
@wrap_param_attr_default()
def conv_projection(input,
filter_size,
num_filters,
num_channels=None,
stride=1,
padding=0,
filter_size_y=None,
stride_y=None,
padding_y=None,
groups=1,
param_attr=None,
trans=False):
"""
Different from img_conv_layer and conv_op, conv_projection is a Projection,
which can be used in mixed_layer and concat_layer. It uses cudnn to implement
convolution and only supports GPU mode.
The example usage is:
.. code-block:: python
proj = conv_projection(input=input1,
filter_size=3,
num_filters=64,
num_channels=64)
:param input: The input of this layer.
:type input: LayerOutput
:param filter_size: The dimensions of the filter kernel. If the parameter is
set to one integer, the two dimensions on x and y axises
will be same when filter_size_y is not set. If it is set
to a list, the first element indicates the dimension on
the x axis, and the second is used to specify the dimension
on the y axis when filter_size_y is not provided.
:type filter_size: int | tuple | list
:param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
is not set, it will be set automatically according to filter_size.
:type filter_size_y: int
:param num_filters: The number of filters.
:type num_filters: int
:param num_channels: The number of the input channels.
:type num_channels: int
:param stride: The strides. If the parameter is set to one integer, the strides
on x and y axises will be same when stride_y is not set. If it is
set to a list, the first element indicates the stride on the x axis,
and the second is used to specify the stride on the y axis when
stride_y is not provided.
:type stride: int | tuple | list
:param stride_y: The stride on the y axis.
:type stride_y: int
:param padding: The padding sizes. If the parameter is set to one integer, the padding
sizes on x and y axises will be same when padding_y is not set. If it
is set to a list, the first element indicates the padding size on the
x axis, and the second is used to specify the padding size on the y axis
when padding_y is not provided.
:type padding: int | tuple | list
:param padding_y: The padding size on the y axis.
:type padding_y: int
:param groups: The group number.
:type groups: int
:param param_attr: The parameter attribute of the convolution. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param trans: Whether it is ConvTransProjection or ConvProjection
:type trans: bool
:return: A Projection Object.
:rtype: ConvTransProjection | ConvProjection
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if filter_size_y is None:
if isinstance(filter_size, collections.Sequence):
assert len(filter_size) == 2
filter_size, filter_size_y = filter_size
else:
filter_size_y = filter_size
if stride_y is None:
if isinstance(stride, collections.Sequence):
assert len(stride) == 2
stride, stride_y = stride
else:
stride_y = stride
if padding_y is None:
if isinstance(padding, collections.Sequence):
assert len(padding) == 2
padding, padding_y = padding
else:
padding_y = padding
if param_attr.attr.get('initial_smart'):
# special initial for conv layers.
init_w = (2.0 / (filter_size**2 * num_channels))**0.5
param_attr.attr["initial_mean"] = 0.0
param_attr.attr["initial_std"] = init_w
param_attr.attr["initial_strategy"] = 0
param_attr.attr["initial_smart"] = False
projCls = ConvTransProjection if trans else ConvProjection
proj = projCls(
input_layer_name=input.name,
num_filters=num_filters,
conv_conf=Conv(
filter_size=filter_size,
padding=padding,
stride=stride,
channels=num_channels,
filter_size_y=filter_size_y,
padding_y=padding_y,
stride_y=stride_y,
groups=groups),
**param_attr.attr)
proj.origin = input
return proj
@wrap_name_default("pad")
@layer_support()
def pad_layer(input,
pad_c=None,
pad_h=None,
pad_w=None,
name=None,
layer_attr=None):
"""
This operation pads zeros to the input data according to pad_c,pad_h
and pad_w. pad_c, pad_h, pad_w specify the size in the corresponding
dimension. And the input data shape is NCHW.
For example, pad_c=[2,3] means padding 2 zeros before the input data
and 3 zeros after the input data in the channel dimension. pad_h means
padding zeros in the height dimension. pad_w means padding zeros in the
width dimension.
For example,
.. code-block:: python
input(2,2,2,3) = [
[ [[1,2,3], [3,4,5]],
[[2,3,5], [1,6,7]] ],
[ [[4,3,1], [1,8,7]],
[[3,8,9], [2,3,5]] ]
]
pad_c=[1,1], pad_h=[0,0], pad_w=[0,0]
output(2,4,2,3) = [
[ [[0,0,0], [0,0,0]],
[[1,2,3], [3,4,5]],
[[2,3,5], [1,6,7]],
[[0,0,0], [0,0,0]] ],
[ [[0,0,0], [0,0,0]],
[[4,3,1], [1,8,7]],
[[3,8,9], [2,3,5]],
[[0,0,0], [0,0,0]] ]
]
The simply usage is:
.. code-block:: python
pad = pad_layer(input=ipt,
pad_c=[4,4],
pad_h=[0,0],
pad_w=[2,2])
:param input: The input of this layer.
:type input: LayerOutput
:param pad_c: The padding size in the channel dimension.
:type pad_c: list | None
:param pad_h: The padding size in the height dimension.
:type pad_h: list | None
:param pad_w: The padding size in the width dimension.
:type pad_w: list | None
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param name: The name of this layer. It is optional.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if pad_c is not None:
assert isinstance(pad_c, collections.Sequence) and len(pad_c) == 2
else:
pad_c = [0, 0]
if pad_h is not None:
assert isinstance(pad_h, collections.Sequence) and len(pad_h) == 2
else:
pad_h = [0, 0]
if pad_w is not None:
assert isinstance(pad_w, collections.Sequence) and len(pad_w) == 2
else:
pad_w = [0, 0]
assert input.num_filters is not None
in_ch = input.num_filters
out_ch = in_ch + pad_c[0] + pad_c[1]
l = Layer(
name=name,
type=LayerType.PAD_LAYER,
inputs=Input(
input.name,
pad=Pad(
channels=in_ch,
pad_c=pad_c,
pad_h=pad_h,
pad_w=pad_w, )),
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
layer_type=LayerType.PAD_LAYER,
parents=[input],
num_filters=out_ch,
size=l.config.size)
@wrap_name_default()
@layer_support()
def conv_shift_layer(a, b, name=None, layer_attr=None):
"""
This layer performs cyclic convolution on two inputs. For example:
- a[in]: contains M elements.
- b[in]: contains N elements (N should be odd).
- c[out]: contains M elements.
.. math::
c[i] = \sum_{j=-(N-1)/2}^{(N-1)/2}a_{i+j} * b_{j}
In this formula:
- a's index is computed modulo M. When it is negative, then get item from
the right side (which is the end of array) to the left.
- b's index is computed modulo N. When it is negative, then get item from
the right size (which is the end of array) to the left.
The example usage is:
.. code-block:: python
conv_shift = conv_shift_layer(a=layer1, b=layer2)
:param name: The name of this layer. It is optional.
:type name: basestring
:param a: The first input of this layer.
:type a: LayerOutput
:param b: The second input of this layer.
:type b: LayerOutput
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
assert b.size is None or b.size % 2 == 1 # size of b must be odd.
Layer(
name=name,
type=LayerType.CONV_SHIFT_LAYER,
inputs=[a.name, b.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.CONV_SHIFT_LAYER, parents=[a, b], size=a.size)
@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default(act=LinearActivation())
@layer_support(ERROR_CLIPPING, DROPOUT)
def tensor_layer(a,
b,
size,
act=None,
name=None,
param_attr=None,
bias_attr=None,
layer_attr=None):
"""
This layer performs tensor operation on two inputs.
For example:
.. math::
y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1
In this formular:
- :math:`a`: the first input contains M elements.
- :math:`b`: the second input contains N elements.
- :math:`y_{i}`: the i-th element of y.
- :math:`W_{i}`: the i-th learned weight, shape if [M, N]
- :math:`b^\mathrm{T}`: the transpose of :math:`b_{2}`.
The simple usage is:
.. code-block:: python
tensor = tensor_layer(a=layer1, b=layer2, size=1000)
:param name: The name of this layer. It is optional.
:type name: basestring
:param a: The first input of this layer.
:type a: LayerOutput
:param b: The second input of this layer.
:type b: LayerOutput
:param size: The dimension of this layer.
:type size: int
:param act: Activation type. LinearActivation is the default activation.
:type act: BaseActivation
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute,
no bias is defined. If this parameter is set to True,
the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
Layer(
name=name,
size=size,
type=LayerType.TENSOR_LAYER,
active_type=act.name,
bias=ParamAttr.to_bias(bias_attr),
inputs=[Input(a.name, **param_attr.attr), Input(b.name)],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.TENSOR_LAYER, parents=[a, b], activation=act, size=size)
@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default()
@layer_support(DROPOUT, ERROR_CLIPPING)
def selective_fc_layer(input,
size,
select=None,
act=None,
name=None,
pass_generation=False,
has_selected_colums=True,
mul_ratio=0.02,
param_attr=None,
bias_attr=None,
layer_attr=None):
"""
Selectived fully connected layer. Different from fc_layer, the output
of this layer can be sparse. It requires an additional input to indicate
several selected columns for output. If the selected columns is not
specified, selective_fc_layer acts exactly like fc_layer.
The simple usage is:
.. code-block:: python
sel_fc = selective_fc_layer(input=input, size=128, act=TanhActivation())
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput | list | tuple
:param select: The layer to select columns to output. It should be a sparse
binary matrix, and is treated as the mask of selective fc. If
it is not set or set to None, selective_fc_layer acts exactly
like fc_layer.
:type select: LayerOutput
:param size: The dimension of this layer, which should be equal to that of
the layer 'select'.
:type size: int
:param act: Activation type. TanhActivation is the default activation.
:type act: BaseActivation
:param pass_generation: The flag which indicates whether it is during generation.
:type pass_generation: bool
:param has_selected_colums: The flag which indicates whether the parameter 'select'
has been set. True is the default.
:type has_selected_colums: bool
:param mul_ratio: A ratio helps to judge how sparse the output is and determine
the computation method for speed consideration.
:type mul_ratio: float
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute,
no bias is defined. If this parameter is set to True,
the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
assert not isinstance(param_attr, collections.Sequence)
param_attr = [param_attr]
else:
if isinstance(param_attr, collections.Sequence):
assert len(input) == len(param_attr)
else:
if "parameter_name" in param_attr.attr and len(input) > 1:
logger.fatal(
"When the name field of param_attr is manually specified "
"and the input is a list, the param_attr should also be a "
"list with each item being the param_attr for each input "
"item. If only one named param_attr is provided, all the "
"input items would share this parameter.")
param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]
assert isinstance(input, collections.Sequence)
assert isinstance(select, LayerOutput)
if select.size is not None:
assert select.size == size
Layer(
inputs=[
Input(ipt.name, **attr.attr) for ipt, attr in zip(input, param_attr)
] + [select.name],
name=name,
type=LayerType.SEL_FC_LAYER,
size=size,
bias=ParameterAttribute.to_bias(bias_attr),
active_type=act.name,
selective_fc_pass_generation=pass_generation,
has_selected_colums=has_selected_colums,
selective_fc_full_mul_ratio=mul_ratio,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.SEL_FC_LAYER,
list(input) + [select],
activation=act,
size=size)
@wrap_name_default()
@layer_support()
def sampling_id_layer(input, name=None, layer_attr=None):
"""
A layer for sampling id from a multinomial distribution from the input layer.
Sampling one id for one sample.
The simple usage is:
.. code-block:: python
samping_id = sampling_id_layer(input=input)
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
l = Layer(
name=name,
type=LayerType.SAMPLING_ID_LAYER,
inputs=[Input(input.name)],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.SAMPLING_ID_LAYER, input, size=l.config.size)
@wrap_name_default()
@layer_support()
def slope_intercept_layer(input,
name=None,
slope=1.0,
intercept=0.0,
layer_attr=None):
"""
This layer for applying a slope and an intercept to the input.
.. math::
y = slope * x + intercept
The simple usage is:
.. code-block:: python
scale = slope_intercept_layer(input=input, slope=-1.0, intercept=1.0)
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param slope: The scale factor.
:type slope: float
:param intercept: The offset.
:type intercept: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.SLOPE_INTERCEPT_LAYER,
slope=slope,
intercept=intercept,
inputs=[Input(input.name)],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.SLOPE_INTERCEPT_LAYER, input, size=input.size)
@wrap_name_default()
@layer_support()
def linear_comb_layer(weights, vectors, size=None, name=None, layer_attr=None):
"""
A layer for weighted sum of vectors takes two inputs.
- Input: size of weights is M
size of vectors is M*N
- Output: a vector of size=N
.. math::
z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)
where :math:`0 \le i \le N-1`
Or in the matrix notation:
.. math::
z = x^\mathrm{T} Y
In this formular:
- :math:`x`: weights
- :math:`y`: vectors.
- :math:`z`: the output.
Note that the above computation is for one sample. Multiple samples are
processed in one batch.
The simple usage is:
.. code-block:: python
linear_comb = linear_comb_layer(weights=weight, vectors=vectors,
size=elem_dim)
:param weights: The weight layer.
:type weights: LayerOutput
:param vectors: The vector layer.
:type vectors: LayerOutput
:param size: The dimension of this layer.
:type size: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(weights, LayerOutput) and isinstance(vectors, LayerOutput)
if vectors.size is not None and weights.size is not None:
assert vectors.size % weights.size == 0
if size is None:
size = vectors.size / weights.size
else:
assert size == vectors.size / weights.size
Layer(
name=name,
type=LayerType.LINEAR_COMBINATION_LAYER,
size=size,
inputs=[Input(weights.name), Input(vectors.name)],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.LINEAR_COMBINATION_LAYER, [weights, vectors], size=size)
convex_comb_layer = linear_comb_layer
@wrap_name_default()
@layer_support()
def block_expand_layer(input,
block_x=0,
block_y=0,
stride_x=0,
stride_y=0,
padding_x=0,
padding_y=0,
num_channels=None,
name=None,
layer_attr=None):
"""
Expand feature map to minibatch matrix.
- matrix width is: block_y * block_x * num_channels
- matirx height is: outputH * outputW
.. math::
outputH = 1 + (2 * padding_y + imgSizeH - block_y + stride_y - 1) / stride_y
outputW = 1 + (2 * padding_x + imgSizeW - block_x + stride_x - 1) / stride_x
The expanding method is the same with ExpandConvLayer, but saved the transposed
value. After expanding, output.sequenceStartPositions will store timeline.
The number of time steps is outputH * outputW and the dimension of each
time step is block_y * block_x * num_channels. This layer can be used after
convolutional neural network, and before recurrent neural network.
The simple usage is:
.. code-block:: python
block_expand = block_expand_layer(input=layer,
num_channels=128,
stride_x=1,
stride_y=1,
block_x=1,
block_x=3)
:param input: The input of this layer.
:type input: LayerOutput
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param block_x: The width of sub block.
:type block_x: int
:param block_y: The width of sub block.
:type block_y: int
:param stride_x: The stride size in horizontal direction.
:type stride_x: int
:param stride_y: The stride size in vertical direction.
:type stride_y: int
:param padding_x: The padding size in horizontal direction.
:type padding_x: int
:param padding_y: The padding size in vertical direction.
:type padding_y: int
:param name: The name of this layer. It is optional.
:type name: basestring.
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
l = Layer(
name=name,
inputs=Input(
input.name,
block_expand=BlockExpand(
channels=num_channels,
block_x=block_x,
block_y=block_y,
stride_x=stride_x,
stride_y=stride_y,
padding_x=padding_x,
padding_y=padding_y)),
type=LayerType.BLOCK_EXPAND,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.BLOCK_EXPAND, parents=[input], size=l.config.size)
@wrap_name_default()
@layer_support()
def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None):
"""
A layer to do max out on convolutional layer output.
- Input: the output of a convolutional layer.
- Output: feature map size same as the input's, and its channel number is
(input channel) / groups.
So groups should be larger than 1, and the num of channels should be able
to be devided by groups.
Reference:
`Maxout Networks
<http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf>`_
`Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
<https://arxiv.org/pdf/1312.6082v4.pdf>`_
.. math::
& out = \max_k (in[n, k, o_c , s])
& out_{i * s + j} = \max_k in_{ k * o_{c} * s + i * s + j}
& s = \\frac{input.size}{ num\_channels}
& o_{c} = \\frac{num\_channels}{groups}
& 0 \le i < o_{c}
& 0 \le j < s
& 0 \le k < groups
The simple usage is:
.. code-block:: python
maxout = maxout_layer(input,
num_channels=128,
groups=4)
:param input: The input of this layer.
:type input: LayerOutput
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param groups: The group number of input layer.
:type groups: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input.activation, LinearActivation)
assert groups > 1
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
assert num_channels % groups == 0
l = Layer(
name=name,
inputs=Input(
input.name, maxout=MaxOut(
channels=num_channels, groups=groups)),
type=LayerType.MAXOUT,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.MAXOUT, parents=[input], size=l.config.size)
@wrap_name_default()
@layer_support()
def ctc_layer(input,
label,
size=None,
name=None,
norm_by_times=False,
layer_attr=None):
"""
Connectionist Temporal Classification (CTC) is designed for temporal
classication task. e.g. sequence labeling problems where the
alignment between the inputs and the target labels is unknown.
Reference:
`Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks
<http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf>`_
Note:
Considering the 'blank' label needed by CTC, you need to use (num_classes + 1)
as the size of the input, where num_classes is the category number.
And the 'blank' is the last category index. So the size of 'input' layer (e.g.
fc_layer with softmax activation) should be (num_classes + 1). The size of
ctc_layer should also be (num_classes + 1).
The example usage is:
.. code-block:: python
ctc = ctc_layer(input=input,
label=label,
size=9055,
norm_by_times=True)
:param input: The input of this layer.
:type input: LayerOutput
:param label: The input label.
:type label: LayerOutput
:param size: The dimension of this layer, which must be equal to (category number + 1).
:type size: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param norm_by_times: Whether to do normalization by times. False is the default.
:type norm_by_times: bool
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert isinstance(label, LayerOutput)
if label.size is not None:
if size is not None:
assert size == label.size + 1
else:
size = label.size + 1
Layer(
name=name,
type=LayerType.CTC_LAYER,
size=size,
norm_by_times=norm_by_times,
inputs=[input.name, label.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.CTC_LAYER, [input, label], size=size)
@wrap_name_default()
@layer_support()
def warp_ctc_layer(input,
label,
size=None,
name=None,
blank=0,
norm_by_times=False,
layer_attr=None):
"""
A layer intergrating the open-source `warp-ctc
<https://github.com/baidu-research/warp-ctc>`_ library, which is used in
`Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin
<https://arxiv.org/pdf/1512.02595v1.pdf>`_, to compute Connectionist Temporal
Classification (CTC) loss. Besides, another `warp-ctc
<https://github.com/gangliao/warp-ctc>`_ repository, which is forked from
the official one, is maintained to enable more compiling options. During the
building process, PaddlePaddle will clone the source codes, build and
install it to :code:`third_party/install/warpctc` directory.
Reference:
`Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks
<http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf>`_
Note:
- Let num_classes represents the category number. Considering the 'blank'
label needed by CTC, you need to use (num_classes + 1) as the size of
warp_ctc layer.
- You can set 'blank' to any value ranged in [0, num_classes], which
should be consistent with those used in your labels.
- As a native 'softmax' activation is interated to the warp-ctc library,
'linear' activation is expected to be used instead in the 'input' layer.
The example usage is:
.. code-block:: python
ctc = warp_ctc_layer(input=input,
label=label,
size=1001,
blank=1000,
norm_by_times=False)
:param input: The input of this layer.
:type input: LayerOutput
:param label: The input label.
:type label: LayerOutput
:param size: The dimension of this layer, which must be equal to (category number + 1).
:type size: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param blank: The 'blank' label used in ctc.
:type blank: int
:param norm_by_times: Whether to do normalization by times. False is the default.
:type norm_by_times: bool
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert isinstance(label, LayerOutput)
if label.size is not None:
if size is not None:
assert size == label.size + 1
else:
size = label.size + 1
Layer(
name=name,
type=LayerType.WARP_CTC_LAYER,
size=size,
blank=blank,
norm_by_times=norm_by_times,
inputs=[input.name, label.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.WARP_CTC_LAYER, parents=[input, label], size=size)
@wrap_name_default()
@wrap_param_attr_default()
@layer_support()
def crf_layer(input,
label,
size=None,
weight=None,
param_attr=None,
name=None,
coeff=1.0,
layer_attr=None):
"""
A layer for calculating the cost of sequential conditional random
field model.
The example usage is:
.. code-block:: python
crf = crf_layer(input=input,
label=label,
size=label_dim)
:param input: The first input layer.
:type input: LayerOutput
:param label: The input label.
:type label: LayerOutput
:param size: The category number.
:type size: int
:param weight: The weight layer defines a weight for each sample in the
mini-batch. It is optional.
:type weight: LayerOutput
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param name: The name of this layer. It is optional.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert isinstance(label, LayerOutput)
assert weight is None or isinstance(weight, LayerOutput)
if input.size is not None and label.size is not None:
assert input.size == label.size
if size is None:
size = input.size
else:
assert size == input.size
ipts = [Input(input.name, **param_attr.attr), Input(label.name)]
if weight is not None:
ipts.append(Input(weight.name))
Layer(
name=name,
type=LayerType.CRF_LAYER,
size=size,
inputs=ipts,
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
parents = [input, label]
if weight is not None:
parents.append(weight)
# The size for LayerOutput means the dimension of the output.
# It's different from the meaning of crf layer, which is the number of
# classes.
return LayerOutput(name, LayerType.CRF_LAYER, parents, size=1)
@wrap_name_default()
@wrap_param_attr_default()
@layer_support()
def crf_decoding_layer(input,
size,
label=None,
param_attr=None,
name=None,
layer_attr=None):
"""
A layer for calculating the decoding sequence of sequential conditional
random field model. The decoding sequence is stored in output.ids.
If the input 'label' is provided, it is treated as the ground-truth label, and
this layer will also calculate error. output.value[i] is 1 for an incorrect
decoding and 0 for the correct.
The example usage is:
.. code-block:: python
crf_decoding = crf_decoding_layer(input=input,
size=label_dim)
:param input: The first input layer.
:type input: LayerOutput
:param size: The dimension of this layer.
:type size: int
:param label: The input label.
:type label: LayerOutput | None
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert label is None or isinstance(label, LayerOutput)
ipts = [Input(input.name, **param_attr.attr)]
if label is not None:
ipts.append(Input(label.name))
Layer(
name=name,
type=LayerType.CRF_DECODING_LAYER,
size=size,
inputs=ipts,
**ExtraLayerAttribute.to_kwargs(layer_attr))
parents = [input]
if label is not None:
parents.append(label)
# The size for LayerOutput means the dimension of the output.
# It's different from the meaning of crf layer, which is the number of
# classes.
return LayerOutput(name, LayerType.CRF_DECODING_LAYER, parents, size=1)
"""
Following are cost Layers.
"""
@wrap_bias_attr_default(has_bias=True)
@wrap_param_attr_default()
@wrap_name_default()
@layer_support()
def nce_layer(input,
label,
num_classes=None,
param_attr=None,
weight=None,
num_neg_samples=10,
neg_distribution=None,
name=None,
bias_attr=None,
layer_attr=None):
"""
Noise-contrastive estimation.
Reference:
`A fast and simple algorithm for training neural probabilistic language
models. <https://www.cs.toronto.edu/~amnih/papers/ncelm.pdf>`_
The example usage is:
.. code-block:: python
cost = nce_layer(input=[layer1, layer2], label=layer2,
param_attr=[attr1, attr2], weight=layer3,
num_classes=3, neg_distribution=[0.1,0.3,0.6])
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The first input of this layer.
:type input: LayerOutput | list | tuple | collections.Sequence
:param label: The input label.
:type label: LayerOutput
:param weight: The weight layer defines a weight for each sample in the
mini-batch. It is optional.
:type weight: LayerOutput
:param num_classes: The number of classes.
:type num_classes: int
:param act: Activation type. SigmoidActivation is the default activation.
:type act: BaseActivation
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param num_neg_samples: The number of sampled negative labels. 10 is the
default value.
:type num_neg_samples: int
:param neg_distribution: The discrete noisy distribution over the output
space from which num_neg_samples negative labels
are sampled. If this parameter is not set, a
uniform distribution will be used. A user-defined
distribution is a list whose length must be equal
to the num_classes. Each member of the list defines
the probability of a class given input x.
:type neg_distribution: list | tuple | collections.Sequence | None
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute,
no bias is defined. If this parameter is set to True,
the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
assert not isinstance(param_attr, collections.Sequence)
param_attr = [param_attr]
else:
if isinstance(param_attr, collections.Sequence):
assert len(input) == len(param_attr)
else:
param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]
assert isinstance(input, collections.Sequence)
assert isinstance(label, LayerOutput)
assert label.layer_type == LayerType.DATA
if num_classes is None:
num_classes = label.size
if neg_distribution is not None:
assert isinstance(neg_distribution, collections.Sequence)
assert len(neg_distribution) == num_classes
assert abs(sum(neg_distribution) - 1.0) < 1e-5
ipts_for_layer = []
parents = []
for each_input, attr in zip(input, param_attr):
assert isinstance(each_input, LayerOutput)
ipts_for_layer.append(Input(each_input.name, **attr.attr))
parents.append(each_input)
ipts_for_layer.append(label.name)
parents.append(label)
if weight is not None:
assert isinstance(weight, LayerOutput)
assert weight.layer_type == LayerType.DATA
ipts_for_layer.append(weight.name)
parents.append(weight)
l = Layer(
name=name,
type=LayerType.NCE_LAYER,
num_classes=num_classes,
neg_sampling_dist=neg_distribution,
active_type=SigmoidActivation().name,
num_neg_samples=num_neg_samples,
inputs=ipts_for_layer,
bias=ParamAttr.to_bias(bias_attr),
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.NCE_LAYER,
parents=parents,
size=l.config.size,
activation=SigmoidActivation())
@wrap_name_default()
@layer_support()
def rank_cost(left,
right,
label,
weight=None,
name=None,
coeff=1.0,
layer_attr=None):
"""
A cost Layer for learning to rank using gradient descent.
Reference:
`Learning to Rank using Gradient Descent
<http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf>`_
.. math::
C_{i,j} & = -\\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}})
o_{i,j} & = o_i - o_j
\\tilde{P_{i,j}} & = \\{0, 0.5, 1\\} \ or \ \\{0, 1\\}
In this formula:
- :math:`C_{i,j}` is the cross entropy cost.
- :math:`\\tilde{P_{i,j}}` is the label. 1 means positive order
and 0 means reverse order.
- :math:`o_i` and :math:`o_j`: the left output and right output.
Their dimension is one.
The example usage is:
.. code-block:: python
cost = rank_cost(left=out_left,
right=out_right,
label=label)
:param left: The first input, the size of this layer is 1.
:type left: LayerOutput
:param right: The right input, the size of this layer is 1.
:type right: LayerOutput
:param label: Label is 1 or 0, means positive order and reverse order.
:type label: LayerOutput
:param weight: The weight layer defines a weight for each sample in the
mini-batch. It is optional.
:type weight: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert left.size == 1
assert right.size == 1
assert label.size == 1
ipts = [left.name, right.name, label.name]
parents = [left, right, label]
if weight is not None:
ipts.append(weight.name)
parents.append(weight)
Layer(
name=name,
type=LayerType.RANK_COST,
inputs=ipts,
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.RANK_COST, parents=parents, size=1)
@wrap_name_default()
@layer_support()
def lambda_cost(input,
score,
name,
NDCG_num=5,
max_sort_size=-1,
layer_attr=None):
"""
lambdaCost for lambdaRank LTR approach.
The example usage is:
.. code-block:: python
cost = lambda_cost(input=input,
score=score,
NDCG_num=8,
max_sort_size=-1)
:param input: The first input of this layer, which is often a document
samples list of the same query and whose type must be sequence.
:type input: LayerOutput
:param score: The scores of the samples.
:type input: LayerOutput
:param NDCG_num: The size of NDCG (Normalized Discounted Cumulative Gain),
e.g., 5 for NDCG@5. It must be less than or equal to the
minimum size of the list.
:type NDCG_num: int
:param max_sort_size: The size of partial sorting in calculating gradient. If
max_sort_size is equal to -1 or greater than the number
of the samples in the list, then the algorithm will sort
the entire list to compute the gradient. In other cases,
max_sort_size must be greater than or equal to NDCG_num.
:type max_sort_size: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput) and isinstance(score, LayerOutput)
if score.size is not None:
assert score.size == 1
Layer(
name=name,
type=LayerType.LAMBDA_COST,
inputs=[input.name, score.name],
NDCG_num=NDCG_num,
max_sort_size=max_sort_size,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.LAMBDA_COST, parents=[input, score], size=1)
@wrap_name_default()
@layer_support()
def cross_entropy(input,
label,
name=None,
coeff=1.0,
weight=None,
layer_attr=None):
"""
A loss layer for multi class entropy.
The example usage is:
.. code-block:: python
cost = cross_entropy(input=input_layer,
label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param weight: The weight layer defines a weight for each sample in the
mini-batch. It is optional.
:type weight: LayerOutout
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
ipts, parents = __cost_input__(input, label, weight)
Layer(
name=name,
type=LayerType.CROSS_ENTROPY,
inputs=ipts,
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1)
@wrap_name_default()
@layer_support()
def cross_entropy_with_selfnorm(input,
label,
name=None,
coeff=1.0,
softmax_selfnorm_alpha=0.1,
layer_attr=None):
"""
A loss layer for multi class entropy with selfnorm.
Input should be a vector of positive numbers, without normalization.
The example usage is:
.. code-block:: python
cost = cross_entropy_with_selfnorm(input=input_layer,
label=label_layer)
:param input: The first input layer.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param softmax_selfnorm_alpha: The scale factor affects the cost.
:type softmax_selfnorm_alpha: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.CROSS_ENTROPY_WITH_SELFNORM,
inputs=[input.name, label.name],
coeff=coeff,
softmax_selfnorm_alpha=softmax_selfnorm_alpha,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.CROSS_ENTROPY_WITH_SELFNORM,
parents=[input, label],
size=1)
@wrap_name_default()
@layer_support()
def sum_cost(input, name=None, layer_attr=None):
"""
A loss layer which calculates the sum of the input as loss.
The example usage is:
.. code-block:: python
cost = sum_cost(input=input_layer)
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
"""
assert isinstance(input, LayerOutput)
Layer(
name=name,
type=LayerType.SUM_COST,
inputs=[input.name],
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(name, LayerType.SUM_COST, parents=[input], size=1)
@wrap_name_default()
@layer_support()
def huber_regression_cost(input,
label,
name=None,
delta=1.0,
coeff=1.0,
layer_attr=None):
"""
In statistics, the Huber loss is a loss function used in robust regression,
that is less sensitive to outliers in data than the squared error loss.
Given a prediction f(x), a label y and :math:`\delta`, the loss function
is defined as:
.. math::
loss = 0.5*(y-f(x))^{2}, | y-f(x) | < \delta
loss = \delta | y-f(x) | - 0.5 \delta ^2, otherwise
The example usage is:
.. code-block:: python
cost = huber_regression_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param delta: The difference between the observed and predicted values.
:type delta: float
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
"""
assert isinstance(input, LayerOutput)
Layer(
name=name,
type=LayerType.HUBER_REGRESSION,
inputs=[input.name, label.name],
delta=delta,
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.HUBER_REGRESSION, parents=[input, label], size=1)
@wrap_name_default()
@layer_support()
def huber_classification_cost(input,
label,
name=None,
coeff=1.0,
layer_attr=None):
"""
For classification purposes, a variant of the Huber loss called modified Huber
is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
a true binary class label :math:`y\in \{-1, 1 \}`, the modified Huber
loss is defined as:
.. math:
loss = \max ( 0, 1-yf(x) )^2, yf(x) \geq -1
loss = -4yf(x), otherwise
The example usage is:
.. code-block:: python
cost = huber_classification_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
if input.size is not None:
assert input.size == 1
Layer(
name=name,
type=LayerType.HUBER_CLASSIFICATION,
inputs=[input.name, label.name],
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.HUBER_CLASSIFICATION, parents=[input, label], size=1)
@wrap_name_default()
@layer_support()
def multi_binary_label_cross_entropy(input,
label,
name=None,
coeff=1.0,
layer_attr=None):
"""
A loss layer for multi binary label cross entropy.
The example usage is:
.. code-block:: python
cost = multi_binary_label_cross_entropy(input=input_layer,
label=label_layer)
:param input: The first input layer.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if input.activation is None or \
not isinstance(input.activation, SigmoidActivation):
logger.log(logging.WARN,
("%s is not a recommended activation for "
"multi_binary_label_cross_entropy, sigmoid is better") %
repr(input.activation))
Layer(
name=name,
type=LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,
inputs=[input.name, label.name],
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,
parents=[input, label],
size=1)
class BeamInput(object):
"""
Define the input for cross_entropy_over_beam layer.
A beam is made up of a triple: the first one is scores over all
candidates; the second one is indices of top k selected candidates; the
third one is the index of ground truth, which is also always called
gold.
"""
def __init__(self, candidate_scores, selected_candidates, gold):
assert isinstance(candidate_scores, LayerOutput)
self.candidate_scores = candidate_scores
assert candidate_scores.size == 1
assert isinstance(selected_candidates, LayerOutput)
self.selected_candidates = selected_candidates
assert isinstance(gold, LayerOutput)
self.gold = gold
@wrap_name_default()
@layer_support()
def cross_entropy_over_beam(input, name=None):
"""
This layer is used in learning to search models, which is to solve complex
joint prediction problems based on learning to search through a
problem-defined search space.
Specifically, the learning to search process for this layer begins with
searching a target sequence from a nested sequence. In the first search
step, top beam size sequences with highest scores, indices of these top k
sequences in the original nested sequence, and the ground truth (also
called gold) altogether (a triple) make up of the first beam.
Then, several special positions, for example, start and end positions
that define meaningful segments are searched. In these searches, top k
positions with highest scores are selected, and then sequence, starting
from the selected starts till ends of the sequences (or a fixed position)
are taken to search next.
We call the possible top k results returned in one search the beam. This
search process can be repeated for pre-defined turns and leads to several
beam expansions.
Finally, the layer cross_entropy_over_beam takes all the beam expansions
which contain several candidate targets found along the multi-step search.
cross_entropy_over_beam calculates cross entropy over the expanded beams
which all the candidates in the beam as the normalized factor.
Note that, if gold falls off the beam at search step t, then the cost is
calculated over the beam at step t.
This cost layer always works together with kmax_seq_score_layer,
sub_nested_seq_layer, and sequence_slice_layer to trim the input to form a
sub-search space.
The example usage is:
.. code-block:: python
cost = cross_entropy_over_beam(input=[
BeamInput(
candidate_scores=beam1_candidates,
selected_candidates=beam1_topk,
gold=gold1),
BeamInput(
candidate_scores=beam2_candidates,
selected_candidates=beam2_topk,
gold=gold2),
])
:param input: Input beams for this layer.
:type input: BeamInput
:param name: The name of this layer. It is optional.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, BeamInput):
input = [input]
else:
assert isinstance(input, list), (
'input for cross_entropy_over_beam shold be a python list '
'of BeamInput object.')
for ipt in input:
assert isinstance(ipt, BeamInput), (
'input for cross_entropy_over_beam '
'should be a BeamInput object.')
ipts = []
parents = []
for beam in input:
parents += [beam.candidate_scores, beam.selected_candidates, beam.gold]
ipts += [
beam.candidate_scores.name, beam.selected_candidates.name,
beam.gold.name
]
Layer(name=name, type=LayerType.CROSS_ENTROPY_OVER_BEAM, inputs=ipts)
return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1)
@wrap_name_default()
@layer_support()
def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
"""
This is a L1 loss but more smooth. It requires that the
sizes of input and label are equal. The formula is as follows,
.. math::
L = \sum_{i} smooth_{L1}(input_i - label_i)
in which
.. math::
smooth_{L1}(x) = \\begin{cases} 0.5x^2& \\text{if} \\ |x| < 1 \\\\ |x|-0.5& \\text{otherwise} \end{cases}
Reference:
`Fast R-CNN
<https://arxiv.org/pdf/1504.08083v2.pdf>`_
The example usage is:
.. code-block:: python
cost = smooth_l1_cost(input=input_layer,
label=label_layer)
:param input: The input layer.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default value.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert isinstance(label, LayerOutput)
assert input.size == label.size
Layer(
name=name,
type=LayerType.SMOOTH_L1,
inputs=[input.name, label.name],
coeff=coeff,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.SMOOTH_L1, parents=[input, label], size=1)
@wrap_name_default()
def multiplex_layer(input, name=None, layer_attr=None):
"""
This layer multiplex multiple layers according to the indexes,
which are provided by the first input layer.
inputs[0]: the indexes of the layers to form the output of size batchSize.
inputs[1:N]; the candidate output data.
For each index i from 0 to batchSize - 1, the i-th row of the output is the
the same to the i-th row of the (index[i] + 1)-th layer.
For each i-th row of output:
.. math::
y[i][j] = x_{x_{0}[i] + 1}[i][j], j = 0,1, ... , (x_{1}.width - 1)
where, y is output. :math:`x_{k}` is the k-th input layer and
:math:`k = x_{0}[i] + 1`.
The example usage is:
.. code-block:: python
maxid = multiplex_layer(input=layers)
:param input: Input layers.
:type input: list of LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, collections.Sequence)
assert len(input) > 2, 'multiplex_layer should have more than 2 inputs'
for i in range(1, len(input)):
assert isinstance(input[i], LayerOutput)
assert input[i].size == input[1].size, \
"All the input layers except the first one should have the same size"
l = Layer(
name=name,
type='multiplex',
inputs=[x.name for x in input],
size=input[1].size,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.MULTIPLEX_LAYER,
parents=input,
size=l.config.size)
@wrap_name_default("dropout")
def dropout_layer(input, dropout_rate, name=None):
"""
The example usage is:
.. code-block:: python
dropout = dropout_layer(input=input_layer, dropout_rate=0.5)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param dropout_rate: The probability of dropout.
:type dropout_rate: float
:return: LayerOutput object.
:rtype: LayerOutput
"""
return addto_layer(
name=name,
input=input,
act=LinearActivation(),
bias_attr=False,
layer_attr=ExtraAttr(drop_rate=dropout_rate))
@wrap_name_default()
@wrap_act_default(act=LinearActivation())
@wrap_param_attr_default()
@layer_support(DROPOUT)
def row_conv_layer(input,
context_len,
act=None,
name=None,
param_attr=None,
layer_attr=None):
"""
The row convolution is called lookahead convolution. It is firstly
introduced in paper of `Deep Speech 2: End-to-End Speech Recognition
in English and Mandarin <https://arxiv.org/pdf/1512.02595v1.pdf>`_ .
The bidirectional RNN that learns representation for a sequence by
performing a forward and a backward pass through the entire sequence.
However, unlike unidirectional RNNs, bidirectional RNNs are challenging
to deploy in an online and low-latency setting. The lookahead convolution
incorporates information from future subsequences in a computationally
efficient manner to improve unidirectional RNNs.
The connection of row convolution is different from the 1D sequence
convolution. Assumed that, the future context-length is k, that is to say,
it can get the output at timestep t by using the the input feature from t-th
timestep to (t+k+1)-th timestep. Assumed that the hidden dim of input
activations are d, the activations r_t for the new layer at time-step t are:
.. math::
r_{t,r} = \sum_{j=1}^{k + 1} {w_{i,j}h_{t+j-1, i}}
\quad \\text{for} \quad (1 \leq i \leq d)
Note:
The `context_len` is `k + 1`. That is to say, the lookahead step
number plus one equals context_len.
.. code-block:: python
row_conv = row_conv_layer(input=input_layer, context_len=3)
:param input: The input of this layer.
:type input: LayerOutput
:param context_len: The context length equals the lookahead step number
plus one.
:type context_len: int
:param act: Activation Type. LinearActivation is the default activation.
:type act: BaseActivation
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert context_len > 0, "the context_len must be greatet than 0."
Layer(
inputs=[Input(input.name, **param_attr.attr)],
name=name,
context_length=context_len,
type=LayerType.ROW_CONV_LAYER,
active_type=act.name,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.ROW_CONV_LAYER, input, activation=act, size=input.size)
@layer_support()
@wrap_name_default()
def prelu_layer(input,
name=None,
partial_sum=1,
channel_shared=None,
num_channels=None,
param_attr=None,
layer_attr=None):
"""
The Parametric Relu activation that actives outputs with a learnable weight.
Reference:
`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification <http://arxiv.org/pdf/1502.01852v1.pdf>`_
.. math::
z_i &\\quad if \\quad z_i > 0 \\\\
a_i * z_i &\\quad \\mathrm{otherwise}
The example usage is:
.. code-block:: python
prelu = prelu_layer(input=layers, partial_sum=1)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param partial_sum: this parameter makes a group of inputs share the same weight.
- partial_sum = 1, indicates the element-wise activation: each element has a weight.
- partial_sum = number of elements in one channel, indicates the channel-wise activation, elements in a channel share the same weight.
- partial_sum = number of outputs, indicates all elements share the same weight.
:type partial_sum: int
:param channel_shared: whether or not the parameter are shared across channels.
- channel_shared = True, we set the partial_sum to the number of outputs.
- channel_shared = False, we set the partial_sum to the number of elements in one channel.
:type channel_shared: bool
:param num_channels: number of input channel.
:type num_channels: int
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), 'prelu_layer accepts only one input.'
if not param_attr:
param_attr = ParamAttr(initial_mean=0.25, initial_std=0.0)
else:
assert isinstance(param_attr, ParameterAttribute)
if num_channels is None:
assert input.num_filters is not None, \
'the input channel cannot be detected, please specify the num_channels parameter'
num_channels = input.num_filters
if channel_shared is not None:
assert isinstance(channel_shared, bool)
assert (input.height != 0 and input.width != 0), \
'input height and widht must be setted'
if channel_shared:
partial_sum = input.height * input.width * num_channels
else:
partial_sum = input.height * input.width
l = Layer(
name=name,
type=LayerType.PRELU,
inputs=Input(input.name, **param_attr.attr),
partial_sum=partial_sum,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.PRELU,
parents=input,
num_filters=num_channels,
size=l.config.size)
@wrap_name_default()
@layer_support(ERROR_CLIPPING, DROPOUT)
@wrap_act_default(act=LinearActivation())
def gated_unit_layer(input,
size,
act=None,
name=None,
gate_attr=None,
gate_param_attr=None,
gate_bias_attr=True,
inproj_attr=None,
inproj_param_attr=None,
inproj_bias_attr=True,
layer_attr=None):
"""
The gated unit layer implements a simple gating mechanism over the input.
The input :math:`X` is first projected into a new space :math:`X'`, and
it is also used to produce a gate weight :math:`\sigma`. Element-wise
product between :math:`X'` and :math:`\sigma` is finally returned.
Reference:
`Language Modeling with Gated Convolutional Networks
<https://arxiv.org/abs/1612.08083>`_
.. math::
y=\\text{act}(X \cdot W + b)\otimes \sigma(X \cdot V + c)
The example usage is:
.. code-block:: python
gated_unit = gated_unit_layer(size=128, input=input_layer))
:param input: The input of this layer.
:type input: LayerOutput
:param size: The dimension of this layer's output.
:type size: int
:param act: Activation type of the projection. LinearActivation is the default
activation.
:type act: BaseActivation
:param name: The name of this layer. It is optional.
:type name: basestring
:param gate_attr: The extra layer attribute of the gate. See ExtraLayerAttribute for
details.
:type gate_attr: ExtraLayerAttribute | None
:param gate_param_attr: The parameter attribute of the gate. See ParameterAttribute
for details.
:type gate_param_attr: ParameterAttribute
:param gate_bias_attr: The bias attribute of the gate. If this parameter is set to False or
an object whose type is not ParameterAttribute, no bias is defined.
If this parameter is set to True, the bias is initialized to zero.
:type gate_bias_attr: ParameterAttribute | bool | None | Any
:param inproj_attr: Extra layer attributes of the projection. See ExtraLayerAttribute for
details.
:type inproj_attr: ExtraLayerAttribute | None
:param inproj_param_attr: The parameter attribute of the projection. See ParameterAttribute
for details.
:type inproj_param_attr: ParameterAttribute
:param inproj_bias_attr: The bias attribute of the projection. If this parameter is set to False
or an object whose type is not ParameterAttribute, no bias is defined.
If this parameter is set to True, the bias is initialized to zero.
:type inproj_bias_attr: ParameterAttribute | bool | None | Any
:param layer_attr: Extra layer attribute of the product. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(
input, LayerOutput), 'The gated linear unit accepts only one input.'
input_proj = fc_layer(
input=input,
name="%s_input_proj" % name,
size=size,
act=act,
layer_attr=inproj_attr,
param_attr=inproj_param_attr,
bias_attr=inproj_bias_attr)
gate = fc_layer(
size=size,
name="%s_gate" % name,
act=SigmoidActivation(),
input=input,
layer_attr=gate_attr,
param_attr=gate_param_attr,
bias_attr=gate_bias_attr)
return mixed_layer(
name="%s_gated_act" % name,
input=dotmul_operator(input_proj, gate),
layer_attr=layer_attr)
@layer_support()
@wrap_name_default('switch_order')
def switch_order_layer(input,
name=None,
reshape_axis=None,
act=None,
layer_attr=None):
"""
This layer switch dimension order of image input.
From order "batchSize, channels, height, width"
to order "batchSize, height, width, channels".
The example usage is:
.. code-block:: python
reshape_axis = 3
switch = switch_order(input=layer, name='switch', reshape_axis=reshape_axis)
reshape = {'height':[ 0, 1, 2], 'width':[3]}
:param input: The input of this layer.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param reshape_axis: Specify the axises of 'height'. Its value should be positive and less than 4.
:type reshape_axis: int
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert reshape_axis != None and (reshape_axis > 0 and reshape_axis < 4)
height = [ele for ele in xrange(reshape_axis)]
width = [ele for ele in range(reshape_axis, 4)]
reshape = {'height': height, 'width': width}
l = Layer(
name=name,
inputs=input.name,
reshape=reshape,
type=LayerType.SWITCH_ORDER_LAYER,
active_type=act.name,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.SWITCH_ORDER_LAYER,
activation=act,
parents=input,
size=l.config.size)
@wrap_name_default()
@layer_support()
def crop_layer(input, offset, axis=2, shape=None, name=None, layer_attr=None):
"""
This layer crops images according to the offset and shape. Users can set
the crop shape through the argument 'shape' explicitly or by specifying a
reference input layer.
The example usage is:
.. code-block:: python
crop = crop_layer(input=[image_input, reference_input], axis=2, offset=[2, 3])
:param input: The input of this layer. If two inputs are given, the second one
will be regarded as the reference.
And the input must be 4-dims and in NCHW order.
:type input: LayerOutput | Sequence
:param offset: The crop offset.
:type offset: Sequence
:param axis: The start axis to be cropped. For image input layer:
- 0: batch size
- 1: channels
- 2: height
- 3: width
:type axis: int
:param shape: The shape to be cropped to. Default is None.
:type shape: Sequence | None
:param name: The name of this layer. It is optional.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
else:
assert isinstance(input, collections.Sequence)
l = Layer(
inputs=[x.name for x in input],
axis=axis,
offset=offset,
shape=shape,
name=name,
type=LayerType.CROP_LAYER,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.CROP_LAYER,
parents=input,
size=l.config.size)
@wrap_name_default()
@layer_support()
def sub_nested_seq_layer(input, selected_indices, name=None):
"""
The sub_nested_seq_layer accepts two inputs: the first one is a nested
sequence; the second one is a set of selceted indices in the nested sequence.
Then sub_nest_seq_layer trims the first nested sequence input according
to the selected indices to form a new output. This layer is useful in
beam training.
The example usage is:
.. code-block:: python
sub_nest_seq = sub_nested_seq_layer(input=data, selected_indices=selected_ids)
:param input: The input of this layer. It is a nested sequence.
:type input: LayerOutput
:param selected_indices: A set of sequence indices in the nested sequence.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), (
'The first input of '
'sub_nested_seq_layer must be a Paddle layer.')
assert isinstance(selected_indices, LayerOutput), (
'The second input of '
'sub_nested_seq_layer must be a Paddle layer.')
l = Layer(
inputs=input.name,
selected_indices=selected_indices.name,
name=name,
type=LayerType.SUB_NESTED_SEQ)
return LayerOutput(
name=name,
layer_type=LayerType.SUB_NESTED_SEQ,
parents=input,
size=l.config.size)
@wrap_name_default("clip")
def clip_layer(input, min, max, name=None):
"""
A layer for clipping the input value by the threshold.
.. math::
out[i] = \min (\max (in[i],p_{1} ),p_{2} )
.. code-block:: python
clip = clip_layer(input=input_layer, min=-10, max=10)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput.
:param min: The lower threshold for clipping.
:type min: float
:param max: The upper threshold for clipping.
:type max: float
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.CLIP_LAYER,
inputs=[input.name],
min=min,
max=max)
return LayerOutput(
name, LayerType.CLIP_LAYER, parents=[input], size=input.size)
@wrap_name_default()
def seq_slice_layer(input, starts, ends, name=None):
"""
seq_slice_layer will return one or several sub-sequences from the
input sequence layer given start and end indices.
- If only start indices are given, and end indices are set to None,
this layer slices the input sequence from the given start indices
to its end.
- If only end indices are given, and start indices are set to None,
this layer slices the input sequence from its beginning to the
given end indices.
- If start and end indices are both given, they should have the same
number of elements.
If start or end indices contains more than one elements, the input sequence
will be sliced for multiple times.
.. code-block:: python
seq_silce = seq_slice_layer(input=input_seq,
starts=start_pos, ends=end_pos)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer, which should be a sequence.
:type input: LayerOutput
:param starts: The start indices to slice the input sequence.
:type starts: LayerOutput | None
:param ends: The end indices to slice the input sequence.
:type ends: LayerOutput | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), (
'The first input of seq_slice layer must be a PaddlePaddle layer.')
if starts is not None:
assert isinstance(starts, LayerOutput), (
'The start indices for seq_slice layer '
'must be a PaddlePaddle layer.')
if ends is not None:
assert isinstance(ends, LayerOutput), (
'The end indices for seq_slice layer must be a PaddlePaddle layer.')
assert starts is not None or ends is not None, (
'start and end indices '
'cannot be set to None at the same time, at least one of '
'them should be given.')
if starts is not None and ends is not None:
assert starts.size == ends.size, (
'If start and end indices are both given to seq_slice_layer, '
'they should have the same width.')
Layer(
name=name,
type=LayerType.SEQ_SLICE,
inputs=input.name,
starts=starts.name if starts is not None else None,
ends=ends.name if ends is not None else None)
return LayerOutput(
name, LayerType.SEQ_SLICE, parents=[input], size=input.size)
@wrap_name_default()
@layer_support()
def kmax_seq_score_layer(input, name=None, beam_size=1):
"""
This layer accepts one input which is scores over a sequence or a nested
sequence, and returns indices of beam_size sequences with highest scores.
.. code-block:: python
kmax_indices = kmax_seq_score_layer(input=input_layer, beam_size)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer. It stores scores over a sequence or
a nested sequence and its size must be 1.
:type input: LayerOutput
:param beam_size: The indices of the sequences with top beam_size scores are returned.
:type beam_size: int
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), ("kmax_seq_score_layer "
"accepts only one input.")
assert input.size == 1, (
"input of kmax_seq_score_layer is a score "
"over a sequence or a nested sequence, so its width must be 1.")
Layer(
name=name,
type=LayerType.KMAX_SEQ_SCORE,
inputs=[input.name],
beam_size=beam_size)
return LayerOutput(
name, LayerType.KMAX_SEQ_SCORE, parents=[input], size=input.size)
@wrap_name_default("conv3d")
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default(act=ReluActivation())
@layer_support(DROPOUT)
def img_conv3d_layer(input,
filter_size,
num_filters,
name=None,
num_channels=None,
act=None,
groups=1,
stride=1,
padding=0,
bias_attr=None,
param_attr=None,
shared_biases=True,
layer_attr=None,
trans=False,
layer_type=None):
"""
The example usage is:
.. code-block:: python
conv = img_conv3d_layer(input=data, filter_size=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,
act=ReluActivation())
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param filter_size: The dimensions of the filter kernel along three axises. If the parameter
is set to one integer, the three dimensions will be same.
:type filter_size: int | tuple | list
:param num_filters: The number of filters. It is as same as the output image channel.
:type num_filters: int
:param act: Activation type. ReluActivation is the default activation.
:type act: BaseActivation
:param groups: The number of the filter groups.
:type groups: int
:param stride: The strides of the convolution along three axises. If the parameter
is set to one integer, the three strides will be same.
:type stride: int | tuple | list
:param padding: The numbers of padding along three axises. If the parameter is set to
one integer, they will be same.
:type padding: int | tuple | list
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param param_attr: The parameter attribute of the convolution. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param shared_biases: Whether biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr: The extra layer attributes. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param trans: True if it is a convTransLayer, False if it is a convLayer
:type trans: bool
:param layer_type: Specify the layer type. If the parameter is set, it must be "deconv3d"
when trans=True. If not set, it will be automatically set to "deconv3d"
when trans=True and "conv3d" when trans=False.
:type layer_type: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if isinstance(filter_size, collections.Sequence):
assert len(filter_size) == 3
filter_size, filter_size_y, filter_size_z = filter_size
else:
filter_size_y = filter_size
filter_size_z = filter_size
if isinstance(stride, collections.Sequence):
assert len(stride) == 3
stride, stride_y, stride_z = stride
else:
stride_y = stride
stride_z = stride
if isinstance(padding, collections.Sequence):
assert len(padding) == 3
padding, padding_y, padding_z = padding
else:
padding_y = padding
padding_z = padding
if param_attr.attr.get('initial_smart'):
# special initial for conv layers.
init_w = (2.0 / (filter_size**2 * num_channels))**0.5
param_attr.attr["initial_mean"] = 0.0
param_attr.attr["initial_std"] = init_w
param_attr.attr["initial_strategy"] = 0
param_attr.attr["initial_smart"] = False
if layer_type:
if trans:
assert layer_type in ["deconv3d"]
lt = layer_type
else:
lt = LayerType.DECONV3D_LAYER if trans else LayerType.CONV3D_LAYER
l = Layer(
name=name,
inputs=Input(
input.name,
conv=Conv3D(
filter_size=filter_size,
padding=padding,
stride=stride,
channels=num_channels,
groups=groups,
filter_size_y=filter_size_y,
padding_y=padding_y,
stride_y=stride_y,
filter_size_z=filter_size_z,
padding_z=padding_z,
stride_z=stride_z),
**param_attr.attr),
active_type=act.name,
num_filters=num_filters,
bias=ParamAttr.to_bias(bias_attr),
shared_biases=shared_biases,
type=lt,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
lt,
parents=[input],
activation=act,
num_filters=num_filters,
size=l.config.size)
@wrap_name_default("scale_shift")
@wrap_param_attr_default()
@wrap_bias_attr_default()
def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None):
"""
A layer applies a linear transformation to each element in each row of
the input matrix. For each element, the layer first re-scales it and then
adds a bias to it.
This layer is very like the SlopeInterceptLayer, except the scale and
bias are trainable.
.. math::
y = w * x + b
.. code-block:: python
scale_shift = scale_shift_layer(input=input_layer, bias_attr=False)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param param_attr: The parameter attribute of scaling. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer(
name=name,
type=LayerType.SCALE_SHIFT_LAYER,
inputs=Input(input.name, **param_attr.attr),
bias=ParamAttr.to_bias(bias_attr))
return LayerOutput(
name, LayerType.SCALE_SHIFT_LAYER, parents=[input], size=input.size)
@wrap_name_default("resize")
def resize_layer(input, size, name=None):
"""
The resize layer resizes the input matrix with a shape of [Height, Width]
into the output matrix with a shape of [Height x Width / size, size],
where size is the parameter of this layer indicating the output dimension.
:param input: The input of this layer.
:type input: LayerOutput.
:param name: The name of this layer. It is optional.
:type name: basestring
:param size: The resized output dimension of this layer.
:type size: int
:return: A LayerOutput object.
:rtype: LayerOutput
"""
Layer(name=name, type=LayerType.RESIZE, inputs=Input(input.name), size=size)
return LayerOutput(name, LayerType.RESIZE, parents=[input], size=input.size)
@wrap_act_default(act=LinearActivation())
@wrap_name_default('sub_seq')
def sub_seq_layer(input, offsets, sizes, act=None, bias_attr=None, name=None):
"""
sub_seq_layer will return sub-sequences from the input sequences. For each
sequence in the input sequence layer, sub_seq_layer will slice it by given
offset and size. Please notice that, number of offset value and size value
both are equal to the number of sequence in the input layer.
.. code-block:: python
sub_seq = sub_seq_layer(input=input_seq, offsets=offsets, sizes=sizes)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer, which should be sequence.
:type input: LayerOutput
:param offsets: The offset indices to slice the input sequence, which should
be sequence type.
:type offsets: LayerOutput
:param sizes: The sizes of the sub-sequences, which should be sequence type.
:type sizes: LayerOutput
:param act: Activation type, LinearActivation is the default activation.
:type act: BaseActivation.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), (
'The first input of sub_seq_layer layer must be a PaddlePaddle layer.')
assert isinstance(offsets, LayerOutput), (
'The offset indices for sub_seq_layer, '
'must be a PaddlePaddle layer.')
assert isinstance(sizes, LayerOutput), (
'The sizes of sub-sequences, must be a PaddlePaddle layer.')
Layer(
name=name,
type=LayerType.SUB_SEQ_LAYER,
inputs=[input.name, offsets.name, sizes.name],
active_type=act.name,
bias=ParamAttr.to_bias(bias_attr))
return LayerOutput(
name,
LayerType.SUB_SEQ_LAYER,
parents=[input, offsets, sizes],
size=input.size)
@wrap_name_default('scale_sub_region')
def scale_sub_region_layer(input, indices, value, name=None):
"""
Given an image or feature map with CHW information, scale_sub_region_layer
can be used to multiply a real value to values of a sub continuous region.
You can provide start and end indices of CHW for each instance.
Please notice that all start indices are counting from 1.
The shape of indices should be [batch_size, 6] and the layout for each row
is [C_Start, C_End, H_Start, H_End, W_Start, W_End].
.. code-block:: python
scale_sub_region = scale_sub_region_layer(input=input,
indices=indices,
value=value)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer which should contains CHW information.
:type input: LayerOutput
:param indices: Start index and end index for C H W, the input value should
be a 2-D matrix with shape [batch_size, 6].
:type indices: LayerOutput.
:param value: value to multiply.
:type value: float
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), (
'The first input of scale_sub_region_layer, '
'must be a PaddlePaddle layer.')
assert isinstance(indices, LayerOutput), (
'The start and end indices for CHW, must be a PaddlePaddle layer.')
assert isinstance(value, float), (
'The value to multiply, must be a real value.')
Layer(
name=name,
type=LayerType.SCALE_SUB_REGION_LAYER,
inputs=[input.name, indices.name],
value=value)
return LayerOutput(
name,
LayerType.SCALE_SUB_REGION_LAYER,
parents=[input, indices],
num_filters=input.num_filters,
size=input.size)
@wrap_name_default()
@wrap_act_default(act=LinearActivation())
@wrap_param_attr_default()
@layer_support()
def factorization_machine(input,
factor_size,
act=None,
name=None,
param_attr=None,
layer_attr=None):
"""
The Factorization Machine models pairwise feature interactions as inner
product of the learned latent vectors corresponding to each input feature.
The Factorization Machine can effectively capture feature interactions
especially when the input is sparse.
This implementation only consider the 2-order feature interactions using
Factorization Machine with the formula:
.. math::
y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \\rangle x_i x_j
Note:
X is the input vector with size n. V is the factor matrix. Each row of V
is the latent vector corresponding to each input dimesion. The size of
each latent vector is k.
For details of Factorization Machine, please refer to the paper:
Factorization machines.
.. code-block:: python
first_order = paddle.layer.fc(input=input,
size=1,
act=paddle.activation.Linear())
second_order = paddle.layer.factorization_machine(input=input,
factor_size=10)
fm = paddle.layer.addto(input=[first_order, second_order],
act=paddle.activation.Linear(),
bias_attr=False)
:param input: The input layer. Supported input types: all input data types
on CPU, and only dense input types on GPU.
:type input: LayerOutput
:param factor_size: The hyperparameter that defines the dimensionality of
the latent vector size.
:type context_len: int
:param act: Activation Type. Default is linear activation.
:type act: BaseActivation
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param layer_attr: Extra Layer config.
:type layer_attr: ExtraLayerAttribute|None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert factor_size > 0, "the factor_size must be greater than 0."
Layer(
inputs=[Input(input.name, **param_attr.attr)],
name=name,
factor_size=factor_size,
type=LayerType.FACTORIZATION_MACHINE,
active_type=act.name,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name, LayerType.FACTORIZATION_MACHINE, input, activation=act, size=1)
| 266,912 | 34.069373 | 142 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/default_decorators.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import inspect
from .attrs import ParamAttr
from .activations import TanhActivation
from paddle.trainer.config_parser import *
__all__ = [
'wrap_name_default', 'wrap_param_attr_default', 'wrap_bias_attr_default',
'wrap_act_default', 'wrap_param_default'
]
def __default_not_set_callback__(kwargs, name):
return name not in kwargs or kwargs[name] is None
def wrap_param_default(param_names=None,
default_factory=None,
not_set_callback=__default_not_set_callback__):
assert param_names is not None
assert isinstance(param_names, list) or isinstance(param_names, tuple)
for each_param_name in param_names:
assert isinstance(each_param_name, basestring)
def __impl__(func):
@functools.wraps(func)
def __wrapper__(*args, **kwargs):
if len(args) != 0:
argspec = inspect.getargspec(func)
num_positional = len(argspec.args)
if argspec.defaults:
num_positional -= len(argspec.defaults)
if not argspec.varargs and len(args) > num_positional:
logger.fatal(
"Must use keyword arguments for non-positional args")
for name in param_names:
if not_set_callback(kwargs, name): # Not set
kwargs[name] = default_factory(func)
return func(*args, **kwargs)
if hasattr(func, 'argspec'):
__wrapper__.argspec = func.argspec
else:
__wrapper__.argspec = inspect.getargspec(func)
return __wrapper__
return __impl__
class DefaultNameFactory(object):
def __init__(self, name_prefix):
self.__counter__ = 0
self.__name_prefix__ = name_prefix
def __call__(self, func):
if self.__name_prefix__ is None:
self.__name_prefix__ = func.__name__
tmp = "__%s_%d__" % (self.__name_prefix__, self.__counter__)
self.__check_name__(tmp)
self.__counter__ += 1
return tmp
def __check_name__(self, nm):
"""
@TODO(yuyang18): Implement it!
@param nm:
@return:
"""
pass
def reset(self):
self.__counter__ = 0
_name_factories = []
def reset_hook():
for factory in _name_factories:
factory.reset()
register_parse_config_hook(reset_hook)
def wrap_name_default(name_prefix=None, name_param="name"):
"""
Decorator to set "name" arguments default to "{name_prefix}_{invoke_count}".
.. code:: python
@wrap_name_default("some_name")
def func(name=None):
print name # name will never be None. If name is not set,
# name will be "some_name_%d"
:param name_prefix: name prefix. wrapped function's __name__ if None.
:type name_prefix: basestring
:return: a decorator to set default name
:rtype: callable
"""
factory = DefaultNameFactory(name_prefix)
_name_factories.append(factory)
return wrap_param_default([name_param], factory)
def wrap_param_attr_default(param_names=None, default_factory=None):
"""
Setting Default Parameter Attributes Decorator.
:param default_factory:
:param param_names: Parameter Attribute's Names, list of string
:type param_names: list
:return: decorator
"""
if param_names is None:
param_names = ['param_attr']
if default_factory is None:
default_factory = lambda _: ParamAttr()
return wrap_param_default(param_names, default_factory)
def wrap_bias_attr_default(param_names=None,
default_factory=None,
has_bias=True):
if param_names is None:
param_names = ['bias_attr']
if default_factory is None:
default_factory = lambda _: ParamAttr(initial_std=0., initial_mean=0.)
def __bias_attr_not_set__(kwargs, name):
if has_bias:
return name not in kwargs or kwargs[name] is None or \
kwargs[name] == True
else:
return name in kwargs and kwargs[name] == True
return wrap_param_default(param_names, default_factory,
__bias_attr_not_set__)
def wrap_act_default(param_names=None, act=None):
if param_names is None:
param_names = ["act"]
if act is None:
act = TanhActivation()
return wrap_param_default(param_names, lambda _: act)
| 5,117 | 30.018182 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/layer_math.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .layers import LayerOutput, mixed_layer, identity_projection, \
slope_intercept_layer, scaling_layer, repeat_layer
from .attrs import is_compatible_with
from .default_decorators import *
import activations as act
from paddle.trainer.config_parser import logger
__all__ = []
def register_unary_math_op(op_name, act):
def op(input, name=None):
return mixed_layer(
input=[identity_projection(input=input)], name=name, act=act)
op = wrap_name_default(op_name)(op)
op.__doc__ = type(act).__doc__
globals()[op_name] = op
__all__.append(op_name)
register_unary_math_op('exp', act.ExpActivation())
register_unary_math_op('log', act.LogActivation())
register_unary_math_op('abs', act.AbsActivation())
register_unary_math_op('sigmoid', act.SigmoidActivation())
register_unary_math_op('tanh', act.TanhActivation())
register_unary_math_op('square', act.SquareActivation())
register_unary_math_op('relu', act.ReluActivation())
register_unary_math_op('sqrt', act.SqrtActivation())
register_unary_math_op('reciprocal', act.ReciprocalActivation())
def add(layeroutput, other):
if is_compatible_with(other, float):
return slope_intercept_layer(input=layeroutput, intercept=other)
if not isinstance(other, LayerOutput):
logger.fatal("LayerOutput can only be added with"
" another LayerOutput or a number")
if layeroutput.size == other.size:
return mixed_layer(input=[
identity_projection(input=layeroutput),
identity_projection(input=other)
])
if other.size != 1 and layeroutput.size != 1:
logger.fatal("Two LayerOutput can be added only if they have equal size"
" or one of their sizes is 1. sizes are %s and %s" %
(layeroutput.size, other.size))
elif layeroutput.size == 1:
tmp = layeroutput
layeroutput = other
other = tmp
other = repeat_layer(other, layeroutput.size)
return mixed_layer(input=[
identity_projection(input=layeroutput), identity_projection(input=other)
])
LayerOutput.__radd__ = add
LayerOutput.__add__ = add
def sub(layeroutput, other):
if is_compatible_with(other, float):
return slope_intercept_layer(input=layeroutput, intercept=-other)
if not isinstance(other, LayerOutput):
logger.fatal("LayerOutput can only be subtracted with"
" another Layeroutput or a number")
neg = slope_intercept_layer(input=other, slope=-1.0)
return add(layeroutput, neg)
LayerOutput.__sub__ = sub
def rsub(layeroutput, other):
neg = slope_intercept_layer(input=layeroutput, slope=-1.0)
return add(neg, other)
LayerOutput.__rsub__ = rsub
def mul(layeroutput, other):
if is_compatible_with(other, float):
return slope_intercept_layer(input=layeroutput, slope=other)
if not isinstance(other, LayerOutput):
logger.fatal("LayerOutput can only be multiplied with"
" another Layeroutput or a number")
elif layeroutput.size == 1:
return scaling_layer(input=other, weight=layeroutput)
elif other.size == 1:
return scaling_layer(input=layeroutput, weight=other)
else:
logger.fatal("At least one of the operand of '*' must be a number"
" or a LayerOutput with size=1")
LayerOutput.__mul__ = mul
LayerOutput.__rmul__ = mul
| 4,029 | 34.350877 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/activations.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = [
"TanhActivation", "SigmoidActivation", "SoftmaxActivation",
"IdentityActivation", "LinearActivation", 'SequenceSoftmaxActivation',
'ExpActivation', "ReluActivation", "BReluActivation", "SoftReluActivation",
"STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation",
"LogActivation", "SqrtActivation", "ReciprocalActivation",
"SoftSignActivation"
]
class BaseActivation(object):
"""
A mark for activation class.
Each activation inherit BaseActivation, which has two parameters.
:param name: activation name in paddle config.
:type name: basestring
:param support_hppl: True if supported by hppl. HPPL is a library used by paddle
internally. Currently, lstm layer can only use activations
supported by hppl.
:type support_hppl: bool
"""
def __init__(self, name, support_hppl):
self.name = name
self.support_hppl = support_hppl
def __repr__(self):
return self.name
class TanhActivation(BaseActivation):
"""
Tanh activation.
.. math::
f(z)=tanh(z)=\\frac{e^z-e^{-z}}{e^z+e^{-z}}
"""
def __init__(self):
BaseActivation.__init__(self, 'tanh', True)
class SigmoidActivation(BaseActivation):
"""
Sigmoid activation.
.. math::
f(z) = \\frac{1}{1+exp(-z)}
"""
def __init__(self):
BaseActivation.__init__(self, 'sigmoid', True)
class SoftmaxActivation(BaseActivation):
"""
Softmax activation for simple input
.. math::
P(y=j|x) = \\frac{e^{x_j}} {\\sum^K_{k=1} e^{x_k} }
"""
def __init__(self):
BaseActivation.__init__(self, 'softmax', False)
class SequenceSoftmaxActivation(BaseActivation):
"""
Softmax activation for one sequence. The dimension of input feature must be
1 and a sequence.
.. code:: python
result = softmax(for each_feature_vector[0] in input_feature)
for i, each_time_step_output in enumerate(output):
each_time_step_output = result[i]
"""
def __init__(self):
BaseActivation.__init__(self, 'sequence_softmax', False)
class IdentityActivation(BaseActivation):
"""
Identity Activation.
Just do nothing for output both forward/backward.
"""
def __init__(self):
BaseActivation.__init__(self, '', False)
LinearActivation = IdentityActivation
class ReluActivation(BaseActivation):
"""
Relu activation.
forward. :math:`y = max(0, z)`
derivative:
.. math::
1 &\\quad if z > 0 \\\\
0 &\\quad\\mathrm{otherwize}
"""
def __init__(self):
BaseActivation.__init__(self, 'relu', True)
class BReluActivation(BaseActivation):
"""
BRelu Activation.
forward. :math:`y = min(24, max(0, z))`
derivative:
.. math::
1 &\\quad if 0 < z < 24 \\\\
0 &\\quad \\mathrm{otherwise}
"""
def __init__(self):
BaseActivation.__init__(self, 'brelu', False)
class SoftReluActivation(BaseActivation):
"""
SoftRelu Activation.
"""
def __init__(self):
BaseActivation.__init__(self, 'softrelu', False)
class STanhActivation(BaseActivation):
"""
Scaled Tanh Activation.
.. math::
f(z) = 1.7159 * tanh(2/3*z)
"""
def __init__(self):
BaseActivation.__init__(self, 'stanh', False)
class AbsActivation(BaseActivation):
"""
Abs Activation.
Forward: :math:`f(z) = abs(z)`
Derivative:
.. math::
1 &\\quad if \\quad z > 0 \\\\
-1 &\\quad if \\quad z < 0 \\\\
0 &\\quad if \\quad z = 0
"""
def __init__(self):
BaseActivation.__init__(self, 'abs', False)
class SquareActivation(BaseActivation):
"""
Square Activation.
.. math::
f(z) = z^2.
"""
def __init__(self):
BaseActivation.__init__(self, 'square', False)
class ExpActivation(BaseActivation):
"""
Exponential Activation.
.. math::
f(z) = e^z.
"""
def __init__(self):
BaseActivation.__init__(self, 'exponential', False)
class LogActivation(BaseActivation):
"""
Logarithm Activation.
.. math::
f(z) = log(z)
"""
def __init__(self):
BaseActivation.__init__(self, 'log', False)
class SqrtActivation(BaseActivation):
"""
Square Root Activation.
.. math::
f(z) = sqrt(z)
"""
def __init__(self):
BaseActivation.__init__(self, 'sqrt', False)
class ReciprocalActivation(BaseActivation):
"""
Reciprocal Activation.
.. math::
f(z)=\\frac{1}{z}
"""
def __init__(self):
BaseActivation.__init__(self, 'reciprocal', False)
class SoftSignActivation(BaseActivation):
"""
SoftSign Activation.
.. math::
f(z)=\\frac{z}{1 + |z|}
"""
def __init__(self):
BaseActivation.__init__(self, 'softsign', False)
| 5,582 | 20.147727 | 84 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/__init__.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from activations import *
from data_sources import *
from poolings import *
from evaluators import *
from layers import *
from networks import *
from optimizers import *
from attrs import *
from config_parser_utils import *
# This will enable operator overload for LayerOutput
import layer_math
| 905 | 33.846154 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/config_parser_utils.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import paddle.trainer.config_parser as config_parser
from paddle.proto.TrainerConfig_pb2 import OptimizationConfig
'''
This file is a wrapper of formal config_parser. The main idea of this file is to
separete different config logic into different function, such as network configuration
and optimizer configuration.
'''
__all__ = [
"parse_trainer_config", "parse_network_config", "parse_optimizer_config",
"reset_parser"
]
def parse_trainer_config(trainer_conf, config_arg_str):
return config_parser.parse_config(trainer_conf, config_arg_str)
def parse_network_config(network_conf, config_arg_str=''):
config = config_parser.parse_config(network_conf, config_arg_str)
return config.model_config
def parse_optimizer_config(optimizer_conf, config_arg_str=''):
config_parser.settings = copy.deepcopy(config_parser.DEFAULT_SETTING)
optimizer_conf()
opt_config = OptimizationConfig()
for k, v in config_parser.settings.iteritems():
if v is None:
continue
opt_config.__setattr__(k, v)
return opt_config
def reset_parser():
config_parser.begin_parse()
| 1,749 | 32.653846 | 86 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/poolings.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
"""
__all__ = [
"BasePoolingType", "MaxPooling", "AvgPooling", "MaxWithMaskPooling",
"CudnnMaxPooling", "CudnnAvgPooling", "CudnnAvgInclPadPooling",
"SumPooling", "SquareRootNPooling"
]
class BasePoolingType(object):
"""
Base Pooling Type.
Note these pooling types are used for sequence input, not for images.
Each PoolingType contains one parameter:
:param name: pooling layer type name used by paddle.
:type name: basestring
"""
def __init__(self, name):
self.name = name
class MaxPooling(BasePoolingType):
"""
Max pooling.
Return the very large values for each dimension in sequence or time steps.
.. math::
max(samples\\_of\\_a\\_sequence)
:param output_max_index: True if output sequence max index instead of max
value. None means use default value in proto.
:type output_max_index: bool|None
"""
def __init__(self, output_max_index=None):
BasePoolingType.__init__(self, "max")
self.output_max_index = output_max_index
class MaxWithMaskPooling(BasePoolingType):
"""
MaxWithMask pooling.
Not only return the very large values for each dimension in sequence or time steps,
but also the location indices of found maxinum values.
"""
def __init__(self):
BasePoolingType.__init__(self, "max-pool-with-mask")
class CudnnMaxPooling(BasePoolingType):
"""
Cudnn max pooling only support GPU. Return the maxinum value in the
pooling window.
"""
def __init__(self):
BasePoolingType.__init__(self, "cudnn-max-pool")
class CudnnAvgPooling(BasePoolingType):
"""
Cudnn average pooling only support GPU. Return the average value in the
pooling window.
"""
def __init__(self):
BasePoolingType.__init__(self, "cudnn-avg-pool")
class CudnnAvgInclPadPooling(BasePoolingType):
"""
Cudnn average pooling only support GPU. Return the average value in the
pooling window taking into account the padding cells.
"""
def __init__(self):
BasePoolingType.__init__(self, "cudnn-avg-incl-pad-pool")
class AvgPooling(BasePoolingType):
"""
Average pooling.
Return the average values for each dimension in sequence or time steps.
.. math::
sum(samples\\_of\\_a\\_sequence)/sample\\_num
"""
STRATEGY_AVG = "average"
STRATEGY_SUM = "sum"
STRATEGY_SQROOTN = "squarerootn"
def __init__(self, strategy=STRATEGY_AVG):
BasePoolingType.__init__(self, "average")
self.strategy = strategy
class SumPooling(AvgPooling):
"""
Sum pooling.
Return the sum values of each dimension in sequence or time steps.
.. math::
sum(samples\\_of\\_a\\_sequence)
"""
def __init__(self):
AvgPooling.__init__(self, AvgPooling.STRATEGY_SUM)
class SquareRootNPooling(AvgPooling):
"""
Square Root Pooling.
Return the square root values of each dimension in sequence or time steps.
.. math::
sum(samples\\_of\\_a\\_sequence)/sqrt(sample\\_num)
"""
def __init__(self):
AvgPooling.__init__(self, AvgPooling.STRATEGY_SQROOTN)
| 3,816 | 24.61745 | 87 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/attrs.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.config_parser import *
__all__ = [
'HookAttr', 'ParamAttr', 'ExtraAttr', 'ParameterAttribute',
'ExtraLayerAttribute'
]
def convert_and_compare(x, Type):
"""
Convert x to be the same type as Type and then convert back to
check whether there is a loss of information
:param x: object to be checked
:param Type: target type to check x over
"""
return type(x)(Type(x)) == x
def is_compatible_with(x, Type):
"""
Check if x has a type compatible with Type
:param x: object to be checked
:param Type: target type to check x over
"""
if type(x) == Type:
return True
try:
if float == Type or int == Type:
# avoid those types that can be converted to float/int but not very
# meaningful and could potentially lead to error
# i.e., str and bool typed value should not be used for initializing float/int variable
if not isinstance(x, str) and not isinstance(x, bool):
return convert_and_compare(x, Type)
elif bool == Type:
# should not use string type to initialize bool variable
if not isinstance(x, str):
return convert_and_compare(x, Type)
else:
return False
except:
return False
class HookAttribute(object):
"""
Hook Attribute object. As a member of ParameterAttribute class, the hook is an auxiliary operation that occurs
during training process of a layer with parameters, such as img_conv layer, fc layer.
:param type: Hook type, currently supported types:
'pruning' : user specify a sparsity_ratio before training started, and the
network will prune the parameters based on the sparsity_ratio.
eg: The definition of Hook object can be hk = HookAttribute('pruning', 0.6)
The specific usage can be paddle.layer.img_conv(input=img, filter_size=3,
num_channels=3, num_filters=64,
param_attr=ParameterAttribute(update_hooks=hk) )
The pruning details can be found https://arxiv.org/pdf/1506.02626.pdf
:type type: string
:param sparsity_ratio: Must be specified if hook type is 'pruning',
it represents the ratio of the zero elements to be set by the Parameter.
:type sparsity_ratio: float or None
"""
def __init__(self, type, sparsity_ratio=None):
self.type = type
self.sparsity_ratio = sparsity_ratio
if self.sparsity_ratio is not None:
assert is_compatible_with(
self.sparsity_ratio,
float), 'sparisity_ratio must be float type'
assert self.sparsity_ratio <= 1 and self.sparsity_ratio >= 0, 'sparsity_ratio must be a float between [0, 1] '
def __call__(self):
return ParameterHook(self.type, sparsity_ratio=self.sparsity_ratio)
class ParameterAttribute(object):
"""
Parameter Attributes object. To fine-tuning network training process, user
can set attribute to control training details, such as l1,l2 rate / learning
rate / how to init param.
NOTE: IT IS A HIGH LEVEL USER INTERFACE.
:param is_static: True if this parameter will be fixed while training.
:type is_static: bool
:param initial_std: Gauss Random initialization standard deviation.
None if not using Gauss Random initialize parameter.
:type initial_std: float or None
:param initial_mean: Gauss Random initialization mean.
None if not using Gauss Random initialize parameter.
:type initial_mean: float or None
:param initial_max: Uniform initialization max value.
:type initial_max: float or None
:param initial_min: Uniform initialization min value.
:type initial_min: float or None
:param l1_rate: the l1 regularization factor
:type l1_rate: float or None
:param l2_rate: the l2 regularization factor
:type l2_rate: float or None
:param learning_rate: The parameter learning rate. None means 1.
The learning rate when optimize is LEARNING_RATE =
GLOBAL_LEARNING_RATE * PARAMETER_LEARNING_RATE
* SCHEDULER_FACTOR.
:type learning_rate: float or None
:param momentum: The parameter momentum. None means use global value.
:type momentum: float or None
:param gradient_clipping_threshold: gradient clipping threshold. If gradient
value larger than some value, will be
clipped.
:type gradient_clipping_threshold: float
:param sparse_update: Enable sparse update for this parameter. It will
enable both local and remote sparse update.
:type sparse_update: bool
:param update_hooks: A HookAttribute object.
:type update_hooks: HookAttribute
:param initializer: If not None, it should be a callable object which accepts
a parameter name and returns numpy array for the initial
value of the parameter
:type initializer: callable object
"""
def __init__(self,
name=None,
is_static=False,
initial_std=None,
initial_mean=None,
initial_max=None,
initial_min=None,
l1_rate=None,
l2_rate=None,
learning_rate=None,
momentum=None,
gradient_clipping_threshold=None,
sparse_update=False,
update_hooks=None,
initializer=None):
self.attr = {}
if is_static:
self.attr['is_static'] = True
if initial_std is None and initial_mean is None and initial_max \
is None and initial_min is None:
self.attr['initial_smart'] = True
elif is_compatible_with(initial_std, float) or \
is_compatible_with(initial_mean, float):
if initial_std is not None:
self.attr['initial_std'] = initial_std
if initial_mean is not None:
self.attr['initial_mean'] = initial_mean
self.attr['initial_strategy'] = 0 # Gauss Random
elif is_compatible_with(initial_max, float) and \
is_compatible_with(initial_min, float):
initial_max = initial_max
initial_min = initial_min
assert initial_min < initial_max
initial_mean = (initial_max + initial_min) / 2
initial_std = initial_mean - initial_min
self.attr['initial_mean'] = initial_mean
self.attr['initial_std'] = initial_std
self.attr['initial_strategy'] = 1 # Uniform Random
else:
raise RuntimeError("Unexpected branch.")
if not is_static and is_compatible_with(l1_rate, float):
self.attr['decay_rate_l1'] = l1_rate
if not is_static and is_compatible_with(l2_rate, float):
self.attr['decay_rate'] = l2_rate
if not is_static and is_compatible_with(learning_rate, float):
self.attr['learning_rate'] = learning_rate
if not is_static and is_compatible_with(momentum, float):
self.attr['momentum'] = momentum
if name is not None:
self.attr['parameter_name'] = name
if sparse_update:
self.attr['sparse_update'] = True
self.attr['sparse_remote_update'] = True
if gradient_clipping_threshold is not None and \
is_compatible_with(gradient_clipping_threshold, float):
self.attr['gradient_clipping_threshold'] = \
gradient_clipping_threshold
if initializer is not None:
self.attr['initializer'] = initializer
if update_hooks:
self.attr['update_hooks'] = update_hooks
def set_default_parameter_name(self, name):
"""
Set default parameter name. If parameter not set, then will use default
parameter name.
:param name: default parameter name.
:type name: basestring
"""
if 'parameter_name' not in self.attr:
self.attr['parameter_name'] = name
@staticmethod
def to_bias(bias_attr):
if isinstance(bias_attr, ParameterAttribute):
return Bias(**bias_attr.attr)
else:
return False
class ExtraLayerAttribute(object):
"""
Some high level layer attributes config. You can set all attributes here,
but some layer doesn't support all attributes. If you set an attribute to a
layer that not support this attribute, paddle will print an error and core.
:param error_clipping_threshold: Error clipping threshold.
:type error_clipping_threshold: float
:param drop_rate: Dropout rate. Dropout will create a mask on layer output.
The dropout rate is the zero rate of this mask. The
details of what dropout is please refer to `here
<https://www.cs.toronto.edu/~hinton/absps/
JMLRdropout.pdf>`_.
:type drop_rate: float
:param device: device ID of layer. device=-1, use CPU. device>=0, use GPU.
The details allocation in parallel_nn please refer to `here
<http://www.paddlepaddle.org/doc/ui/cmd_argument/
use_case.html#case-2-specify-layers-in-different-devices>`_.
:type device: int
"""
def __init__(self,
error_clipping_threshold=None,
drop_rate=None,
device=None):
self.attr = dict()
if error_clipping_threshold is not None:
error_clipping_threshold = float(error_clipping_threshold)
if error_clipping_threshold < 0:
raise ValueError("Error clipping must > 0")
self.attr['error_clipping_threshold'] = error_clipping_threshold
if drop_rate is not None:
drop_rate = float(drop_rate)
if drop_rate < 0:
raise ValueError("Dropout rate must > 0")
self.attr["drop_rate"] = drop_rate
if isinstance(device, int):
self.attr["device"] = device
def check(self, layer_name):
for key in self.attr:
if not hasattr(self, 'can_%s' % key) or \
not getattr(self, 'can_%s' % key):
raise NotImplementedError("Layer %s does not support %s" %
(layer_name, key))
@staticmethod
def to_kwargs(attr):
if attr is None:
return dict()
else:
return attr.attr
HookAttr = HookAttribute
ParamAttr = ParameterAttribute
ExtraAttr = ExtraLayerAttribute
| 11,697 | 39.199313 | 122 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/data_sources.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Data Sources are helpers to define paddle training data or testing data.
"""
from paddle.trainer.config_parser import *
from .utils import deprecated
try:
import cPickle as pickle
except ImportError:
import pickle
__all__ = ['define_py_data_sources2']
def define_py_data_source(file_list,
cls,
module,
obj,
args=None,
async=False,
data_cls=PyData):
"""
Define a python data source.
For example, the simplest usage in trainer_config.py as follow:
.. code-block:: python
define_py_data_source("train.list", TrainData, "data_provider", "process")
Or. if you want to pass arguments from trainer_config to data_provider.py, then
.. code-block:: python
define_py_data_source("train.list", TrainData, "data_provider", "process",
args={"dictionary": dict_name})
:param data_cls:
:param file_list: file list name, which contains all data file paths
:type file_list: basestring
:param cls: Train or Test Class.
:type cls: TrainData or TestData
:param module: python module name.
:type module: basestring
:param obj: python object name. May be a function name if using
PyDataProviderWrapper.
:type obj: basestring
:param args: The best practice is using dict to pass arguments into
DataProvider, and use :code:`@init_hook_wrapper` to
receive arguments.
:type args: string or picklable object
:param async: Load Data asynchronously or not.
:type async: bool
:return: None
:rtype: None
"""
if isinstance(file_list, list):
file_list_name = 'train.list'
if cls == TestData:
file_list_name = 'test.list'
with open(file_list_name, 'w') as f:
f.writelines(file_list)
file_list = file_list_name
if not isinstance(args, basestring) and args is not None:
args = pickle.dumps(args, 0)
cls(
data_cls(
files=file_list,
load_data_module=module,
load_data_object=obj,
load_data_args=args,
async_load_data=async))
def define_py_data_sources(train_list,
test_list,
module,
obj,
args=None,
train_async=False,
data_cls=PyData):
"""
The annotation is almost the same as define_py_data_sources2, except that
it can specific train_async and data_cls.
:param data_cls:
:param train_list: Train list name.
:type train_list: basestring
:param test_list: Test list name.
:type test_list: basestring
:param module: python module name. If train and test is different, then
pass a tuple or list to this argument.
:type module: basestring or tuple or list
:param obj: python object name. May be a function name if using
PyDataProviderWrapper. If train and test is different, then pass
a tuple or list to this argument.
:type obj: basestring or tuple or list
:param args: The best practice is using dict() to pass arguments into
DataProvider, and use :code:`@init_hook_wrapper` to receive
arguments. If train and test is different, then pass a tuple
or list to this argument.
:type args: string or picklable object or list or tuple.
:param train_async: Is training data load asynchronously or not.
:type train_async: bool
:return: None
:rtype: None
"""
def __is_splitable__(o):
return (isinstance(o, list) or
isinstance(o, tuple)) and hasattr(o, '__len__') and len(o) == 2
assert train_list is not None or test_list is not None
assert module is not None and obj is not None
test_module = module
train_module = module
if __is_splitable__(module):
train_module, test_module = module
test_obj = obj
train_obj = obj
if __is_splitable__(obj):
train_obj, test_obj = obj
if args is None:
args = ""
train_args = args
test_args = args
if __is_splitable__(args):
train_args, test_args = args
if train_list is not None:
define_py_data_source(train_list, TrainData, train_module, train_obj,
train_args, train_async, data_cls)
if test_list is not None:
define_py_data_source(test_list, TestData, test_module, test_obj,
test_args, False, data_cls)
def define_py_data_sources2(train_list, test_list, module, obj, args=None):
"""
Define python Train/Test data sources in one method. If train/test use
the same Data Provider configuration, module/obj/args contain one argument,
otherwise contain a list or tuple of arguments. For example\:
.. code-block:: python
define_py_data_sources2(train_list="train.list",
test_list="test.list",
module="data_provider"
# if train/test use different configurations,
# obj=["process_train", "process_test"]
obj="process",
args={"dictionary": dict_name})
The related data provider can refer to :ref:`api_pydataprovider2_sequential_model` .
:param train_list: Train list name.
:type train_list: basestring
:param test_list: Test list name.
:type test_list: basestring
:param module: python module name. If train and test is different, then
pass a tuple or list to this argument.
:type module: basestring or tuple or list
:param obj: python object name. May be a function name if using
PyDataProviderWrapper. If train and test is different, then pass
a tuple or list to this argument.
:type obj: basestring or tuple or list
:param args: The best practice is using dict() to pass arguments into
DataProvider, and use :code:`@init_hook_wrapper` to receive
arguments. If train and test is different, then pass a tuple
or list to this argument.
:type args: string or picklable object or list or tuple.
:return: None
:rtype: None
"""
def py_data2(files, load_data_module, load_data_object, load_data_args,
**kwargs):
data = create_data_config_proto()
data.type = 'py2'
data.files = files
data.load_data_module = load_data_module
data.load_data_object = load_data_object
data.load_data_args = load_data_args
data.async_load_data = False
return data
define_py_data_sources(
train_list=train_list,
test_list=test_list,
module=module,
obj=obj,
args=args,
data_cls=py_data2)
| 7,727 | 35.11215 | 88 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/layers_test_config.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
num_classes = 5
x = data_layer(name="input1", size=3)
y = data_layer(name="input2", size=5)
z = out_prod_layer(input1=x, input2=y)
x1 = fc_layer(input=x, size=5)
y1 = fc_layer(input=y, size=5)
z1 = mixed_layer(
act=LinearActivation(),
input=[
conv_operator(
img=x1,
filter=y1,
filter_size=1,
num_filters=5,
num_channels=5,
stride=1)
])
assert z1.size > 0
y2 = fc_layer(input=y, size=15)
z2 = rotate_layer(input=y2, height=5, width=3)
cos1 = cos_sim(a=x1, b=y1)
cos3 = cos_sim(a=x1, b=y2, size=3)
linear_comb = linear_comb_layer(weights=x1, vectors=y2, size=3)
out = fc_layer(
input=[cos1, cos3, linear_comb, z, z1, z2],
size=num_classes,
act=SoftmaxActivation())
print_layer(input=[out])
outputs(classification_cost(out, data_layer(name="label", size=num_classes)))
dotmul = mixed_layer(
input=[dotmul_operator(
a=x1, b=x1), dotmul_projection(input=y1)])
proj_with_attr_init = mixed_layer(
input=full_matrix_projection(
input=y1,
param_attr=ParamAttr(
learning_rate=0, initial_mean=0, initial_std=0)),
bias_attr=ParamAttr(
initial_mean=0, initial_std=0, learning_rate=0),
act=LinearActivation(),
size=5,
name='proj_with_attr_init')
# for ctc
tmp = fc_layer(
input=[x1, dotmul, proj_with_attr_init],
size=num_classes + 1,
act=SoftmaxActivation())
ctc = ctc_layer(input=tmp, label=y, size=num_classes + 1)
ctc_eval = ctc_error_evaluator(input=tmp, label=y)
settings(
batch_size=10,
learning_rate=2e-3,
learning_method=AdamOptimizer(),
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25)
| 2,378 | 26.344828 | 77 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/layers_test.py
|
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.config_parser import parse_config_and_serialize
if __name__ == '__main__':
parse_config_and_serialize(
'trainer_config_helpers/tests/layers_test_config.py', '')
# layers_test_config.py
| 828 | 38.47619 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/test_reset_hook.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from paddle.trainer.config_parser import parse_config
class TestParse(unittest.TestCase):
def test_parse(self):
a = parse_config('trainer_config_helpers/tests/layers_test_config.py',
'')
b = parse_config('trainer_config_helpers/tests/layers_test_config.py',
'')
self.assertEqual(a, b)
if __name__ == '__main__':
unittest.main()
| 1,043 | 33.8 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/util_layers.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
a = data_layer(name='a', size=10)
b = data_layer(name='b', size=10)
result = addto_layer(input=[a, b])
concat1 = concat_layer(input=[a, b])
concat2 = concat_layer(
input=[identity_projection(input=a), identity_projection(input=b)])
outputs(result, concat1, concat2)
| 977 | 33.928571 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_row_l2_norm_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300)
row_l2_norm = row_l2_norm_layer(input=data)
outputs(row_l2_norm)
| 766 | 35.52381 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_pad.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2016, height=48, width=42)
conv = img_conv_layer(
input=data,
filter_size=3,
num_channels=1,
num_filters=16,
padding=1,
act=LinearActivation(),
bias_attr=True)
pool = img_pool_layer(input=conv, pool_size=2, stride=2, pool_type=MaxPooling())
pad = pad_layer(input=pool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
outputs(pad)
| 1,097 | 30.371429 | 80 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300, height=10, width=10)
prelu = prelu_layer(input=data, num_channels=3)
prelu = prelu_layer(input=data, partial_sum=1, num_channels=3)
prelu = prelu_layer(input=data, partial_sum=5, num_channels=3)
prelu = prelu_layer(input=data, channel_shared=True, num_channels=3)
prelu = prelu_layer(input=data, channel_shared=False, num_channels=3)
outputs(prelu)
| 1,050 | 41.04 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_roi_pool_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
data = data_layer(name='data', size=3 * 14 * 14, height=14, width=14)
rois = data_layer(name='rois', size=10)
conv = img_conv_layer(
input=data,
filter_size=3,
num_channels=3,
num_filters=16,
padding=1,
act=LinearActivation(),
bias_attr=True)
roi_pool = roi_pool_layer(
input=conv,
rois=rois,
pooled_width=7,
pooled_height=7,
spatial_scale=1. / 16)
outputs(roi_pool)
| 1,077 | 27.368421 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_repeat_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din = data_layer(name='data', size=30)
outputs(
repeat_layer(
input=din, num_repeats=10, as_row_vector=True),
repeat_layer(
input=din, num_repeats=10, act=TanhActivation(), as_row_vector=False))
| 925 | 34.615385 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_seq_concat_reshape.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din1 = data_layer(name='data1', size=30)
din2 = data_layer(name='data2', size=30)
opts = []
opts.append(seq_concat_layer(a=din1, b=din2))
opts.append(seq_reshape_layer(input=din1, reshape_size=5))
outputs(opts)
| 918 | 33.037037 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_spp_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=100, learning_rate=1e-5)
data = data_layer(name='data', size=3200, height=20, width=10)
spp = spp_layer(
input=data, pyramid_height=2, num_channels=16, pool_type=MaxPooling())
outputs(spp)
| 874 | 34 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_seq_slice_layer.py
|
#!/usr/bin/env python
#coding=utf-8
from paddle.trainer_config_helpers import *
input_seq = data_layer("word", size=128)
starts = data_layer("starts", size=5)
ends = data_layer("ends", size=5)
seq_slice1 = seq_slice_layer(input=input_seq, starts=starts, ends=ends)
seq_slice2 = seq_slice_layer(input=input_seq, starts=starts, ends=None)
seq_slice3 = seq_slice_layer(input=input_seq, starts=None, ends=ends)
outputs(seq_slice1, seq_slice2, seq_slice3)
| 454 | 31.5 | 71 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers_with_weight.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
data = data_layer(name='input', size=300)
lbl = data_layer(name='label', size=1)
wt = data_layer(name='weight', size=1)
fc = fc_layer(input=data, size=10, act=SoftmaxActivation())
outputs(
classification_cost(
input=fc, label=lbl, weight=wt),
square_error_cost(
input=fc, label=lbl, weight=wt),
nce_layer(
input=fc,
label=data_layer(
name='multi_class_label', size=500),
weight=wt))
| 1,153 | 32.941176 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_hsigmoid.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
din = data_layer(name='data', size=100)
label = data_layer(name='label', size=10)
outputs(hsigmoid(input=din, label=label, num_classes=10))
| 846 | 35.826087 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_scale_sub_region_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2016, height=48, width=42)
indices = data_layer(name='indices', size=6)
scale_sub_region = scale_sub_region_layer(
input=data, indices=indices, value=0.0)
outputs(scale_sub_region)
| 928 | 34.730769 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_rnn_group.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
seq = data_layer(name='seq_input', size=100)
sub_seq = data_layer(name='sub_seq_input', size=100)
lbl = data_layer(name='label', size=1)
def generate_rnn_simple(name):
def rnn_simple(s):
m = memory(name=name, size=200)
fc = fc_layer(input=[s, m], size=200, name=name)
return fc
return rnn_simple
def generate_rnn_simple_no_name():
def rnn_simple(s):
m = memory(name=None, size=200)
fc = fc_layer(input=[s, m], size=200)
m.set_input(fc)
return fc
return rnn_simple
with mixed_layer() as lstm_param: # test lstm unit, rnn group
lstm_param += full_matrix_projection(input=seq, size=100 * 4)
with mixed_layer() as gru_param:
gru_param += full_matrix_projection(input=seq, size=100 * 3)
outputs(
last_seq(input=recurrent_group(
step=generate_rnn_simple('rnn_forward'), input=seq)),
first_seq(input=recurrent_group(
step=generate_rnn_simple('rnn_back'), input=seq, reverse=True)),
last_seq(input=recurrent_group(
step=generate_rnn_simple('rnn_subseq_forward'),
input=SubsequenceInput(input=sub_seq))),
last_seq(input=lstmemory_group(
input=lstm_param, size=100)),
last_seq(input=gru_group(
input=gru_param, size=100)),
last_seq(input=recurrent_group(
step=generate_rnn_simple_no_name(), input=seq)), )
| 2,072 | 31.904762 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/last_first_seq.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din = data_layer(name='data', size=30)
seq_op = [first_seq, last_seq]
agg_level = [AggregateLevel.TO_SEQUENCE, AggregateLevel.TO_NO_SEQUENCE]
opts = []
for op in seq_op:
for al in agg_level:
opts.append(op(input=din, agg_level=al))
for op in seq_op:
opts.append(
op(input=din, agg_level=AggregateLevel.TO_NO_SEQUENCE, stride=5))
outputs(opts)
| 1,078 | 28.972222 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_factorization_machine.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
data = data_layer(name='data', size=1024)
fm = factorization_machine(input=data, factor_size=10)
outputs(fm)
| 769 | 34 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_multibox_loss_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
input_loc = data_layer(name='input_loc', size=16, height=16, width=1)
input_conf = data_layer(name='input_conf', size=8, height=1, width=8)
priorbox = data_layer(name='priorbox', size=32, height=4, width=8)
label = data_layer(name='label', size=24, height=4, width=6)
multibox_loss = multibox_loss_layer(
input_loc=input_loc,
input_conf=input_conf,
priorbox=priorbox,
label=label,
num_classes=21,
overlap_threshold=0.5,
neg_pos_ratio=3.0,
neg_overlap=0.5,
background_id=0,
name='test_multibox_loss')
outputs(multibox_loss)
| 1,273 | 30.85 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/math_ops.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
x = data_layer(name='data', size=100)
x = layer_math.exp(x)
x = layer_math.sqrt(x)
x = layer_math.reciprocal(x)
x = layer_math.log(x)
x = layer_math.abs(x)
x = layer_math.sigmoid(x)
x = layer_math.tanh(x)
x = layer_math.square(x)
x = layer_math.relu(x)
y = 1 + x
y = y + 1
y = x + y
y = y - x
y = y - 2
y = 2 - y
y = 2 * y
y = y * 3
z = data_layer(name='data_2', size=1)
y = y * z
y = z * y
y = y + z
y = z + y
outputs(y)
| 1,127 | 25.232558 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_clip_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300)
clip = clip_layer(input=data, min=-10, max=10)
outputs(clip)
| 762 | 35.333333 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_multiplex_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
index = data_layer(name='index', size=1)
din1 = data_layer(name='data1', size=30)
din2 = data_layer(name='data2', size=30)
din3 = data_layer(name='data3', size=30)
dout = multiplex_layer([index, din1, din2, din3])
outputs(dout)
| 935 | 33.666667 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/unused_layers.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-4)
probs = data_layer(name='probs', size=100)
outputs(
sampling_id_layer(input=probs), # It seems not support training
# It seems this layer is not correct, and should be rewrite.
# block_expand_layer(input=probs, channel=1, block_x=1, block_y=3),
)
| 966 | 36.192308 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py
|
#!/usr/bin/env python
#coding=utf-8
from paddle.trainer_config_helpers import *
data = data_layer(name="input_seq", size=128)
scores = fc_layer(input=data, size=1, act=ExpActivation())
kmax_seq_id = kmax_seq_score_layer(input=scores, beam_size=5)
outputs(kmax_seq_id)
| 270 | 26.1 | 61 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_dot_prod_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
vec1 = data_layer(name='vector1', size=10)
vec2 = data_layer(name='vector2', size=10)
dot_product = dot_prod_layer(input1=vec1, input2=vec2)
outputs(dot_product)
| 821 | 36.363636 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_pooling3D_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=100, learning_rate=1e-5)
data_2d = data_layer(name='data_2d', size=6000, height=20, width=10)
pool_2d = img_pool_layer(
name="pool___2d",
input=data_2d,
num_channels=30,
pool_size=5,
stride=3,
padding=1,
pool_type=AvgPooling())
outputs(pool_2d)
data_3d = data_layer(
name='data_3d_1', size=60000, depth=10, height=20, width=10)
pool_3d_1 = img_pool3d_layer(
name="pool_3d_1",
input=data_3d,
num_channels=30,
pool_size=5,
stride=3,
padding=1,
pool_type=AvgPooling())
outputs(pool_3d_1)
pool_3d_2 = img_pool3d_layer(
name="pool_3d_2",
input=data_3d,
num_channels=30,
pool_size=[5, 5, 5],
stride=[3, 3, 3],
padding=[1, 1, 1],
pool_type=MaxPooling())
outputs(pool_3d_2)
| 1,437 | 26.132075 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_row_conv.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2560)
row_conv = row_conv_layer(input=data, context_len=19, act=ReluActivation())
outputs(row_conv)
| 843 | 34.166667 | 75 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/img_trans_layers.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-3, batch_size=1000)
img = data_layer(name='image', size=227 * 227)
# the parse_conv in config_parse.py is not strictly accurate when filter_size
# is not square. So here set square filter_size.
img_conv = img_conv_layer(
input=img,
num_channels=1,
num_filters=64,
filter_size=(32, 32),
padding=(1, 1),
stride=(1, 1),
act=LinearActivation(),
trans=True)
img_bn = batch_norm_layer(input=img_conv, act=ReluActivation())
img_norm = img_cmrnorm_layer(input=img_bn, size=32)
img_pool = img_pool_layer(input=img_conv, pool_size=32, pool_type=MaxPooling())
outputs(img_pool, img_norm)
| 1,298 | 32.307692 | 79 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_sub_nested_seq_select_layer.py
|
#!/usr/bin/env python
#coding=utf-8
from paddle.trainer_config_helpers import *
beam_size = 5
data = data_layer(name='input_seq', size=300)
selected_ids = data_layer(name='input', size=beam_size)
sub_nest_seq = sub_nested_seq_layer(input=data, selected_indices=selected_ids)
outputs(sub_nest_seq)
| 300 | 24.083333 | 78 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_deconv3d_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
num_channels = 3
filter_size = 3
filter_size_y = 3
filter_size_z = 3
stride = 2
stride_y = 2
stride_z = 2
padding = 1
padding_y = 1
padding_z = 1
groups = 1
data = data_layer(
name='data', size=12096 * num_channels, height=48, width=42, depth=6)
# first
deconv3d_1 = img_conv3d_layer(
input=data,
name='deconv3d_1',
num_filters=16,
num_channels=num_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
bias_attr=True,
shared_biases=True,
trans=True,
layer_type="deconv3d",
act=LinearActivation())
# second
deconv3d_2 = img_conv3d_layer(
input=data,
name='deconv3d_2',
num_filters=16,
num_channels=num_channels,
filter_size=[filter_size, filter_size_y, filter_size_z],
stride=[stride, stride_y, stride_z],
padding=[padding, padding_y, padding_z],
groups=groups,
bias_attr=True,
shared_biases=True,
trans=True,
layer_type="deconv3d",
act=LinearActivation())
outputs(deconv3d_2)
| 1,720 | 25.476923 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_resize_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300)
resized = resize_layer(input=data, size=150)
outputs(resized)
| 763 | 35.380952 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/shared_lstm.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
data_1 = data_layer(name='data_a', size=100)
data_2 = data_layer(name='data_b', size=100)
mixed_param = ParamAttr(name='mixed_param')
with mixed_layer(size=400, bias_attr=False) as m1:
m1 += full_matrix_projection(input=data_1, param_attr=mixed_param)
with mixed_layer(size=400, bias_attr=False) as m2:
m2 += full_matrix_projection(input=data_2, param_attr=mixed_param)
lstm_param = ParamAttr(name='lstm_param')
lstm_bias = ParamAttr(name='lstm_bias', initial_mean=0., initial_std=0.)
lstm1 = lstmemory_group(
input=m1,
param_attr=lstm_param,
lstm_bias_attr=lstm_bias,
input_proj_bias_attr=False)
lstm2 = lstmemory_group(
input=m2,
param_attr=lstm_param,
lstm_bias_attr=lstm_bias,
input_proj_bias_attr=False)
softmax_param = ParamAttr(name='softmax_param')
predict = fc_layer(
input=[last_seq(input=lstm1), last_seq(input=lstm2)],
size=10,
param_attr=[softmax_param, softmax_param],
bias_attr=False,
act=SoftmaxActivation())
outputs(
classification_cost(
input=predict, label=data_layer(
name='label', size=10)))
| 1,810 | 30.77193 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_fc.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din = data_layer(name='data', size=100)
trans = trans_layer(input=din)
hidden = fc_layer(input=trans, size=100, bias_attr=False)
mask = data_layer(name='mask', size=100)
hidden_sel = selective_fc_layer(
input=din, select=mask, size=100, act=SigmoidActivation())
outputs(hidden, hidden_sel)
| 1,004 | 31.419355 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_conv3d_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
num_channels = 3
filter_size = 3
filter_size_y = 3
filter_size_z = 3
stride = 2
stride_y = 2
stride_z = 2
padding = 1
padding_y = 1
padding_z = 1
groups = 1
data = data_layer(
name='data', size=12096 * num_channels, height=48, width=42, depth=6)
# first
conv3d_1 = img_conv3d_layer(
input=data,
name='conv3d_1',
num_filters=16,
num_channels=num_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
bias_attr=True,
shared_biases=True,
trans=False,
layer_type="conv3d",
act=LinearActivation())
# second
conv3d_2 = img_conv3d_layer(
input=data,
name='conv3d_2',
num_filters=16,
num_channels=num_channels,
filter_size=[filter_size, filter_size_y, filter_size_z],
stride=[stride, stride_y, stride_z],
padding=[padding, padding_y, padding_z],
groups=groups,
bias_attr=True,
shared_biases=True,
trans=False,
layer_type="conv3d",
act=LinearActivation())
outputs(conv3d_2)
| 1,707 | 25.6875 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
seq_in = data_layer(name='input', size=200)
labels = data_layer(name='labels', size=5000)
probs = data_layer(name='probs', size=10)
xe_label = data_layer(name='xe-label', size=10)
hidden = fc_layer(input=seq_in, size=4)
outputs(
ctc_layer(
input=seq_in, label=labels),
warp_ctc_layer(
input=seq_in, label=labels, blank=0),
crf_layer(
input=hidden, label=data_layer(
name='crf_label', size=4)),
rank_cost(
left=data_layer(
name='left', size=1),
right=data_layer(
name='right', size=1),
label=data_layer(
name='label', size=1)),
lambda_cost(
input=data_layer(
name='list_feature', size=100),
score=data_layer(
name='list_scores', size=1)),
cross_entropy(
input=probs, label=xe_label),
cross_entropy_with_selfnorm(
input=probs, label=xe_label),
huber_regression_cost(
input=seq_in, label=labels),
huber_classification_cost(
input=data_layer(
name='huber_probs', size=1),
label=data_layer(
name='huber_label', size=1)),
multi_binary_label_cross_entropy(
input=probs, label=xe_label),
sum_cost(input=hidden),
nce_layer(
input=hidden, label=labels))
| 2,015 | 31.516129 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_bi_grumemory.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-4)
din = data_layer(name='data', size=120)
outputs(bidirectional_gru(input=din, size=40, return_seq=True))
| 810 | 35.863636 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/shared_fc.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(learning_rate=1e-4, batch_size=1000)
a = data_layer(name='feature_a', size=200)
b = data_layer(name='feature_b', size=200)
fc_param = ParamAttr(name='fc_param', initial_max=1.0, initial_min=-1.0)
bias_param = ParamAttr(name='bias_param', initial_mean=0.0, initial_std=0.0)
softmax_param = ParamAttr(
name='softmax_param', initial_max=1.0, initial_min=-1.0)
hidden_a = fc_layer(
input=a, size=200, param_attr=fc_param, bias_attr=bias_param)
hidden_b = fc_layer(
input=b, size=200, param_attr=fc_param, bias_attr=bias_param)
predict = fc_layer(
input=[hidden_a, hidden_b],
param_attr=[softmax_param, softmax_param],
bias_attr=False,
size=10,
act=SoftmaxActivation())
outputs(
classification_cost(
input=predict, label=data_layer(
name='label', size=10)))
| 1,482 | 32.704545 | 76 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_grumemory_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-4)
din = data_layer(name='data', size=120)
outputs(
grumemory(
input=din,
size=40,
reverse=True,
gate_act=TanhActivation(),
act=SigmoidActivation()))
| 897 | 31.071429 | 74 |
py
|
Paddle
|
Paddle-master/python/paddle/trainer_config_helpers/tests/configs/test_l2_distance_layer.py
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
outputs(
l2_distance_layer(
x=data_layer(
name='x', size=128), y=data_layer(
name='y', size=128)))
| 797 | 35.272727 | 74 |
py
|
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