seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
for embedding_name, path_to_meta in zip(embedding_names, paths_to_meta):
# Initialize config
embedding = config.embeddings.add()
# Specifiy the embedding variable and the metadata
embedding.tensor_name = embedding_name
embedding.metadata_path = path_to_meta
# Project the embeddings to space dimensions for visualization
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(summary_writer, config)
def add_train_stats(model, hparams):
with tf.variable_scope("stats") as scope:
for i in range(hparams.tacotron_num_gpus):
tf.summary.histogram("mel_outputs %d" % i, model.tower_mel_outputs[i])
tf.summary.histogram("mel_targets %d" % i, model.tower_mel_targets[i])
| tensorflow.contrib.tensorboard.plugins.projector.visualize_embeddings | 10,500 |
import tensorflow as tf
conv(tf.concat([self.enc[1], self.enc[2]], axis=-1), 1, bias=False, name="start_pointer"), -1)
end_logits = tf.squeeze(
conv(tf.concat([self.enc[1], self.enc[3]], axis=-1), 1, bias=False, name="end_pointer"), -1)
self.logits = [mask_logits(start_logits, mask=tf.reshape(self.c_mask, [N, -1])),
mask_logits(end_logits, mask=tf.reshape(self.c_mask, [N, -1]))]
self.logits1, self.logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(self.logits1), axis=2),
tf.expand_dims(tf.nn.softmax(self.logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, self.max_a_len)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
def _compute_loss(self):
def focal_loss(logits, labels, weights=None, alpha=0.25, gamma=2):
logits = tf.nn.sigmoid(logits)
zeros = array_ops.zeros_like(logits, dtype=logits.dtype)
pos_p_sub = array_ops.where(labels > zeros, labels - logits, zeros)
neg_p_sub = array_ops.where(labels > zeros, zeros, logits)
cross_ent = - alpha * (pos_p_sub ** gamma) * tf.log(tf.clip_by_value(logits, 1e-8, 1.0)) \
- (1 - alpha) * (neg_p_sub ** gamma) * tf.log(tf.clip_by_value(1.0 - logits, 1e-8, 1.0))
| tensorflow.matrix_band_part | 10,501 |
import tensorflow as tf
end_label = tf.one_hot(self.end_label, tf.shape(self.logits2)[1], axis=1)
if self.config.loss_type == 'cross_entropy':
start_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=self.logits1, labels=start_label)
end_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=self.logits2, labels=end_label)
self.loss = tf.reduce_mean(start_loss + end_loss)
else:
start_loss = focal_loss(tf.nn.softmax(self.logits1, -1), start_label)
| tensorflow.nn.softmax_cross_entropy_with_logits | 10,502 |
import tensorflow as tf
'''因为rnn_outputs是三维的，这里需要将其转成2维的，
矩阵运算后再转换回来[batch_size, num_steps, num_classes]'''
logits = tf.reshape(tf.matmul(tf.reshape(rnn_outputs, [-1, state_size]), W) +b, \
shape=[batch_size, num_steps, num_classes])
predictions = tf.nn.softmax(logits)
y_as_list = tf.unstack(y, num=num_steps, axis=1)
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,logits=logits)
| tensorflow.nn.softmax | 10,503 |
from tensorflow.python.ops import math_ops
class_id: Class for which we want binary metrics.
weights: `Tensor` whose shape is broadcastable to the the first [D1, ... DN]
dimensions of `predictions_idx` and `labels`.
Returns:
A [D1, ... DN] `Tensor` of false positive counts.
"""
with ops.name_scope(None, 'false_positives', (predictions_idx, labels)):
labels, predictions_idx = _maybe_select_class_id(labels,
predictions_idx,
class_id)
fp = set_ops.set_size(set_ops.set_difference(
predictions_idx, labels, aminusb=True))
fp = math_ops.to_double(fp)
if weights is not None:
weights = math_ops.to_double(weights)
fp = math_ops.mul(fp, weights)
return fp
def _streaming_sparse_false_positive_at_k(predictions_idx,
labels,
k=None,
class_id=None,
weights=None,
name=None):
"""Calculates weighted per step false positives for precision@k.
If `class_id` is specified, calculate binary true positives for `class_id`
only.
| tensorflow.python.ops.math_ops.to_double | 10,504 |
import tensorflow as tf
rand = tf.random.uniform([x_shape[0]])
epoch = tf.where(rand < 0.1, tf.zeros_like(epoch), epoch)
| tensorflow.zeros_like | 10,505 |
import tensorflow as tf
Returns:
A transformed tensor (tf.float32).
"""
def _repeat(x, n_repeats):
with tf.variable_scope('_repeat'):
rep = tf.transpose(
tf.expand_dims(tf.ones(shape=tf.stack([
n_repeats,
])), 1), [1, 0])
rep = tf.to_int32(rep)
x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
return tf.reshape(x, [-1])
def _interpolate(im, x, y, z, out_size):
| tensorflow.stack | 10,506 |
import tensorflow as tf
self.assertEqual(save_path + "-?????-of-00002", val)
meta_graph_filename = save._MetaGraphFilename(val)
self.assertEqual(save_path + ".meta", meta_graph_filename)
# Restore a different "v0" from shard 0 of the saved files.
with tf.Session(
target="",
config=tf.ConfigProto(device_count={"CPU": 2})) as sess:
with sess.graph.device("/cpu:0"):
v0 = tf.Variable(111, name="v0")
save = tf.train.Saver({"v0": v0}, sharded=True)
tf.initialize_all_variables().run()
self.assertEqual(111, v0.eval())
save.restore(sess, save_path + "-00000-of-00002")
| tensorflow.ConfigProto | 10,507 |
import tensorflow as tf
else:
X = self.t_conv(name + '_deconf', X, filter, f_size, 1, (not norm) and use_bias, "VALID", stddev)
if norm == 'I':
X = tf.contrib.layers.instance_norm(X, scope=scope, reuse=reuse)
elif norm == 'B':
X = tf.layers.batch_normalization(X, reuse=reuse, training=is_train, name=name)
elif norm == 'G':
X = tf.contrib.layers.group_norm(X, groups=16, scope=scope, reuse=reuse)
if dropout > 0.0:
X = tf.layers.dropout(X, dropout, training=is_train)
if slope < 1.0:
X = tf.nn.leaky_relu(X, slope) if slope > 0.0 else tf.nn.relu(X)
return X
F = 3
norm = self.args.norm
# print('norm', norm)
# print('skip cons', self.args.skip_connections)
# print('VNET In:', I.get_shape().as_list())
if adaption_net:
# print('ada scope T/R', is_train, reuse_ada)
| tensorflow.nn.relu | 10,508 |
import tensorflow as tf
from datetime import datetime
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
slim = tf.contrib.slim
global first
first = True
classnum=12
testnum = tf.placeholder(tf.int32)
trainnum = tf.placeholder(tf.int32)
validnum = tf.placeholder(tf.int32)
learnrate = tf.placeholder(tf.float32)
def getinputs(path):
filename_queue=tf.train.string_input_producer([path])
reader=tf.TFRecordReader()
_,serialized_example=reader.read(filename_queue)
features=tf.parse_single_example(serialized_example,
features={
'label':tf.FixedLenFeature([], tf.int64),
'img_raw' : tf.FixedLenFeature([], tf.string),
})
| tensorflow.placeholder | 10,509 |
import tensorflow as tf
if inverse:
scales = tf.math.exp(log_sigmas)
log_x = tf.math.log(x)
ldj = log_x
| tensorflow.math.log | 10,510 |
import tensorflow as tf
logger.info('Load the first task saver!')
saver.load_state_dict(np.load(f'./{inittask}/{taskname}.task0.saver.npy', allow_pickle=True)[()])
logger.info('Update all copies! (lazymodel, normalizers_copy)')
tf.get_default_session().run(sync_model_to_lazymodel)
tf.get_default_session().run(copy_normalizers)
logger.info('Loaded normalizers:')
load_norm = tf.get_default_session().run(normalizers_parameters)
logger.info(load_norm)
TASK_NUM = 1
| tensorflow.get_default_session | 10,511 |
import tensorflow as tf
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
| tensorflow.logging.set_verbosity | 10,512 |
import tensorflow as tf
# Benchmark related
def device_and_data_format():
return ("/gpu:0",
"channels_first") if tf.test.is_gpu_available() else ("/cpu:0",
"channels_last")
def random_batch(batch_size, config):
shape = (batch_size,) + config.input_shape
images = tf.random_uniform(shape)
labels = tf.random_uniform(
[batch_size], minval=0, maxval=config.n_classes, dtype=tf.int32)
return images, labels
class MockIterator(object):
def __init__(self, tensors):
self._tensors = [tf.identity(x) for x in tensors]
| tensorflow.random_uniform | 10,513 |
import tensorflow as tf
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
tf.contrib.data.map_and_batch(
| tensorflow.data.TFRecordDataset | 10,514 |
import tensorflow as tf
action = tf.check_numerics(action, 'action')
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
observ, reward, done = tf.py_func(
lambda a: self._batch_env.step(a)[:3], [action],
[observ_dtype, tf.float32, tf.bool], name='step')
observ = tf.check_numerics(observ, 'observ')
reward = tf.check_numerics(reward, 'reward')
return tf.group(
self._observ.assign(observ),
self._action.assign(action),
| tensorflow.check_numerics | 10,515 |
import tensorflow as tf
vdata = np.load(args.data_location)
tf.reset_default_graph()
idim = (36, 64)
| tensorflow.reset_default_graph | 10,516 |
import tensorflow as tf
def __init__(self, batch, hidden, keep_prob=1.0, is_train=None, scope="ptr_net"):
self.gru = tf.contrib.rnn.GRUCell(hidden)
self.batch = batch
self.scope = scope
self.keep_prob = keep_prob
self.is_train = is_train
self.dropout_mask = dropout(tf.ones(
[batch, hidden], dtype=tf.float32), keep_prob=keep_prob, is_train=is_train)
def __call__(self, init, match, d, mask):
with tf.variable_scope(self.scope):
d_match = dropout(match, keep_prob=self.keep_prob,
is_train=self.is_train)
inp, logits1 = pointer(d_match, init * self.dropout_mask, d, mask)
d_inp = dropout(inp, keep_prob=self.keep_prob,
is_train=self.is_train)
_, state = self.gru(d_inp, init)
tf.get_variable_scope().reuse_variables()
_, logits2 = pointer(d_match, state * self.dropout_mask, d, mask)
return logits1, logits2
| tensorflow.variable_scope | 10,517 |
import tensorflow as tf
image_list: a list of image tensors of the same dimension but possibly
varying channel.
crop_height: the height of the image following the crop.
crop_width: the width of the image following the crop.
Returns:
the list of cropped images.
"""
outputs = []
for image in image_list:
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
offset_height = (image_height - crop_height) / 2
offset_width = (image_width - crop_width) / 2
outputs.append(_crop(image, offset_height, offset_width,
crop_height, crop_width))
return outputs
def _smallest_size_at_least(height, width, smallest_side):
| tensorflow.shape | 10,518 |
import tensorflow as tf
return tf.reduce_mean(loss)
loss = tf.map_fn(fn=lambda inp: sample_compute(inp), elems=tf.range(resample), dtype=tf.float32,
parallel_iterations=32)
final_loss = tf.reduce_mean(loss)
return final_loss
| tensorflow.reduce_mean | 10,519 |
import tensorflow as tf
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord)
| tensorflow.train.start_queue_runners | 10,520 |
import tensorflow as tf
self.s = tf.placeholder(tf.float32, [None, self.num_s], name='s1') # input state for agent1
self.S_ = tf.placeholder(tf.float32, [None, self.num_global_s], name='S_') # input Next Global State
self.s_ = tf.placeholder(tf.float32, [None, self.num_s], name='s1_') # input next state for agent1
self.R = tf.placeholder(tf.float32, [None, ], name='R') # input Reward
self.a = tf.placeholder(tf.float32, [None, self.num_a], name='a') # input Action onehot for agent1
self.done = tf.placeholder(tf.float32, [None, ], name='done') # input Done info ???
self.q_m_ = tf.placeholder(tf.float32, [None, ], name='q_value_next_max')
| tensorflow.placeholder | 10,521 |
import tensorflow as tf
def dense(inputs, hidden, use_bias=True, scope="dense"):
with tf.variable_scope(scope):
shape = tf.shape(inputs)
dim = inputs.get_shape().as_list()[-1]
out_shape = [shape[idx] for idx in range(
len(inputs.get_shape().as_list()) - 1)] + [hidden]
flat_inputs = tf.reshape(inputs, [-1, dim])
W = tf.get_variable("W", [dim, hidden])
res = tf.matmul(flat_inputs, W)
if use_bias:
b = tf.get_variable(
"b", [hidden], initializer=tf.constant_initializer(0.))
res = tf.nn.bias_add(res, b)
| tensorflow.reshape | 10,522 |
import tensorflow as tf
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
width: an int32 scalar tensor indicating the current width.
smallest_side: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
new_height: an int32 scalar tensor indicating the new height.
new_width: and int32 scalar tensor indicating the new width.
"""
smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
height = tf.to_float(height)
width = tf.to_float(width)
smallest_side = tf.to_float(smallest_side)
scale = tf.cond(tf.greater(height, width),
lambda: smallest_side / width,
lambda: smallest_side / height)
new_height = tf.to_int32(height * scale)
new_width = tf.to_int32(width * scale)
return new_height, new_width
| tensorflow.convert_to_tensor | 10,523 |
import tensorflow as tf
| tensorflow.where | 10,524 |
import tensorflow as tf
z_s_flat = tf.reshape(z_s, [-1])
y_s_flat = tf.reshape(y_s, [-1])
| tensorflow.reshape | 10,525 |
import tensorflow as tf
one = tf.Variable(1.0)
twos = tf.Variable([2.0, 2.0, 2.0])
init = tf.initialize_all_variables()
save = tf.train.Saver(tf.all_variables())
| tensorflow.initialize_all_variables | 10,526 |
import tensorflow as tf
return True
return False
def _restore_vars(self, sess):
"""
:param sess:
:return: boolean for successful or not
"""
if not self._restore_optimistic:
if self.restore_ckpt_file is None:
logger.warn(
Color.yellow('No checkpoint file for restore vars, checkpoint file is None', bold=True))
return False
self._restore_saver = tf.train.Saver(self._var_list, name='tk_restore')
self._restore_saver.restore(sess, self.restore_ckpt_file)
return True
else:
return self._optimistic_restore_model(sess)
def _optimistic_restore_model(self, sess):
"""
restore weights of same names with model.
:param sess:
:return:
"""
if self.restore_ckpt_file is None:
| tensorflow.train.Saver | 10,527 |
import tensorflow as tf
# Exports to meta_graph
meta_graph_def = slice_saver.export_meta_graph(filename)
with tf.Graph().as_default():
# Restores from MetaGraphDef.
new_saver = tf.train.import_meta_graph(filename)
# Generates a new MetaGraphDef.
new_meta_graph_def = new_saver.export_meta_graph()
# It should be the same as the original.
self.assertProtoEquals(meta_graph_def, new_meta_graph_def)
| tensorflow.train.import_meta_graph | 10,528 |
import tensorflow as tf
with tf.variable_scope(scope):
deconv_weight = tf.Variable(
tf.random_normal([filter_h, filter_w, num_channels_out, num_channels_in], stddev=0.1, dtype=tf.float32))
deconv_bias = tf.Variable(tf.zeros([num_channels_out], dtype=tf.float32))
map = tf.nn.conv2d_transpose(input_data, deconv_weight, output_dims, strides=[1, stride_h, stride_w, 1],
padding=padding)
map = tf.nn.bias_add(map, deconv_bias)
activation = non_linear_fn(map)
# print(scope, 'out', activation.get_shape().as_list())
return activation
def self_attention(x, channels, act_func=tf.nn.relu, scope='attention'):
| tensorflow.nn.bias_add | 10,529 |
import tensorflow as tf
return None
def build_generator(self,image,reuse=False,name='generator'):
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
| tensorflow.variable_scope | 10,530 |
import tensorflow as tf
initializer=tf.constant_initializer(0), trainable=False)
self.dropout = tf.placeholder_with_default(0.0, (), name="dropout")
| tensorflow.placeholder_with_default | 10,531 |
import tensorflow as tf
v = self.get_slot(var, "v")
v_t = v.assign(beta2_t * v + (1. - beta2_t) * tf.square(grad))
m = self.get_slot(var, "m")
m_t = m.assign(beta1_t * m + (1. - beta1_t) * grad)
v_t_hat = tf.div(v_t, 1. - beta2_t)
m_t_hat = tf.div(m_t, 1. - beta1_t)
g_t = tf.div(m_t_hat, tf.sqrt(v_t_hat) + eps)
g_t_1 = self.get_slot(var, "g")
g_t = g_t_1.assign(g_t)
| tensorflow.div | 10,532 |
import tensorflow as tf
vx_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
vz_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
x_t = tf.gather(x, l)
x_t_len = tf.strings.length(x_t)
x_t = tf.string_split([x_t], delimiter='').values
z_t = tf.gather(y, m)
z_t_len = tf.strings.length(z_t)
z_t = tf.string_split([z_t], delimiter='').values
for i in tf.range(start=0, limit=x_t_len - self._p + 1, delta=1, dtype=None, name='range'):
u = tf.string_join(x_t[i:i + self._p], '')
vx_keys, r = tf.cond(
tf.greater(vx.lookup(u), -1),
true_fn=lambda: (vx_keys, tf.add(vx.lookup(u), 1)),
false_fn=lambda: (tf.concat([vx_keys, tf.reshape(u, (-1, 1))], axis=0),
| tensorflow.strings.length | 10,533 |
import tensorflow as tf
params_size_t = self._cell.params_size()
self._rnn_params = tf.get_variable(
"lstm_params",
initializer=tf.random_uniform(
[params_size_t], -config.init_scale, config.init_scale),
validate_shape=False)
c = tf.zeros([config.num_layers, self.batch_size, config.hidden_size],
tf.float32)
h = tf.zeros([config.num_layers, self.batch_size, config.hidden_size],
tf.float32)
self._initial_state = (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),)
outputs, h, c = self._cell(inputs, h, c, self._rnn_params, is_training)
outputs = tf.transpose(outputs, [1, 0, 2])
outputs = tf.reshape(outputs, [-1, config.hidden_size])
return outputs, (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),)
def _get_lstm_cell(self, config, is_training):
if config.rnn_mode == BASIC:
return tf.contrib.rnn.BasicLSTMCell(
config.hidden_size, forget_bias=0.0, state_is_tuple=True,
reuse=not is_training)
if config.rnn_mode == BLOCK:
return tf.contrib.rnn.LSTMBlockCell(
config.hidden_size, forget_bias=0.0)
raise ValueError("rnn_mode %s not supported" % config.rnn_mode)
def _build_rnn_graph_lstm(self, inputs, config, is_training):
"""Build the inference graph using canonical LSTM cells."""
| tensorflow.contrib.rnn.LSTMStateTuple | 10,534 |
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten
# self.glimpses = tf.concat([glimpse1,glimpse2,glimpse3],axis=-1)
# Block 1
conv1a = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[8, 8], strides=4, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(self.inputs)
conv1b = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1a)
conv1c = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1b)
pool1 = MaxPool2D(pool_size=[2,2])(conv1c)
# Block 2
conv2a = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool1)
conv2b = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2a)
conv2c = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2b)
pool2 = MaxPool2D(pool_size=[2,2])(conv2c)
# Block 3
conv3a = Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool2)
conv3b = Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv3a)
conv3c = Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv3b)
pool3 = MaxPool2D(pool_size=[2,2])(conv3c)
# final convolutional layer
#removed GOAL_SIZE
conv4 = Conv2D(padding="valid", filters=RNN_SIZE-loc_layer_size, kernel_size=[2, 2], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=None)(pool3)
# FC layers
| tensorflow.keras.layers.MaxPool2D | 10,535 |
import tensorflow as tf
l = (LinearWrap(img)
.Conv2D('conv0', NF, nl=LeakyReLU)
.Conv2D('conv1', NF * 2)
.Conv2D('conv2', NF * 4)
.Conv2D('conv3', NF * 8, stride=1)
.Conv2D('conv4', 1, stride=1, nl=tf.identity, use_bias=True)())
return l
def _build_graph(self, inputs):
A, B = inputs
with tf.name_scope('preprocess'):
A = tf.transpose(A / 128.0 - 1.0, [0, 3, 1, 2])
B = tf.transpose(B / 128.0 - 1.0, [0, 3, 1, 2])
def viz3(name, a, b, c):
with tf.name_scope(name):
im = tf.concat([a, b, c], axis=3)
im = tf.transpose(im, [0, 2, 3, 1])
im = (im + 1.0) * 128
im = tf.clip_by_value(im, 0, 255)
| tensorflow.name_scope | 10,536 |
import tensorflow as tf
if common_layers.is_on_tpu():
_remove_summaries() # summaries not currently working on TPU
return tf.contrib.tpu.TPUEstimatorSpec(
tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
else:
return tf.estimator.EstimatorSpec(
tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
def estimator_spec_eval(self, features, logits, labels, loss, losses_dict):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
| tensorflow.estimator.EstimatorSpec | 10,537 |
import tensorflow as tf
self.y = tf.compat.v1.placeholder(tf.int32, shape = (None, self.config.data["num_categories"]), name="y") # ex. (50000, 10)
self.train = tf.compat.v1.placeholder(tf.bool)
| tensorflow.compat.v1.placeholder | 10,538 |
import tensorflow as tf
flags.DEFINE_integer("num_gpus", 2, "Total number of GPUs to use.")
flags.DEFINE_bool("multi_worker", True, "Multi-worker training.")
# My additional flags
tf.app.flags.DEFINE_boolean("use_original_ckpt", True, "use original ckpt")
flags.DEFINE_integer("task_index", 0, "task_index")
flags.DEFINE_string(
"worker", "localhost:3000,localhost:3001", "specify workers in the cluster"
| tensorflow.app.flags.DEFINE_boolean | 10,539 |
import tensorflow as tf
locs, scales = tf.map_fn(loop_hyper_deocder, zs, dtype=(tf.float32, tf.float32),
parallel_iterations=1, back_prop=False)
lower_bound = 1e-9# TODO
scales = tf.maximum(scales, lower_bound)
print("Hyper Decoder")
ys = conditional_entropy_model.decompress(y_strings, locs, scales, y_min_v, y_max_v, y_shape)
print("Entropy Decoder")
def loop_synthesis(element):
y = tf.expand_dims(element[0], 0)
x_coori = tf.expand_dims(element[1], 0)
x_coori= tf.cast(x_coori,tf.float32)
x = synthesis_transform(x_coori,y)
return tf.squeeze(x, [0])
element=[ys,x_coori]
xs = tf.map_fn(loop_synthesis, element, dtype=tf.float32, parallel_iterations=1, back_prop=False)
print("Synthesis Transform")
return xs
###################################### write & read binary files. ######################################
| tensorflow.cast | 10,540 |
import tensorflow as tf
tf.transpose(self.x, [1, 0, 2]),
initializer=state,
parallel_iterations=1)
rnn_outputs = \
tf.scan(
self.output_step_scan,
rnn_states,
initializer=tf.zeros([self.N_batch, self.N_out]),
parallel_iterations= 1)
return tf.transpose(rnn_outputs, [1, 0, 2]), tf.unstack(rnn_states)
# fix spectral radius of recurrent matrix
def initial_W(self):
| tensorflow.zeros | 10,541 |
import tensorflow as tf
ha = tf.matmul(varphis, param_eta * tf.matmul(Kt, prec) + Wsa) + wa
# hss(s): eta * (\varphi(s)^T * K^T * \Sigma^{-1} * K * \varphi(s))
varphisKt = tf.matmul(varphis, Kt)
hss = param_eta * tf.reduce_sum(tf.matmul(varphisKt, prec) * varphisKt, axis=1)
Haa = param_eta * prec + Waa
# Haa = 0.5 * (Haa + TT.transpose(Haa))
| tensorflow.matmul | 10,542 |
import tensorflow as tf
regressor = XLNetRegressor(hparams=hparams)
logits = regressor(inputs)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
logits_ = sess.run(logits)
self.assertEqual(logits_.shape,
(batch_size, max_time))
| tensorflow.global_variables_initializer | 10,543 |
import tensorflow as tf
# How to set placements on multiple devices.
# Here, assume we have three devies CPU:0, GPU:0, and GPU:1
if tf.test.is_built_with_cuda():
with tf.device('/cpu:0'):
a = tf.constant([1.0, 3.0, 5.0], shape=[1, 3])
b = tf.constant([2.0, 4.0, 6.0], shape=[3, 1])
with tf.device('/gpu:1'):
c = tf.matmul(a,b)
c = tf.reshape(c, [-1])
with tf.device('/gpu:2'):
d = tf.matmul(b,a)
flat_d = tf.reshape(d, [-1])
combined = tf.mul(c, flat_d)
print(sess.run(combined)) | tensorflow.matmul | 10,544 |
import tensorflow as tf
if tf_output1_dtype == tf.string:
cast1 = tf.dtypes.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
out0 = tf.identity(cast0, "TENSOR_OUTPUT0")
out1 = tf.identity(cast1, "TENSOR_OUTPUT1")
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel", input_dtype,
| tensorflow.identity | 10,545 |
import tensorflow as tf
def inference(self,images):
images=tf.cast(images,tf.float32)/255.0
l1 = tf.matmul(images, self.w1)+self.b1
l1=tf.nn.relu(l1)
l2 = tf.matmul(l1, self.w2)+self.b2
l2=tf.nn.relu(l2)
l3=tf.matmul(l2, self.w3)+self.b3
l3=tf.nn.relu(l3)
out=tf.matmul(l3, self.w4)+self.b4
return out
| tensorflow.nn.relu | 10,546 |
from tensorflow.python.framework import ops
update_op = size.assign(new_size)
if metrics_collections:
ops.add_to_collections(metrics_collections, value)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return value, update_op
# pylint: enable=invalid-slice-index
| tensorflow.python.framework.ops.add_to_collections | 10,547 |
import tensorflow as tf
# load pre-trained data from file
# glove = np.load(params['glove'])['embeddings'] # np.array
# training the embedding during training
glove = np.zeros(
(self.params["embedding_vocabulary_size"], self.params["embedding_dim"]),
dtype=np.float32,
)
# Add OOV word embedding
embedding_array = np.vstack([glove, [[0.0] * self.params["embedding_dim"]]])
embedding_variable = tf.Variable(
embedding_array, dtype=tf.float32, trainable=True
)
# embedding_variable = tf.get_variable(
# 'embedding_variable',
# shape=(self.params["embedding_vocabulary_size"] + 1, self.params["embedding_dim"]),
# dtype=tf.float32,
# initializer=tf.contrib.layers.xavier_initializer(),
# regularizer=tf.contrib.layers.l2_regularizer(self.params["regularizer_rate"]),
# trainable=True
# )
| tensorflow.Variable | 10,548 |
import tensorflow as tf
self.conv4 = tf.layers.conv2d(self.pool3,
self.config.cifar10_cnn["num_filters"],
self.config.cifar10_cnn["filter_size"],
padding='same', activation=tf.nn.relu)
self.drop3 = tf.layers.dropout(self.conv4, self.config.cifar10_cnn["keep_prob"], training=self.train)
# b. Flatten input data
self.flatten = tf.reshape(self.drop3, [-1, self.config.cifar10_cnn["fc1_nb_units"]])
| tensorflow.layers.dropout | 10,549 |
import tensorflow as tf
with client_graph.as_default():
decoded_x = stage.decode(encoded_x, decode_params, shape=shape)
with self.session(graph=client_graph):
decoded_x = self.evaluate(decoded_x)
return TestData(x, encoded_x, decoded_x, state, state_update_tensors,
updated_state)
def _non_adaptive_one_to_many_encode_decode():
"""Implementation of the method for `EncodingStageInterface`."""
server_graph = tf.Graph()
with server_graph.as_default():
x = input_fn()
shape = py_utils.static_or_dynamic_shape(x)
encode_params, decode_params = stage.get_params()
encoded_x = stage.encode(x, encode_params)
# Get all values out of TensorFlow as Python constants. This is a trivial
# example of communication happening outside of TensorFlow.
with self.session(graph=server_graph):
| tensorflow.Graph | 10,550 |
import tensorflow as tf
batch_size = 8
inp = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
out = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
weights = [tf.ones_like(inp[0], dtype=tf.float32) for _ in range(8)]
with tf.variable_scope("root"):
_, losses = SampleGRUSeq2Seq(inp, out, weights)
updates = []
params = tf.global_variables()
optimizer = tf.train.AdamOptimizer(0.03, epsilon=1e-5)
for i in range(len(buckets)):
full_grads = tf.gradients(losses[i], params)
grads, _ = tf.clip_by_global_norm(full_grads, 30.0)
update = optimizer.apply_gradients(zip(grads, params))
updates.append(update)
sess.run([tf.global_variables_initializer()])
steps = 6
for _ in range(steps):
bucket = random.choice(np.arange(len(buckets)))
length = buckets[bucket][0]
i = [np.array([np.random.randint(9) + 1 for _ in range(batch_size)],
dtype=np.int32) for _ in range(length)]
# 0 is our "GO" symbol here.
o = [np.array([0] * batch_size, dtype=np.int32)] + i
feed = {}
for i1, i2, o1, o2 in zip(inp[:length], i[:length],
out[:length], o[:length]):
feed[i1.name] = i2
feed[o1.name] = o2
| tensorflow.global_variables_initializer | 10,551 |
import tensorflow as tf
# Test case 1, 2.
x = tf.placeholder_with_default(input=1, shape=[])
# None would fire an exception were it actually executed.
self.assertTrue(normal._is_scalar_helper(x.shape, lambda: None))
self.assertTrue(
normal._is_scalar_helper(tf.TensorShape(None), lambda: tf.shape(x)))
x = tf.placeholder_with_default(input=[1], shape=[1])
# None would fire an exception were it actually executed.
self.assertFalse(normal._is_scalar_helper(x.shape, lambda: None))
self.assertFalse(
normal._is_scalar_helper(tf.TensorShape(None), lambda: tf.shape(x)))
# There's no notion of partially known shapes in eager mode, so exit
# early.
if tf.executing_eagerly():
return
# Test case 3.
x = tf.placeholder_with_default(input=1, shape=None)
is_scalar = normal._is_scalar_helper(x.shape, lambda: tf.shape(x))
self.assertTrue(self.evaluate(is_scalar))
| tensorflow.TensorShape | 10,552 |
import tensorflow as tf
self.in_window = tf.less(
batch_shaped(band_features["closest_time_diff"]), band_time_diff
)
# Before and after flux.
before_flux = batch_win_shaped(band_features["before_flux"])
after_flux = batch_win_shaped(band_features["after_flux"])
before_time = batch_win_shaped(band_features["before_time"])
after_time = batch_win_shaped(band_features["after_time"])
self.dtime = batch_2win_shaped(
tf.concat([before_time, after_time], axis=1) - tile_to_2win(closest_time),
)
self.dflux = batch_2win_shaped(
tf.concat([before_flux, after_flux], axis=1) - tile_to_2win(closest_flux),
)
# Masking tensor.
left_mask = _left_mask(
batch_shaped(
band_features["before_padding"]),
window_size)
| tensorflow.concat | 10,553 |
import tensorflow as tf
------
ValueError
If input tensor is not 2D.
"""
if weight_init is None:
num_features = tensor.get_shape()[-1].value
weight_init = tf.truncated_normal([num_features, size], stddev=0.01)
if bias_init is None:
bias_init = tf.zeros([size])
with tf.name_scope(name, 'fully_connected', [tensor]):
w = tf.Variable(weight_init, name='w', dtype=tf.float32)
b = tf.Variable(bias_init, name='b', dtype=tf.float32)
return tf.nn.xw_plus_b(tensor, w, b)
def weight_decay(penalty_type, penalty):
"""Add weight decay.
Args:
model: TensorflowGraph.
Returns:
A scalar tensor containing the weight decay cost.
| tensorflow.Variable | 10,554 |
import tensorflow as tf
lengths = tf.to_float(tf.expand_dims(encoder_input_length, axis=1))
mask = tf.sequence_mask(encoder_input_length, maxlen=tf.shape(hidden_states)[1])
weights = tf.to_float(mask) / lengths
weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1)
return weighted_average, weights
def last_state_attention(hidden_states, encoder_input_length, *args, **kwargs):
weights = tf.one_hot(encoder_input_length - 1, tf.shape(hidden_states)[1])
weights = tf.to_float(weights)
weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1)
return weighted_average, weights
def local_attention(state, hidden_states, encoder, encoder_input_length, pos=None, scope=None, context=None, **kwargs):
batch_size = tf.shape(state)[0]
attn_length = tf.shape(hidden_states)[1]
| tensorflow.to_float | 10,555 |
import tensorflow as tf
'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer(
'save_checkpoints_secs', 3600,
'The frequency with which the model is saved, in seconds.')
# model related configuration
tf.app.flags.DEFINE_integer(
'train_image_size', 384,
'The size of the input image for the model to use.')
tf.app.flags.DEFINE_integer(
'heatmap_size', 96,
'The size of the output heatmap of the model.')
tf.app.flags.DEFINE_string(
'backbone', 'seresnext50',#or seresnext50 seresnet50
'The backbone network to use for feature pyramid.')
tf.app.flags.DEFINE_float(
'heatmap_sigma', 1.,
'The sigma of Gaussian which generate the target heatmap.')
tf.app.flags.DEFINE_float(
'bbox_border', 25.,
'The nearest distance of the crop border to al keypoints.')
tf.app.flags.DEFINE_integer(
'train_epochs', 50,
'The number of epochs to use for training.')
tf.app.flags.DEFINE_integer(
'epochs_per_eval', 20,
'The number of training epochs to run between evaluations.')
tf.app.flags.DEFINE_integer(
'batch_size', 10,
| tensorflow.app.flags.DEFINE_float | 10,556 |
from tensorflow.python.framework import random_seed
def _infer_model(self, x, batch_size=None, axis=None, proba=False):
# Converts inputs into tf.DataFrame / tf.Series.
batch_size = -1 if batch_size is None else batch_size
input_fn, feed_fn = _get_predict_input_fn(x, batch_size)
checkpoint_path = saver.latest_checkpoint(self._model_dir)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
contrib_framework.create_global_step(g)
features, _ = input_fn()
feed_dict = feed_fn() if feed_fn is not None else None
predictions = self._get_predict_ops(features)
if not isinstance(predictions, dict):
predictions = {'predictions': predictions}
| tensorflow.python.framework.random_seed.set_random_seed | 10,557 |
from tensorflow.python.framework import ops
training_op = control_flow_ops.group(*all_grads)
self._BenchmarkOp(training_op, "cudnn_lstm %s %s" %
(config_name, self._GetConfigDesc(config)))
def benchmarkTfRNNLSTMTraining(self):
test_configs = self._GetTestConfig()
for config_name, config in test_configs.items():
num_layers = config["num_layers"]
num_units = config["num_units"]
batch_size = config["batch_size"]
seq_length = config["seq_length"]
with ops.Graph().as_default(), ops.device("/device:GPU:0"):
inputs = seq_length * [
array_ops.zeros([batch_size, num_units], dtypes.float32)
]
initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127)
cell = rnn_cell.LSTMCell(
num_units=num_units, initializer=initializer, state_is_tuple=True)
multi_cell = rnn_cell.MultiRNNCell(
[cell() for _ in range(num_layers)])
outputs, final_state = core_rnn.static_rnn(
multi_cell, inputs, dtype=dtypes.float32)
| tensorflow.python.framework.ops.Graph | 10,558 |
import tensorflow as tf
if use_pool:
l = tf.nn.max_pool(l, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
| tensorflow.nn.max_pool | 10,559 |
import tensorflow as tf
"url.")
tf.flags.DEFINE_string(
"tpu_zone", None,
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string(
"gcp_project", None,
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
| tensorflow.flags.DEFINE_string | 10,560 |
import tensorflow as tf
name='%s_omega' % self.layer_name,
dtype=self.dtype,
initializer=initialization.xavier_initializer(
shape=bias_shape,
dtype=self.dtype,
uniform=self.normal_initializer,
mask=None)))
else:
setattr(
self,
'kappa_%s' % layer,
tf.constant(1.))
setattr(
self,
'omega_%s' % layer,
tf.constant(1.))
if self.adapation:
setattr(
self,
'eta_%s' % layer,
tf.get_variable(
name='%s_eta' % self.layer_name,
| tensorflow.constant | 10,561 |
import tensorflow as tf
idx_z0_y0_x1 = base_z0_y0 + x1_clip
idx_z0_y1_x0 = base_z0_y1 + x0_clip
idx_z0_y1_x1 = base_z0_y1 + x1_clip
idx_z1_y0_x0 = base_z1_y0 + x0_clip
idx_z1_y0_x1 = base_z1_y0 + x1_clip
idx_z1_y1_x0 = base_z1_y1 + x0_clip
idx_z1_y1_x1 = base_z1_y1 + x1_clip
# Use indices to lookup pixels in the flat image and restore
# channels dim
im_flat = tf.reshape(im, tf.stack([-1, channels]))
im_flat = tf.to_float(im_flat)
i_z0_y0_x0 = tf.gather(im_flat, idx_z0_y0_x0)
i_z0_y0_x1 = tf.gather(im_flat, idx_z0_y0_x1)
i_z0_y1_x0 = tf.gather(im_flat, idx_z0_y1_x0)
i_z0_y1_x1 = tf.gather(im_flat, idx_z0_y1_x1)
i_z1_y0_x0 = tf.gather(im_flat, idx_z1_y0_x0)
i_z1_y0_x1 = tf.gather(im_flat, idx_z1_y0_x1)
i_z1_y1_x0 = tf.gather(im_flat, idx_z1_y1_x0)
i_z1_y1_x1 = tf.gather(im_flat, idx_z1_y1_x1)
# Finally calculate interpolated values.
x0_f = tf.to_float(x0)
x1_f = tf.to_float(x1)
y0_f = tf.to_float(y0)
y1_f = tf.to_float(y1)
z0_f = tf.to_float(z0)
z1_f = tf.to_float(z1)
# Check the out-of-boundary case.
x0_valid = tf.to_float(
| tensorflow.gather | 10,562 |
import tensorflow as tf
mse_loss_list.append(0.5 * tf.losses.mean_squared_error(targets_list[pred_ind], pred_outputs[pred_ind],
weights=1.0 / tf.cast(cur_batch_size, tf.float32),
scope='loss_{}'.format(pred_ind),
loss_collection=None,#tf.GraphKeys.LOSSES,
# mean all elements of all pixels in all batch
reduction=tf.losses.Reduction.MEAN))# SUM, SUM_OVER_BATCH_SIZE, default mean by all elements
temp_loss = tf.reduce_mean(tf.reshape(tf.losses.mean_squared_error(targets_list[-1], pred_outputs[-1], weights=1.0, loss_collection=None, reduction=tf.losses.Reduction.NONE), [cur_batch_size, config.class_num_joints[(params['model_scope'] if 'all' not in params['model_scope'] else '*')], -1]), axis=-1)
num_topk = config.class_num_joints[(params['model_scope'] if 'all' not in params['model_scope'] else '*')] // 2
gather_col = tf.nn.top_k(temp_loss, k=num_topk, sorted=True)[1]
gather_row = tf.reshape(tf.tile(tf.reshape(tf.range(cur_batch_size), [-1, 1]), [1, num_topk]), [-1, 1])
gather_indcies = tf.stop_gradient(tf.stack([gather_row, tf.reshape(gather_col, [-1, 1])], axis=-1))
select_targets = tf.gather_nd(targets_list[-1], gather_indcies)
select_heatmap = tf.gather_nd(pred_outputs[-1], gather_indcies)
mse_loss_list.append(tf.losses.mean_squared_error(select_targets, select_heatmap,
weights=1.0 / tf.cast(cur_batch_size, tf.float32),
scope='loss_{}'.format(len(pred_outputs) - 1),
loss_collection=None,#tf.GraphKeys.LOSSES,
# mean all elements of all pixels in all batch
reduction=tf.losses.Reduction.MEAN))
else:
for pred_ind in list(range(len(pred_outputs))):
mse_loss_list.append(tf.losses.mean_squared_error(targets_list[pred_ind], pred_outputs[pred_ind],
weights=1.0 / tf.cast(cur_batch_size, tf.float32),
scope='loss_{}'.format(pred_ind),
loss_collection=None,#tf.GraphKeys.LOSSES,
# mean all elements of all pixels in all batch
| tensorflow.gather_nd | 10,563 |
import tensorflow as tf
grads_pruned += [(grad[0] * self.masks[idx_mask], grad[1])]
return grads_pruned
def __save_model(self):
"""Save the current model."""
save_path = self.saver_train.save(self.sess_train, FLAGS.ws_save_path, self.global_step)
tf.logging.info('model saved to ' + save_path)
def __restore_model(self, is_train):
"""Restore a model from the latest checkpoint files.
Args:
* is_train: whether to restore a model for training
"""
| tensorflow.logging.info | 10,564 |
import tensorflow as tf
features={
'label':tf.FixedLenFeature([], tf.int64),
'img_raw' : tf.FixedLenFeature([], tf.string),
})
image=tf.decode_raw(features['img_raw'],tf.uint8)
label=tf.cast(features['label'],tf.int32)
image=tf.reshape(image,[4096,1])
return image,label
def get_batch(image,label,batch_size,crop_size):
#print(image.shape)
#print(label.shape)
images,labels=tf.train.shuffle_batch([image,label],
batch_size=batch_size,num_threads=10,capacity=10000,min_after_dequeue=200)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
def get_test_batch(image,label,batch_size):
images,labels=tf.train.batch([image,label],batch_size=batch_size)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
def get_valid_batch(image,label,batch_size):
images,labels=tf.train.batch([image,label],batch_size=batch_size)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
| tensorflow.train.shuffle_batch | 10,565 |
import tensorflow as tf
tf.abs(self.W_rec) * self.rec_Connectivity,
self.Dale_rec, name="in_1"),
transpose_b=True, name="1")
+ tf.matmul(
rnn_in,
tf.abs(self.W_in) * self.input_Connectivity,
transpose_b=True, name="2")
+ self.b_rec) \
+ np.sqrt(2.0 * self.alpha * self.rec_noise * self.rec_noise)\
* tf.random_normal(state.get_shape(), mean=0.0, stddev=1.0)
| tensorflow.abs | 10,566 |
import tensorflow as tf
self.data_format = data_format
if( data_format =='NCHW' ):
self.strides = [1, 1, d_h, d_w]
else:
self.strides = [1, d_h, d_w, 1]
def __call__(self,input_var,name=None,**xargs):
shapes = tf.shape(input_var)
if( self.data_format == 'NCHW' ):
shapes = tf.stack([shapes[0],tf.shape(self.b)[0],shapes[2]*self.strides[2],shapes[3]*self.strides[3]])
else:
shapes = tf.stack([shapes[0],shapes[1]*self.strides[1],shapes[2]*self.strides[2],tf.shape(self.b)[0]])
return tf.nn.bias_add(
tf.nn.conv2d_transpose(input_var,self.w,output_shape=shapes,
data_format=self.data_format,
strides=self.strides,padding='SAME'),
self.b,data_format=self.data_format,name=name)
def get_variables(self):
return {'w':self.w,'b':self.b}
class WeightNormTransposedConv2d(object):
def __init__(self,name,input_dim,out_dim,
| tensorflow.shape | 10,567 |
import tensorflow as tf
if mode == 'eval':
for checkpoint in _get_next_checkpoint():
tf.logging.info('Starting to evaluate.')
try:
| tensorflow.logging.info | 10,568 |
import tensorflow as tf
class MaxToKeepTest(tf.test.TestCase):
def testNonSharded(self):
save_dir = os.path.join(self.get_temp_dir(), "max_to_keep_non_sharded")
try:
gfile.DeleteRecursively(save_dir)
except OSError:
pass # Ignore
gfile.MakeDirs(save_dir)
with self.test_session() as sess:
v = tf.Variable(10.0, name="v")
save = tf.train.Saver({"v": v}, max_to_keep=2)
tf.initialize_all_variables().run()
self.assertEqual([], save.last_checkpoints)
s1 = save.save(sess, os.path.join(save_dir, "s1"))
self.assertEqual([s1], save.last_checkpoints)
self.assertTrue(gfile.Exists(s1))
s2 = save.save(sess, os.path.join(save_dir, "s2"))
self.assertEqual([s1, s2], save.last_checkpoints)
self.assertTrue(gfile.Exists(s1))
| tensorflow.Variable | 10,569 |
import tensorflow as tf
final_loss = tf.reduce_sum(loss)
return final_loss, cstr_pct
def contra_traj_lossV8(pred, tgt, horizon=12):
horizon_pred, horizon_tgt = horizon_sumV1(pred, horizon), horizon_sumV1(tgt, horizon)
# horizon_pred, horizon_tgt = horizon_sumV2(pred, tgt, horizon)
horizon_pred1, horizon_pred2 = tf.split(horizon_pred, 2, axis=0)
horizon_tgt1, horizon_tgt2 = tf.split(horizon_tgt, 2, axis=0)
pred_flat1, pred_flat2 = tf.reshape(horizon_pred1, [-1, 1]), tf.reshape(horizon_pred2, [1, -1])
tgt_flat1, tgt_flat2 = tf.reshape(horizon_tgt1, [-1, 1]), tf.reshape(horizon_tgt2, [1, -1])
tgt_dif = tgt_flat1 - tgt_flat2
pred_dif = pred_flat1 - pred_flat2
geq = tf.cast(tgt_dif > 0, tf.bool)
tgt_posi_dif = tf.where(geq, tgt_dif, -tgt_dif)
pred_posi_dif = tf.where(geq, pred_dif, -pred_dif)
loss = tf.maximum(0., tgt_posi_dif - pred_posi_dif)
| tensorflow.reshape | 10,570 |
import tensorflow as tf
_debug(self.conv5)
if self.test_classification:
with tf.variable_scope('logits'):
print('Building unit: logits')
self.score = tf.reduce_mean(self.conv5, axis=[1, 2])
| tensorflow.variable_scope | 10,571 |
from tensorflow.python.ops import clip_ops
global_step: Optional global_step. If provided, `decay = decay*n/(n+1)`.
This provides a quicker adaptation of the mean for the first steps.
report_summary: If `True`, will add histogram summaries of the `max_norm`.
epsilon: Small value chosen to avoid zero variance.
name: The name for this operation is used to scope operations and summaries.
Returns:
A function for applying gradient clipping.
"""
def gradient_clipping(grads_and_vars):
"""Internal function for adaptive clipping."""
grads, variables = zip(*grads_and_vars)
norm = clip_ops.global_norm(grads)
max_norm, log_mean = _adaptive_max_norm(norm, std_factor, decay,
global_step, epsilon, name)
# reports the max gradient norm for debugging
if report_summary:
summary.scalar("global_norm/adaptive_max_gradient_norm", max_norm)
# factor will be 1. if norm is smaller than max_norm
factor = array_ops.where(norm < max_norm, array_ops.ones_like(norm),
math_ops.exp(log_mean) / norm)
if static_max_norm is not None:
| tensorflow.python.ops.clip_ops.global_norm | 10,572 |
import tensorflow as tf
with tf.variable_scope(scope):
d_memory = dropout(memory, keep_prob=keep_prob, is_train=is_train)
s0 = tf.nn.tanh(dense(d_memory, hidden, scope="s0"))
s = dense(s0, 1, use_bias=False, scope="s")
s1 = softmax_mask(tf.squeeze(s, [2]), mask)
a = tf.expand_dims(tf.nn.softmax(s1), axis=2)
res = tf.reduce_sum(a * memory, axis=1)
return res
| tensorflow.squeeze | 10,573 |
import tensorflow as tf
def build_cnet(self, state_in, name, reuse=False, batch_size=64):
reg = tf.contrib.layers.l2_regularizer(1e-3)
with tf.variable_scope(name, reuse=reuse):
layer_c1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
| tensorflow.variable_scope | 10,574 |
import tensorflow as tf
self.config = config
self._activation_fn = tf.nn.relu
self._embedding_initializers = {
'embeddings': tf.truncated_normal_initializer(stddev=0.01),
}
self._embedding_regularizers = {}
self._initializers = {
"w": tf.contrib.layers.xavier_initializer(),
}
self._regularizers = {
'w': tf.contrib.layers.l2_regularizer(config.l2)
}
self._construct_placeholders()
self._construct_weights()
self._construct()
tf.summary.scalar('Model/Loss', tf.get_collection(GraphKeys.LOSSES)[0])
self.summary = tf.summary.merge_all()
def _construct(self):
"""
Construct the model; main part of it goes here
"""
| tensorflow.contrib.layers.l2_regularizer | 10,575 |
import tensorflow as tf
x1 = x0 + 1
y0 = tf.to_int32(tf.floor(y))
y1 = y0 + 1
z0 = tf.to_int32(tf.floor(z))
z1 = z0 + 1
x0_clip = tf.clip_by_value(x0, zero, max_x)
x1_clip = tf.clip_by_value(x1, zero, max_x)
y0_clip = tf.clip_by_value(y0, zero, max_y)
y1_clip = tf.clip_by_value(y1, zero, max_y)
z0_clip = tf.clip_by_value(z0, zero, max_z)
z1_clip = tf.clip_by_value(z1, zero, max_z)
dim3 = width
dim2 = width * height
dim1 = width * height * depth
base = _repeat(
tf.range(num_batch) * dim1, out_depth * out_height * out_width)
base_z0_y0 = base + z0_clip * dim2 + y0_clip * dim3
base_z0_y1 = base + z0_clip * dim2 + y1_clip * dim3
base_z1_y0 = base + z1_clip * dim2 + y0_clip * dim3
| tensorflow.clip_by_value | 10,576 |
from tensorflow.python.framework import ops
def _calc_bias_add_flops(graph, node):
"""Calculates the computing needed for BiasAdd."""
input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
input_shape.assert_is_fully_defined()
input_count = np.prod(input_shape.as_list())
return ops.OpStats("flops", input_count)
@ops.RegisterStatistics("BiasAdd", "weight_parameters")
def _calc_bias_add_weight_params(graph, node):
| tensorflow.python.framework.ops.OpStats | 10,577 |
import tensorflow as tf
output = tf.matmul(scores, facts) # [B, 1, H]
# output = tf.reshape(output, [-1, tf.shape(facts)[-1]])
else:
scores = tf.reshape(scores, [-1, tf.shape(facts)[1]])
output = facts * tf.expand_dims(scores, -1)
output = tf.reshape(output, tf.shape(facts))
return output
| tensorflow.expand_dims | 10,578 |
import tensorflow as tf
input_use_counts = [0] * len(cell_inputs + blocks)
for (idx1, _, idx2, _) in cell_arch:
input_use_counts[idx1] += 1
input_use_counts[idx2] += 1
# Concat only unused blocks
with tf.variable_scope('combine'):
block_use_counts = input_use_counts[len(cell_inputs):]
out_blocks = [block for (block, use_count) in zip(blocks, block_use_counts) if use_count == 0]
comb_ch = len(out_blocks) * block_ch
X = tf.concat(out_blocks, axis=3)
| tensorflow.variable_scope | 10,579 |
import tensorflow as tf
class MetaGraphTest(tf.test.TestCase):
def _TestDir(self, test_name):
test_dir = os.path.join(self.get_temp_dir(), test_name)
if os.path.exists(test_dir):
shutil.rmtree(test_dir)
gfile.MakeDirs(test_dir)
return test_dir
def testAddCollectionDef(self):
test_dir = self._TestDir("good_collection")
filename = os.path.join(test_dir, "metafile")
with self.test_session():
# Creates a graph.
v0 = tf.Variable(10.0, name="v0")
var = tf.Variable(tf.constant(0, dtype=tf.int64))
count_up_to = var.count_up_to(3)
input_queue = tf.FIFOQueue(30, tf.float32, shared_name="collection_queue")
qr = tf.train.QueueRunner(input_queue, [count_up_to])
tf.initialize_all_variables()
# Creates a saver.
save = tf.train.Saver({"v0": v0})
# Adds a set of collections.
tf.add_to_collection("int_collection", 3)
tf.add_to_collection("float_collection", 3.5)
tf.add_to_collection("string_collection", "hello")
tf.add_to_collection("variable_collection", v0)
# Add QueueRunners.
tf.train.add_queue_runner(qr)
# Adds user_defined proto in three formats: string, bytes and Any.
| tensorflow.constant | 10,580 |
import tensorflow as tf
update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
q_values = q_func(observations_ph.get(), num_actions, scope="q_func")
deterministic_actions = tf.argmax(q_values, axis=1)
batch_size = tf.shape(observations_ph.get())[0]
random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
_act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],
outputs=output_actions,
| tensorflow.stack | 10,581 |
import tensorflow as tf
y0_f = tf.to_float(y0)
y1_f = tf.to_float(y1)
z0_f = tf.to_float(z0)
z1_f = tf.to_float(z1)
# Check the out-of-boundary case.
x0_valid = tf.to_float(
tf.less_equal(x0, max_x) & tf.greater_equal(x0, 0))
x1_valid = tf.to_float(
tf.less_equal(x1, max_x) & tf.greater_equal(x1, 0))
y0_valid = tf.to_float(
tf.less_equal(y0, max_y) & tf.greater_equal(y0, 0))
y1_valid = tf.to_float(
tf.less_equal(y1, max_y) & tf.greater_equal(y1, 0))
z0_valid = tf.to_float(
tf.less_equal(z0, max_z) & tf.greater_equal(z0, 0))
z1_valid = tf.to_float(
tf.less_equal(z1, max_z) & tf.greater_equal(z1, 0))
w_z0_y0_x0 = tf.expand_dims(((x1_f - x) * (y1_f - y) *
(z1_f - z) * x1_valid * y1_valid * z1_valid),
1)
w_z0_y0_x1 = tf.expand_dims(((x - x0_f) * (y1_f - y) *
(z1_f - z) * x0_valid * y1_valid * z1_valid),
1)
| tensorflow.less_equal | 10,582 |
import tensorflow as tf
# Individual components of the loss that will need summaries.
clone_loss = None
regularization_loss = None
# Compute and aggregate losses on the clone device.
with tf.device(clone.device):
all_losses = []
clone_losses = tf.get_collection(tf.GraphKeys.LOSSES, clone.scope)
if clone_losses:
| tensorflow.device | 10,583 |
import tensorflow as tf
# +np.diag(np.ones(self.N_rec)*(1-self.alpha)))))
# add diagnal matrix 1-alpha to account for persistance tau
return (1.1/rho) * W # - .9*np.diag(np.ones(self.N_rec)*(1-self.alpha)) #correct for tau
# vanishing gradient regularization, Omega, as in Pascanu
# NOTE: this is RELU specific
def dOmega_dWrec(self):
# states in shape timesteps, batch, n_rec
states = self.states
dxt_list = tf.gradients(self.error, states)
#dxt_list[0] = tf.Print(dxt_list[0], [dxt_list[0]], "dxt 0: ")
test = tf.gradients(states[0], states[-1])
dxt = tf.stack(dxt_list)
xt = tf.stack(states)
num = (1 - self.alpha) * dxt + tf.tensordot(self.alpha * dxt ,
| tensorflow.gradients | 10,584 |
import tensorflow as tf
np.savetxt(FLAGS.attack_method + '_' + FLAGS.dataset + '/tSNE/tSNEisadv_' + f1, is_adv_all)
return None
def tSNE_visual_carliniLi(hps, num_batch):
# Construct graph
images, labels = input_name.build_input(
FLAGS.dataset, FLAGS.eval_data_path, hps.batch_size, FLAGS.mode) # FLAGS.mode='attack', batch_size=200
Res = model_name.ResNet(hps, images, FLAGS.mode, Reuse=False)
Res.build_graph()
saver = tf.train.Saver()
# Open session and restore checkpoint
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
tf.train.start_queue_runners(sess)
sess.run(tf.global_variables_initializer())
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root) # Choose dir according to rt
tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)
saver.restore(sess, ckpt_state.model_checkpoint_path)
model_carlini = models_carlini(hps)
if FLAGS.attack_method == 'carliniLi':
attack_carlini = attacks.carliniLi.CarliniLi(sess, model_carlini, largest_const=10 ** -3)
elif FLAGS.attack_method == 'carliniL2':
attack_carlini = attacks.carliniL2.CarliniL2(sess, model_carlini, batch_size=10, max_iterations=1000, confidence=0,binary_search_steps=3)
adv_image = tf.placeholder(tf.float32, shape=[hps.batch_size, image_size, image_size, num_channel])
_, logits_nor = model_carlini.predict(images, tsne_logits=True)
_, logits_adv = model_carlini.predict(adv_image, tsne_logits=True)
dim_logits = logits_nor.shape[1]
| tensorflow.train.get_checkpoint_state | 10,585 |
import tensorflow as tf
Builds the computation graph with Tensorflow
"""
start_t = time.time()
self._setup_placeholders()
self._embed()
self._encode()
self._fuse()
self._decode()
self._compute_loss()
self._create_train_op()
self.logger.info('Time to build graph: {} s'.format(time.time() - start_t))
param_num = total_params(tf.trainable_variables())
self.logger.info('There are {} parameters in the model'.format(param_num))
"""
:description: Placeholders
"""
def _setup_placeholders(self):
if self.demo:
self.c = tf.placeholder(tf.int32, [None, self.config.max_p_len], "context")
| tensorflow.trainable_variables | 10,586 |
import tensorflow as tf
with tf.contrib.summary.create_file_writer(
logdir=model_dir, filename_suffix=".host_call").as_default():
with tf.contrib.summary.always_record_summaries():
for i, name in enumerate(metric_names):
if reduce_fn is None:
scalar = args[i][0]
else:
scalar = reduce_fn(args[i])
with tf.contrib.summary.record_summaries_every_n_global_steps(
100, global_step=step):
tf.contrib.summary.scalar(prefix + name, scalar, step=step)
return tf.contrib.summary.all_summary_ops()
global_step_tensor = tf.reshape(tf.train.get_or_create_global_step(), [1])
other_tensors = [tf.reshape(monitor_dict[key], [1]) for key in metric_names]
return host_call_fn, [global_step_tensor] + other_tensors
def two_stream_loss(FLAGS, features, labels, mems, is_training):
"""Pretraining loss with two-stream attention Transformer-XL."""
#### Unpack input
mem_name = "mems"
mems = mems.get(mem_name, None)
inp_k = tf.transpose(features["input_k"], [1, 0])
inp_q = tf.transpose(features["input_q"], [1, 0])
| tensorflow.reshape | 10,587 |
import tensorflow as tf
Random Network Distillation
"""
def random_net_distill(x_ph, a_ph, hidden_sizes=(400,300), activation=tf.nn.relu,
output_activation=tf.tanh, action_space=None, dropout_rate=0.1):
act_dim = a_ph.shape.as_list()[-1]
act_limit = action_space.high[0]
with tf.variable_scope('rnd_targ_act'):
rnd_targ_act = act_limit * mlp(x_ph, list(hidden_sizes) + [act_dim], activation, output_activation)
with tf.variable_scope('rnd_pred_act'):
# rnd_pred_act = act_limit * mlp(x_ph, list(hidden_sizes) + [act_dim], activation, output_activation)
rnd_pred_act_in_dim = x_ph.shape.as_list()[1]
rnd_pred_act_dropout_mask_generator = DropoutMaskGenerator(rnd_pred_act_in_dim,
| tensorflow.variable_scope | 10,588 |
import tensorflow as tf
"lookup_table",
dtype=tf.float32,
shape=[len(vocab), size_layers],
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01),
)
lookup_table = tf.concat((tf.zeros(shape=[1, size_layers]), lookup_table[1:, :]), 0)
forward = tf.nn.embedding_lookup(lookup_table, self.X)
self.Y = tf.placeholder(tf.float32, (None, None, n_mels * resampled))
self.decoder_inputs = tf.concat((tf.zeros_like(self.Y[:, :1, :]), self.Y[:, :-1, :]), 1)
self.decoder_inputs = self.decoder_inputs[:, :, -n_mels:]
self.Z = tf.placeholder(tf.float32, (None, None, fourier_window_size // 2 + 1))
batch_size = tf.shape(self.X)[0]
seq_lens = tf.count_nonzero(tf.reduce_sum(self.decoder_inputs, -1), 1, dtype=tf.int32) + 1
def cells(reuse=False):
return tf.contrib.rnn.DropoutWrapper(
tf.nn.rnn_cell.LSTMCell(
size_layers, initializer=tf.orthogonal_initializer(), reuse=reuse
),
| tensorflow.placeholder | 10,589 |
import tensorflow as tf
hparams = imagetransformer_latent_tiny()
hparams.mode = tf.estimator.ModeKeys.TRAIN
block_dim = int(hparams.hidden_size // hparams.num_blocks)
block_v_size = 2**(hparams.bottleneck_bits /
(hparams.num_residuals * hparams.num_blocks))
block_v_size = int(block_v_size)
means = tf.get_variable(
name="means",
shape=[hparams.num_residuals,
hparams.num_blocks,
block_v_size,
block_dim],
initializer=tf.uniform_unit_scaling_initializer())
hparams.bottleneck = functools.partial(
discretization.discrete_bottleneck,
hidden_size=hparams.hidden_size,
z_size=hparams.bottleneck_bits,
filter_size=hparams.filter_size,
startup_steps=hparams.startup_steps,
bottleneck_kind=hparams.bottleneck_kind,
num_blocks=hparams.num_blocks,
num_residuals=hparams.num_residuals,
reshape_method=hparams.reshape_method,
beta=hparams.vq_beta,
| tensorflow.uniform_unit_scaling_initializer | 10,590 |
from tensorflow.python.ops import control_flow_ops
def _create_slots(self, var_list):
# Create slots for the first and second moments.
for v in var_list:
self._zeros_slot(v, "g", self._name)
def _apply_dense(self, grad, var):
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
g_t = grad
g_t_1 = self.get_slot(var, "g")
g_t = g_t_1.assign(g_t)
var_update = state_ops.assign_sub(var,
2. * lr_t * g_t - lr_t * g_t_1) # Adam would be lr_t * g_t
return control_flow_ops.group(*[var_update, g_t])
def _apply_sparse(self, grad, var):
raise NotImplementedError("Sparse gradient updates are not supported.")
class OptimisticAdamOptimizer(optimizer.Optimizer):
def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999,
epsilon=1e-8,
use_locking=False, name="Adamirror"):
super(OptimisticAdamOptimizer, self).__init__(use_locking, name)
self._lr = learning_rate
self._beta1 = beta1
| tensorflow.python.ops.control_flow_ops.group | 10,591 |
import tensorflow as tf
m = model
# Do initialization of all variables
sess.run(tf.global_variables_initializer())
# Load datasets with defaults
sess.run([m.train_dataset_init_op, m.pred_dataset_init_op], feed_dict={
m.ph.train_images: np.zeros((1, w, h, in_ch)),
m.ph.train_classes: np.zeros((1,)),
m.ph.pred_images: np.zeros((1, w, h, in_ch)),
m.ph.pred_classes: np.zeros((1,))
})
def _make_session(self):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
return sess
def _feed_dataset_to_model(self, images, classes=None, is_train=False):
m = self._model
utils.logger.log('Feeding dataset to model...')
# Mock classes if required
classes = classes or [0 for _ in range(len(images))]
if is_train:
self._sess.run(m.train_dataset_init_op, feed_dict={
| tensorflow.ConfigProto | 10,592 |
import tensorflow as tf
self.w4=tf.get_variable('w4', [512,classnum],initializer=tf.contrib.layers.xavier_initializer_conv2d())
self.b1 = tf.get_variable('b1', [2048],initializer=tf.constant_initializer(0.0))
self.b2 = tf.get_variable('b2', [3072],initializer=tf.constant_initializer(0.0))
self.b3 = tf.get_variable('b3', [512],initializer=tf.constant_initializer(0.0))
self.b4 = tf.get_variable('b4', [classnum],initializer=tf.constant_initializer(0.0))
def inference(self,images):
images=tf.cast(images,tf.float32)/255.0
l1 = tf.matmul(images, self.w1)+self.b1
l1=tf.nn.relu(l1)
l2 = tf.matmul(l1, self.w2)+self.b2
l2=tf.nn.relu(l2)
l3=tf.matmul(l2, self.w3)+self.b3
l3=tf.nn.relu(l3)
| tensorflow.cast | 10,593 |
import tensorflow as tf
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L1,
do_function_inlining=True)
graph_options = tf.GraphOptions(optimizer_options=optimizer_options)
config = tf.ConfigProto(allow_soft_placement=True,
graph_options=graph_options)
if params.device_list:
| tensorflow.ConfigProto | 10,594 |
import tensorflow as tf
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import array_ops
import numpy as np
def safe_get(name, *args, **kwargs):
""" Same as tf.get_variable, except flips on reuse_variables automatically """
try:
return tf.get_variable(name, *args, **kwargs)
except ValueError:
tf.get_variable_scope().reuse_variables()
return tf.get_variable(name, *args, **kwargs)
def init_weights(shape, name=None):
shape = tuple(shape)
weights = np.random.normal(scale=0.01, size=shape).astype('f')
return safe_get(name, list(shape), initializer=tf.constant_initializer(weights), dtype=tf.float32)
def init_bias(shape, name=None):
return safe_get(name, initializer=tf.zeros(shape, dtype=tf.float32))
| tensorflow.get_variable | 10,595 |
import tensorflow as tf
def general_conv2d(self, input_data, filters = 64, kernel_size = 7, stride = 1, stddev = 0.02, activation_function = "relu", padding = "VALID", do_norm=True, relu_factor = 0, name="conv2d"):
with tf.variable_scope(name):
conv = tf.layers.conv2d(input_data, filters, kernel_size, stride, padding, activation=None)
if do_norm:
conv = tf.layers.batch_normalization(conv, momentum=0.9)
if activation_function == "relu":
conv = tf.nn.relu(conv, name = 'relu')
if activation_function == "leakyrelu":
| tensorflow.layers.batch_normalization | 10,596 |
import tensorflow as tf
# Build a new graph with different initialization.
v0 = tf.Variable(-1.0)
| tensorflow.Variable | 10,597 |
import tensorflow as tf
else: # 创建worker两个网络的具体步骤
with tf.variable_scope(scope): # 这里的scope传入的是worker的名字
self.global_step = globalAC.global_step
self.obs_space = N_S
self.act_space = N_A
self.k = 16
self.g_dim = 256
self.c = 10
self.vf_hidden_size = 128 # for value function network
self.alpha = 0.5 # for build loss
self.batch_processor = FeudalBatchProcessor(self.c)
self.build_model() # build feudal policy model
with tf.name_scope('local_grad'):
grads = tf.gradients(self.loss, self.var_list)
grads, _ = tf.clip_by_global_norm(grads, 40)
with tf.name_scope('sync'): # worker和global的同步过程
with tf.name_scope('pull'): # 获取global参数,复制到local—net
self.pull_params_op = tf.group(*[v1.assign(v2)
for v1, v2 in zip(self.var_list, globalAC.var_list)])
with tf.name_scope('push'): # 将参数传送到gloabl中去
self.update_params_op = OPT.apply_gradients(zip(grads, globalAC.var_list))
# 其中传送的是local—net的actor和critic的参数梯度grads,具体计算在上面定义
# apply_gradients是tf.train.Optimizer中自带的功能函数，将求得的梯度参数更新到global中
self.inc_step = self.global_step.assign_add(tf.shape(self.obs)[0])
self.train_op = tf.group(self.update_params_op, self.inc_step)
# GLOBALE_STEP += tf.shape(self.obs)[0]
| tensorflow.gradients | 10,598 |
import tensorflow as tf
config = tf.ConfigProto(log_device_placement=False)
if self.on_gpu:
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.sess.run(init)
checkpoint_file = self.model_dir
| tensorflow.Session | 10,599 |
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