seed
stringlengths 25
2.89k
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stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
self.saver = tf.train.Saver()
# Loss functions and training
loss_pg = - tf.reduce_mean(pi.log_prob(batch['actions']) * batch['advantage']) - 0.01 * tf.reduce_mean(pi.entropy())
loss_vf = 0.5 * tf.reduce_mean(tf.square(batch['rewards'] - self.vf))
self.a_grads = tf.gradients(loss_pg, self.pi_params)
self.c_grads = tf.gradients(loss_vf, self.vf_params)
self.a_grads, _ = tf.clip_by_global_norm(self.a_grads, 20.0)
self.c_grads, _ = tf.clip_by_global_norm(self.c_grads, 20.0)
opt = tf.train.AdamOptimizer(self.LR)
self.update_a_op = opt.apply_gradients(zip(self.a_grads, self.pi_params))
self.update_c_op = opt.apply_gradients(zip(self.c_grads, self.vf_params))
self.sess.run(tf.global_variables_initializer())
# Tensorboard
if summary_dir is not None:
self.writer = tf.summary.FileWriter(summary_dir)
| tensorflow.train.AdamOptimizer | 11,900 |
import tensorflow as tf
def main(self):
with tf.Graph().as_default() as graph, tf.device('/cpu:0'):
num_gpu = len(cfgs.GPU_GROUP.strip().split(','))
global_step = slim.get_or_create_global_step()
lr = self.warmup_lr(cfgs.LR, global_step, cfgs.WARM_SETP, num_gpu)
tf.summary.scalar('lr', lr)
optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM)
r3det_gwd = build_whole_network.DetectionNetworkR3DetGWD(cfgs=self.cfgs,
is_training=True)
with tf.name_scope('get_batch'):
if cfgs.IMAGE_PYRAMID:
shortside_len_list = tf.constant(cfgs.IMG_SHORT_SIDE_LEN)
shortside_len = tf.random_shuffle(shortside_len_list)[0]
else:
shortside_len = cfgs.IMG_SHORT_SIDE_LEN
img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \
self.reader.next_batch(dataset_name=cfgs.DATASET_NAME,
batch_size=cfgs.BATCH_SIZE * num_gpu,
shortside_len=shortside_len,
is_training=True)
| tensorflow.name_scope | 11,901 |
import tensorflow as tf
p = defaults
source_vocab_size = self._encoders["inputs"].vocab_size
p.input_modality = {
"inputs": (registry.Modalities.SYMBOL, source_vocab_size)
}
p.target_modality = (registry.Modalities.CLASS_LABEL, self.num_classes)
def example_reading_spec(self):
data_fields = {
"inputs": tf.VarLenFeature(tf.int64),
"targets": tf.FixedLenFeature([1], tf.int64),
}
data_items_to_decoders = None
return (data_fields, data_items_to_decoders)
def txt_line_iterator(txt_path):
"""Iterate through lines of file."""
| tensorflow.VarLenFeature | 11,902 |
import tensorflow as tf
# instead.
output_layer = model.get_sequence_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias",[], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.reduce_sum(tf.multiply(output_layer,output_weights),-1)
logits = tf.add(logits, output_bias)
probabilities=tf.sigmoid(logits)
# labels=tf.constant(labels,dtype=tf.int32)
per_example_loss=tf.losses.sigmoid_cross_entropy(multi_class_labels=labels, logits=logits,reduction=Reduction.NONE)
| tensorflow.variable_scope | 11,903 |
import tensorflow as tf
Maximum pairwise distance allowed.
Returns
-------
tf.Tensor:
Of shape (n_test, n_support)
"""
test = tf.expand_dims(test, 1)
support = tf.expand_dims(support, 0)
g = -tf.maximum(tf.reduce_sum(tf.square(test - support), 2), max_dist_sq)
return g
def add_bias(tensor, init=None, name=None):
| tensorflow.expand_dims | 11,904 |
import tensorflow as tf
assert padding in ['SAME', 'VALID']
x = tf.keras.layers.Conv3D(filters, kernel_size, activation=activation, kernel_initializer=initialization, use_bias=use_bias, kernel_regularizer=reg_l2)(x)
| tensorflow.keras.layers.Conv3D | 11,905 |
import tensorflow as tf
x = tf.image.random_brightness(x, max_delta=0.8*s)
x = tf.image.random_contrast(x, lower=lower, upper=upper)
x = tf.image.random_saturation(x, lower=lower, upper=upper)
x = tf.image.random_hue(x, max_delta=0.2*s)
x = tf.clip_by_value(x, 0, 1)
return x
def color_drop(image):
image = tf.image.rgb_to_grayscale(image)
image = tf.tile(image, [1, 1, 1, 3])
return image
# pylint: disable=not-callable
@gin.configurable(blacklist=["kwargs"])
class CLGAN(modular_gan.ModularGAN):
"""Self-Supervised GAN with Contrastive Loss"""
| tensorflow.image.rgb_to_grayscale | 11,906 |
import tensorflow as tf
# 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))
x = tf.placeholder_with_default(input=[1], shape=None)
is_scalar = normal._is_scalar_helper(x.shape, lambda: tf.shape(x))
self.assertFalse(self.evaluate(is_scalar))
def _GetFakeDistribution(self):
class FakeDistribution(tfd.Distribution):
"""Fake Distribution for testing _set_sample_static_shape."""
def __init__(self, batch_shape=None, event_shape=None):
self._static_batch_shape = tf.TensorShape(batch_shape)
self._static_event_shape = tf.TensorShape(event_shape)
super(FakeDistribution, self).__init__(
| tensorflow.shape | 11,907 |
import tensorflow as tf
training_decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(
decoder=training_decoder,
impute_finished=True,
maximum_iterations=tf.reduce_max(seq_lens),
)
| tensorflow.reduce_max | 11,908 |
from tensorflow.python.framework import ops
total_var = contrib_variables.local_variable(
array_ops.zeros([], dtype=dtypes.float64), name=total_scope)
batch_total = math_ops.reduce_sum(average_precision, name='batch_total')
total_update = state_ops.assign_add(total_var, batch_total, name='update')
# Divide total by max to get mean, for both vars and the update ops.
mean_average_precision = _safe_scalar_div(total_var, max_var, name='mean')
update = _safe_scalar_div(total_update, max_update, name=scope)
if metrics_collections:
ops.add_to_collections(metrics_collections, mean_average_precision)
if updates_collections:
ops.add_to_collections(updates_collections, update)
return mean_average_precision, update
def _select_class_id(ids, selected_id):
"""Filter all but `selected_id` out of `ids`.
Args:
ids: `int64` `Tensor` or `SparseTensor` of IDs.
selected_id: Int id to select.
| tensorflow.python.framework.ops.add_to_collections | 11,909 |
import tensorflow as tf
eval_examples.append(PaddingInputExample())
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
if not tf.gfile.Exists(eval_file) or not FLAGS.data_converted:
file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
| tensorflow.logging.info | 11,910 |
import tensorflow as tf
dist /= tf.reshape(self.hparams.scales,
[1, 1, self.hparams.moe_num_experts])
nearest_idx = tf.argmax(-dist, axis=-1)
nearest_hot = tf.one_hot(nearest_idx, self.hparams.block_v_size)
| tensorflow.argmax | 11,911 |
import tensorflow as tf
is_training_const = utils.constant_value(is_training)
if is_training_const is None or is_training_const:
update_mean_op, update_second_moment_op = utils.smart_cond(
is_training,
build_update_ops,
build_no_ops,
)
# Every new connection creates a new op which adds its contribution
# to the running average when ran.
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_mean_op)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_second_moment_op)
def _build(self, input_batch, is_training=True, test_local_stats=True):
"""Connects the BatchNorm module into the graph.
Args:
input_batch: A Tensor of arbitrary dimension. By default, the final
dimension is not reduced over when computing the minibatch statistics.
is_training: A boolean to indicate if the module should be connected in
| tensorflow.add_to_collection | 11,912 |
import tensorflow as tf
with tf.variable_scope("convnet"):
# original architecture
out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu)
out = layers.flatten(out)
with tf.variable_scope("action_value"):
out = layers.fully_connected(out, num_outputs=512, activation_fn=tf.nn.relu)
out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None)
return out
def simple_model(img_in, num_actions, scope, reuse=False, num_filters=64):
with tf.variable_scope(scope, reuse=reuse):
out = img_in
gauss_initializer = initializers.xavier_initializer(uniform=False) # stddev = 1/n
with tf.variable_scope("convnet"):
out = layers.convolution2d(
out, num_outputs=num_filters, kernel_size=8, stride=4,
activation_fn=tf.nn.relu, weights_initializer=gauss_initializer,
trainable=False)
out = layers.flatten(out)
with tf.variable_scope("action_value"):
out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None)
return out
| tensorflow.variable_scope | 11,913 |
import tensorflow as tf
:return: sample from the MVN of shape N x D
"""
eps = tf.random_normal(tf.shape(mean), dtype=settings.float_type) # N x P
if cov_structure == "diag":
sample = mean + tf.sqrt(cov) * eps # N x P
elif cov_structure == "full":
cov = cov + (tf.eye(tf.shape(mean)[1], dtype=settings.float_type) * settings.numerics.jitter_level)[None, ...] # N x P x P
chol = tf.cholesky(cov) # N x P x P
return mean + (tf.matmul(chol, eps[..., None])[..., 0]) # N x P
else:
raise NotImplementedError # pragma: no cover
| tensorflow.shape | 11,914 |
import tensorflow as tf
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps,
use_tpu, optimizer)
output_spec = contrib_tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
| tensorflow.logging.info | 11,915 |
from tensorflow.python.framework import ops
with ops.control_dependencies([maybe_reallocate_op]):
append_values_op = array[size:new_size].assign(batch_values)
with ops.control_dependencies([append_values_op]):
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
| tensorflow.python.framework.ops.add_to_collections | 11,916 |
import tensorflow as tf
inputs=embed_inputs,
dtype=tf.float32,
sequence_length=self.seq_len,
) ## (batch_size, seq_len, num_hidden)
# rnn_outputs = tf.transpose(rnn_outputs, perm=[1,0,2]) ## (seq_len, batch_size, num_hidden) NOT NEEDED ANY MORE
last_outputs = self.last_relevant(rnn_outputs, self.seq_len) ## (batch_size, num_hidden)
with tf.variable_scope('output', reuse=forward_only):
with tf.variable_scope('softmax'):
W = tf.get_variable('W', [self.num_hidden, self.num_classes],
# initializer=tf.random_uniform_initializer(-0.003, 0.003))
initializer=tf.contrib.layers.xavier_initializer())
# initializer=tf.truncated_normal_initializer(stddev=0.1))
b = tf.get_variable('b', [self.num_classes], initializer=tf.constant_initializer(0.1))
logits = tf.matmul(last_outputs, W) + b
| tensorflow.variable_scope | 11,917 |
import tensorflow as tf
"src_lang": tf.TensorShape([None, ]),
"trg_lang": tf.TensorShape([None, ])}
| tensorflow.TensorShape | 11,918 |
import tensorflow as tf
dc_g_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_g_fake), logits=d_g_fake))
dc_g_loss = dc_g_loss_fake + dc_g_loss_real
# Categorical Discrimminator Loss
dc_c_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_c_real), logits=d_c_real))
dc_c_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_c_fake), logits=d_c_fake))
dc_c_loss = dc_c_loss_fake + dc_c_loss_real
# Generator loss
| tensorflow.ones_like | 11,919 |
import tensorflow as tf
precise_logits = tf.cast(logits, tf.float32) if (
logits.dtype == tf.float16) else logits
onehot_labels = tf.cast(onehot_labels, precise_logits.dtype)
predictions = tf.nn.sigmoid(logits)
predictions_pt = tf.where(tf.equal(onehot_labels, 1), predictions, 1.-predictions)
# add small value to avoid 0
epsilon = 1e-8
alpha_t = tf.scalar_mul(alpha, tf.ones_like(onehot_labels, dtype=tf.float32))
alpha_t = tf.where(tf.equal(onehot_labels, 1.0), alpha_t, 1-alpha_t)
losses = tf.reduce_sum(-alpha_t * tf.pow(1. - predictions_pt, gamma) * tf.log(predictions_pt+epsilon),
name=name, axis=1)
return losses
def Evaluate(sess):
test_acc = 0.0
test_loss = 0.0
for it in range(test_iteration):
| tensorflow.log | 11,920 |
import tensorflow as tf
def resnet_block(x, block_name='ResBlock', channel_nr=64, scale = 1, pad='SAME'):
tmp = conv3d(x, kernel_size=3, filters=channel_nr, padding=pad, activation=None, use_bias=False, initialization=None)
tmp = tf.keras.layers.LeakyReLU(alpha=0.2)(tmp)
tmp = conv3d(tmp, kernel_size=3, filters=channel_nr, padding=pad, activation=None, use_bias=False, initialization=None)
| tensorflow.keras.layers.LeakyReLU | 11,921 |
import tensorflow as tf
h_conv1 = self.conv2d('h_conv1', s, W_conv1, 4, b_conv1) # 构造第一个卷积层输出为conv1
h_pool1 = self.max_pool_2x2('h_pool1', h_conv1)
h_conv2 = self.conv2d('h_conv2', h_pool1, W_conv2, 2, b_conv2)
h_conv3 = self.conv2d('h_conv3', h_conv2, W_conv3, 1, b_conv3)
h_conv3_flat = tf.reshape(h_conv3, [-1, 1600], 'h_conv3_flat')
h_fc1 = tf.nn.relu(tf.add(tf.matmul(h_conv3_flat, W_fc1), b_fc1, 'h_fc1'))
readout = tf.add(tf.matmul(h_fc1, W_fc2), b_fc2, 'h_fc2')
return s, readout, h_fc1
def creat_optimizer(self,readout):
action = tf.placeholder(tf.float32,[None,self.ACTIONS])
| tensorflow.matmul | 11,922 |
import tensorflow as tf
step_size = tf.cast(step_size, dtype=tf.float32)
max_lr = tf.cast(max_lr, dtype=tf.float32)
if mode == 'tri':
periodic_comp = tf.mod((global_step + step_size / 4) / step_size, 1)
first_factor = tf.abs(periodic_comp - 0.5)
second_factor = 2 * (max_lr - learning_rate)
second_comp = learning_rate
| tensorflow.mod | 11,923 |
import tensorflow as tf
transform_input_data, model_preprocess_fn=model_preprocess_fn,
image_resizer_fn=image_resizer_fn,
num_classes=num_classes,
data_augmentation_fn=None)
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=False,
num_additional_channels=predict_input_config.num_additional_channels)
input_dict = transform_fn(decoder.decode(example))
images = tf.cast(input_dict[fields.InputDataFields.image], dtype=tf.float32)
images = tf.expand_dims(images, axis=0)
true_image_shape = tf.expand_dims(
input_dict[fields.InputDataFields.true_image_shape], axis=0)
return tf.estimator.export.ServingInputReceiver(
features={
fields.InputDataFields.image: images,
fields.InputDataFields.true_image_shape: true_image_shape},
| tensorflow.cast | 11,924 |
import tensorflow as tf
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
| tensorflow.nn.log_softmax | 11,925 |
import tensorflow as tf
require_init = tf.reduce_any(tf.is_nan(self.g))
init_ops = tf.cond(require_init,_init,lambda : [self.g,self.b])
with tf.control_dependencies(init_ops):
w = tf.expand_dims(self.g,axis=0) * tf.nn.l2_normalize(self.v,axis=0)
return tf.matmul(input_var,w)+self.b
def get_variables(self):
#TODO: self.v should be l2-normalized or not? / currently not.
return {'v':self.v,'b':self.b,'g':self.g}
class SymPadConv2d(object): #Resize and Convolution(upsacle by 2)
def __init__(self,name,input_dim,output_dim,
k_h=3,k_w=3,stddev=0.02) :
assert k_h%2==1 and k_w%2==1, 'kernel size should be odd numbers to ensure exact size'
with tf.variable_scope(name) :
self.w = tf.get_variable('w', [k_h, k_w, input_dim, output_dim],
initializer=tf.random_normal_initializer(stddev=stddev))
self.b = tf.get_variable('b',[output_dim], initializer=tf.constant_initializer(0.0))
self.padding = [ [0,0],[k_h//2,k_h//2],[k_w//2,k_w//2],[0,0] ]
def __call__(self,input_var,name=None,**kwargs):
_,h,w,c = input_var.shape.as_list()
_t = tf.image.resize_nearest_neighbor(input_var, [h*2, w*2])
_t = tf.pad(_t,self.padding, mode='SYMMETRIC')
return tf.nn.bias_add(
tf.nn.conv2d(_t, self.w,
data_format='NHWC', #we can't use cudnn due to resize method...
strides=[1,1,1,1], padding="VALID"),
| tensorflow.variable_scope | 11,926 |
import tensorflow as tf
dec_op, _ = seq2seq(enc_inp, dec_inp, feed_previous=feed_previous)
net_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope_name)
optimizer = tf.train.AdamOptimizer(0.03, epsilon=1e-5)
update_op = optimizer.minimize(
tf.nn.seq2seq.sequence_loss(dec_op, targets, weights),
| tensorflow.train.AdamOptimizer | 11,927 |
import tensorflow as tf
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(logits1), axis=2),
tf.expand_dims(tf.nn.softmax(logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, config.ans_limit)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
| tensorflow.matrix_band_part | 11,928 |
import tensorflow as tf
@tfe.run_all_tests_in_graph_and_eager_modes
class ParetoTest(test_case.TestCase):
def _scipy_pareto(self, concentration, scale):
# In scipy pareto is defined with scale = 1, so we need to scale.
return stats.pareto(concentration, scale=scale)
def testParetoShape(self):
scale = tf.constant([2.] * 5)
concentration = tf.constant([2.] * 5)
pareto = tfd.Pareto(concentration, scale)
self.assertEqual(self.evaluate(pareto.batch_shape_tensor()), (5,))
self.assertEqual(pareto.batch_shape, tf.TensorShape([5]))
self.assertAllEqual(self.evaluate(pareto.event_shape_tensor()), [])
self.assertEqual(pareto.event_shape, tf.TensorShape([]))
def testParetoShapeBroadcast(self):
scale = tf.constant([[3., 2.]])
concentration = tf.constant([[4.], [5.], [6.]])
pareto = tfd.Pareto(concentration, scale)
self.assertAllEqual(self.evaluate(pareto.batch_shape_tensor()), (3, 2))
self.assertAllEqual(pareto.batch_shape, tf.TensorShape([3, 2]))
self.assertAllEqual(self.evaluate(pareto.event_shape_tensor()), [])
self.assertEqual(pareto.event_shape, tf.TensorShape([]))
| tensorflow.TensorShape | 11,929 |
import tensorflow as tf
Computes the offset array used to upsample indices with TensorFlow.
:param shape: [list] Window shape.
"""
center = [(ss - 1) // 2 for ss in shape]
axes = [tf.range(-cc, ss - cc, dtype=tf.int32) for cc, ss in zip(center, shape)]
# Broadcast and match dimension.
if len(shape) > 1:
for jj in range(len(shape)):
for ii in range(len(shape) + 1):
if ii != jj:
axes[jj] = tf.expand_dims(axes[jj], ii)
for jj in range(len(shape)):
shape_ = [ss for ss in shape] + [1]
shape_[jj] = 1
axes[jj] = tf.tile(axes[jj], shape_)
offset = tf.concat(axes, len(shape))
return offset
def _get_offset_array(shape):
"""
| tensorflow.expand_dims | 11,930 |
import tensorflow as tf
# with `layer_input`.
with tf.variable_scope("output"):
attention_output = dense_layer_3d_proj(
attention_output,
hidden_size,
attention_head_size,
create_initializer(initializer_range),
None,
name="dense")
attention_output = dropout(attention_output, hidden_dropout_prob)
attention_output = layer_norm(attention_output + layer_input)
with tf.variable_scope("ffn_1"):
with tf.variable_scope("intermediate"):
intermediate_output = dense_layer_2d(
attention_output,
intermediate_size,
create_initializer(initializer_range),
intermediate_act_fn,
num_attention_heads=num_attention_heads,
name="dense")
with tf.variable_scope("output"):
ffn_output = dense_layer_2d(
| tensorflow.variable_scope | 11,931 |
from tensorflow.contrib.tpu.python.tpu.datasets import StreamingFilesDataset
if epochs_between_evals > 1:
raise ValueError("epochs_between_evals > 1 not supported for file "
"based dataset.")
epoch_data_dir = self._result_queue.get(timeout=300)
if not self._is_training:
self._result_queue.put(epoch_data_dir) # Eval data is reused.
file_pattern = os.path.join(
epoch_data_dir, rconst.SHARD_TEMPLATE.format("*"))
dataset = StreamingFilesDataset(
files=file_pattern, worker_job="worker",
num_parallel_reads=rconst.NUM_FILE_SHARDS, num_epochs=1,
sloppy=not self._deterministic)
map_fn = functools.partial(self._deserialize, batch_size=batch_size)
dataset = dataset.map(map_fn, num_parallel_calls=16)
else:
types = {movielens.USER_COLUMN: rconst.USER_DTYPE,
| tensorflow.contrib.tpu.python.tpu.datasets.StreamingFilesDataset | 11,932 |
import tensorflow as tf
for ns in [d, h, w]:
x = tf.split(x, ns, 1)
x = tf.concat([tf.squeeze(v, 1) for v in x], 3)
return tf.reshape(x, (batch_size, d*r, h*r, w*r, 1))
def subpixel_conv3d(x, r, out_channels):
x = tf.split(x, out_channels, 4)
x = tf.concat([phase_shift_3d(v, r) for v in x], 4)
return x
def pixel_shuffler_3d(x, r, k, out_channels, name):
in_channels = x.get_shape.as_list()[4]
| tensorflow.split | 11,933 |
import tensorflow as tf
b6 = utils.bias_variable([4096], name="b6")
conv6 = utils.conv2d_basic(pool5, W6, b6)
relu6 = tf.nn.relu(conv6, name="relu6")
if FLAGS.debug:
utils.add_activation_summary(relu6)
relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)
W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
b7 = utils.bias_variable([4096], name="b7")
conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)
relu7 = tf.nn.relu(conv7, name="relu7")
if FLAGS.debug:
utils.add_activation_summary(relu7)
relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)
W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8")
b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8")
conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)
# annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")
| tensorflow.nn.relu | 11,934 |
import tensorflow as tf
if encoder.position_bias and input_length is not None and time is not None:
src_pos = tf.tile(tf.expand_dims(tf.range(time_steps), axis=0), [batch_size, 1])
trg_pos = tf.tile(tf.reshape(time, [1, 1]), [batch_size, time_steps])
src_len = tf.tile(tf.expand_dims(input_length, axis=1), [1, time_steps]) # - 1
pos_feats = tf.to_float(tf.stack([src_pos, trg_pos, src_len], axis=2))
pos_feats = tf.log(1 + pos_feats)
y += dense(pos_feats, encoder.attn_size, use_bias=False, name='P_a')
| tensorflow.stack | 11,935 |
import tensorflow as tf
wh = tf.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale))
gh = tf.get_variable("gh", [nh*4], initializer=tf.constant_initializer(1.0))
bh = tf.get_variable("bh", [nh*4], initializer=tf.constant_initializer(0.0))
b = tf.get_variable("b", [nh*4], initializer=tf.constant_initializer(0.0))
gc = tf.get_variable("gc", [nh], initializer=tf.constant_initializer(1.0))
bc = tf.get_variable("bc", [nh], initializer=tf.constant_initializer(0.0))
c, h = tf.split(axis=1, num_or_size_splits=2, value=s)
for idx, (x, m) in enumerate(zip(xs, ms)):
c = c*(1-m)
h = h*(1-m)
z = _ln(tf.matmul(x, wx), gx, bx) + _ln(tf.matmul(h, wh), gh, bh) + b
i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
i = tf.nn.sigmoid(i)
f = tf.nn.sigmoid(f)
o = tf.nn.sigmoid(o)
u = tf.tanh(u)
c = f*c + i*u
h = o*tf.tanh(_ln(c, gc, bc))
xs[idx] = h
s = tf.concat(axis=1, values=[c, h])
return xs, s
def conv_to_fc(x):
| tensorflow.matmul | 11,936 |
from tensorflow.python.framework import constant_op
features = {
'language':
sparse_tensor.SparseTensor(
values=('en', 'fr', 'zh'),
indices=((0, 0), (0, 1), (2, 0)),
dense_shape=(3, 2))
}
labels = constant_op.constant(((1,), (0,), (0,)))
return features, labels
# The given hash_bucket_size results in variables larger than the
# default min_slice_size attribute, so the variables are partitioned.
sparse_feature = feature_column.sparse_column_with_hash_bucket(
'language', hash_bucket_size=2e7)
| tensorflow.python.framework.constant_op.constant | 11,937 |
import tensorflow as tf
def _to_chars(line):
"""string scalar -> Dataset of characters (string scalars)."""
chars, = tf.py_func(_split_string, [line + "\n"], [tf.string])
chars.set_shape([None])
return tf.data.Dataset.from_tensor_slices(chars)
return (tf.data.TextLineDataset([filename])
.flat_map(_to_chars)
.repeat()
.batch(tf.to_int64(sequence_size))
.shuffle(1000)
.batch(tf.to_int64(batch_size)))
| tensorflow.to_int64 | 11,938 |
import tensorflow as tf
layer_c2 = tf.layers.dense(layer_c1, 256, tf.nn.relu, kernel_regularizer=reg)
lstm_c = tf.nn.rnn_cell.LSTMCell(num_units=256)
| tensorflow.nn.rnn_cell.LSTMCell | 11,939 |
import tensorflow as tf
def contra_step_lossV5(pred, tgt, resample=1):
# p = tf.print('begin loss v5', [resample, pred.shape,tgt.shape])
# with tf.control_dependencies([p]):
pred_flat = tf.reshape(pred, [-1])
tgt_flat = tf.reshape(tgt, [-1])
batch = tf.stack([pred_flat, tgt_flat], 1)
num_sam = tools.shape(batch)[0]
index = tf.range(num_sam)
divider = tf.constant(resample, dtype=tf.float32)
def sample_compute(cur_loss, i):
batch1 = tf.gather(batch, tf.random.shuffle(index))
batch2 = tf.gather(batch, tf.random.shuffle(index))
pred1 = tf.slice(batch1, [0, 0], [num_sam, 1])
pred2 = tf.slice(batch2, [0, 0], [num_sam, 1])
tgt1 = tf.slice(batch1, [0, 1], [num_sam, 1])
tgt2 = tf.slice(batch2, [0, 1], [num_sam, 1])
loss = cur_loss + compute_contra_loss(pred1, pred2, tgt1, tgt2)
print(loss)
return (loss, i + 1)
# def sample_compute(i):
# batch1 = tf.gather(batch, tf.random.shuffle(index))
# batch2 = tf.gather(batch, tf.random.shuffle(index))
| tensorflow.random.shuffle | 11,940 |
import tensorflow as tf
if mode == tf.estimator.ModeKeys.TRAIN:
train_op, update_learning_rate = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, adjust_lr, use_hvd,
use_compression, use_fp16, clip, cos_decay, use_lamb, previous_train_steps, post_train_steps)
logging_hook = tf.train.LoggingTensorHook({"loss": total_loss, "learning_rate": update_learning_rate}, every_n_iter=FLAGS.hooking_frequence)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
training_hooks=[logging_hook])
elif mode == tf.estimator.ModeKeys.EVAL:
| tensorflow.estimator.EstimatorSpec | 11,941 |
import tensorflow as tf
value_dtype=tf.int64,
default_value=-1)
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),
tf.constant(1, dtype=tf.int64, name='constant'))
)
vx.insert(u, r)
| tensorflow.string_split | 11,942 |
import tensorflow as tf
name: the name of the tf summary.
"""
with tf.name_scope(name):
private_samples -= tf.reduce_mean(private_samples, 0)
shared_samples -= tf.reduce_mean(shared_samples, 0)
| tensorflow.reduce_mean | 11,943 |
import tensorflow as tf
ignored_matches: a bool tensor of shape of [batch, N], representing
whether each box is an ignored matches or not. An ignored matches is the
match that is neither positive or negative.
"""
matched_gt_boxes, matched_gt_classes, matched_gt_indices, matched_iou, _ = (
box_ops.box_matching(boxes, gt_boxes, gt_classes))
positive_matches = tf.greater(
matched_iou, self._config_dict['foreground_iou_threshold'])
negative_matches = tf.logical_and(
tf.greater_equal(
matched_iou, self._config_dict['background_iou_low_threshold']),
tf.less(
matched_iou, self._config_dict['background_iou_high_threshold']))
| tensorflow.greater | 11,944 |
import tensorflow as tf
class AssignOpTest(tf.test.TestCase):
def _initAssignFetch(self, x, y, use_gpu=False):
"""Initialize a param to init and update it with y."""
super(AssignOpTest, self).setUp()
with self.test_session(use_gpu=use_gpu):
p = tf.Variable(x)
assign = tf.assign(p, y)
p.initializer.run()
new_value = assign.eval()
return p.eval(), new_value
def _initAssignAddFetch(self, x, y, use_gpu=False):
"""Initialize a param to init, and compute param += y."""
with self.test_session(use_gpu=use_gpu):
p = tf.Variable(x)
| tensorflow.assign | 11,945 |
import tensorflow as tf
[0, 0]], dtype=tf.float32)
mask5 = tf.constant([[1, 0],
[1, 0]], dtype=tf.float32)
masks1 = tf.stack([mask0, mask1, mask2])
masks2 = tf.stack([mask3, mask4, mask5])
ious = isu.get_pairwise_iou_matrix(masks1, masks2)
expected_ious = tf.constant([[0.5, 0.0, 1.0/3.0],
[0.75, 0.0, 0.25],
[0.75, 0.0, 2.0/3.0]],
dtype=tf.float32)
self.assertAllClose(ious.numpy(), expected_ious.numpy())
| tensorflow.constant | 11,946 |
import tensorflow as tf
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
if task_name != "sts-b":
probabilities = tf.nn.softmax(logits, axis=-1)
predictions = tf.argmax(probabilities, axis=-1, output_type=tf.int32)
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
else:
| tensorflow.nn.dropout | 11,947 |
import tensorflow as tf
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
def get_next_sentence_output(bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
| tensorflow.reduce_sum | 11,948 |
import tensorflow as tf
# Traditional U-Net
def build_Unet_Arch(self, input_data, name="Unet_Arch"):
self.base_number_of_features = 32
with tf.variable_scope(name):
# Encoder definition
o_c1 = self.general_conv2d(input_data, self.base_number_of_features, 3, stride = 1, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_conv2d_1')
o_mp1 = tf.layers.max_pooling2d(o_c1, 2, 2, name = name + '_maxpooling_1')
o_c2 = self.general_conv2d(o_mp1, self.base_number_of_features * 2, 3, stride = 1, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_conv2d_2')
o_mp2 = tf.layers.max_pooling2d(o_c2, 2, 2, name = name + '_maxpooling_2')
o_c3 = self.general_conv2d(o_mp2, self.base_number_of_features * 4, 3, stride = 1, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_conv2d_3')
o_mp3 = tf.layers.max_pooling2d(o_c3, 2, 2, name = name + '_maxpooling_3')
o_c4 = self.general_conv2d(o_mp3, self.base_number_of_features * 8, 3, stride = 1, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_conv2d_4')
o_mp4 = tf.layers.max_pooling2d(o_c4, 2, 2, name = name + '_maxpooling_4')
o_c5 = self.general_conv2d(o_mp4, self.base_number_of_features * 16, 3, stride = 1, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_conv2d_5')
# Decoder definition
o_d1 = self.general_deconv2d(o_c5, self.base_number_of_features * 8, 3, stride = 2, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_deconv2d_1')
o_me1 = tf.concat([o_d1, o_c4], 3) # Skip connection
o_d2 = self.general_deconv2d(o_me1, self.base_number_of_features * 4, 3, stride = 2, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_deconv2d_2')
o_me2 = tf.concat([o_d2, o_c3], 3) # Skip connection
o_d3 = self.general_deconv2d(o_me2, self.base_number_of_features * 2, 3, stride = 2, padding = 'SAME', activation_function = 'relu', do_norm = False, name = name + '_deconv2d_3')
o_me3 = tf.concat([o_d3, o_c2], 3) # Skip connection
| tensorflow.layers.max_pooling2d | 11,949 |
import tensorflow as tf
start = time()
self.sess.run([self.pi_new_params, self.vf_new_params, self.data_iter.initializer],
feed_dict={self.state: s, self.actions: a, self.rewards: r, self.advantage: adv})
while True:
try:
summary, step, _ = self.sess.run([self.summarise, self.global_step, self.train_op])
except tf.errors.OutOfRangeError:
break
print('\rTrained in %.3fs. Global step %i' % (time() - start, step+1))
return summary
class PPO_HC(PPO):
def build_anet(self, state_in, name, reuse=False):
reg = tf.contrib.layers.l2_regularizer(1e-3)
with tf.variable_scope(name, reuse=reuse):
layer_a1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
layer_a2 = tf.layers.dense(layer_a1, 256, tf.nn.relu, kernel_regularizer=reg)
mu = tf.layers.dense(layer_a2, self.a_dim, tf.nn.tanh, kernel_regularizer=reg)
sigma = tf.layers.dense(layer_a2, self.a_dim, tf.nn.softplus, kernel_regularizer=reg)
# sigma = tf.get_variable(name='pi_sigma', shape=self.a_dim, initializer=tf.constant_initializer(0.5))
sigma = tf.clip_by_value(sigma, 0.0, 1.0)
norm_dist = tf.distributions.Normal(loc=mu * self.a_bound, scale=sigma)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
return norm_dist, params
class PPO_LSTM(Base):
| tensorflow.contrib.layers.l2_regularizer | 11,950 |
import tensorflow as tf
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.FIFOQueue | 11,951 |
import tensorflow as tf
initializer=tf.random_normal_initializer())
alpha_logstd = tf.get_variable('alpha_logstd_layer'+str(h),
shape=[1, 1, n_basis, n_out],
initializer=tf.random_normal_initializer())
alpha_std = tf.exp(alpha_logstd)
# Compute epsilon from {n_samples} standard Gaussian
# epsilon = tf.random_normal([n_samples, 1, n_out*2, n_out])
epsilon = tf.random_uniform([n_samples, 1, n_basis, n_out])
hyp_params = tf.get_variable('hyp_params_layer'+str(h),
shape=[2],
initializer=tf.random_normal_initializer())
l1, l2 = tf.nn.sigmoid(hyp_params[0]), tf.exp(hyp_params[1])
epsilon = tf.sinh(epsilon*l2)/tf.cosh(epsilon*l2)**l1/l2
# Compute A_{h+1}
A = tf.tile(alpha_mean+epsilon*alpha_std, [1, tf.shape(X)[0], 1, 1])
# Compute z_{h}A_{h+1}
Z1 = tf.matmul(Z, A[:,:,:n_basis//2,:])/tf.sqrt(n_basis*.5)
Z2 = tf.matmul(Z, A[:,:,n_basis//2:,:])/tf.sqrt(n_basis*.5)
# Compute u_{h+1} and v_{h+1}
U, V = tf.cos(Z1)+tf.cos(Z2), tf.sin(Z1)+tf.sin(Z2)
Z = tf.concat([U, V], 3)/tf.sqrt(n_out*1.)
KL += tf.reduce_mean(alpha_std**2+alpha_mean**2-2*alpha_logstd-1)/2.
# Output layer
else:
F = tf.squeeze(tf.layers.dense(Z, n_out), [2])
return F, KL | tensorflow.shape | 11,952 |
import tensorflow as tf
# Axis 0 - Batch.
# Axis 1 - Input Frames, 4 frames.
# Axis 2, 3 - Height & Width.
# Axis 4 - Channels RGB, 3 colours.
x = tf.transpose(observations, [0, 2, 3, 1, 4])
x_shape = common_layers.shape_list(x)
x = tf.reshape(x, x_shape[:-2] + [-1])
dropout = getattr(self.hparams, "dropout_ppo", 0.0)
| tensorflow.transpose | 11,953 |
import tensorflow as tf
with tf.variable_scope('mixed'):
#add fake pc as a rotation class
num_to_add = int(max(self.batch_size/self.num_angles, 1))
idx = tf.range(0, self.batch_size, 1)
idx = tf.random_shuffle(idx)[0:num_to_add]
self.fake_to_add = tf.gather(self.generator_out, idx)
self.mixed_pc = tf.concat([self.real_pc_rotated, self.fake_to_add], 0)
self.mixed_label = tf.concat([self.rot_label_pl, tf.constant(self.num_angles, shape = (num_to_add,))], axis = 0)
mixed_idx = tf.range(0, self.mixed_label.get_shape().as_list()[0], 1)
mixed_idx = tf.random_shuffle(mixed_idx)[0:self.batch_size]
self.mixed_pc = tf.gather(self.mixed_pc, mixed_idx)
self.mixed_label = tf.gather(self.mixed_label, mixed_idx)
self.mixed_pred, mixed_end_points = self.get_pred(self.mixed_pc)
| tensorflow.constant | 11,954 |
import tensorflow as tf
# Downloads data at url into data_dir/basename(url). The dataset has a header
# row (color_name, r, g, b) followed by comma-separated lines.
path = maybe_download(os.path.basename(url), data_dir, url)
# This chain of commands loads our data by:
# 1. skipping the header; (.skip(1))
# 2. parsing the subsequent lines; (.map(parse))
# 3. shuffling the data; (.shuffle(...))
# 3. grouping the data into padded batches (.padded_batch(...)).
dataset = tf.data.TextLineDataset(path).skip(1).map(parse).shuffle(
buffer_size=10000).padded_batch(
batch_size, padded_shapes=([None], [None, None], []))
return dataset
# pylint: disable=not-callable
class RNNColorbot(tf.keras.Model):
"""Multi-layer (LSTM) RNN that regresses on real-valued vector labels.
| tensorflow.data.TextLineDataset | 11,955 |
import tensorflow as tf
c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
state_in = (c_in, h_in)
rnn_in = tf.expand_dims(self.h3, [0])
step_size = tf.shape(inputs)[:1]
state_in = tf.nn.rnn_cell.LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size,
time_major=False)
| tensorflow.expand_dims | 11,956 |
import tensorflow as tf
def print_examples_and_shapes(x):
if np.random.uniform() < debug_print_examples_rate:
tf.print(
{
'inputs_shape': tf.size(x['inputs']),
'targets_shape': tf.size(x['targets']),
'inputs': x['inputs'],
'targets': x['targets'],
},
output_stream=logging.info)
| tensorflow.size | 11,957 |
import tensorflow as tf
else: return tf.no_op()
else:
return tf.assert_equal(shape_a[0], shape_b[0])
| tensorflow.assert_equal | 11,958 |
import tensorflow as tf
# Remove episode indices.
num_episodes = tf.count_nonzero(
input_tensor=tf.gather(params=self.terminal_memory, indices=indices),
axis=0,
dtype=util.tf_dtype('int')
)
num_episodes = tf.minimum(x=num_episodes, y=self.episode_count)
assignment = tf.assign(
ref=self.episode_indices[:self.episode_count - num_episodes],
value=self.episode_indices[num_episodes: self.episode_count]
)
# Decrement episode count.
| tensorflow.minimum | 11,959 |
import tensorflow as tf
'learning_rate_decay_factor', 0.96, 'Learning rate decay factor.')
tf.app.flags.DEFINE_float(
'decay_steps', 1000,
'Number of epochs after which learning rate decays.')
# for learning rate piecewise_constant decay
tf.app.flags.DEFINE_string(
'decay_boundaries', '60000, 800000',
'Learning rate decay boundaries by global_step (comma-separated list).')
tf.app.flags.DEFINE_string(
'lr_decay_factors', '1, 0.6, 0.1',
| tensorflow.app.flags.DEFINE_string | 11,960 |
from tensorflow.python.ops import math_ops
def _prob(self, x):
return math_ops.exp(self._log_prob(x))
def _log_cdf(self, x):
return math_ops.log(self._cdf(x))
def _cdf(self, x):
x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
self.validate_args else [], x)
# Note that igammac returns the upper regularized incomplete gamma
# function Q(a, x), which is what we want for the CDF.
return math_ops.igammac(self.alpha, self.beta / x)
@distribution_util.AppendDocstring(
"""This is defined to be
```
entropy = alpha - log(beta) + log(Gamma(alpha))
+ (1-alpha)digamma(alpha)
```
where digamma(alpha) is the digamma function.""")
| tensorflow.python.ops.math_ops.igammac | 11,961 |
import tensorflow as tf
"""
检查所给路径是否已存在，如果存在删除原有日志。并创建日志写入对象
参数
----
logPath ：string，日志存储路径
返回
----
summaryWriter ：FileWriter，日志写入器
"""
if tf.gfile.Exists(logPath):
tf.gfile.DeleteRecursively(logPath)
summaryWriter = tf.summary.FileWriter(logPath, graph=tf.get_default_graph())
return summaryWriter
| tensorflow.gfile.Exists | 11,962 |
import tensorflow as tf
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
writer.write(output_line)
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
if FLAGS.do_serve:
def serving_input_fn():
with tf.variable_scope("foo"):
feature_spec = {
"input_ids": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
"segment_ids": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
"label_ids": tf.FixedLenFeature([], tf.int64),
}
serialized_tf_example = tf.placeholder(dtype=tf.string,
shape=[None],
name='input_example_tensor')
receiver_tensors = {'examples': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
estimator._export_to_tpu = False # this is important
| tensorflow.FixedLenFeature | 11,963 |
import tensorflow as tf
# FC layers to find next location
loc_layer1 = Dense(units=loc_layer_size)(prev_loc)
loc_layer2 = Dense(units=loc_layer_size)(loc_layer1)
# Concatenationation of above layers, followed by FC layer
concat = tf.concat([flat1b, loc_layer2],1) # goal_layer2
h1 = Dense(units=RNN_SIZE)(concat)
h2 = Dense(units=RNN_SIZE)(h1)
self.h3 = tf.nn.relu(h2+concat)
#Recurrent network for temporal dependencies
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(RNN_SIZE,state_is_tuple=True)
c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
state_in = (c_in, h_in)
rnn_in = tf.expand_dims(self.h3, [0])
step_size = tf.shape(inputs)[:1]
state_in = tf.nn.rnn_cell.LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size,
| tensorflow.nn.rnn_cell.BasicLSTMCell | 11,964 |
from tensorflow.python.framework import ops
Args:
x: A `Tensor` or `SparseTensor`.
dtype: The destination type.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` with same shape as `x`.
Raises:
TypeError: If `x` cannot be cast to the `dtype`.
"""
with ops.op_scope([x], name, "Cast") as name:
if isinstance(x, ops.SparseTensor):
values_cast = cast(x.values, dtype, name=name)
return ops.SparseTensor(x.indices, values_cast, x.shape)
else:
# TODO(touts): Handle what Josh said.
#
# Could return ops.convert_to_tensor(x, dtype=dtype, ...) here, but that
# allows some conversions that cast() can't do, e.g. casting numbers to
# strings.
x = ops.convert_to_tensor(x, name="x")
if x.dtype.base_dtype == dtype:
return x
return gen_math_ops.cast(x, dtype, name=name)
def to_float(x, name="ToFloat"):
| tensorflow.python.framework.ops.SparseTensor | 11,965 |
import tensorflow as tf
def conv_to_fc(x):
nh = np.prod([v.value for v in x.get_shape()[1:]])
x = tf.reshape(x, [-1, nh])
return x
def discount_with_dones(rewards, dones, gamma):
discounted = []
r = 0
for reward, done in zip(rewards[::-1], dones[::-1]):
r = reward + gamma*r*(1.-done) # fixed off by one bug
discounted.append(r)
return discounted[::-1]
def find_trainable_variables(key):
return tf.trainable_variables(key)
def make_path(f):
return os.makedirs(f, exist_ok=True)
def constant(p):
return 1
def linear(p):
return 1-p
def middle_drop(p):
eps = 0.75
if 1-p<eps:
| tensorflow.trainable_variables | 11,966 |
from tensorflow.python.ops import common_shapes
@ops.RegisterShape("MaxPoolWithArgmax")
def _MaxPoolWithArgMaxShape(op):
"""Shape function for MaxPoolWithArgmax op."""
return common_shapes.max_pool_shape(op) * 2
@ops.RegisterShape("AvgPoolGrad")
def _AvgPoolGradShape(op):
| tensorflow.python.ops.common_shapes.max_pool_shape | 11,967 |
import tensorflow as tf
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
| tensorflow.logging.info | 11,968 |
from tensorflow.python.ops import init_ops
padding='SAME',
data_format=None,
rate=1,
activation_fn=nn.relu,
normalizer_fn=None,
normalizer_params=None,
weights_initializer=initializers.xavier_initializer(),
weights_regularizer=None,
biases_initializer=init_ops.zeros_initializer(),
biases_regularizer=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
use_spectral_norm=False,
is_training=False,
| tensorflow.python.ops.init_ops.zeros_initializer | 11,969 |
import tensorflow as tf
one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
output = tf.matmul(one_hot_input_ids, embedding_table)
| tensorflow.matmul | 11,970 |
import tensorflow as tf
degree_l = tf.convert_to_tensor(value=degree_l)
order_m = tf.convert_to_tensor(value=order_m)
x = tf.convert_to_tensor(value=x)
| tensorflow.convert_to_tensor | 11,971 |
import tensorflow as tf
:description: Placeholders
"""
def _setup_placeholders(self):
if self.demo:
self.c = tf.placeholder(tf.int32, [None, self.config.max_p_len], "context")
self.q = tf.placeholder(tf.int32, [None, self.config.max_q_len], "question")
self.ch = tf.placeholder(tf.int32, [None, self.config.max_p_len, self.config.max_ch_len], "context_char")
self.qh = tf.placeholder(tf.int32, [None, self.config.max_q_len, self.config.max_ch_len], "question_char")
self.start_label = tf.placeholder(tf.int32, [None], "answer_label1")
self.end_label = tf.placeholder(tf.int32, [None], "answer_label2")
else:
self.c = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_p_len],
"context")
self.q = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_q_len],
"question")
self.ch = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_p_len,
| tensorflow.placeholder | 11,972 |
from tensorflow.python.framework import tensor_util
def _MaxPoolWithArgMaxShape(op):
"""Shape function for MaxPoolWithArgmax op."""
return common_shapes.max_pool_shape(op) * 2
@ops.RegisterShape("AvgPoolGrad")
def _AvgPoolGradShape(op):
"""Shape function for the AvgPoolGrad op."""
orig_input_shape = tensor_util.constant_value(op.inputs[0])
if orig_input_shape is not None:
return [tensor_shape.TensorShape(orig_input_shape.tolist())]
else:
# NOTE(mrry): We could in principle work out the shape from the
# gradients and the attrs, but if we do not know orig_input_shape
# statically, then we are unlikely to know the shape of the
# gradients either.
| tensorflow.python.framework.tensor_util.constant_value | 11,973 |
from tensorflow.python.framework import tensor_util
x = ops.convert_to_tensor(x, name="x")
def slice_shape(start_sum, size, name):
"""Closure to slice out shape."""
start_sum = start_sum if start_sum else (
array_ops.zeros((), dtype=dtypes.int32, name="zero"),)
if (x.get_shape().ndims is not None and
self._is_all_constant_helper(size, *start_sum)):
start = sum(tensor_util.constant_value(s) for s in start_sum)
stop = start + tensor_util.constant_value(size)
slice_ = x.get_shape()[start:stop].as_list()
if all(s is not None for s in slice_):
return ops.convert_to_tensor(slice_, dtype=dtypes.int32, name=name)
# Fall-through intended.
return array_ops.slice(array_ops.shape(x), (sum(start_sum),), (size,))
sample_ndims = self.get_sample_ndims(x, name=name)
return (slice_shape((), sample_ndims,
| tensorflow.python.framework.tensor_util.constant_value | 11,974 |
import tensorflow as tf
tf.random.uniform([]) < self._merge_prob)
def _MergeOneToken(tokens, i):
return tf.expand_dims(
self._MergeTokens((tokens[i], tokens[i + 1])), axis=-1)
def _MergeCandidates(tokens, candidates):
"""Merge in the reverse binary tree."""
best_id = tf.argmin(candidates, output_type=tf.int32)
# Perform the merge at position best_id.
tokens = tf.concat(
[tokens[:best_id], [candidates[best_id]], tokens[best_id + 2:]],
axis=0)
# Recompute the merge candidates.
# Only the neighbors of best_id need to be recomputed.
empty = tf.zeros([0], dtype=candidates.dtype)
| tensorflow.argmin | 11,975 |
import tensorflow as tf
for mtype, (classes, sdfs, poses) in enumerate([
(labeled_classes, labeled_sdfs, labeled_poses),
(predicted_classes, predicted_sdfs, predicted_poses)]):
for i in range(classes.shape[0]):
if class_id == classes[i]:
sdf = tf.expand_dims(sdfs[i], -1)
sdf = sdf * -1.0 # inside positive, outside zero
samples_object = centernet_utils.transform_pointcloud(
tf.reshape(samples_world, [1, 1, -1, 3]),
tf.reshape(poses[2][i], [1, 1, 3]),
tf.reshape(poses[0][i], [1, 1, 3, 3]),
tf.reshape(poses[1][i], [1, 1, 3]), inverse=True) * 2.0
samples_object = \
(samples_object * (29.0/32.0) / 2.0 + 0.5) * 32.0 - 0.5
samples = tf.squeeze(samples_object)
interpolated = trilinear.interpolate(sdf, samples)
sdf_values += tf.math.sign(tf.nn.relu(interpolated + self.tol))
status2 = False
if status2:
| tensorflow.reshape | 11,976 |
import tensorflow as tf
Returns:
A list of tf.Summary values if hook_args.hparams contains the
reference file and the translated file.
"""
decode_hparams = hook_args.decode_hparams
if (decode_hparams.decode_reference is None or
decode_hparams.decode_to_file is None):
return None
values = []
bleu = 100 * bleu_hook.bleu_wrapper(
decode_hparams.decode_reference, decode_hparams.decode_to_file)
values.append(tf.Summary.Value(tag="BLEU", simple_value=bleu))
tf.logging.info("%s: BLEU = %6.2f" % (decode_hparams.decode_to_file, bleu))
return values
def _preprocess_sgm(line, is_sgm):
"""Preprocessing to strip tags in SGM files."""
if not is_sgm:
return line
# In SGM files, remove <srcset ...>, <p>, <doc ...> lines.
if line.startswith("<srcset") or line.startswith("</srcset"):
return ""
if line.startswith("<doc") or line.startswith("</doc"):
return ""
if line.startswith("<p>") or line.startswith("</p>"):
| tensorflow.logging.info | 11,977 |
import tensorflow as tf
context_emb = tf.concat(context_emb_list, 2) # [num_sentences, max_sentence_length, emb]
head_emb = tf.concat(head_emb_list, 2) # [num_sentences, max_sentence_length, emb]
context_emb = tf.nn.dropout(context_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
head_emb = tf.nn.dropout(head_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length) # [num_sentence, max_sentence_length]
| tensorflow.nn.dropout | 11,978 |
import tensorflow as tf
)
mu = 2 * tf.layers.dense(inputs=l1,
units=action_dim, # number of hidden units
activation=tf.nn.tanh,
name='mu',
trainable=trainable
)
sigma = tf.layers.dense(inputs=l1,
units=action_dim, # output units
activation=tf.nn.softplus, # get action probabilities
name='sigma',
trainable=trainable
)
norm_dist = tf.distributions.Normal(loc=mu, scale=sigma)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
return norm_dist, params
def choose_action(self, s):
# 決定下一步該怎麼做
# s = s[np.newaxis, :]
s = s.reshape(-1, 84, 84, 3)
a = self.sess.run(self.sample_op, {self.tfs: s})[0]
return np.clip(a, ACTION_BOUND[0], ACTION_BOUND[1])
def get_v(self, s):
if s.ndim < 4:
| tensorflow.distributions.Normal | 11,979 |
import tensorflow as tf
else:
value_for_priority = qf1_loss_col
# Compute the entropy temperature loss
# it is used when the entropy coefficient is learned
ent_coef_loss, entropy_optimizer = None, None
if not isinstance(self.ent_coef, float):
ent_coef_loss = -tf.reduce_mean(
self.log_ent_coef * tf.stop_gradient(logp_pi + self.target_entropy)*self.weight_ph)
entropy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate_ph)
# Compute the policy loss
# Alternative: policy_kl_loss = tf.reduce_mean(logp_pi - min_qf_pi)
policy_kl_loss = tf.reduce_mean((self.ent_coef * logp_pi - min_qf_pi)*self.weight_ph)
actor_for_priority = tf.reduce_mean(self.ent_coef * logp_pi - min_qf_pi,1)
# NOTE: in the original implementation, they have an additional
# regularization loss for the Gaussian parameters
# this is not used for now
# policy_loss = (policy_kl_loss + policy_regularization_loss)
min_q = tf.minimum(dtm_qf1,dtm_qf2)
Q_filter = tf.cast((qf1 > min_q)|(qf2 > min_q),tf.float32)
#Q_filter_1 = tf.cast(qf1 > min_q,tf.float32)
#Q_filter_2 = tf.cast(qf2 > min_q,tf.float32)
im_loss1 = tf.square(self.actions_ph - self.deterministic_actions_ph)*Q_filter*self.is_demo_ph
#im_loss2 = tf.square(self.actions_ph - self.deterministic_actions_ph)*Q_filter_2*self.is_demo_ph
#actor_loss_di1 = tf.reduce_mean(im_loss1)
| tensorflow.reduce_mean | 11,980 |
import tensorflow as tf
"""Numerically more stable variance computation."""
if mean is None:
mean = tf.reduce_mean(input_, axes)
res = tf.square(input_ - mean)
| tensorflow.reduce_mean | 11,981 |
import tensorflow as tf
self.y2 = tf.placeholder(tf.int32, [None, config.test_para_limit],"answer_index2")
else:
self.c, self.q, self.ch, self.qh, self.y1, self.y2, self.qa_id = batch.get_next()
# self.word_unk = tf.get_variable("word_unk", shape = [config.glove_dim], initializer=initializer())
self.word_mat = tf.get_variable("word_mat", initializer=tf.constant(
word_mat, dtype=tf.float32), trainable=False)
self.char_mat = tf.get_variable(
"char_mat", initializer=tf.constant(char_mat, dtype=tf.float32))
self.c_mask = tf.cast(self.c, tf.bool)
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
if opt:
| tensorflow.constant | 11,982 |
import tensorflow as tf
with tf.variable_scope('scop'):
self.w1=tf.get_variable('w1', [4096,1024],initializer=tf.contrib.layers.xavier_initializer_conv2d())
self.w2=tf.get_variable('w2', [1024,classnum],initializer=tf.contrib.layers.xavier_initializer_conv2d())
self.b1 = tf.get_variable('b1', [1024],initializer=tf.constant_initializer(0.0))
| tensorflow.contrib.layers.xavier_initializer_conv2d | 11,983 |
import tensorflow as tf
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
output_z_ = lrelu(linear(trans_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(tf.concat([output_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(tf.concat([output_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(tf.concat([output_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(tf.concat([output_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(tgtimg_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(tf.concat([truthoutput_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(tf.concat([truthoutput_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(tf.concat([truthoutput_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(tf.concat([truthoutput_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
| tensorflow.concat | 11,984 |
import tensorflow as tf
features = {
"inputs": observations,
"epoch": tf.constant(epoch + 1),
"input_action": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
"input_reward": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
"targets": tf.zeros(obs_shape[:1] + [num_target_frames] + obs_shape[2:]),
"target_action": tf.zeros(
obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
"target_reward": tf.zeros(
obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
| tensorflow.zeros | 11,985 |
import tensorflow as tf
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
| tensorflow.reshape | 11,986 |
import tensorflow as tf
assert(len(x.get_shape()) == 2)
assert(len(idx.get_shape()) == 1)
idx_flattened = tf.range(0, x.shape[0]) * x.shape[1] + idx
y = tf.gather(tf.reshape(x, [-1]), # flatten input
idx_flattened) # use flattened indices
return y
def check_shape(ts,shapes):
i = 0
for (t,shape) in zip(ts,shapes):
assert t.get_shape().as_list()==shape, "id " + str(i) + " shape " + str(t.get_shape()) + str(shape)
i += 1
def avg_norm(t):
return tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(t), axis=-1)))
def gradient_add(g1, g2, param):
print([g1, g2, param.name])
assert (not (g1 is None and g2 is None)), param.name
if g1 is None:
return g2
elif g2 is None:
return g1
else:
return g1 + g2
def q_explained_variance(qpred, q):
_, vary = tf.nn.moments(q, axes=[0, 1])
| tensorflow.square | 11,987 |
import tensorflow as tf
encoder_state = tf.concat(encoder_states, 1)
return encoder_outputs, encoder_state, new_encoder_input_length
def compute_energy(hidden, state, encoder, time=None, input_length=None, prev_weights=None, **kwargs):
batch_size = tf.shape(hidden)[0]
time_steps = tf.shape(hidden)[1]
if encoder.attn_keep_prob is not None:
state_noise_shape = [1, tf.shape(state)[1]] if encoder.pervasive_dropout else None
state = tf.nn.dropout(state, keep_prob=encoder.attn_keep_prob, noise_shape=state_noise_shape)
hidden_noise_shape = [1, 1, tf.shape(hidden)[2]] if encoder.pervasive_dropout else None
hidden = tf.nn.dropout(hidden, keep_prob=encoder.attn_keep_prob, noise_shape=hidden_noise_shape)
| tensorflow.shape | 11,988 |
import tensorflow as tf
return update_scale_expr
# Functionality to update the threshold for parameter space noise.
update_param_noise_threshold_expr = param_noise_threshold.assign(tf.cond(update_param_noise_threshold_ph >= 0,
lambda: update_param_noise_threshold_ph, lambda: param_noise_threshold))
# Put everything together.
deterministic_actions = tf.argmax(q_values_perturbed, 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))
updates = [
update_eps_expr,
tf.cond(reset_ph, lambda: perturb_vars(original_scope="q_func", perturbed_scope="perturbed_q_func"), lambda: tf.group(*[])),
| tensorflow.stack | 11,989 |
import tensorflow as tf
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 | 11,990 |
import tensorflow as tf
loss_box = tf.reduce_mean(tf.reduce_sum(
out_loss_box,
axis=dim
))
return loss_box
def _add_losses(self, sigma_rpn=3.0):
with tf.variable_scope('loss_' + self._tag):
# RPN, class loss
rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2])
rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1])
# 得到前景和背景anchor的index
rpn_select = tf.where(tf.not_equal(rpn_label, -1))
rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2])
rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1])
rpn_cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label))
| tensorflow.reshape | 11,991 |
from tensorflow.contrib.layers.python.layers import utils
return mean, variance
def build_moving_stats():
return (
tf.identity(self._moving_mean),
tf.identity(self._moving_variance),
)
mean, variance = utils.smart_cond(
use_batch_stats,
build_batch_stats,
build_moving_stats,
)
return mean, variance
| tensorflow.contrib.layers.python.layers.utils.smart_cond | 11,992 |
import tensorflow as tf
tf.multiply(smooth_l1_option2, tf.abs(tf.subtract(smooth_l1_sign, 1.0))))
outside_mul = tf.multiply(bbox_outside_weights, smooth_l1_result)
| tensorflow.multiply | 11,993 |
import tensorflow as tf
all_top_5_ops.append(results[2])
if self.variable_mgr.retain_tower_updates(device_num):
# Retain the Batch Normalization updates operations only from the
# first tower. Ideally, we should grab the updates from all towers but
# these stats accumulate extremely fast so we can ignore the other
# stats from the other towers without significant detriment.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, name_scope)
staging_delta_ops = list(self.variable_mgr.staging_delta_ops)
if not update_ops:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, name_scope)
enqueue_ops.append(tf.group(*gpu_copy_stage_ops))
if self.variable_mgr.supports_staged_vars():
| tensorflow.get_collection | 11,994 |
import tensorflow as tf
self.a_grads = tf.gradients(loss_pg, self.pi_params)
self.c_grads = tf.gradients(loss_vf, self.vf_params)
self.a_grads, _ = tf.clip_by_global_norm(self.a_grads, 20.0)
self.c_grads, _ = tf.clip_by_global_norm(self.c_grads, 20.0)
opt = tf.train.AdamOptimizer(self.LR)
self.update_a_op = opt.apply_gradients(zip(self.a_grads, self.pi_params))
self.update_c_op = opt.apply_gradients(zip(self.c_grads, self.vf_params))
self.sess.run(tf.global_variables_initializer())
# Tensorboard
if summary_dir is not None:
self.writer = tf.summary.FileWriter(summary_dir)
tf.summary.scalar('Loss/Policy', loss_pg)
tf.summary.scalar('Loss/Value', loss_vf)
| tensorflow.global_variables_initializer | 11,995 |
import tensorflow as tf
shortcut = tf.identity(x)
if pool_first:
if in_channel == filters:
if strides == 1:
shortcut = tf.identity(x)
else:
shortcut= tf.pad(x, tf.constant([[0,0],[1,1],[1,1],[0,0]]), "CONSTANT")
shortcut = tf.nn.max_pool(shortcut, [1, strides, strides, 1], [1, strides, strides, 1], 'VALID')
else:
shortcut = self._conv('shortcut_conv', x, padding='VALID',
num_filters=filters, kernel_size=(1, 1), stride=(strides, strides),
bias=self.bias)
else:
| tensorflow.constant | 11,996 |
import tensorflow as tf
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
| tensorflow.one_hot | 11,997 |
import tensorflow as tf
# Activation
if softmax_stag:
scores = tf.nn.softmax(scores) # [B, 1, T]
# Weighted sum
if mode == 'SUM':
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))
if return_alphas:
return output, scores
return output
class VecAttGRUCell(RNNCell):
| tensorflow.expand_dims | 11,998 |
import tensorflow as tf
gate_sig = tf.nn.sigmoid(gate)
combination_res = gate_sig * new_context_act + (1 - gate_sig) * rep_map # bs,bn,bl,vec
with tf.variable_scope('restore_original_length'):
combination_res_reshape = tf.reshape(combination_res, [bs, bn * bl, ivec]) # bs,bn*bl,vec
output = combination_res_reshape[:, :sl, :]
return output | tensorflow.reshape | 11,999 |
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