seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
tf.summary.scalar('Loss/Policy', loss_pg)
tf.summary.scalar('Loss/Value', loss_vf)
tf.summary.scalar('Loss/Entropy', loss_entropy)
tf.summary.scalar('Loss/Total', loss)
tf.summary.scalar('Var/Epsilon', epsilon_decay)
tf.summary.scalar('Var/Policy Mode', tf.reduce_mean(pi.mode()))
tf.summary.scalar('Var/Policy Sigma', tf.reduce_mean(pi.stddev()))
tf.summary.scalar('Var/Value', tf.reduce_mean(self.vf))
self.summarise = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES))
# AC net
def build_anet(self, state_in, name, reuse=False, batch_size=64):
reg = None
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)
lstm_a = tf.nn.rnn_cell.LSTMCell(num_units=256)
lstm_a = tf.nn.rnn_cell.DropoutWrapper(lstm_a, output_keep_prob=self.keep_prob)
state_init_a = lstm_a.zero_state(batch_size=batch_size, dtype=tf.float32)
lstm_ain = tf.expand_dims(layer_a2, axis=1)
out_a, state_final_a = tf.nn.dynamic_rnn(cell=lstm_a, inputs=lstm_ain, initial_state=state_init_a)
cell_out_a = tf.reshape(out_a, [-1, 256])
mu = tf.layers.dense(cell_out_a, self.a_dim, tf.nn.tanh, kernel_regularizer=reg)
sigma = tf.layers.dense(cell_out_a, 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)
| tensorflow.variable_scope | 14,300 |
import tensorflow as tf
class PPO(Base):
def __init__(self, env, summary_dir='./', gpu=False):
self.LR = 1e-4
self.MINIBATCH = 64
self.EPOCHS = 8
self.EPSILON = 0.2
self.EPS_LEN = 100000
# GPU setup
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, device_count={'GPU': gpu})
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
# Placeholders
self.sess = tf.Session(config=config)
self.s_dim, self.a_dim = env.observation_space.shape, env.action_space.shape[0]
self.a_bound = (env.action_space.high - env.action_space.low) / 2
self.actions = tf.placeholder(tf.float32, [None, self.a_dim], 'action')
self.state = tf.placeholder(tf.float32, [None, self.s_dim[0]], 'state')
self.advantage = tf.placeholder(tf.float32, [None, 1], 'advantage')
self.rewards = tf.placeholder(tf.float32, [None, 1], 'discounted_r')
# Dateset with experiennce replay
self.dataset = tf.data.Dataset.from_tensor_slices({'state': self.state, 'actions': self.actions,
'rewards': self.rewards, 'advantage': self.advantage})
self.dataset = self.dataset.shuffle(buffer_size=10000)
self.dataset = self.dataset.batch(self.MINIBATCH)
self.dataset = self.dataset.cache()
self.dataset = self.dataset.repeat(self.EPOCHS)
| tensorflow.Session | 14,301 |
from tensorflow.python.framework import ops
def register_gradient():
if "GuidedBackProp" not in ops._gradient_registry._registry:
@ops.RegisterGradient("GuidedBackProp")
def _GuidedBackProp(op, grad):
| tensorflow.python.framework.ops.RegisterGradient | 14,302 |
import tensorflow as tf
def batch_to_seq(h, nbatch, nsteps, flat=False):
if flat:
h = tf.reshape(h, [nbatch, nsteps])
else:
h = tf.reshape(h, [nbatch, nsteps, -1])
return [tf.squeeze(v, [1]) for v in tf.split(axis=1, num_or_size_splits=nsteps, value=h)]
def seq_to_batch(h, flat = False):
shape = h[0].get_shape().as_list()
| tensorflow.split | 14,303 |
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)
| tensorflow.reduce_sum | 14,304 |
import tensorflow as tf
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
| tensorflow.logging.info | 14,305 |
import tensorflow as tf
cell = tf.contrib.rnn.MultiRNNCell(
[make_cell() for _ in range(config.num_layers)], state_is_tuple=True)
self._initial_state = cell.zero_state(config.batch_size, tf.float32)
state = self._initial_state
outputs = []
with tf.variable_scope('RNN'):
for time_step in range(self.num_steps):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(inputs[:, time_step, :], state)
outputs.append(cell_output)
output = tf.reshape(tf.concat(outputs, 1), [-1, config.hidden_size])
return output, state
| tensorflow.variable_scope | 14,306 |
import tensorflow as tf
with tf.name_scope('Train'):
train_input = DataInput(config=config, data=train_data, name='TrainInput')
with tf.variable_scope('Model', reuse=None, initializer=initializer):
m = Model(is_training=True, config=config, input_=train_input, graph=train_graph)
tf.summary.scalar('Training Loss', m.cost)
tf.summary.scalar('Learning rate', m.lr)
latest_ckpt = tf.train.latest_checkpoint(FLAGS.save_path)
with train_graph.as_default():
sv = tf.train.Supervisor(logdir=FLAGS.save_path)
config_proto = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
with sv.managed_session(config=config_proto) as train_sess:
#with tf.Session(config=config_proto) as train_sess:
train_sess.run(tf.global_variables_initializer())
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.)
m.assign_lr(train_sess, config.learning_rate * lr_decay)
train_perplexity = run_epoch(train_sess, m, #eval_op=m.train_op,
verbose=True)
print('Epoch {} Train Perplexity: {:.3f}'.format(i + 1,
| tensorflow.ConfigProto | 14,307 |
from tensorflow.contrib.metrics.python.ops import metric_ops
metric_ops.streaming_precision_at_thresholds, threshold)
# Recall for positive examples.
metrics[_MetricKeys.RECALL_MEAN % threshold] = _streaming_at_threshold(
metric_ops.streaming_recall_at_thresholds, threshold)
return metrics
def _float_weights_or_none(weights):
if weights is None:
return None
return math_ops.to_float(weights)
def _labels_streaming_mean(unused_predictions, labels, weights=None):
return metric_ops.streaming_mean(labels, weights=weights)
def _predictions_streaming_mean(predictions, unused_labels, weights=None):
return metric_ops.streaming_mean(predictions, weights=weights)
def _streaming_auc(predictions, labels, weights=None):
return metric_ops.streaming_auc(
predictions, labels, weights=_float_weights_or_none(weights))
def _accuracy_at_threshold(threshold):
def _accuracy_metric(predictions, labels, weights=None):
| tensorflow.contrib.metrics.python.ops.metric_ops.streaming_mean | 14,308 |
from tensorflow.python.framework import ops
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"):
model = cudnn_rnn_ops.CudnnLSTM(num_layers, num_units, num_units)
params_size_t = model.params_size()
input_data = variables.Variable(
| tensorflow.python.framework.ops.device | 14,309 |
import tensorflow as tf
# tgt1, tgt2 = tf.split(horizon_tgt, 2, axis=0)
even = [2 * i for i in range(25)]
odd = [2 * i + 1 for i in range(25)]
pred1 = tf.gather(horizon_pred, even)
pred2 = tf.gather(horizon_pred, odd)
tgt1 = tf.gather(horizon_tgt, even)
tgt2 = tf.gather(horizon_tgt, odd)
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
loss = tf.maximum(0.0, ((tgt_larg - tgt_small) - (pred_larg - pred_small)))
loss = tf.reduce_mean(loss)
return loss
| tensorflow.cast | 14,310 |
import tensorflow as tf
if single_file:
dataset_path = os.path.join(dataset_path, 'train_annotated.json')
else:
dataset_path = os.path.join(dataset_path, 'dev_annotated.json')
def load_dataset():
dataset = []
if single_file:
# Opening with GFile allows to use remotely stored files, e.g.
# in a gs bucket.
dataset_handle = tf.io.gfile.GFile(dataset_path, 'r')
for line in dataset_handle:
dataset.append(json.loads(line))
else:
all_files = tf.io.gfile.listdir(dataset_path)
for filename in all_files:
if 'json' in filename:
print('Loading data from file {}'.format(filename))
with tf.io.gfile.GFile(os.path.join(dataset_path, filename)) as f:
for line in f:
dataset.append(json.loads(line))
print('The total size of the dataset {}'.format(len(dataset)))
return dataset[:int(len(dataset) * percentile)]
def drop_annotated_yield_examples(generator=None):
del generator
while True:
passages = set()
| tensorflow.io.gfile.listdir | 14,311 |
import tensorflow as tf
summaries = {
'triplet_loss/Margin':
tf.constant(margin),
'triplet_loss/Anchor/Positive/Distance/Mean':
tf.math.reduce_mean(anchor_positive_distances),
'triplet_mining/Anchor/Positive/Distance/Mean':
tf.math.reduce_mean(anchor_positive_mining_distances),
}
| tensorflow.math.reduce_mean | 14,312 |
import tensorflow as tf
dropout=self.dropout)
def _fuse(self):
with tf.variable_scope("Context_to_Query_Attention_Layer"):
C = tf.tile(tf.expand_dims(self.c_embed_encoding, 2), [1, 1, self.max_q_len, 1])
Q = tf.tile(tf.expand_dims(self.q_embed_encoding, 1), [1, self.max_p_len, 1, 1])
S = trilinear([C, Q, C * Q], input_keep_prob=1.0 - self.dropout)
| tensorflow.variable_scope | 14,313 |
import tensorflow as tf
num_layers = [16,16,10,6]
else:
raise ValueError('depth=%g is a not a valid setting!' % depth)
# input tensors
self.input_x = tf.placeholder(tf.int32, [None, sequence_max_length], name="input_x")
self.input_tags = tf.placeholder(tf.int32, [None, sequence_max_length], name="input_tags")
self.input_deps = tf.placeholder(tf.int32, [None, sequence_max_length], name="input_dependency")
self.input_head = tf.placeholder(tf.int32, [None, sequence_max_length], name="input_head")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
self.is_training = tf.placeholder(tf.bool)
initializer = tf.contrib.layers.variance_scaling_initializer()
# Embedding Lookup 16
with tf.device('/cpu:0'), tf.name_scope("embedding"):
| tensorflow.placeholder | 14,314 |
import tensorflow as tf
Args:
variables: List of variables.
deltas: List of deltas of same length.
Returns:
The step-applied operation. A tf.group of tf.assign_add ops.
"""
if len(variables) != len(deltas):
raise TensorforceError("Invalid variables and deltas lists.")
assignments = list()
for variable, delta in zip(variables, deltas):
assignments.append(tf.assign_add(ref=variable, value=delta))
with tf.control_dependencies(control_inputs=assignments):
return util.no_operation()
def tf_minimize(self, variables, **kwargs):
"""
Performs an optimization step.
Args:
variables: List of variables to optimize.
**kwargs: Additional optimizer-specific arguments. The following arguments are used
by some optimizers:
| tensorflow.assign_add | 14,315 |
import tensorflow as tf
tf.float32, shape=[], name="new_learning_rate")
self._lr_update = tf.assign(self._lr, self._new_lr)
def _build_rnn_graph(self, inputs, config, is_training):
if config.rnn_mode == CUDNN:
return self._build_rnn_graph_cudnn(inputs, config, is_training)
else:
return self._build_rnn_graph_lstm(inputs, config, is_training)
def _build_rnn_graph_cudnn(self, inputs, config, is_training):
"""Build the inference graph using CUDNN cell."""
inputs = tf.transpose(inputs, [1, 0, 2])
self._cell = tf.contrib.cudnn_rnn.CudnnLSTM(
num_layers=config.num_layers,
num_units=config.hidden_size,
input_size=config.hidden_size,
dropout=1 - config.keep_prob if is_training else 0)
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],
| tensorflow.contrib.cudnn_rnn.CudnnLSTM | 14,316 |
import tensorflow as tf
tmp_bboxes = _calc_bounding_boxes()
bboxes = np.vstack((bboxes, tmp_bboxes)) # <class 'tuple'>: (5265, 5)
# non maximum suppression
# refind_idx = util.nms(bboxes, nms_thresh)
refind_idx = tf.image.non_max_suppression(tf.convert_to_tensor(bboxes[:, :4], dtype=tf.float32),
tf.convert_to_tensor(bboxes[:, 4], dtype=tf.float32),
max_output_size=bboxes.shape[0], iou_threshold=nms_thresh)
refind_idx = sess.run(refind_idx)
refined_bboxes = bboxes[refind_idx]
overlay_bounding_boxes(raw_img, refined_bboxes, lw)
if display:
| tensorflow.convert_to_tensor | 14,317 |
import tensorflow as tf
ref=self.episode_indices[-1],
value=tf.where(self.memory_index + num_instances > self.capacity,
self.episode_indices[self.episode_count - 1], self.capacity - 1)
)
with tf.control_dependencies(control_inputs=(assignment,)):
assignment = tf.assign(ref=self.memory_index, value=((self.memory_index + num_instances) % self.capacity))
with tf.control_dependencies(control_inputs=(assignment,)):
return tf.no_op()
def tf_retrieve_indices(self, indices):
"""
Fetches experiences for given indices.
Args:
| tensorflow.control_dependencies | 14,318 |
import tensorflow as tf
vocab = UnicodeCharsVocabulary(vocab_file, max_word_length)
batcher = Batcher(vocab_file, max_word_length)
ids_placeholder = tf.placeholder('int32',
shape=(None, None, max_word_length)
)
model = BidirectionalLanguageModel(options_file, weight_file)
ops = model(ids_placeholder)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sentence_id = 0
with open(dataset_file, 'r') as fin, h5py.File(outfile, 'w') as fout:
for line in fin:
sentence = line.strip().split()
char_ids = batcher.batch_sentences([sentence])
embeddings = sess.run(
ops['lm_embeddings'], feed_dict={ids_placeholder: char_ids}
)
| tensorflow.Session | 14,319 |
from tensorflow.contrib import tpu as contrib_tpu
total_loss, learning_rate, num_train_steps, num_warmup_steps,
use_tpu, optimizer)
output_spec = contrib_tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
| tensorflow.contrib.tpu.TPUEstimatorSpec | 14,320 |
import tensorflow as tf
vocab = data.frequent_vocab_list
embed = wv.load_matrix(args.embedding_size, vocab)
embed = np.array(embed, dtype = np.float32)
with tf.Session(config=config) as sess:
model = create_model(sess, data, args, embed)
if args.mode == "train":
model.train_process(sess, data, args)
else:
| tensorflow.Session | 14,321 |
import tensorflow as tf
weight_decay = 1e-4
momentum = 0.9
total_epochs = 30
iteration = 14089 // 1
# 128 * 14089 ~ 1,803,460
test_iteration = 10
def center_loss(features, label, alfa, nrof_classes):
"""Center loss based on the paper "A Discriminative Feature Learning Approach for Deep Face Recognition"
(http://ydwen.github.io/papers/WenECCV16.pdf)
"""
nrof_features = features.get_shape()[1]
centers = tf.get_variable('centers', [nrof_classes, nrof_features], dtype=tf.float32,
initializer=tf.constant_initializer(0), trainable=False)
label = tf.reshape(label, [-1])
centers_batch = tf.gather(centers, label)
diff = (1 - alfa) * (centers_batch - features)
centers = tf.scatter_sub(centers, label, diff)
# centers = tf.nn.l2_normalize(centers, 1, 1e-10, name='centers_norm')
loss = tf.reduce_mean(tf.square(features - centers_batch))
return loss, centers
def focal_loss(onehot_labels, cls_preds,
alpha=0.25, gamma=2.0, name=None, scope=None):
"""Compute softmax focal loss between logits and onehot labels
logits and onehot_labels must have same shape [batchsize, num_classes] and
the same data type (float16, 32, 64)
Args:
onehot_labels: Each row labels[i] must be a valid probability distribution
| tensorflow.reshape | 14,322 |
import tensorflow as tf
x0 = tf.to_int32(tf.floor(x))
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(
| tensorflow.clip_by_value | 14,323 |
import tensorflow as tf
Sets:
feedable placeholders with general purpose
"""
self.is_training = tf.placeholder_with_default(False, shape=(), name="is_training")
#def create_is_training_node(self):
# self._is_training = tf.placeholder_with_default(False, shape=(), name="is_training")
| tensorflow.placeholder_with_default | 14,324 |
import tensorflow as tf
self.placeholders = placeholders
with tf.variable_scope("embedding"), tf.device('/cpu:0'):
self.embedding = tf.get_variable('word_embedding', trainable=(options.fix_word_vec==False),
| tensorflow.variable_scope | 14,325 |
import tensorflow as tf
# the shape of `tmp` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
tmp1 = tf.tensordot(facts, w1, axes=1)
tmp2 = tf.tensordot(query, w2, axes=1)
tmp2 = tf.reshape(tmp2, [-1, 1, tf.shape(tmp2)[-1]])
tmp = tf.tanh((tmp1 + tmp2) + b)
# For each of the timestamps its vector of size A from `tmp` is reduced with `v` vector
v_dot_tmp = tf.tensordot(tmp, v, axes=1, name='v_dot_tmp') # (B,T) shape
key_masks = mask # [B, 1, T]
# key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
paddings = tf.ones_like(v_dot_tmp) * (-2 ** 32 + 1)
v_dot_tmp = tf.where(key_masks, v_dot_tmp, paddings) # [B, 1, T]
alphas = tf.nn.softmax(v_dot_tmp, name='alphas') # (B,T) shape
# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
#output = tf.reduce_sum(facts * tf.expand_dims(alphas, -1), 1)
output = facts * tf.expand_dims(alphas, -1)
output = tf.reshape(output, tf.shape(facts))
# output = output / (facts.get_shape().as_list()[-1] ** 0.5)
if not return_alphas:
return output
else:
return output, alphas
| tensorflow.nn.softmax | 14,326 |
import tensorflow as tf
'''
if is_max_pool:
# May Name Conflict
x = tf.nn.max_pool(x,kernel_size,strides=strides,padding='SAME',name=layer_name)
else:
x = tf.nn.avg_pool(x,kernel_size,strides=strides,padding='SAME',name=layer_name)
return x
def pool3d(layer_name, x, kernel_size=[1,1,2,2,1], strides=[1,1,2,2,1], is_max_pool=True):
'''
| tensorflow.nn.avg_pool | 14,327 |
import tensorflow as tf
1. / model_options.output_stride)
resize_width = scale_dimension(
model_options.crop_size[1],
1. / model_options.output_stride)
else:
# If crop_size is None, we simply do global pooling.
pool_height = tf.shape(features)[1]
pool_width = tf.shape(features)[2]
image_feature = tf.reduce_mean(
features, axis=[1, 2], keepdims=True)
resize_height = pool_height
resize_width = pool_width
image_feature_activation_fn = tf.nn.relu
image_feature_normalizer_fn = batch_norm
if model_options.aspp_with_squeeze_and_excitation:
image_feature_activation_fn = tf.nn.sigmoid
| tensorflow.reduce_mean | 14,328 |
import tensorflow as tf
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(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*"
| tensorflow.train.Scaffold | 14,329 |
import tensorflow as tf
if nn_type == 'mlp':
with tf.variable_scope('pi'):
pi = act_limit * mlp(x, list(hidden_sizes) + [act_dim], activation, output_activation)
with tf.variable_scope('q1'):
q1 = tf.squeeze(mlp(tf.concat([x, a], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
with tf.variable_scope('q2'):
q2 = tf.squeeze(mlp(tf.concat([x, a], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
with tf.variable_scope('q1', reuse=True):
q1_pi = tf.squeeze(mlp(tf.concat([x, pi], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
elif nn_type == 'mlp_dropout':
with tf.variable_scope('pi'):
pi = act_limit * mlp_dropout(x, list(hidden_sizes)+[act_dim], activation, output_activation)
with tf.variable_scope('q'):
q = tf.squeeze(mlp_dropout(tf.concat([x,a], axis=-1), list(hidden_sizes)+[1], activation, None, dropout_rate), axis=1)
with tf.variable_scope('q', reuse=True):
q_pi = tf.squeeze(mlp_dropout(tf.concat([x,pi], axis=-1), list(hidden_sizes)+[1], activation, None, dropout_rate), axis=1)
elif nn_type == 'mlp_variational':
with tf.variable_scope('pi'):
pi_in_dim = x.shape.as_list()[1]
pi_dropout_mask_generator = DropoutMaskGenerator(pi_in_dim, hidden_sizes, model_prob=1.0 - dropout_rate)
pi_dropout_mask_phs = pi_dropout_mask_generator.generate_dropout_mask_placeholders()
pi, pi_reg = mlp_variational(x, pi_dropout_mask_phs, list(hidden_sizes) + [act_dim], activation, output_activation, dropout_rate)
pi = act_limit * pi
with tf.variable_scope('q1'):
q1_in_ph = tf.concat([x, a], axis=-1)
q1_in_dim = q1_in_ph.shape.as_list()[1]
q1_dropout_mask_generator = DropoutMaskGenerator(q1_in_dim, hidden_sizes, model_prob=1.0 - dropout_rate)
| tensorflow.variable_scope | 14,330 |
import tensorflow as tf
# Setup Index Matrix for one-hot-encoding
identity_mat = tf.diag(tf.ones(shape=[embedding_size]))
| tensorflow.ones | 14,331 |
import tensorflow as tf
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
def test_inference(self,images):
images=tf.cast(images,tf.float32)/255.0
| tensorflow.nn.relu | 14,332 |
import tensorflow as tf
if isinstance(facts, tuple):
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
facts = tf.concat(facts, 2)
print ("querry_size mismatch")
query = tf.concat(values = [
query,
query,
], axis=1)
if time_major:
# (T,B,D) => (B,T,D)
facts = tf.array_ops.transpose(facts, [1, 0, 2])
mask = tf.equal(mask, tf.ones_like(mask))
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
queries = tf.tile(query, [1, tf.shape(facts)[1]])
queries = tf.reshape(queries, tf.shape(facts))
din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag)
d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag)
d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]])
| tensorflow.array_ops.transpose | 14,333 |
import tensorflow as tf
Valid values: `detection`, `classification`. Default 'detection'.
Returns:
A dict mapping variable names to variables.
"""
return {var.op.name: var for var in tf.global_variables()}
def updates(self):
"""Returns a list of update operators for this model.
| tensorflow.global_variables | 14,334 |
import tensorflow as tf
:param name: [string] Name of the variable scope.
:param x: [Tensor] Tensor to apply BN on.
:param is_training [bool] Whether in training mode.
:return: [Tensor] Normalized activation.
"""
bn = tf.contrib.layers.batch_norm(
x, fused=True, scale=True, data_format=data_format, is_training=is_training, scope=name)
return bn
# log.warning('Not using BN to test performance at inference time')
# return x
| tensorflow.contrib.layers.batch_norm | 14,335 |
import tensorflow as tf
def _init():
v_norm = tf.nn.l2_normalize(self.v,axis=[0,1,2])
t = tf.nn.conv2d(input_var,v_norm,self.strides,self.padding,data_format='NHWC')
mu,var = tf.nn.moments(t,axes=[0,1,2])
std = tf.sqrt(var+self.epsilon)
return [tf.assign(self.g,1/std),tf.assign(self.b,-1.*mu/std)]
require_init = tf.reduce_any(tf.is_nan(self.g))
init_ops = tf.cond(require_init,_init,lambda : [self.g,self.b])
| tensorflow.assign | 14,336 |
import tensorflow as tf
tf.summary.scalar('ent_coef', self.ent_coef)
tf.summary.scalar('learning_rate', tf.reduce_mean(self.learning_rate_ph))
# Retrieve parameters that must be saved
self.params = tf_util.get_trainable_vars("model")
self.target_params = tf_util.get_trainable_vars("target/values_fn/vf")
# Initialize Variables and target network
with self.sess.as_default():
self.sess.run(tf.global_variables_initializer())
self.sess.run(target_init_op)
self.summary = tf.summary.merge_all()
def pretrain_sac(self,pretrain_steps):
print("=====SAC Pretraining=====")
for step in range(pretrain_steps):
# Compute current learning_rate
frac = 1.0 - step / pretrain_steps
current_lr = self.learning_rate(frac)
# Update policy and critics (q functions)
policy_loss, qf1_loss, qf2_loss, value_loss,*entropy =self._train_step(step, writer=None,learning_rate=current_lr,pretrain=True)
if step % 50==0:
print("** Pretraining step: |",step/pretrain_steps," Actor loss: |",policy_loss, "Critic loss|",value_loss," Actor expert loss|",entropy[-1] )
# Update target network
| tensorflow.summary.merge_all | 14,337 |
import tensorflow as tf
params = {
self.SEED_PARAM_KEY:
tf.random.uniform((), maxval=tf.int32.max, dtype=tf.int32)
}
| tensorflow.random.uniform | 14,338 |
import tensorflow as tf
tf.compat.v2.summary.scalar(
name="relabel_q_vals",
data=tf.reduce_mean(relabel_q_vals),
step=global_step,
)
max_q = tf.reduce_max(logits_vec, axis=1)
tf.compat.v2.summary.scalar(
name="max_q", data=tf.reduce_mean(max_q), step=global_step)
### End metrics
| tensorflow.reduce_max | 14,339 |
import tensorflow as tf
loop_vars=[n, result, two])
return result
def factorial(n: TensorLike) -> TensorLike:
n = tf.convert_to_tensor(value=n)
return tf.exp(tf.math.lgamma(n + 1))
def generate_l_m_permutations(
max_band: int,
name: str = "spherical_harmonics_generate_l_m_permutations") -> Tuple[TensorLike, TensorLike]:
with tf.name_scope(name):
| tensorflow.math.lgamma | 14,340 |
import tensorflow as tf
with tf.variable_scope(scope, reuse=reuse):
# set up placeholders
obs_t_input = make_obs_ph("obs_t")
act_t_ph = tf.placeholder(tf.int32, [None], name="action")
rew_t_ph = tf.placeholder(tf.float32, [None], name="reward")
obs_tp1_input = make_obs_ph("obs_tp1")
done_mask_ph = tf.placeholder(tf.float32, [None], name="done")
importance_weights_ph = tf.placeholder(tf.float32, [None], name="weight")
# q network evaluation
q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", reuse=True) # reuse parameters from act
q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/q_func")
| tensorflow.placeholder | 14,341 |
from tensorflow.python.framework import ops
rank = array_ops.rank(top_k_predictions)
check_rank_op = control_flow_ops.Assert(
math_ops.greater_equal(rank, 2),
['top_k_predictions must have rank 2 or higher, e.g. [batch_size, k].'])
with ops.control_dependencies([check_rank_op]):
return _streaming_sparse_precision_at_k(
top_k_idx=top_k_predictions,
labels=labels,
| tensorflow.python.framework.ops.control_dependencies | 14,342 |
import tensorflow as tf
boxes (num_priors, 4): real values for priors.
labels (num_priors): labels for priors.
"""
# size: num_priors x num_targets
ious = iou_of(tf.expand_dims(gt_boxes, axis=0), tf.expand_dims(corner_form_priors, axis=1))
# size: num_priors
best_target_per_prior = tf.math.reduce_max(ious, axis=1)
| tensorflow.expand_dims | 14,343 |
import tensorflow as tf
n = tf.random_normal(shape=shape, dtype=tf.float32)
n = tf.reshape(n, shape=(int(shape[0]), -1))
n = tf.nn.l2_normalize(n, dim=1)
n = tf.reshape(n, shape)
return n
| tensorflow.reshape | 14,344 |
import tensorflow as tf
[None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEqual(output.shape, (batch_size, num_classes))
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
is_training=False)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEqual(output.shape, (batch_size,))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
| tensorflow.random_uniform | 14,345 |
from tensorflow.python.ops import array_ops
"""
with self._name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
sample_shape, batch_shape, event_shape = self.get_shape(x)
event_shape = distribution_util.pick_vector(
self._event_ndims_is_0, (1,), event_shape)
batch_shape = distribution_util.pick_vector(
self._batch_ndims_is_0, (1,), batch_shape)
new_shape = array_ops.concat(0, ((-1,), batch_shape, event_shape))
x = array_ops.reshape(x, shape=new_shape)
x = distribution_util.rotate_transpose(x, shift=-1)
return x, sample_shape
def undo_make_batch_of_event_sample_matrices(
self, x, sample_shape, name="undo_make_batch_of_event_sample_matrices"):
"""Reshapes/transposes `Distribution` `Tensor` from B_+E_+S_ to S+B+E.
| tensorflow.python.ops.array_ops.concat | 14,346 |
import tensorflow as tf
# Classification accuracy of encoder
correct_pred = tf.equal(tf.argmax(encoder_output_label_, 1), tf.argmax(y_input, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
| tensorflow.argmax | 14,347 |
import tensorflow as tf
h, w, _ = image.shape
grid = 8
image = image[:h // grid * grid, :w // grid * grid, :]
mask = mask[:h // grid * grid, :w // grid * grid, :]
image = np.expand_dims(image, 0)
mask = np.expand_dims(mask, 0)
input_image = np.concatenate([image, mask], axis=2)
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
input_image = tf.constant(input_image, dtype=tf.float32)
output = model.build_server_graph(FLAGS, input_image)
output = (output + 1.) * 127.5
output = tf.reverse(output, [-1])
output = tf.saturate_cast(output, tf.uint8)
# load pretrained model
| tensorflow.ConfigProto | 14,348 |
import tensorflow as tf
l3=tf.matmul(l2, self.w3)+self.b3
l3=tf.nn.relu(l3)
out=tf.matmul(l3, self.w4)+self.b4
return out
def valid_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
def softmax_loss(self,predicts,labels):
predicts=tf.nn.softmax(predicts)
labels=tf.one_hot(labels,classnum)
loss=-tf.reduce_sum(labels*tf.log(predicts))
return loss
def optimer(self,loss,lr=0.001):
train_step=tf.train.GradientDescentOptimizer(lr).minimize(loss)
return train_step
| tensorflow.matmul | 14,349 |
import tensorflow as tf
def train_z(loss,Z):
return tf.gradients(ys = loss, xs = Z)
| tensorflow.gradients | 14,350 |
import tensorflow as tf
sample: the ground truth information
detections: the predicted detections
Returns:
dict of intermediate results.
"""
result_dict = {'iou_mean': -1, 'iou_min': -1, 'collisions': 0,
'collision_intersection': 0, 'collision_iou': 0}
num_boxes = sample['num_boxes'].numpy()
labeled_boxes_init = tf.gather(
sample['groundtruth_boxes'], axis=1, indices=[1, 0, 3, 2]) * 256.0
for _, metric in self.metrics.items():
if isinstance(metric, ShapeAccuracyMetric):
labels = sample['shapes']
weights = tf.math.sign(labels + 1) # -1 is mapped to zero, else 1
metric.update(labels, detections['shapes_logits'], weights)
elif isinstance(metric, BoxIoUMetric):
scene_id = str(sample['scene_filename'].numpy(), 'utf-8')
| tensorflow.gather | 14,351 |
import tensorflow as tf
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., margin-pred_posi_dif)
cstr_pct = tf.math.count_nonzero(loss, dtype=tf.float32) / tf.cast(tf.reduce_prod(tf.shape(loss)), tf.float32)
final_loss = tf.reduce_mean(loss)
return final_loss, cstr_pct
| tensorflow.maximum | 14,352 |
import tensorflow as tf
# List of dicts to dict of lists
metrics = dict(zip(metrics[0], zip(*[m.values() for m in metrics])))
metrics = {m: np.nanmean(metrics[m], axis=0) for m in metrics}
return metrics
def _checkpoint_var_search(self, checkpoint_path):
reader = tf.train.NewCheckpointReader(checkpoint_path)
saved_shapes = reader.get_variable_to_shape_map()
model_names = tf.model_variables() # Used by tf.slim layers
if not len(tf.model_variables()):
model_names = tf.global_variables() # Fallback when slim is not used
model_names = set([v.name.split(':')[0] for v in model_names])
checkpoint_names = set(saved_shapes.keys())
found_names = model_names & checkpoint_names
missing_names = model_names - checkpoint_names
shape_conflicts = set()
| tensorflow.model_variables | 14,353 |
import tensorflow as tf
return tf.layers.conv2d(input, channels, kernel_size=size, strides=[stride, stride],
padding=padding, kernel_initializer=init, name='conv' + id,
use_bias=use_bias, dilation_rate=(dilation, dilation))
def z_conv(self, id, input, channels, size, stride=1, padding="SAME", use_bias=False, dilation=1):
# zero mean conv
if type(size) == int: size = [size, size]
in_ch = input.get_shape().as_list()[-1]
# init = tf.contrib.layers.variance_scaling_initializer(dtype=tf.float32)
init = tf.truncated_normal_initializer(mean=0.0, stddev=0.02)
filters = tf.get_variable('zero_conv_weights' + id, initializer=init, shape=[size[0], size[1], in_ch, channels])
filters = filters - tf.reduce_mean(filters, axis=[0, 1, 2], keepdims=True)
if padding == "PARTIAL":
with tf.variable_scope('mask'):
_, h, w, _ = input.get_shape().as_list()
slide_window = size[0] * size[1]
mask = tf.ones(shape=[1, h, w, 1])
| tensorflow.truncated_normal_initializer | 14,354 |
import tensorflow as tf
with tf.name_scope('inputs'):
noise, one_hot_labels = _get_generator_inputs(
FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)
# Generate images.
with tf.variable_scope('Generator'): # Same scope as in train job.
images = networks.conditional_generator(
(noise, one_hot_labels), is_training=False)
# Visualize images.
| tensorflow.variable_scope | 14,355 |
from tensorflow.contrib.metrics.python.ops import set_ops
labels, predictions_idx = _maybe_select_class_id(labels,
predictions_idx,
class_id)
fn = set_ops.set_size(set_ops.set_difference(predictions_idx,
labels,
aminusb=False))
| tensorflow.contrib.metrics.python.ops.set_ops.set_difference | 14,356 |
import tensorflow as tf
# Convert RGB to BGR
red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=rgb_scaled)
assert red.get_shape().as_list()[1:] == [224, 224, 1]
assert green.get_shape().as_list()[1:] == [224, 224, 1]
assert blue.get_shape().as_list()[1:] == [224, 224, 1]
bgr = tf.concat(axis=3, values=[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
| tensorflow.concat | 14,357 |
import tensorflow as tf
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
| tensorflow.matmul | 14,358 |
import tensorflow as tf
else:
print("Created model with fresh parameters.")
global_variable = [gv for gv in tf.global_variables() if args.name in gv.name]
sess.run(tf.variables_initializer(global_variable))
return model
def main(args):
if args.debug:
debug()
if args.cuda:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
else:
config = tf.ConfigProto(device_count={'GPU': 0})
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
data_class = MultiTurnDialog.load_class(args.dataset)
wordvec_class = WordVector.load_class(args.wvclass)
if wordvec_class == None:
wordvec_class = Glove
if args.cache:
data = try_cache(data_class, (args.datapath,), args.cache_dir)
vocab = data.frequent_vocab_list
| tensorflow.ConfigProto | 14,359 |
import tensorflow as tf
assert n_filters > projection_dim
with tf.variable_scope('CNN_proj') as scope:
W_proj_cnn = tf.get_variable(
"W_proj", [n_filters, projection_dim],
initializer=tf.random_normal_initializer(
mean=0.0, stddev=np.sqrt(1.0 / n_filters)),
dtype=DTYPE)
b_proj_cnn = tf.get_variable(
"b_proj", [projection_dim],
initializer=tf.constant_initializer(0.0),
dtype=DTYPE)
# apply highways layers
def high(x, ww_carry, bb_carry, ww_tr, bb_tr):
carry_gate = tf.nn.sigmoid(tf.matmul(x, ww_carry) + bb_carry)
transform_gate = tf.nn.relu(tf.matmul(x, ww_tr) + bb_tr)
return carry_gate * transform_gate + (1.0 - carry_gate) * x
if use_highway:
highway_dim = n_filters
for i in range(n_highway):
with tf.variable_scope('CNN_high_%s' % i) as scope:
W_carry = tf.get_variable(
'W_carry', [highway_dim, highway_dim],
# glorit init
initializer=tf.random_normal_initializer(
mean=0.0, stddev=np.sqrt(1.0 / highway_dim)),
| tensorflow.matmul | 14,360 |
import tensorflow as tf
anchor_match_mining_distance_matrix=(
anchor_match_mining_distance_matrix)))
negative_distances = tf.boolean_mask(
negative_distances,
mask=negative_distances < negative_distances.dtype.max)
negative_mining_distances = tf.boolean_mask(
negative_mining_distances,
mask=negative_distances < negative_distances.dtype.max)
active_triplet_ratio = (
tf.cast(num_active_triplets, dtype=tf.float32) / num_total_triplets)
active_mining_triplet_ratio = (
tf.cast(num_active_mining_triplets, dtype=tf.float32) /
num_total_triplets)
active_loss = (
loss / tf.math.maximum(1e-12, tf.stop_gradient(active_triplet_ratio)))
active_mining_loss = (
mining_loss /
tf.math.maximum(1e-12, tf.stop_gradient(active_mining_triplet_ratio)))
tag = 'SemiHardNegative' if use_semi_hard else 'HardNegative'
summaries = {
# Summaries related to triplet loss computation.
| tensorflow.cast | 14,361 |
import tensorflow as tf
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
loss = tf.maximum(0.0, ((tgt_larg - tgt_small) - (pred_larg - pred_small)))
loss = tf.reduce_mean(loss)
return loss
def apply_optimizers(objectives, trainer, config):
# Make sure all losses are computed and apply loss scales.
processed = []
values = [ob.value for ob in objectives]
| tensorflow.reduce_mean | 14,362 |
import tensorflow as tf
M = tf.placeholder(tf.float32, [None, 2, n_ctx])
Y_train = tf.placeholder(tf.int32, [n_batch_train])
Y = tf.placeholder(tf.int32, [None])
train, logits, clf_losses, lm_losses = mgpu_train(X_train, M_train, Y_train)
clf_loss = tf.reduce_mean(clf_losses)
params = find_trainable_variables('model')
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(tf.global_variables_initializer())
| tensorflow.reduce_mean | 14,363 |
from tensorflow.contrib.learn.python.learn.estimators import run_config
learner_config.num_classes = 2
learner_config.constraints.max_tree_depth = 1
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
classifier = estimator.GradientBoostedDecisionTreeClassifier(
| tensorflow.contrib.learn.python.learn.estimators.run_config.RunConfig | 14,364 |
import tensorflow as tf
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = tf.constant(0.5, shape=[2, 2, 2])
enc_outputs, enc_state = tf.nn.dynamic_rnn(cell, inp, dtype=tf.float32)
attn_states = enc_outputs
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
| tensorflow.nn.dynamic_rnn | 14,365 |
import tensorflow as tf
1) for i in range(num_boxes)], axis=0)
rotations_y = tf.reshape(rotations_y, [-1, 1])
predicted_boxes = tf.concat([detections['translations_3d'],
detections['sizes_3d'],
rotations_y], axis=1)
labeled_classes = tf.cast(sample['groundtruth_valid_classes'], tf.int64)
predicted_classes = tf.cast(detections['detection_classes'], tf.int64)
confidences = detections['detection_scores']
metric.update(scene_id, labeled_boxes, labeled_classes, predicted_boxes,
predicted_classes, confidences)
elif isinstance(metric, IoUMetric):
classes = sample['classes']
mesh_names = sample['mesh_names']
| tensorflow.cast | 14,366 |
import tensorflow as tf
U = tf.get_variable(name="attn_U",
shape=[2 * self.config.hidden_size, 2 * self.config.hidden_size],
initializer=tf.contrib.layers.xavier_initializer(),
# initializer=tf.truncated_normal_initializer(),
# initializer=tf.keras.initializers.lecun_normal(),
dtype=tf.float32)
self.position_emb = tf.reshape(self.position_emb, [-1, 2 * self.config.hidden_size])
shape = tf.shape(output)
output = tf.reshape(output, [-1, 2 * self.config.hidden_size])
atten_hidden = tf.tanh(
tf.add(
tf.matmul(self.position_emb, W),
tf.matmul(output, U)))
alpha = tf.nn.softmax(
tf.reshape(tf.matmul(atten_hidden, V), [-1, shape[1], 1]), axis=1)
output = tf.reshape(output, [-1, shape[1], 2 * self.config.hidden_size])
C = tf.multiply(alpha, output)
return tf.concat([output, C], axis=-1)
def _train_epoch(self, train_batches, dropout):
"""
:param train_batches:
:param dropout:
| tensorflow.matmul | 14,367 |
import tensorflow as tf
predictions = tf.reshape(predictions, [1, -1, heatmap_size*heatmap_size])
pred_max = tf.reduce_max(predictions, axis=-1)
pred_indices = tf.argmax(predictions, axis=-1)
pred_x, pred_y = tf.cast(tf.floormod(pred_indices, heatmap_size), tf.float32), tf.cast(tf.floordiv(pred_indices, heatmap_size), tf.float32)
width, height = tf.cast(width, tf.float32), tf.cast(height, tf.float32)
pred_x, pred_y = pred_x * width / tf.cast(heatmap_size, tf.float32), pred_y * height / tf.cast(heatmap_size, tf.float32)
if clip_at_zero:
pred_x, pred_y = pred_x * tf.cast(pred_max>0, tf.float32), pred_y * tf.cast(pred_max>0, tf.float32)
pred_x = pred_x * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (width / 2.)
pred_y = pred_y * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (height / 2.)
if config.PRED_DEBUG:
pred_indices_ = tf.squeeze(pred_indices)
image_ = tf.squeeze(image) * 255.
pred_heatmap = tf.one_hot(pred_indices_, heatmap_size*heatmap_size, on_value=1., off_value=0., axis=-1, dtype=tf.float32)
pred_heatmap = tf.reshape(pred_heatmap, [-1, heatmap_size, heatmap_size])
if data_format == 'channels_first':
image_ = tf.transpose(image_, perm=(1, 2, 0))
| tensorflow.cast | 14,368 |
import tensorflow as tf
feed_previous=True)
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
def testAttentionDecoder1(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = [tf.constant(0.5, shape=[2, 2])] * 2
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
| tensorflow.constant | 14,369 |
import tensorflow as tf
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(c)
xs[idx] = h
s = tf.concat(axis=1, values=[c, h])
return xs, s
def _ln(x, g, b, e=1e-5, axes=[1]):
u, s = tf.nn.moments(x, axes=axes, keep_dims=True)
x = (x-u)/tf.sqrt(s+e)
x = x*g+b
return x
| tensorflow.concat | 14,370 |
import tensorflow as tf
margin: Margin of the contrastive loss
Returns:
loss: scalar float Tensor
"""
########################
# PUT YOUR CODE HERE #
########################
D = (tf.reduce_sum((channel_1 - channel_2)**2, reduction_indices=1))**0.5
zeros = tf.fill(tf.shape(D), 0.0)
# loss = 0.5*(label*(D**2.) + (1-label) * (tf.reduce_max([zeros, margin - D], reduction_indices=0))**2)
loss = label*(D**2) + (1-label) * (tf.reduce_max([zeros, margin - D**2], 0))
########################
# END OF YOUR CODE #
########################
| tensorflow.reduce_sum | 14,371 |
import tensorflow as tf
Returns
-------
A tensor.
"""
res = tf.nn.elu(x)
if alpha == 1:
return res
else:
return tf.where(x > 0, res, alpha * res)
| tensorflow.nn.elu | 14,372 |
import tensorflow as tf
wrong = tf.to_float(tf.logical_not(tf.nn.in_top_k(logits, label, 1)), name='incorrect_vector')
summary.add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
wd_cost = tf.multiply(1e-5, regularize_cost('fc.*/W', tf.nn.l2_loss),
name='regularize_loss')
summary.add_moving_summary(cost, wd_cost)
self.cost = tf.add_n([wd_cost, cost], name='cost')
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=5e-4, trainable=False)
opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
return optimizer.apply_grad_processors(
| tensorflow.add_n | 14,373 |
from tensorflow.contrib.eager.python.examples.revnet import config as config_
for grad, var in zip(grads, vars_):
if grad is not None:
self.assertEqual(grad.shape, var.shape)
def test_training_graph(self):
"""Test model training in graph mode."""
with tf.Graph().as_default():
config = config_.get_hparams_cifar_38()
config.add_hparam("n_classes", 10)
config.add_hparam("dataset", "cifar-10")
x = tf.random_normal(
shape=(self.config.batch_size,) + self.config.input_shape)
t = tf.random_uniform(
| tensorflow.contrib.eager.python.examples.revnet.config.get_hparams_cifar_38 | 14,374 |
import tensorflow as tf
return img_h0, img_h1, img_h2, img_h3, img_h4, img_z
with tf.variable_scope("conv") as scope:
srcimg_h0, srcimg_h1, srcimg_h2, srcimg_h3, srcimg_h4, srcimg_z = encode(srcimg)
scope.reuse_variables()
tgtimg_h0, tgtimg_h1, tgtimg_h2, tgtimg_h3, tgtimg_h4, tgtimg_z = encode(tgtimg)
tgtctx_h0, tgtctx_h1, tgtctx_h2, tgtctx_h3, tgtctx_h4, tgtctx_z = encode(tgtctx)
with tf.variable_scope("translate") as scope:
trans_h0 = lrelu(linear(tf.nn.dropout(tf.concat([srcimg_z, tgtctx_z], 1), keep_prob), featsize, 'trans_h0'))
trans_z = linear(tf.nn.dropout(trans_h0, keep_prob), featsize, 'trans_z')
self.translated_z = trans_z
s_h, s_w = self.output_height, self.output_width
s_h0, s_h1, s_h2, s_h3 = \
int(s_h/ns0), int(s_h/ns0/ns1), int(s_h/ns0/ns1/ns2), int(s_h/ns0/ns1/ns2/ns3)
s_w0, s_w1, s_w2, s_w3 = \
int(s_w/ns0), int(s_w/ns0/ns1), int(s_w/ns0/ns1/ns2), int(s_w/ns0/ns1/ns2/ns3)
| tensorflow.concat | 14,375 |
import tensorflow as tf
def _add_train_summary(self, var):
tf.summary.histogram('TRAIN/' + var.op.name, var)
# Custom Layers #
def _reshape_layer(self, bottom, num_dim, name):
input_shape = tf.shape(bottom)
with tf.variable_scope(name):
# change the channel to the caffe format
# 18个通道[,18，none，none],分别显示得分，前9个为前景得分，后9个为背景得分
# 第二次[1,2,none,none]
to_caffe = tf.transpose(bottom, [0, 3, 1, 2])
# then force it to have channel 2
#[1,2,none.none],将9个anchor的前景得分和背景得分分开
# 第二次[1,18,none,none]
reshaped = tf.reshape(to_caffe, tf.concat(axis=0, values=[[self._batch_size], [num_dim, -1], [input_shape[2]]]))
# then swap the channel back
# [1,none,none,2], 第一个none应该为（行*9）
# 第二次[1,none,none,18]
to_tf = tf.transpose(reshaped, [0, 2, 3, 1])
return to_tf
| tensorflow.transpose | 14,376 |
from tensorflow.python.framework import tensor_shape
"""Common shape function for binary operators that broadcast their inputs."""
shape_x = op.inputs[0].get_shape()
shape_y = op.inputs[1].get_shape()
if shape_x.ndims is None or shape_y.ndims is None:
return [tensor_shape.unknown_shape()]
# To compute the broadcasted dimensions, we zip together shape_x and shape_y,
# and pad with 1 to make them the same length.
broadcasted_dims = reversed(list(six.moves.zip_longest(
reversed(shape_x.dims),
reversed(shape_y.dims),
fillvalue=tensor_shape.Dimension(1))))
# Next we combine the dimensions according to the numpy broadcasting rules.
# http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
return_dims = []
for (dim_x, dim_y) in broadcasted_dims:
if dim_x.value is None or dim_y.value is None:
# One or both dimensions is unknown. If either dimension is greater than
# 1, we assume that the program is correct, and the other dimension will
# be broadcast to match it.
# TODO(mrry): If we eliminate the shape checks in C++, we must still
# assert that the unknown dim is either 1 or the same as the known dim.
| tensorflow.python.framework.tensor_shape.Dimension | 14,377 |
import tensorflow as tf
with tf.variable_scope(scope, reuse=reuse):
observations_ph = make_obs_ph("observation")
stochastic_ph = tf.placeholder(tf.bool, (), name="stochastic")
update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
| tensorflow.placeholder | 14,378 |
import tensorflow as tf
tf.app.flags.DEFINE_float(
'neg_threshold', 0.4, 'Matching threshold for the negtive examples in the loss function.')
# optimizer related configuration
tf.app.flags.DEFINE_float(
'weight_decay', 0.0005, 'The weight decay on the model weights.')
tf.app.flags.DEFINE_float(
'momentum', 0.9,
'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
tf.app.flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
tf.app.flags.DEFINE_float(
'end_learning_rate', 0.00005,
'The minimal end learning rate used by a polynomial decay learning rate.')
# for learning rate exponential_decay
tf.app.flags.DEFINE_float(
'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',
'The values of learning_rate decay factor for each segment between boundaries (comma-separated list).')
# checkpoint related configuration
tf.app.flags.DEFINE_string(
'checkpoint_path', './model/resnet50',#None,
'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
| tensorflow.app.flags.DEFINE_float | 14,379 |
import tensorflow as tf
z = tf.math.exp(log_sigmas)*x
ldj = tf.math.reduce_sum(log_sigmas, axis=[1,2,3])
else:
z = x*tf.math.exp(-log_sigmas)
ldj = -tf.math.reduce_sum(log_sigmas, axis=[1,2,3])
return z, ldj
class HalfGaussianize(Parameterize):
| tensorflow.math.reduce_sum | 14,380 |
import tensorflow as tf
for (name, value) in monitored_values.items():
tf.summary.scalar(name, value)
summary_op = tf.summary.merge_all()
return (summary_op, monitored_values)
def _make_var(self, name, shape, dtype=None, no_reg=False, initializer=None, init_constant=None, trainable=True):
if initializer is None:
if init_constant is not None:
initializer = tf.constant_initializer(init_constant, dtype=tf.float32)
else:
initializer = tf.contrib.keras.initializers.he_normal()
# Ensure that name is unique by shape too
name += '-shape-{}'.format('x'.join([str(x) for x in shape]))
var = tf.get_variable(name, shape=shape, dtype=dtype, initializer=initializer, trainable=trainable)
# Add L2 regularization node for trainable var
if trainable and not no_reg:
l2_loss = tf.nn.l2_loss(var)
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, l2_loss)
| tensorflow.contrib.keras.initializers.he_normal | 14,381 |
import tensorflow as tf
LR = 0.001
def main(unused_argv):
tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled.
with tf.device('/CPU:0'): # due to b/128333994
if FLAGS.normalize_reward_fns:
action_reward_fns = (
environment_utilities.normalized_sliding_linear_reward_fn_generator(
CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE))
else:
| tensorflow.device | 14,382 |
import tensorflow as tf
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
| tensorflow.contrib.cluster_resolver.TPUClusterResolver | 14,383 |
import tensorflow as tf
Of shape (n_test, n_support)
"""
rnorm_test = tf.rsqrt(
tf.reduce_sum(tf.square(test), 1, keep_dims=True)) + 1e-7
rnorm_support = tf.rsqrt(
tf.reduce_sum(tf.square(support), 1, keep_dims=True)) + 1e-7
test_normalized = test * rnorm_test
support_normalized = support * rnorm_support
# Transpose for mul
support_normalized_t = tf.transpose(support_normalized, perm=[1, 0])
g = tf.matmul(test_normalized, support_normalized_t) # Gram matrix
return g
def elu(x, alpha=1.):
"""Exponential linear unit.
Parameters
----------
x: A tensor or variable to compute the activation function for.
alpha: A scalar, slope of positive section.
| tensorflow.matmul | 14,384 |
import tensorflow as tf
update_mask = tf.layers.conv2d(mask, filters=1, dilation_rate=(dilation, dilation), name='mask' + id,
kernel_size=size, kernel_initializer=tf.constant_initializer(1.0),
| tensorflow.constant_initializer | 14,385 |
import tensorflow as tf
x_duplicate_sample = x_duplicate.sample(name="custom_sample")
self.assertEqual(x.name, "x/")
self.assertEqual(y.name, "y/")
# There's no notion of graph, hence the same name will be reused.
# Tensors also do not have names in eager mode, so exit early.
if tf.executing_eagerly():
return
self.assertTrue(x_sample.name.startswith("x/custom_sample"))
self.assertTrue(x_log_prob.name.startswith("x/custom_log_prob"))
self.assertEqual(x_duplicate.name, "x_1/")
self.assertTrue(x_duplicate_sample.name.startswith(
| tensorflow.executing_eagerly | 14,386 |
import tensorflow as tf
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']))
ignored_matches = tf.logical_and(
tf.less(matched_iou, 0.0),
tf.greater_equal(
matched_iou, self._config_dict['background_iou_high_threshold']))
ignored_matches = tf.logical_and(
ignored_matches,
tf.less(
matched_iou, self._config_dict['foreground_iou_threshold']))
| tensorflow.less | 14,387 |
import tensorflow as tf
# Init dual param values
self.param_eta = init_eta
self.param_omega = init_omega
self.param_eta_non_lin = init_eta
self.param_omega_non_lin = init_omega
param_eta = tf.placeholder(dtype=tf.float32, shape=[], name="param_eta")
param_omega = tf.placeholder(dtype=tf.float32, shape=[], name="param_omega")
old_entropy = tf.placeholder(dtype=tf.float32, shape=[], name="old_entropy")
varphis = tf.placeholder(dtype=tf.float32, shape=[None, None], name="varphis")
Kt = tf.placeholder(dtype=tf.float32, shape=[None, None], name="Kt")
prec = tf.placeholder(dtype=tf.float32, shape=[None, None], name="prec")
Waa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="Waa")
Wsa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="Wsa")
wa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="wa")
| tensorflow.placeholder | 14,388 |
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Runs the op.
print(sess.run(c))
# If we load a graph and want device placement to be forgotten,
# we set a parameter in our session:
config = tf.ConfigProto()
config.allow_soft_placement = True
sess_soft = tf.Session(config=config)
# GPUs
#---------------------------------
# Note that the GPU must have a compute capability > 3.5 for TF to use.
# http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capability
# Careful with GPU memory allocation, TF never releases it. TF starts with almost
# all of the GPU memory allocated. We can slowly grow to that limit with an
| tensorflow.ConfigProto | 14,389 |
import tensorflow as tf
def variational_expectations( Fmu, Fvar, phi, num_gauss_hermite_points=20):
"""
Compute the expected value of a function phi, given a Gaussian
distribution for the input values.
if
q(f) = N(Fmu, Fvar)
then this method computes
\int phi(f) q(f) df.
Here, we implement a default Gauss-Hermite quadrature routine
"""
gh_x, gh_w = hermgauss(num_gauss_hermite_points)
gh_x = gh_x.reshape(1, -1)
gh_w = gh_w.reshape(-1, 1) / np.sqrt(np.pi)
shape = tf.shape(Fmu)
Fmu, Fvar = [tf.reshape(e, (-1, 1)) for e in (Fmu, Fvar)]
X = gh_x * tf.sqrt(2.0 * Fvar) + Fmu
logp = phi(X)
return tf.reshape(tf.matmul(logp, gh_w), shape)
import tensorflow as tf
def block_diagonal(matrices, dtype=tf.float32):
"""Constructs block-diagonal matrices from a list of batched 2D tensors.
Args:
matrices: A list of Tensors with shape [..., N_i, M_i] (i.e. a list of
matrices with the same batch dimension).
dtype: Data type to use. The Tensors in `matrices` must match this dtype.
| tensorflow.reshape | 14,390 |
import tensorflow as tf
self.b,data_format='NHWC',name=name)
def get_variables(self):
return {'w':self.w,'b':self.b}
class WeightNormSymPadConv2d(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.conv2d = WeightNormConv2d('conv',input_dim,output_dim,k_h,k_w,1,1,data_format='NHWC',padding='VALID')
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')
| tensorflow.variable_scope | 14,391 |
import tensorflow as tf
np.log(1. - sm_normal.cdf(1)),
qdist.log_prob(2.).eval(),
atol=0)
def test_log_prob_and_grad_gives_finite_results(self):
with self.test_session():
for dtype in [np.float32, np.float64]:
mu = tf.Variable(0., name="mu", dtype=dtype)
sigma = tf.Variable(1., name="sigma", dtype=dtype)
qdist = distributions.QuantizedDistribution(
base_dist_cls=distributions.Normal,
mu=mu,
sigma=sigma)
x = np.arange(-100, 100, 2).astype(dtype)
tf.initialize_all_variables().run()
| tensorflow.Variable | 14,392 |
import tensorflow as tf
x = tf.to_float(x)
y = tf.to_float(y)
z = tf.to_float(z)
depth_f = tf.to_float(depth)
height_f = tf.to_float(height)
width_f = tf.to_float(width)
| tensorflow.to_float | 14,393 |
from tensorflow.python.framework import ops
"""Moves a list of tensors to a device by concatenating/splitting them."""
# Reset the device setting to avoid weird interactions with device merging
# logic.
with ops.device(None):
if all(tensor.shape == tensor_shape.scalar() for tensor in tensors):
with ops.device(tensors[0].device):
| tensorflow.python.framework.ops.device | 14,394 |
import tensorflow as tf
ema = tf.train.ExponentialMovingAverage(decay=0.9)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(b_train,
| tensorflow.control_dependencies | 14,395 |
import tensorflow as tf
num_decoder_symbols=5, embedding_size=2, output_projection=(w, b))
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [tf.constant(0, tf.int32, shape=[2]) for _ in range(3)]
with tf.variable_scope("other"):
d3, _ = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2,
feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
tf.get_variable_scope().reuse_variables()
d1, _ = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
d2, _ = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
| tensorflow.global_variables_initializer | 14,396 |
import tensorflow as tf
word_t_representation = self.embedding_lookup(word_t)
(state_t, context_t, coverage_t, attn_dist_t, p_gen_t, output_t) = self.one_step_decoder(
state_t_1, context_t_1, coverage_t_1, word_t_representation, encoder_states, encoder_features,
passage_word_idx, passage_mask, v, w_c, word_vocab)
vocab_scores = tf.log(output_t)
greedy_prediction = tf.reshape(tf.argmax(output_t, 1),[-1]) # calcualte greedy
multinomial_prediction = tf.reshape(tf.multinomial(vocab_scores, 1),[-1]) # calculate multinomial
topk_log_probs, topk_ids = tf.nn.top_k(vocab_scores, beam_size) # calculate topK
return (state_t, context_t, coverage_t, attn_dist_t, p_gen_t, output_t, topk_log_probs, topk_ids,
| tensorflow.log | 14,397 |
import tensorflow as tf
@dynamic_batching.batch_fn
def f(a):
return a
f(tf.constant([1]))
# Intentionally using tf.Session() instead of self.test_session() to have
# control over closing the session. test_session() is a cached session.
with tf.Session():
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord)
time.sleep(_SLEEP_TIME)
coord.request_stop() # Calls close operation.
coord.join()
# Session closed.
def test_minimum_batch_size(self):
with self.test_session() as session:
@dynamic_batching.batch_fn_with_options(
minimum_batch_size=2, timeout_ms=1000)
| tensorflow.train.start_queue_runners | 14,398 |
import tensorflow as tf
except ValueError: # auto_reuse doesn't work with LSTM cells
output, new_state = get_cell(input_size, reuse=True)(input_, state)
if decoder.skip_update and decoder.pred_edits and symbol is not None:
is_del = tf.equal(symbol, utils.DEL_ID)
new_state = tf.where(is_del, state, new_state)
if decoder.cell_type.lower() == 'lstm' and decoder.use_lstm_full_state:
output = new_state
| tensorflow.where | 14,399 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.