seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
summary = tf.Summary()
summary.value.add(tag='eval/Accuracy@1', simple_value=precision_at_1)
summary.value.add(tag='eval/Recall@5', simple_value=recall_at_5)
summary_writer.add_summary(summary, global_step)
log_fn('Precision @ 1 = %.4f recall @ 5 = %.4f [%d examples]' %
(precision_at_1, recall_at_5, total_eval_count))
def _benchmark_cnn(self):
"""Run cnn in benchmark mode. When forward_only on, it forwards CNN."""
(enqueue_ops, fetches) = self._build_model()
main_fetch_group = tf.group(*fetches)
execution_barrier = None
if self.job_name and not FLAGS.cross_replica_sync:
execution_barrier = self.add_sync_queues_and_barrier(
'execution_barrier_', [])
global_step = tf.contrib.framework.get_global_step()
with tf.device(self.global_step_device):
with tf.control_dependencies([main_fetch_group]):
inc_global_step = global_step.assign_add(1)
| tensorflow.group | 13,800 |
import tensorflow as tf
# Make sure update_ops are computed before total_loss.
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name='update_barrier')
self.d_losses[-1] = control_flow_ops.with_dependencies([barrier], self.d_losses[-1])
self.g_losses[-1] = control_flow_ops.with_dependencies([barrier], self.g_losses[-1])
| tensorflow.no_op | 13,801 |
import tensorflow as tf
test_dir = self._TestDir("saver_collection")
filename = os.path.join(test_dir, "metafile")
saver0_ckpt = os.path.join(test_dir, "saver0.ckpt")
saver1_ckpt = os.path.join(test_dir, "saver1.ckpt")
with self.test_session(graph=tf.Graph()) as sess:
# Creates a graph.
v0 = tf.Variable(10.0, name="v0")
v1 = tf.Variable(11.0, name="v1")
# Creates 2 savers.
saver0 = tf.train.Saver({"v0": v0}, name="saver0")
saver1 = tf.train.Saver({"v1": v1}, name="saver1")
tf.add_to_collection("savers", saver0)
| tensorflow.Variable | 13,802 |
import tensorflow as tf
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
| tensorflow.nn.dropout | 13,803 |
import tensorflow as tf
loss = interpreter.l2_loss(noisy_decode, self.raw_targets[1], alpha=FLAGS.beta)
self.models += [noisy_decode]
return loss
def _tensor_to_image(self, net):
with tf.name_scope('to_image'):
if FLAGS.new_blur:
net = net[..., :self.batch_shape[-1]]
net = tf.nn.relu(net)
net = tf.cast(net <= 1, net.dtype) * net * 255
net = tf.cast(net, tf.uint8)
return net
def _image_to_tensor(self, image):
with tf.name_scope('args_transform'):
net = tf.cast(image, tf.float32) / 255.
if FLAGS.new_blur:
net = _blur_expand(net)
FLAGS.blur = 0.
return net
| tensorflow.cast | 13,804 |
from tensorflow.python.ops import array_ops
def _resize_image(image, height, width):
image = array_ops.expand_dims(image, 0)
| tensorflow.python.ops.array_ops.expand_dims | 13,805 |
import tensorflow as tf
Returns:
*sampled_idx_indicator*: boolean tensor of shape [N], True for entries which are sampled.
"""
negative_idx = tf.logical_not(labels)
positive_idx = tf.logical_and(labels, indicator)
negative_idx = tf.logical_and(negative_idx, indicator)
# Sample positive and negative samples separately
if sample_size is None:
max_num_pos = tf.reduce_sum(tf.cast(positive_idx, dtype=tf.int32))
else:
max_num_pos = int(positive_fraction * sample_size)
sampled_pos_idx = subsample_indicator(positive_idx, max_num_pos)
num_sampled_pos = tf.reduce_sum(tf.cast(sampled_pos_idx, tf.int32))
if sample_size is None:
negative_positive_ratio = (1 - positive_fraction) / positive_fraction
max_num_neg = tf.cast(negative_positive_ratio * tf.cast(num_sampled_pos, dtype=tf.float32),
dtype=tf.int32)
else:
max_num_neg = sample_size - num_sampled_pos
sampled_neg_idx = subsample_indicator(negative_idx, max_num_neg)
return tf.logical_or(sampled_pos_idx, sampled_neg_idx)
def batch_sample_balanced_positive_negative(indicators,
sample_size,
labels,
| tensorflow.cast | 13,806 |
import tensorflow as tf
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.sin | 13,807 |
import tensorflow as tf
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 = ""
| tensorflow.train.init_from_checkpoint | 13,808 |
import tensorflow as tf
log_y = log_x*scales + mus
ldj += log_sigmas
z = tf.math.exp(log_y)
return z, ldj
else:
scales = tf.math.exp(-log_sigmas)
log_x = tf.math.log(x)
ldj = -log_x
log_y = (log_x - mus)*scales
ldj -= log_sigmas
z = tf.math.exp(log_y)
return z, ldj
class LogGaussianize(Parameterize):
"""
Implementation of Parameterize for a log-Gaussian prior.
"""
def __init__(self, input_shape=None, epsilon=1.0E-3, name='log_gaussianize', *args, **kwargs):
super().__init__(*args, num_parameters=2, input_shape=input_shape, name=name, **kwargs)
self.epsilon = epsilon
| tensorflow.math.exp | 13,809 |
import tensorflow as tf
test_pre = tf.argmax(test_pre, 1)
test_true = tf.argmax(test_labels, 1)
| tensorflow.argmax | 13,810 |
import tensorflow as tf
query,
query,
], axis=1)
if time_major:
# (T,B,D) => (B,T,D)
facts = tf.array_ops.transpose(facts, [1, 0, 2])
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]])
scores = d_layer_3_all
if mask is not None:
mask = tf.equal(mask, tf.ones_like(mask))
key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
paddings = tf.ones_like(scores) * (-2 ** 32 + 1)
scores = tf.where(key_masks, scores, paddings) # [B, 1, T]
# Activation
if softmax_stag:
| tensorflow.layers.dense | 13,811 |
import tensorflow as tf
for g, v in grads_and_vars:
tf.summary.histogram(v.name[:-2] + '_hist', v)
tf.summary.histogram(v.name[:-2] + '_grad_hist', g)
with tf.control_dependencies([train_op]), tf.name_scope('ema'):
ema = tf.train.ExponentialMovingAverage(decay=MOVING_AVERAGE_DECAY, num_updates=global_step)
train_op = ema.apply(tf.trainable_variables())
| tensorflow.name_scope | 13,812 |
import tensorflow as tf
context, beta = self._selector(context, h, reuse=(t!=0))
beta_list.append(beta)
with tf.variable_scope('lstm', reuse=(t!=0)):
_, (c, h) = lstm_cell(inputs=tf.concat(axis=1, values=[x, context]), state=[c, h])
logits = self._decode_lstm(x, h, context, reuse=(t!=0))
sampled_word = tf.argmax(logits, 1)
sampled_word_list.append(sampled_word)
alphas = tf.transpose(tf.stack(alpha_list), (1, 0, 2)) # (N, T, L)
betas = tf.transpose(tf.squeeze(beta_list), (1, 0)) # (N, T)
sampled_captions = tf.transpose(tf.stack(sampled_word_list), (1, 0)) # (N, max_len)
return alphas, betas, sampled_captions
| tensorflow.argmax | 13,813 |
import tensorflow as tf
grads = average_gradients(tower_grads)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
summary_op = tf.summary.merge_all()
# save moving average
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
# batch norm updates
with tf.control_dependencies([variables_averages_op, apply_gradient_op, batch_norm_updates_op]):
train_op = tf.no_op(name='train_op')
saver = tf.train.Saver(tf.global_variables())
summary_writer = tf.summary.FileWriter(FLAGS.checkpoint_path, tf.get_default_graph())
| tensorflow.trainable_variables | 13,814 |
import tensorflow as tf
logits=logits, labels=tf.nn.softmax(logits)))
entropy += curr_ent
prev_layers.append(anchors.read(tf.reduce_sum(index)))
inputs = prev_layers[-1]
for i in range(2): # op_1, op_2
next_c, next_h = stack_lstm(inputs, prev_c, prev_h, self.w_lstm)
prev_c, prev_h = next_c, next_h
logits = tf.matmul(next_h[-1], self.w_soft) + self.b_soft
if self.temperature is not None:
logits /= self.temperature
if self.tanh_constant is not None:
op_tanh = self.tanh_constant / self.op_tanh_reduce
logits = op_tanh * tf.tanh(logits)
if use_bias:
logits += self.b_soft_no_learn
| tensorflow.matmul | 13,815 |
import tensorflow as tf
biases = self.variable('biases', [output_channels], tf.constant_initializer(0.0))
deconv_layer = tf.nn.bias_add(deconv, biases)
if bn:
deconv_layer = self.batch_norm_layer('batch_norm_layer',deconv_layer,training)
if relu:
deconv_layer = tf.nn.relu(deconv_layer, name=scope.name)
print('Deconv layer {0} -> {1}'.format(bottom.get_shape().as_list(),deconv_layer.get_shape().as_list()))
return deconv_layer
def variable(self, name, shape, initializer,regularizer=None):
with tf.device('/cpu:0'):
return tf.get_variable(name, shape, initializer=initializer, regularizer=regularizer, trainable=True)
def fc_layer(self, bottom, in_size, out_size, name):
with tf.variable_scope(name):
weights, biases = self.get_fc_var(in_size, out_size, name)
x = tf.reshape(bottom, [-1, in_size])
fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
tf.summary.histogram('weight', weights)
tf.summary.histogram('bias', biases)
| tensorflow.get_variable | 13,816 |
import tensorflow as tf
"""The actual input function."""
batch_size = params["batch_size"]
name_to_features = {
"input_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_mask":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"masked_lm_positions":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_ids":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
"next_sentence_labels":
tf.FixedLenFeature([1], tf.int64),
}
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
if is_training:
d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
d = d.repeat()
d = d.shuffle(buffer_size=len(input_files))
# `cycle_length` is the number of parallel files that get read.
cycle_length = min(num_cpu_threads, len(input_files))
| tensorflow.FixedLenFeature | 13,817 |
from tensorflow.contrib.learn.python.learn.datasets import base
local_file = base.maybe_download(TEST_IMAGES, train_dir,
| tensorflow.contrib.learn.python.learn.datasets.base.maybe_download | 13,818 |
import tensorflow as tf
"question")
self.ch = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_p_len,
self.config.max_ch_len], "context_char")
self.qh = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_q_len,
self.config.max_ch_len], "question_char")
self.start_label = tf.placeholder(tf.int32, [self.config.batch_size], "answer_label1")
self.end_label = tf.placeholder(tf.int32, [self.config.batch_size], "answer_label2")
self.position_emb = position_embedding(self.c, 2 * self.config.hidden_size)
self.c_mask = tf.cast(self.c, tf.bool) # index 0 is padding symbol N x self.max_p_num, max_p_len
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
| tensorflow.placeholder | 13,819 |
import tensorflow as tf
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
feature = InputFeatures(
| tensorflow.logging.info | 13,820 |
from tensorflow.contrib.learn.python.learn.estimators import run_config
run_config.TaskType.PS: ['fake_ps_0', 'fake_ps_1']
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config.RunConfig()
# Because we did not start a distributed cluster, we need to pass an
# empty ClusterSpec, otherwise the device_setter will look for
# distributed jobs, such as "/job:ps" which are not present.
config._cluster_spec = server_lib.ClusterSpec({})
| tensorflow.contrib.learn.python.learn.estimators.run_config.RunConfig | 13,821 |
import tensorflow as tf
def loss(self, logits, forward_only=None):
cost = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=tf.cast(self.y, tf.float32))
mean_cost = tf.reduce_mean(cost)
y_pred = tf.argmax(logits, 1)
correct_pred = tf.equal(y_pred, tf.argmax(self.y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
if forward_only:
str_summary_type = 'eval'
| tensorflow.argmax | 13,822 |
import tensorflow as tf
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with tf.Session() as sess:
input0_tensor = tf.get_default_graph().get_tensor_by_name(
"TENSOR_INPUT0:0")
input1_tensor = tf.get_default_graph().get_tensor_by_name(
"TENSOR_INPUT1:0")
output0_tensor = tf.get_default_graph().get_tensor_by_name(
"TENSOR_OUTPUT0:0")
| tensorflow.get_default_graph | 13,823 |
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
GMM.SCORES: _streaming_sum(loss),
}
return model_fn_lib.ModelFnOps(mode=mode, predictions=predictions,
eval_metric_ops=eval_metric_ops,
| tensorflow.contrib.learn.python.learn.estimators.model_fn.ModelFnOps | 13,824 |
import tensorflow as tf
gru_fw, outputs[-1] * mask_fw, seq_len, initial_state=init_fw, dtype=tf.float32)
with tf.variable_scope("bw_{}".format(layer)):
inputs_bw = tf.reverse_sequence(
outputs[-1] * mask_bw, seq_lengths=seq_len, seq_dim=1, batch_dim=0)
out_bw, _ = tf.nn.dynamic_rnn(
gru_bw, inputs_bw, seq_len, initial_state=init_bw, dtype=tf.float32)
out_bw = tf.reverse_sequence(
out_bw, seq_lengths=seq_len, seq_dim=1, batch_dim=0)
| tensorflow.nn.dynamic_rnn | 13,825 |
import tensorflow as tf
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
train_op = control_flow_ops.with_dependencies([update_op], total_loss,
name='train_op')
else:
clones_losses = []
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
for clone in clones:
with tf.name_scope(clone.scope):
clone_loss = _gather_clone_loss(clone, len(clones),
regularization_losses)
if clone_loss is not None:
clones_losses.append(clone_loss)
# Only use regularization_losses for the first clone
regularization_losses = None
if clones_losses:
total_loss = tf.add_n(clones_losses, name='total_loss')
# Add the summaries from the first clone. These contain the summaries
| tensorflow.name_scope | 13,826 |
import tensorflow as tf
if params['use_ohkm']:
base_learning_rate = 1. * base_learning_rate
for pred_ind in list(range(len(pred_outputs) - 1)):
mse_loss_list.append(0.5 * tf.losses.mean_squared_error(targets_list[pred_ind], pred_outputs[pred_ind],
weights=1.0 / tf.cast(cur_batch_size, tf.float32),
scope='loss_{}'.format(pred_ind),
loss_collection=None,#tf.GraphKeys.LOSSES,
# mean all elements of all pixels in all batch
reduction=tf.losses.Reduction.MEAN))# SUM, SUM_OVER_BATCH_SIZE, default mean by all elements
temp_loss = tf.reduce_mean(tf.reshape(tf.losses.mean_squared_error(targets_list[-1], pred_outputs[-1], weights=1.0, loss_collection=None, reduction=tf.losses.Reduction.NONE), [cur_batch_size, config.class_num_joints[(params['model_scope'] if 'all' not in params['model_scope'] else '*')], -1]), axis=-1)
num_topk = config.class_num_joints[(params['model_scope'] if 'all' not in params['model_scope'] else '*')] // 2
gather_col = tf.nn.top_k(temp_loss, k=num_topk, sorted=True)[1]
gather_row = tf.reshape(tf.tile(tf.reshape(tf.range(cur_batch_size), [-1, 1]), [1, num_topk]), [-1, 1])
gather_indcies = tf.stop_gradient(tf.stack([gather_row, tf.reshape(gather_col, [-1, 1])], axis=-1))
select_targets = tf.gather_nd(targets_list[-1], gather_indcies)
select_heatmap = tf.gather_nd(pred_outputs[-1], gather_indcies)
mse_loss_list.append(tf.losses.mean_squared_error(select_targets, select_heatmap,
weights=1.0 / tf.cast(cur_batch_size, tf.float32),
scope='loss_{}'.format(len(pred_outputs) - 1),
loss_collection=None,#tf.GraphKeys.LOSSES,
# mean all elements of all pixels in all batch
reduction=tf.losses.Reduction.MEAN))
else:
for pred_ind in list(range(len(pred_outputs))):
| tensorflow.range | 13,827 |
import tensorflow as tf
for path in paths:
spectrograms.append(np.load("spectrogram/" + path + ".npy"))
if spectrograms[-1].shape[0] > max_x:
max_x = spectrograms[-1].shape[0]
return spectrograms, max_x
# In[4]:
tf.reset_default_graph()
sess = tf.InteractiveSession()
model = Model()
sess.run(tf.global_variables_initializer())
# In[5]:
for e in range(30):
pbar = tqdm(range(0, len(text_files), batch_size), desc="minibatch loop")
total_cost, total_acc = 0, 0
| tensorflow.InteractiveSession | 13,828 |
import tensorflow as tf
loss=work.softmax_loss(inf,batch_label)
opti=work.optimer(loss,learnrate)
test_image_batch,test_label_batch=get_test_batch(test_image,test_label,testnum)
test_inf=work.test_inference(test_image_batch)
test_labels=tf.one_hot(test_label_batch,classnum)
test_pre = tf.reshape(test_inf, [testnum, classnum])
correct_prediction=tf.equal(tf.argmax(test_inf,1),tf.argmax(test_labels,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
test_pre = tf.argmax(test_pre, 1)
test_true = tf.argmax(test_labels, 1)
valid_image_batch,valid_label_batch=get_valid_batch(valid_image,valid_label,validnum)
valid_inf=work.valid_inference(valid_image_batch)
valid_labels=tf.one_hot(valid_label_batch,classnum)
#train_step=tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
valid_pre = tf.reshape(valid_inf, [validnum, classnum])
valid_correct_prediction=tf.equal(tf.argmax(valid_inf,1),tf.argmax(valid_labels,1))
| tensorflow.argmax | 13,829 |
import tensorflow as tf
)
encoded_history = reduce_max(conv3, [1, 2])
with tf.name_scope("Decoder"):
second_to_last_user_utterance = encoded_utterances[:, history_length - 3, 0, :]
last_system_utterance = encoded_utterances[:, history_length - 2, 0, :]
last_user_utterance = encoded_utterances[:, history_length - 1, 0, :]
| tensorflow.name_scope | 13,830 |
import tensorflow as tf
else:
if num_channels_in != output_length:
conv_weight = tf.Variable(tf.truncated_normal([1, 1, num_channels_in, output_length], stddev=0.1, dtype=tf.float32))
conv = tf.nn.conv2d(input_data, conv_weight, strides=[1, 1, 1, 1], padding='SAME')
| tensorflow.truncated_normal | 13,831 |
import tensorflow as tf
Returns
-------
tf.Tensor
A biased tensor with the same shape as the input tensor.
"""
if init is None:
init = tf.zeros([tensor.get_shape()[-1].value])
with tf.name_scope(name, tensor.op.name, [tensor]):
b = tf.Variable(init, name='b')
return tf.nn.bias_add(tensor, b)
def dropout(tensor, dropout_prob, training=True, training_only=True):
"""Random dropout.
This implementation supports "always-on" dropout (training_only=False), which
| tensorflow.Variable | 13,832 |
import tensorflow as tf
upsampled_features = tf.image.resize_bilinear(
image_features,
[self._mask_height, self._mask_width],
align_corners=True)
upsampled_features = slim.conv2d(
upsampled_features,
num_outputs=self._mask_prediction_conv_depth,
kernel_size=[2, 2])
mask_predictions = slim.conv2d(upsampled_features,
num_outputs=self.num_classes,
activation_fn=None,
kernel_size=[3, 3])
instance_masks = tf.expand_dims(tf.transpose(mask_predictions,
perm=[0, 3, 1, 2]),
axis=1,
name='MaskPredictor')
predictions_dict[MASK_PREDICTIONS] = instance_masks
return predictions_dict
class ConvolutionalBoxPredictor(BoxPredictor):
"""Convolutional Box Predictor.
Optionally add an intermediate 1x1 convolutional layer after features and
| tensorflow.transpose | 13,833 |
import tensorflow as tf
from tensorflow.python.training import moving_averages
from collections import defaultdict
# TODO(rbharath): What does this line do?
py_all = all
# TODO(rbharath): REMOVE GLOBAL VARS! BREAKS DEEPCHEM STYLE!
_UID_PREFIXES = defaultdict(int)
# This dictionary holds a mapping {graph: learning_phase}.
# A learning phase is a bool tensor used to run Keras models in
# either train mode (learning_phase == 1) or test mode (learning_phase == 0).
_GRAPH_LEARNING_PHASES = {}
def _to_tensor(x, dtype):
x = tf.convert_to_tensor(x)
if x.dtype != dtype:
x = tf.cast(x, dtype)
return x
def learning_phase():
"""Returns the learning phase flag.
The learning phase flag is a bool tensor (0 = test, 1 = train)
to be passed as input to any Keras function
that uses a different behavior at train time and test time.
"""
graph = tf.get_default_graph()
| tensorflow.convert_to_tensor | 13,834 |
import tensorflow as tf
input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))
if tf.random.uniform(()) > 0.5:
input_image = tf.image.flip_left_right(input_image)
| tensorflow.random.uniform | 13,835 |
import tensorflow as tf
prev_c,
prev_h,
anchors,
anchors_w_1,
arc_seq,
tf.constant([0.0], dtype=tf.float32, name="entropy"),
tf.constant([0.0], dtype=tf.float32, name="log_prob"),
]
loop_outputs = tf.while_loop(_condition, _body, loop_vars,
parallel_iterations=1)
arc_seq = loop_outputs[-3].stack()
arc_seq = tf.reshape(arc_seq, [-1])
entropy = tf.reduce_sum(loop_outputs[-2])
log_prob = tf.reduce_sum(loop_outputs[-1])
last_c = loop_outputs[-7]
| tensorflow.while_loop | 13,836 |
import tensorflow as tf
tgt_mask = tf.cast(tgt_mask, tf.float32)
lm_loss = tf.cast(lm_loss, tf.float32)
total_loss = tf.reduce_sum(lm_loss * tgt_mask) / tf.reduce_sum(tgt_mask)
monitor_dict["total_loss"] = total_loss
| tensorflow.reduce_sum | 13,837 |
import tensorflow as tf
of the input tensor t after padding, assuming length <= t.shape[0].
Returns:
padded_t: the padded tensor, whose first dimension is length. If the length
is an integer, the first dimension of padded_t is set to length
statically.
"""
t_rank = tf.rank(t)
t_shape = tf.shape(t)
t_d0 = t_shape[0]
pad_d0 = tf.expand_dims(length - t_d0, 0)
pad_shape = tf.cond(
tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
lambda: tf.expand_dims(length - t_d0, 0))
padded_t = tf.concat([t, tf.zeros(pad_shape, dtype=t.dtype)], 0)
if not _is_tensor(length):
padded_t = _set_dim_0(padded_t, length)
return padded_t
| tensorflow.expand_dims | 13,838 |
from tensorflow.python.ops import array_ops
b_grads = b_module.bspmm(a_indices, a_values, a_shape, grad, adjoint_a=True, adjoint_b=False)
bg_row=tf.shape(b_grads[0])[0]
bg_col=tf.shape(b_grads[0])[1]
b_grads = tf.reshape(b_grads, (numTensors * bg_row, bg_col))
if adj_b:
b_grads = [array_ops.transpose(b_g) for b_g in b_grads]
for t in range(numTensors):
rows = a_indices[t][:, 0]
cols = a_indices[t][:, 1]
parts_a = array_ops.gather(grad[t], rows if not adj_a else cols)
parts_b = array_ops.gather(b_list[t] if not adj_b else array_ops.transpose(b_list[t]), cols if not adj_a else rows)
a_values_grads.append(math_ops.reduce_sum(parts_a * parts_b, reduction_indices=1))
return_val = [None for _ in range(numTensors)] + a_values_grads + [None for _ in range(numTensors)] + [b_grads]
return tuple(return_val)
| tensorflow.python.ops.array_ops.gather | 13,839 |
import tensorflow as tf
max_chars_total=self.max_chars_for_vocab)
def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""Generates training/dev data.
Args:
data_dir: a string
tmp_dir: a string
task_id: an optional integer
Returns:
shard or shards for which data was generated.
"""
tf.logging.info("generate_data task_id=%s" % task_id)
encoder = self.get_or_create_vocab(data_dir, tmp_dir)
assert task_id >= 0 and task_id < self.num_generate_tasks
if task_id < self.num_train_shards:
out_file = self.training_filepaths(
data_dir, self.num_train_shards, shuffled=False)[task_id]
else:
out_file = self.dev_filepaths(
data_dir, self.num_dev_shards,
shuffled=False)[task_id - self.num_train_shards]
generator_utils.generate_files(
self.example_generator(encoder, tmp_dir, task_id), [out_file])
| tensorflow.logging.info | 13,840 |
import tensorflow as tf
logits = tf.nn.softmax(softmax_mask(outputs, mask))
outputs = tf.matmul(logits, memory)
res = tf.concat([inputs, outputs], axis=2)
with tf.variable_scope("gate"):
dim = res.get_shape().as_list()[-1]
d_res = dropout(res, keep_prob=keep_prob, is_train=is_train)
gate = tf.nn.sigmoid(dense(d_res, dim, use_bias=False))
return res * gate
def dense(inputs, hidden, use_bias=True, scope="dense"):
with tf.variable_scope(scope):
shape = tf.shape(inputs)
dim = inputs.get_shape().as_list()[-1]
out_shape = [shape[idx] for idx in range(
len(inputs.get_shape().as_list()) - 1)] + [hidden]
flat_inputs = tf.reshape(inputs, [-1, dim])
W = tf.get_variable("W", [dim, hidden])
res = tf.matmul(flat_inputs, W)
if use_bias:
b = tf.get_variable(
"b", [hidden], initializer=tf.constant_initializer(0.))
res = tf.nn.bias_add(res, b)
res = tf.reshape(res, out_shape)
return res
| tensorflow.shape | 13,841 |
import tensorflow as tf
if len(facts.get_shape().as_list()) == 2:
facts = tf.expand_dims(facts, 1)
if time_major:
# (T,B,D) => (B,T,D)
facts = tf.array_ops.transpose(facts, [1, 0, 2])
# Trainable parameters
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]
query = tf.layers.dense(query, facts_size, activation=None, name='f1' + stag)
query = prelu(query)
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]])
scores = d_layer_3_all
# Mask
# key_masks = tf.sequence_mask(facts_length, tf.shape(facts)[1]) # [B, T]
key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
paddings = tf.ones_like(scores) * (-2 ** 32 + 1)
if not forCnn:
scores = tf.where(key_masks, scores, paddings) # [B, 1, T]
# Scale
| tensorflow.concat | 13,842 |
import tensorflow as tf
inside_mul = tf.multiply(bbox_inside_weights, tf.subtract(bbox_pred, bbox_targets))
smooth_l1_sign = tf.cast(tf.less(tf.abs(inside_mul), 1.0 / sigma2), tf.float32)
smooth_l1_option1 = tf.multiply(tf.multiply(inside_mul, inside_mul), 0.5 * sigma2)
smooth_l1_option2 = tf.subtract(tf.abs(inside_mul), 0.5 / sigma2)
| tensorflow.abs | 13,843 |
import tensorflow as tf
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])
with tf.control_dependencies(init_ops):
w = tf.reshape(self.g,[1,1,1,tf.shape(self.v)[-1]]) * tf.nn.l2_normalize(self.v,axis=[0,1,2])
return tf.nn.bias_add(
tf.nn.conv2d(input_var, w,data_format='NHWC',
strides=self.strides, padding=self.padding),
self.b,data_format='NHWC',name=name)
| tensorflow.shape | 13,844 |
import tensorflow as tf
features[TIMESERIES_COL] = tf.squeeze(features[TIMESERIES_COL], axis = [2])
return tf.estimator.export.ServingInputReceiver(features, feature_placeholders)
# Create custom estimator's train and evaluate function
def train_and_evaluate(output_dir, use_keras):
if use_keras:
estimator = make_keras_estimator(output_dir)
else:
estimator = tf.estimator.Estimator(model_fn = simple_rnn,
model_dir = output_dir)
train_spec = tf.estimator.TrainSpec(read_dataset('train.csv',
tf.estimator.ModeKeys.TRAIN,
512),
max_steps = 1000)
exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)
eval_spec = tf.estimator.EvalSpec(read_dataset('valid.csv',
tf.estimator.ModeKeys.EVAL,
| tensorflow.estimator.Estimator | 13,845 |
import tensorflow as tf
Returns:
A tensor with the log loss.
"""
with tf.name_scope(name):
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
predictions = tf.to_float(predictions)
labels = tf.to_float(labels)
losses = -tf.multiply(labels, tf.log(predictions + eps)) - tf.multiply(
(1 - labels), tf.log(1 - predictions + eps))
return tf.losses.compute_weighted_loss(losses, weights)
def kappa_loss(predictions, labels, y_pow=1, eps=1e-15, num_ratings=5, batch_size=32, name='kappa'):
"""Define a kappa loss, Its a continuous differentiable approximation of
discrete kappa loss.
Args:
predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
| tensorflow.losses.compute_weighted_loss | 13,846 |
import tensorflow as tf
input_ = tf.concat([input_, context], axis=1)
input_size = input_.get_shape()[1].value
initializer = CellInitializer(decoder.cell_size) if decoder.orthogonal_init else None
with tf.variable_scope(tf.get_variable_scope(), initializer=initializer):
try:
output, new_state = get_cell(input_size)(input_, state)
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
return output, new_state
def update_pos(pos, symbol, max_pos=None):
if not decoder.pred_edits:
return pos
| tensorflow.equal | 13,847 |
import tensorflow as tf
with tf.name_scope(name, "click_weighted_pairwise_loss",[output]):
sliced_output = tf.unstack(output, axis=1)
sliced_label = tf.unstack(labels, axis=1)
sliced_propensity = tf.unstack(propensity_weights, axis=1)
for i in range(len(sliced_output)):
for j in range(i+1, len(sliced_output)):
cur_label_weight = tf.math.sign(sliced_label[i] - sliced_label[j])
cur_propensity = sliced_propensity[i] * sliced_label[i] + sliced_propensity[j] * sliced_label[j]
cur_pair_loss = -tf.exp(sliced_output[i]) / (tf.exp(sliced_output[i]) + tf.exp(sliced_output[j]))
if loss == None:
loss = cur_label_weight * cur_pair_loss * cur_propensity
loss += cur_label_weight * cur_pair_loss * cur_propensity
batch_size = tf.shape(labels[0])[0]
return tf.reduce_sum(loss) / tf.cast(batch_size, dtypes.float32) #/ (tf.reduce_sum(propensity_weights)+1)
| tensorflow.exp | 13,848 |
from tensorflow.python.framework import constant_op
"""
# The last 10 bits of 359 and 1024+359 are identical.
# As a result, all the crosses collide.
t1 = constant_op.constant([[359], [359 + 1024]])
t2 = constant_op.constant([list(range(10)), list(range(10))])
cross = sparse_feature_cross_op.sparse_feature_cross(
| tensorflow.python.framework.constant_op.constant | 13,849 |
import tensorflow as tf
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
| tensorflow.contrib.tpu.TPUConfig | 13,850 |
import tensorflow as tf
else:
return tf.reshape(tf.stack(values=h, axis=1), [-1])
def lstm(xs, ms, s, scope, nh, init_scale=1.0):
nbatch, nin = [v.value for v in xs[0].get_shape()]
with tf.variable_scope(scope):
wx = tf.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale))
wh = tf.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale))
b = tf.get_variable("b", [nh*4], 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 = tf.matmul(x, wx) + tf.matmul(h, wh) + 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)
| tensorflow.split | 13,851 |
from tensorflow.python.ops import common_shapes
Args:
op: Input operation.
Returns:
Shape of both inputs to `op`.
"""
return [op.inputs[0].get_shape().merge_with(op.inputs[1].get_shape())]
ops.RegisterShape("L2Loss")(common_shapes.scalar_shape)
ops.RegisterShape("LRN")(common_shapes.unchanged_shape_with_rank(4))
@ops.RegisterShape("LRNGrad")
def _LRNGradShape(op):
"""Shape function for LRNGrad op."""
in_grads_shape = op.inputs[0].get_shape().with_rank(4)
in_image_shape = op.inputs[1].get_shape().with_rank(4)
out_image_shape = op.inputs[2].get_shape().with_rank(4)
return [in_grads_shape.merge_with(in_image_shape).merge_with(out_image_shape)]
| tensorflow.python.ops.common_shapes.unchanged_shape_with_rank | 13,852 |
import tensorflow as tf
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_real_example = None
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
(total_loss, per_example_loss, probabilities, logits, predictions) = \
create_model(config, is_training, input_ids, input_mask,
segment_ids, label_ids, num_labels,
use_one_hot_embeddings, task_name)
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
| tensorflow.cast | 13,853 |
import tensorflow as tf
with self.test_session() as session:
@dynamic_batching.batch_fn
def f(a, b):
return a + b
output0 = f(tf.constant([1]), tf.constant([2]))
output1 = f(tf.constant([[2]]), tf.constant([3]))
tp = pool.ThreadPool(2)
f0 = tp.apply_async(session.run, [output0])
f1 = tp.apply_async(session.run, [output1])
| tensorflow.constant | 13,854 |
import tensorflow as tf
# first task or maxstep, update the model
if not test and (TASK_NUM == 0 or TASK_NUM % maxstep == 0):
logger.info(f"task_num={TASK_NUM}, sync_model_to_lazymodel")
tf.get_default_session().run(sync_model_to_lazymodel)
if test:
| tensorflow.get_default_session | 13,855 |
import tensorflow as tf
return self.fc8
def avg_pool(self, bottom, name):
return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def max_pool(self, bottom, name):
return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def conv_layer(self, bottom, name):
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
| tensorflow.nn.max_pool | 13,856 |
import tensorflow as tf
value = tf.layers.dense(x, 1, name="value")
return {"target_policy": logits, "target_value": value}
@registry.register_model
class DenseBitwiseCategoricalPolicy(PolicyBase):
"""Dense network with bitwise input and categorical output."""
def body(self, features):
observations = features["inputs"]
flat_x = tf.layers.flatten(observations)
with tf.variable_scope("dense_bitwise"):
flat_x = discretization.int_to_bit_embed(flat_x, 8, 32)
x = tf.layers.dense(flat_x, 256, activation=tf.nn.relu)
x = tf.layers.dense(flat_x, 128, activation=tf.nn.relu)
logits = tf.layers.dense(x, self.hparams.problem.num_actions)
value = tf.layers.dense(x, 1)[..., 0]
| tensorflow.layers.flatten | 13,857 |
import tensorflow as tf
dtype=tf.float32)
(nms_masks1,
nms_scores1,
nms_classes1) = isu.instance_non_maximum_suppression_2d_scores(
masks,
scores,
3,
min_score_thresh=0.65,
min_iou_thresh=0.5,
is_class_agnostic=True)
nms_masks_expected1 = tf.stack([mask0, mask5, mask4])
nms_scores_expected1 = tf.constant([1.0, 0.8, 0.7], dtype=tf.float32)
nms_classes_expected1 = tf.constant([1, 2, 2], dtype=tf.int32)
(nms_masks2,
nms_scores2,
nms_classes2) = isu.instance_non_maximum_suppression_2d_scores(
masks,
scores,
3,
min_score_thresh=0.65,
| tensorflow.stack | 13,858 |
import tensorflow as tf
def build_placeholders(self):
# standard for all policies
self.obs = tf.placeholder(tf.float32, [None,
self.obs_space]) # ! self.obs = tf.placeholder(tf.float32, [None] + list(self.obs_space))
# ! self.obs_space = env.observation_space.shape
self.r = tf.placeholder(tf.float32, (None,1))
self.ac = tf.placeholder(tf.float32, (None, self.act_space))
self.adv = tf.placeholder(tf.float32, [None]) # unused
# specific to FeUdal
self.prev_g = tf.placeholder(tf.float32, (None, None, self.g_dim))
| tensorflow.placeholder | 13,859 |
import tensorflow as tf
from tensorflow.python.platform import googletest
class IntegratedGradientsTest(googletest.TestCase):
"""
To run:
"python -m saliency.integrated_gradients_test" from the PAIR-code/saliency
directory.
"""
def testIntegratedGradientsGetMask(self):
with tf.Graph().as_default() as graph:
x = tf.placeholder(shape=[None, 3], dtype=tf.float32)
y = 5 * x[:, 0] + x[:, 0] * x[:, 1] + tf.sin(x[:, 2])
with tf.Session() as sess:
# Calculate the value of `y` at the baseline.
x_baseline_val = np.array([[0.5, 0.8, 1.0]], dtype=np.float)
y_baseline_val = sess.run(y, feed_dict={x: x_baseline_val})
# Calculate the value of `y` at the input.
x_input_val = np.array([[1.0, 2.0, 3.0]], dtype=np.float)
y_input_val = sess.run(y, feed_dict={x: x_input_val})
| tensorflow.Graph | 13,860 |
import tensorflow as tf
for direction in ['forward', 'backward']:
if direction == 'forward':
layer_input = self.embedding
else:
layer_input = tf.reverse_sequence(
self.embedding,
sequence_lengths,
seq_axis=1,
batch_axis=0
)
for i in range(n_lstm_layers):
if projection_dim < lstm_dim:
# are projecting down output
lstm_cell = tf.nn.rnn_cell.LSTMCell(
lstm_dim, num_proj=projection_dim,
cell_clip=cell_clip, proj_clip=proj_clip)
else:
lstm_cell = tf.nn.rnn_cell.LSTMCell(
lstm_dim,
cell_clip=cell_clip, proj_clip=proj_clip)
if use_skip_connections:
# ResidualWrapper adds inputs to outputs
if i == 0:
# don't add skip connection from token embedding to
# 1st layer output
pass
| tensorflow.nn.rnn_cell.LSTMCell | 13,861 |
import tensorflow as tf
output = tf.nn.relu(pre_activation, name=scope.name)
return output
# 定义一个池化层，默认为max_pooling
def max_pool_2x2(self,name, x):
with tf.variable_scope(name) as scope:
maxpool = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], padding='SAME')
return maxpool
# 创建DQN
def creat_network(self):
| tensorflow.variable_scope | 13,862 |
import tensorflow as tf
with tf.name_scope('pooling_for_un_head'):
undep_idxs = tf.tile(tf.expand_dims(dep_org_idx, 1), [1, sl_unhead, 1]) # [bs, sluh, sld]
unhead_idxs = tf.tile(tf.expand_dims(unhead_org_idx, 2), [1, 1, sl_dep]) # [bs, sluh, sld]
if direction is None:
| tensorflow.expand_dims | 13,863 |
import tensorflow as tf
return crop
def random_apply(fn, image, prob=1.):
b, *_ = image.get_shape().as_list()
chance = tf.less(tf.random_uniform([b], 0, 1.0), prob)
return tf.where(chance, fn(image), tf.identity(image))
def color_distortion(image, s=1.0):
lower, upper, x = (1 - 0.8 * s), (1 + 0.8 * s), image
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):
| tensorflow.image.random_hue | 13,864 |
import tensorflow as tf
@dynamic_batching.batch_fn_with_options(maximum_batch_size=2)
def f(a, b):
batch_size = tf.shape(a)[0]
return a + b, tf.tile([batch_size], [batch_size])
outputs = [
f(tf.constant([1]), tf.constant([2])),
f(tf.constant([1]), tf.constant([2])),
f(tf.constant([1]), tf.constant([2])),
f(tf.constant([1]), tf.constant([2])),
f(tf.constant([1]), tf.constant([2])),
]
tf.train.start_queue_runners()
results = session.run(outputs)
for value, batch_size in results:
| tensorflow.constant | 13,865 |
import tensorflow as tf
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__(
dtype=tf.float32,
reparameterization_type=tfd.NOT_REPARAMETERIZED,
validate_args=True,
allow_nan_stats=True,
| tensorflow.TensorShape | 13,866 |
import tensorflow as tf
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
query = tf.layers.dense(query, facts_size, activation=None, name='f1' + stag)
query = prelu(query)
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)
| tensorflow.shape | 13,867 |
import tensorflow as tf
hparams.n_samples = sample_size
tf.reset_default_graph()
with tf.Graph().as_default():
energy_fn, _, _ = l2hmc.get_scg_energy_fn()
| tensorflow.Graph | 13,868 |
import tensorflow as tf
if bias:
b = bias_variable([shape[-1]])
h = h + b
return h
def deconv2d(x, shape, output_shape, name, bias=False, stride=2, padding='SAME'):
with tf.variable_scope(name):
W = weight_variable(shape)
h = tf.nn.conv2d_transpose(x, W, output_shape, strides=[1, stride, stride, 1], padding=padding)
if bias:
b = bias_variable([shape[-2]])
h = h + b
return h
def conv3d(x, shape, name, bias=False, stride=2, padding='SAME'):
with tf.variable_scope(name):
W = weight_variable(shape)
h = tf.nn.conv3d(x, W, strides=[1, stride, stride, stride, 1], padding=padding)
if bias:
b = bias_variable([shape[-1]])
h = h + b
return h
def deconv3d(x, shape, output_shape, name, bias=False, stride=2, padding='SAME'):
with tf.variable_scope(name):
W = weight_variable(shape)
h = tf.nn.conv3d_transpose(x, W, output_shape, strides=[1, stride, stride, stride, 1], padding=padding)
if bias:
b = bias_variable([shape[-2]])
h = h + b
| tensorflow.variable_scope | 13,869 |
import tensorflow as tf
v = variable_scope.get_variable("v", [options.attention_vec_size])
v = tf.expand_dims(tf.expand_dims(v, axis=0), axis=0)
| tensorflow.expand_dims | 13,870 |
import tensorflow as tf
for classes, sdfs, poses in [(predicted_classes,
predicted_sdfs,
predicted_poses)]:
for i in range(classes.shape[0]):
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]),
| tensorflow.expand_dims | 13,871 |
import tensorflow as tf
'batch_edge_type_idx': tf.placeholder(tf.int32, shape=(), name='batch_edge_type_idx'),
'batch_row_edge_type': tf.placeholder(tf.int32, shape=(), name='batch_row_edge_type'),
'batch_col_edge_type': tf.placeholder(tf.int32, shape=(), name='batch_col_edge_type'),
'degrees': tf.placeholder(tf.int32),
'dropout': tf.placeholder_with_default(0., shape=()),
}
placeholders.update({
'adj_mats_%d,%d,%d' % (i, j, k): tf.sparse_placeholder(tf.float32)
| tensorflow.placeholder_with_default | 13,872 |
import tensorflow as tf
"""
# Reshape input to (batch_size, num_nodes, input_dim)
output_size = self._num_units
batch_size = inputs.get_shape()[0].value
inputs = tf.reshape(inputs, [batch_size, self._num_nodes, -1])
input_size = inputs.get_shape()[2].value
dtype = inputs.dtype
x = inputs
x0 = tf.transpose(x, perm=[1, 2,0]) # (num_nodes, total_arg_size, batch_size)
x0 = tf.reshape(x0, shape=[self._num_nodes, input_size * batch_size])
x = tf.expand_dims(x0, axis=0)
scope = tf.get_variable_scope()
with tf.variable_scope(scope):
if self._max_diffusion_step == 0:
pass
else:
for support in self._supports:
x1 = tf.sparse_tensor_dense_matmul(support, x0)
| tensorflow.reshape | 13,873 |
from tensorflow.python.ops import array_ops
labels_sizes = set_ops.set_size(labels)
return math_ops.minimum(labels_sizes, k, name=scope)
# For dense Tensor, calculate scalar count based on last dimension, and
# tile across labels shape.
labels_shape = array_ops.shape(labels)
labels_size = labels_shape[-1]
num_relevant_scalar = math_ops.minimum(labels_size, k)
return array_ops.fill(labels_shape[0:-1], num_relevant_scalar, name=scope)
| tensorflow.python.ops.array_ops.shape | 13,874 |
from tensorflow.python.framework import tensor_shape
return constant_op.constant([], dtype=dtypes.int32)
def _event_shape(self):
return tensor_shape.scalar()
@distribution_util.AppendDocstring(_poisson_sample_note)
| tensorflow.python.framework.tensor_shape.scalar | 13,875 |
import tensorflow as tf
with tf.device('/gpu:2'):
d = tf.matmul(b,a)
flat_d = tf.reshape(d, [-1])
| tensorflow.reshape | 13,876 |
import tensorflow as tf
w = tf.get_variable(name='weights',
trainable=is_pretrain,
shape=[kernel_size[0],kernel_size[1],in_channels,out_channels],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable(name='bias',
trainable=is_pretrain,
shape=[out_channels],
initializer=tf.constant_initializer(0.0))
x = tf.nn.conv2d(x,w,strides,padding='SAME',name='conv')
x = tf.nn.bias_add(x,b,name='bias_add')
x = tf.nn.relu(x,name='relu')
return x
def pool(layer_name, x, kernel_size=[1,2,2,1], strides=[1,2,2,1], is_max_pool=True):
'''
| tensorflow.nn.conv2d | 13,877 |
import tensorflow as tf
batch_size, nf = x.get_shape().as_list()
h = linear(x, [nf, n_kernels*dim_per_kernel], 'h1')
activation = tf.reshape(h, (batch_size, n_kernels, dim_per_kernel))
big = tf.eye(batch_size)
big = tf.expand_dims(big, 1)
abs_dif = tf.reduce_sum(tf.abs(tf.expand_dims(activation, 3) - tf.expand_dims(tf.transpose(activation, [1, 2, 0]), 0)), 2)
mask = 1. - big
masked = tf.exp(-abs_dif) * mask
| tensorflow.expand_dims | 13,878 |
import tensorflow as tf
# Create connected layers: fc1, fc2
with tf.contrib.framework.arg_scope([tf.contrib.layers.fully_connected],
normalizer_fn=tf.contrib.layers.batch_norm,
| tensorflow.contrib.framework.arg_scope | 13,879 |
from tensorflow.contrib.framework import deprecated_arg_values
x=x,
input_fn=input_fn,
batch_size=batch_size,
outputs=[key],
as_iterable=as_iterable)
if as_iterable:
return (pred[key] for pred in preds)
return preds[key].reshape(-1)
@deprecated_arg_values(
estimator.AS_ITERABLE_DATE,
estimator.AS_ITERABLE_INSTRUCTIONS,
as_iterable=False)
def predict_proba(self,
x=None,
input_fn=None,
batch_size=None,
as_iterable=True):
| tensorflow.contrib.framework.deprecated_arg_values | 13,880 |
import tensorflow as tf
def __call__(self, global_step):
warmup_lr = self._params.warmup_learning_rate
warmup_steps = self._params.warmup_steps
init_lr = self._params.init_learning_rate
lr_levels = self._params.learning_rate_levels
lr_steps = self._params.learning_rate_steps
linear_warmup = (
warmup_lr + tf.cast(global_step, dtype=tf.float32) / warmup_steps *
(init_lr - warmup_lr))
learning_rate = tf.where(global_step < warmup_steps, linear_warmup, init_lr)
for next_learning_rate, start_step in zip(lr_levels, lr_steps):
learning_rate = tf.where(global_step >= start_step, next_learning_rate,
learning_rate)
return learning_rate
| tensorflow.cast | 13,881 |
import tensorflow as tf
lstm_input = tf.transpose(x, perm=[1, 0, 2])
outputs, _ = tf.lite.experimental.nn.dynamic_rnn(
| tensorflow.lite.experimental.nn.dynamic_rnn | 13,882 |
import tensorflow as tf
upper_pad = tf.maximum(0, image_height - cutout_center_height - length // 2)
left_pad = tf.maximum(0, cutout_center_width - length // 2)
right_pad = tf.maximum(0, image_width - cutout_center_width - length // 2)
cutout_shape = [image_height - (lower_pad + upper_pad),
image_width - (left_pad + right_pad)]
padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]]
mask = tf.pad(
tf.zeros(cutout_shape, dtype=images.dtype),
padding_dims, constant_values=1)
patch = tf.ones_like(images, dtype=images.dtype) * replace,
mask = tf.expand_dims(mask, -1)
mask = tf.tile(mask, [1, 1, num_channels])
images = tf.where(
tf.equal(mask, 0),
| tensorflow.zeros | 13,883 |
import tensorflow as tf
if cfgs.ADD_BOX_IN_TENSORBOARD:
detections_in_img = self.drawer.draw_boxes_with_categories_and_scores(
img_batch=tf.expand_dims(img[0, :, :, :], axis=0),
boxes=outputs[0],
scores=outputs[1],
| tensorflow.expand_dims | 13,884 |
import tensorflow as tf
dummy_scores = tf.zeros([k, 1]) # [k, 1]
for i in range(self.config["coref_depth"]):
with tf.variable_scope("coref_layer", reuse=(i > 0)):
top_antecedent_emb = tf.gather(top_span_emb, top_antecedents) # [k, c, emb]
top_antecedent_scores = top_fast_antecedent_scores + self.get_slow_antecedent_scores(top_span_emb, top_antecedents, top_antecedent_emb, top_antecedent_offsets, top_span_speaker_ids, genre_emb) # [k, c]
top_antecedent_weights = tf.nn.softmax(tf.concat([dummy_scores, top_antecedent_scores], 1)) # [k, c + 1]
top_antecedent_emb = tf.concat([tf.expand_dims(top_span_emb, 1), top_antecedent_emb], 1) # [k, c + 1, emb]
attended_span_emb = tf.reduce_sum(tf.expand_dims(top_antecedent_weights, 2) * top_antecedent_emb, 1) # [k, emb]
with tf.variable_scope("f"):
f = tf.sigmoid(util.projection(tf.concat([top_span_emb, attended_span_emb], 1), util.shape(top_span_emb, -1))) # [k, emb]
top_span_emb = f * attended_span_emb + (1 - f) * top_span_emb # [k, emb]
top_antecedent_scores = tf.concat([dummy_scores, top_antecedent_scores], 1) # [k, c + 1]
top_antecedent_cluster_ids = tf.gather(top_span_cluster_ids, top_antecedents) # [k, c]
top_antecedent_cluster_ids += tf.to_int32(tf.log(tf.to_float(top_antecedents_mask))) # [k, c]
same_cluster_indicator = tf.equal(top_antecedent_cluster_ids, tf.expand_dims(top_span_cluster_ids, 1)) # [k, c]
non_dummy_indicator = tf.expand_dims(top_span_cluster_ids > 0, 1) # [k, 1]
pairwise_labels = tf.logical_and(same_cluster_indicator, non_dummy_indicator) # [k, c]
dummy_labels = tf.logical_not(tf.reduce_any(pairwise_labels, 1, keepdims=True)) # [k, 1]
top_antecedent_labels = tf.concat([dummy_labels, pairwise_labels], 1) # [k, c + 1]
loss = self.softmax_loss(top_antecedent_scores, top_antecedent_labels) # [k]
loss = tf.reduce_sum(loss) # []
| tensorflow.concat | 13,885 |
import tensorflow as tf
train_X_stds = np.std(train_X_reshaped, axis=0, keepdims=True)
def standardization(x):
x_reshaped = x.reshape([x.shape[0], -1])
result = (x_reshaped - train_X_means) / (train_X_stds + 1e-9)
return result.reshape(x.shape)
normalized_test_X = standardization(test_X)
with tf.Session() as sess, tf.summary.FileWriter(
"./tf_logs/fashion_minst_multi_task_learning/" + str(datetime.now().timestamp()),
graph=tf.get_default_graph()) as f:
sess.run(tf.global_variables_initializer())
# similar logic as mnist's next_batch()
epoch = 0
index_in_epoch = 0
while epoch < n_epoch:
for _ in range(m // n_batch_size + 1):
start = index_in_epoch
if start + n_batch_size > m:
epoch += 1
| tensorflow.get_default_graph | 13,886 |
import tensorflow as tf
# Trainable parameters
w1 = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1))
w2 = tf.Variable(tf.random_normal([input_size, attention_size], stddev=0.1))
b = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
v = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
with tf.name_scope('v'):
# Applying fully connected layer with non-linear activation to each of the B*T timestamps;
# 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:
| tensorflow.tensordot | 13,887 |
import tensorflow as tf
if linear_loss is not None:
values.append(tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/eval_linear_loss",
simple_value=linear_loss))
test_summary = tf.Summary(value=values)
summary_writer.add_summary(test_summary, step)
def time_string():
return datetime.now().strftime("%Y-%m-%d %H:%M")
def model_train_mode(args, feeder, hparams, global_step):
with tf.variable_scope("Tacotron_model", reuse=tf.AUTO_REUSE) as scope:
model = create_model("Tacotron", hparams)
model.initialize(feeder.inputs, feeder.input_lengths, feeder.speaker_embeddings,
feeder.mel_targets, feeder.token_targets,
targets_lengths=feeder.targets_lengths, global_step=global_step,
is_training=True, split_infos=feeder.split_infos)
model.add_loss()
model.add_optimizer(global_step)
stats = add_train_stats(model, hparams)
return model, stats
| tensorflow.variable_scope | 13,888 |
import tensorflow as tf
def testGraphExtension(self):
self._testGraphExtensionSave()
self._testGraphExtensionRestore()
def testStrippedOpListDef(self):
with self.test_session():
# Creates a graph.
v0 = tf.Variable(0.0)
var = tf.Variable(10.0)
tf.add(v0, var)
@function.Defun(x=tf.float32)
def minus_one(x):
return x - 1
minus_one(tf.identity(v0))
save = tf.train.Saver({"v0": v0})
| tensorflow.Variable | 13,889 |
import tensorflow as tf
model_target_y = models(hps, images, FLAGS.RCE_train, logits=False)
target_y64 = tf.argmin(model_target_y,axis=1)
else:
target_y64=target_labels
target_y = tf.cast(target_y64, tf.int32)
adversarial_sample = attacks.jsma.jsma(models, images, hps, RCE_train, target_y,epochs=epoch_jsma, eps=eps,
clip_min=-0.5, clip_max=0.5, pair=False, min_proba=0.0)
elif method=='smda':
print('Attacking method is smda')
if target_labels==None:
print('Target label is the argmin label')
model_target_y = models(hps, images, FLAGS.RCE_train, logits=False)
target_y64 = tf.argmin(model_target_y,axis=1)
else:
target_y64=target_labels
target_y = tf.cast(target_y64, tf.int32)
adversarial_sample = attacks.smda.smda(models, images, hps, RCE_train, target_y, epochs=epoch_jsma, eps=eps,
clip_min=-0.5, clip_max=0.5, min_proba=0.0)
else:
print('Not recognized method')
adversarial_sample = None
return adversarial_sample
def tSNE_visual(hps,num_batch):
| tensorflow.argmin | 13,890 |
import tensorflow as tf
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 | 13,891 |
import tensorflow as tf
tf.app.flags.DEFINE_integer('seed', 1, "initial random seed")
tf.app.flags.DEFINE_bool('validation', False, "")
tf.app.flags.DEFINE_integer('batch_size', 32, "the number of examples in a batch")
tf.app.flags.DEFINE_integer('ul_batch_size', 128, "the number of unlabeled examples in a batch")
tf.app.flags.DEFINE_integer('eval_batch_size', 100, "the number of eval examples in a batch")
tf.app.flags.DEFINE_integer('eval_freq', 5, "")
tf.app.flags.DEFINE_integer('num_epochs', 120, "the number of epochs for training")
tf.app.flags.DEFINE_integer('epoch_decay_start', 80, "epoch of starting learning rate decay")
tf.app.flags.DEFINE_integer('num_iter_per_epoch', 400, "the number of updates per epoch")
tf.app.flags.DEFINE_float('learning_rate', 0.001, "initial leanring rate")
tf.app.flags.DEFINE_float('mom1', 0.9, "initial momentum rate")
tf.app.flags.DEFINE_float('mom2', 0.5, "momentum rate after epoch_decay_start")
tf.app.flags.DEFINE_string('method', 'vat', "{vat, vatent, baseline}")
if FLAGS.dataset == 'cifar10':
from cifar10 import inputs, unlabeled_inputs
elif FLAGS.dataset == 'svhn':
from svhn import inputs, unlabeled_inputs
else:
raise NotImplementedError
NUM_EVAL_EXAMPLES = 5000
| tensorflow.app.flags.DEFINE_string | 13,892 |
import tensorflow as tf
| tensorflow.get_logger | 13,893 |
import tensorflow as tf
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
if FLAGS.do_eval:
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
result = estimator.evaluate(
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
| tensorflow.logging.info | 13,894 |
from tensorflow.python.ops import math_ops
math_ops.equal(
array_ops.constant(0.0, dtype=dtypes.float64), denominator),
lambda: array_ops.constant(0.0, dtype=dtypes.float64),
lambda: math_ops.div(numerator, denominator),
name=name)
| tensorflow.python.ops.math_ops.div | 13,895 |
import tensorflow as tf
Omega = tf.square(bounded - 1.0)
Omega = tf.reduce_sum(tf.reduce_mean(Omega, axis=1)) / (1.0 * tf.reduce_sum(nelems))
| tensorflow.reduce_sum | 13,896 |
import tensorflow as tf
def _create_model(self, train_triples):
# Count unique items to determine embedding matrix sizes
entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2]))
rel_cnt = len(set(train_triples[:,1]))
init_sd = 1.0 / np.sqrt(self.embedding_size)
# Embedding variables
entity_var_shape = [entity_cnt, self.embedding_size]
rel_var_shape = [rel_cnt, self.embedding_size]
entity_init = tf.truncated_normal(entity_var_shape, stddev=init_sd)
rel_init = tf.truncated_normal(rel_var_shape, stddev=init_sd)
# Ensure maxnorm constraints are initially satisfied
entity_init = dense_maxnorm(entity_init, self.maxnorm)
self.entity_embedding_vars = tf.Variable(entity_init)
self.rel_embedding_vars = tf.Variable(rel_init)
# Embedding layer for each (head, rel, tail) triple being fed in as input
head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input)
tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input)
rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input)
# Relationship vector acts as a translation in entity embedding space
| tensorflow.truncated_normal | 13,897 |
import tensorflow as tf
aux_logits = self._add_fully_connected(X, (ch,), K, no_reg=True)
return aux_logits
def _compute_predictions(self, logits, classes):
probs = tf.nn.softmax(logits)
preds = tf.argmax(logits, axis=1, output_type=tf.int32)
corrects = tf.equal(preds, classes)
return (probs, corrects)
def _compute_loss(self, logits, aux_logits_list, classes, **knobs):
reg_decay = knobs['reg_decay']
aux_loss_mul = knobs['aux_loss_mul'] # Multiplier for auxiliary loss
| tensorflow.equal | 13,898 |
import tensorflow as tf
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])
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}
| tensorflow.expand_dims | 13,899 |
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