seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
self.saver = tf.train.Saver(tf.global_variables())
def _get_lstm_cell(self, config, is_training):
if config.rnn_mode == BASIC:
return tf.contrib.rnn.BasicLSTMCell(
config.hidden_size, forget_bias=0., state_is_tuple=True,
reuse=not is_training)
if config.rnn_mode == BLOCK:
return tf.contrib.rnn.LSTMBlockCell(
config.hidden_size, forget_bias=0.)
raise ValueError('rnn_mode {} not supported'.format(config.rnn_mode))
def _build_rnn_graph(self, inputs, config, is_training):
def make_cell():
cell = self._get_lstm_cell(config, is_training)
| tensorflow.contrib.rnn.LSTMBlockCell | 5,300 |
import tensorflow as tf
self.run_op_benchmark(
sess, st_deserialized_op, min_iters=2000,
name="benchmark_very_large_2d_float_st_serialization")
if __name__ == "__main__":
tf.test.main()
| tensorflow.test.main | 5,301 |
import tensorflow as tf
# horizon_pred, horizon_tgt = horizon_sumV2(pred, tgt, horizon)
pred_flat1, pred_flat2 = tf.reshape(horizon_pred, [-1, 1]), tf.reshape(horizon_pred, [1, -1])
tgt_flat1, tgt_flat2 = tf.reshape(horizon_tgt, [-1, 1]), tf.reshape(horizon_tgt, [1, -1])
tgt_dif = tgt_flat1 - tgt_flat2
pred_dif = pred_flat1 - pred_flat2
geq = tf.cast(tgt_dif > 0, tf.bool)
tgt_posi_dif = tf.where(geq, tgt_dif, -tgt_dif)
pred_posi_dif = tf.where(geq, pred_dif, -pred_dif)
loss = tf.maximum(0., tgt_posi_dif - pred_posi_dif)
cstr_pct = tf.math.count_nonzero(loss, dtype=tf.float32) / tf.cast(tf.reduce_prod(tf.shape(loss)), tf.float32)
unorm_w = tf.exp((tgt_flat1 + tgt_flat2)/temp)
loss = unorm_w * loss / (tf.reduce_sum(unorm_w))
a = tf.print(tf.reduce_sum(unorm_w))
with tf.control_dependencies([a]):
final_loss = tf.reduce_sum(loss)
return final_loss, cstr_pct
def contra_traj_lossV8(pred, tgt, horizon=12):
horizon_pred, horizon_tgt = horizon_sumV1(pred, horizon), horizon_sumV1(tgt, horizon)
# horizon_pred, horizon_tgt = horizon_sumV2(pred, tgt, horizon)
horizon_pred1, horizon_pred2 = tf.split(horizon_pred, 2, axis=0)
horizon_tgt1, horizon_tgt2 = tf.split(horizon_tgt, 2, axis=0)
pred_flat1, pred_flat2 = tf.reshape(horizon_pred1, [-1, 1]), tf.reshape(horizon_pred2, [1, -1])
tgt_flat1, tgt_flat2 = tf.reshape(horizon_tgt1, [-1, 1]), tf.reshape(horizon_tgt2, [1, -1])
tgt_dif = tgt_flat1 - tgt_flat2
pred_dif = pred_flat1 - pred_flat2
geq = tf.cast(tgt_dif > 0, tf.bool)
| tensorflow.reduce_sum | 5,302 |
import tensorflow as tf
tf.TensorShape([None, None, None, None, None]),
tf.TensorShape([]),
],
back_prop=False,
parallel_iterations=1)
if inputs_old is not None: # Restore to not confuse Estimator.
features["inputs"] = inputs_old
# Reassign targets back to the previous value.
if targets_old is not None:
features["targets"] = targets_old
losses = {"training": loss}
if "partial_targets" in features:
partial_target_length = common_layers.shape_list(
features["partial_targets"])[1]
result = tf.slice(result, [0, partial_target_length, 0, 0],
[-1, -1, -1, -1])
return {
"outputs": result,
"scores": None,
"logits": logits,
"losses": losses,
}
def sample(self, features):
"""Run the model and extract samples.
Args:
features: an map of string to `Tensor`.
| tensorflow.slice | 5,303 |
import tensorflow as tf
if tf.DeviceSpec.from_string(device.name).device_type == "TPU":
return (32,)
return (16, 32)
def _force_device_sync(self):
"""Shamelessly copied from `resnet50_test.py`."""
tf.constant(1.).cpu()
def _report(self, label, start, num_iters, device, batch_size, data_format):
avg_time = (time.time() - start) / num_iters
dev = tf.DeviceSpec.from_string(device).device_type.lower()
name = "%s_%s_batch_%d_%s" % (label, dev, batch_size, data_format)
extras = {"examples_per_sec": batch_size / avg_time}
self.report_benchmark(
iters=num_iters, wall_time=avg_time, name=name, extras=extras)
def _benchmark_eager_apply(self,
label,
device_and_format,
defun=False,
| tensorflow.DeviceSpec.from_string | 5,304 |
import tensorflow as tf
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)
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
def lnlstm(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))
gx = tf.get_variable("gx", [nh*4], initializer=tf.constant_initializer(1.0))
bx = tf.get_variable("bx", [nh*4], initializer=tf.constant_initializer(0.0))
wh = tf.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale))
gh = tf.get_variable("gh", [nh*4], initializer=tf.constant_initializer(1.0))
bh = tf.get_variable("bh", [nh*4], initializer=tf.constant_initializer(0.0))
| tensorflow.nn.moments | 5,305 |
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))
| tensorflow.logging.info | 5,306 |
import tensorflow as tf
estimator_spec = tf.estimator.EstimatorSpec(mode=mode,
loss=loss, train_op=train_op,
training_hooks=training_hooks)
if output_type == "sess":
try:
pred_label = tf.argmax(distillation_loss["st_logits"], axis=-1, output_type=tf.int32)
correct = tf.equal(
tf.cast(tf.ones_like(label_ids, dtype=tf.int32), tf.int32),
tf.cast(pred_label, tf.int32)
)
st_accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
pred_label = tf.argmax(distillation_loss["te_logits"], axis=-1, output_type=tf.int32)
correct = tf.equal(
tf.cast(tf.zeros_like(label_ids, dtype=tf.int32), tf.int32),
tf.cast(pred_label, tf.int32)
)
te_accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
| tensorflow.cast | 5,307 |
import tensorflow as tf
from __future__ import print_function
import numpy as np
import tensorflow as tf
class AssignOpTest(tf.test.TestCase):
# NOTE(mrry): We exclude thess tests from the TSAN TAP target, because they
# contain benign and deliberate data races when multiple threads update
# the same parameters without a lock.
def testParallelUpdateWithoutLocking(self):
with self.test_session() as sess:
ones_t = tf.fill([1024, 1024], 1.0)
p = tf.Variable(tf.zeros([1024, 1024]))
adds = [tf.assign_add(p, ones_t, use_locking=False)
for _ in range(20)]
tf.initialize_all_variables().run()
def run_add(add_op):
sess.run(add_op)
threads = [self.checkedThread(target=run_add, args=(add_op,))
for add_op in adds]
for t in threads:
t.start()
for t in threads:
t.join()
| tensorflow.zeros | 5,308 |
import tensorflow as tf
import variable_mgr
tf.flags.DEFINE_string('model', 'trivial', 'name of the model to run')
# The code will first check if it's running under benchmarking mode
# or evaluation mode, depending on FLAGS.eval:
# Under the evaluation mode, this script will read a saved model,
# and compute the accuracy of the model against a validation dataset.
# Additional ops for accuracy and top_k predictors are only used under this
# mode.
# Under the benchmarking mode, user can specify whether nor not to use
# the forward-only option, which will only compute the loss function.
# forward-only cannot be enabled with eval at the same time.
tf.flags.DEFINE_boolean('eval', False, 'whether use eval or benchmarking')
tf.flags.DEFINE_boolean('forward_only', False, """whether use forward-only or
training for benchmarking""")
tf.flags.DEFINE_integer('batch_size', 0, 'batch size per compute device')
tf.flags.DEFINE_integer('num_batches', 100,
'number of batches to run, excluding warmup')
tf.flags.DEFINE_integer('num_warmup_batches', None,
'number of batches to run before timing')
tf.flags.DEFINE_integer('autotune_threshold', None,
'The autotune threshold for the models')
tf.flags.DEFINE_integer('num_gpus', 1, 'the number of GPUs to run on')
tf.flags.DEFINE_integer('display_every', 10,
"""Number of local steps after which progress is printed
| tensorflow.flags.DEFINE_boolean | 5,309 |
import tensorflow as tf
logging.info(" [*] printing trainable variables")
else:
try: # TF1.0+
t_vars = tf.global_variables()
except Exception: # TF0.12
t_vars = tf.all_variables()
logging.info(" [*] printing global variables")
for idx, v in enumerate(t_vars):
logging.info(" var {:3}: {:15} {}".format(idx, str(v.get_shape()), v.name))
| tensorflow.all_variables | 5,310 |
import tensorflow as tf
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
| tensorflow.nn.log_softmax | 5,311 |
import tensorflow as tf
self.discriminator_loss = self.D_B_loss + self.D_A_loss
self.loss_GABA_sum = tf.summary.scalar("g_loss_a2b", self.loss_GABA)
self.loss_GBAB_sum = tf.summary.scalar("g_loss_b2a", self.loss_GBAB)
self.g_total_loss_sum = tf.summary.scalar("g_loss", self.generator_loss)
self.g_sum = tf.summary.merge([self.loss_GABA_sum,self.loss_GBAB_sum,self.g_total_loss_sum])
| tensorflow.summary.scalar | 5,312 |
import tensorflow as tf
Return:
A tuple of list: (nodes, adjcents)
nodes: A list of N + 1 `tf.Tensor` of `int64`, N is the number of
hops. Specify node set of each hop, including the root.
adjcents: A list of N `tf.SparseTensor` of `int64`. Specify adjacent
matrix between hops.
"""
nodes = tf.reshape(nodes, [-1])
nodes_list = [nodes]
adj_list = []
for hop_edge_types in edge_types:
neighbor, weight, _ = get_full_neighbor(nodes, hop_edge_types)
next_nodes, next_idx = tf.unique(neighbor.values, out_idx=tf.int64)
next_indices = tf.stack([neighbor.indices[:, 0], next_idx], 1)
next_values = weight.values
next_shape = tf.stack([tf.size(nodes), tf.size(next_nodes)])
next_shape = tf.cast(next_shape, tf.int64)
next_adj = tf.SparseTensor(next_indices, next_values, next_shape)
next_adj = tf.sparse_reorder(next_adj)
nodes_list.append(next_nodes)
adj_list.append(next_adj)
nodes = next_nodes
return nodes_list, adj_list
| tensorflow.unique | 5,313 |
import tensorflow as tf
eval_metrics = (metric_fn,
[per_example_loss, label_ids, logits, is_real_example])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions={"probabilities": probabilities},
scaffold_fn=scaffold_fn)
return output_spec
return model_fn
| tensorflow.contrib.tpu.TPUEstimatorSpec | 5,314 |
import tensorflow as tf
# The larger local steps is, the lower is the variance in our policy gradients estimate
# on the one hand; but on the other hand, we get less frequent parameter updates, which
# slows down learning. In this code, we found that making local steps be much
# smaller than 20 makes the algorithm more difficult to tune and to get to work.
self.runner = RunnerThread(env, pi, 20)
grads = tf.gradients(self.loss, pi.var_list)
tf.summary.scalar("model/policy_loss", pi_loss / bs)
tf.summary.scalar("model/value_loss", vf_loss / bs)
tf.summary.scalar("model/entropy", entropy / bs)
tf.summary.image("model/state", pi.x)
tf.summary.scalar("model/grad_global_norm", tf.global_norm(grads))
tf.summary.scalar("model/var_global_norm", tf.global_norm(pi.var_list))
self.summary_op = tf.summary.merge_all()
grads, _ = tf.clip_by_global_norm(grads, 40.0)
# copy weights from the parameter server to the local model
self.sync = tf.group(*[v1.assign(v2) for v1, v2 in zip(pi.var_list, self.network.var_list)])
grads_and_vars = list(zip(grads, self.network.var_list))
self.inc_step = self.global_step.assign_add(tf.shape(pi.x)[0])
# each worker has a different set of adam optimizer parameters
opt = tf.train.AdamOptimizer(1e-4)
self.train_op = tf.group(opt.apply_gradients(grads_and_vars), self.inc_step)
self.summary_writer = None
| tensorflow.summary.merge_all | 5,315 |
import tensorflow as tf
# with char embeddings init between -1 and 1
#w_init = tf.random_normal_initializer(
# mean=0.0,
# stddev=np.sqrt(2.0 / (width * char_embed_dim))
#)
# Kim et al 2015, +/- 0.05
w_init = tf.random_uniform_initializer(
minval=-0.05, maxval=0.05)
elif cnn_options['activation'] == 'tanh':
# glorot init
w_init = tf.random_normal_initializer(
mean=0.0,
stddev=np.sqrt(1.0 / (width * char_embed_dim))
| tensorflow.random_uniform_initializer | 5,316 |
import tensorflow as tf
Args:
prev_rnn_state: The state of the RNN from the previous timestep.
prev_latent_encoded: Float Tensor of shape
[batch_size, encoded_latent_size], the previous latent state z_{t-1}
run through latent_encoder.
inputs: A Tensor of shape [batch_size, data_size], the current inputs to
the model. Most often this is x_{t-1}, the previous token in the
observation sequence.
Returns:
rnn_out: The output of the RNN.
rnn_state: The new state of the RNN.
"""
inputs_encoded = self.data_encoder(tf.to_float(inputs))
rnn_inputs = tf.concat([inputs_encoded, prev_latent_encoded], axis=1)
rnn_out, rnn_state = self.rnn_cell(rnn_inputs, prev_rnn_state)
return rnn_out, rnn_state
def transition(self, rnn_out):
"""Computes the transition distribution p(z_t|h_t).
Note that p(z_t | h_t) = p(z_t| z_{1:t-1}, x_{1:t-1})
Args:
rnn_out: The output of the rnn for the current timestep.
Returns:
| tensorflow.to_float | 5,317 |
import tensorflow as tf
file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
| tensorflow.logging.info | 5,318 |
import tensorflow as tf
@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]
return {"target_policy": logits, "target_value": value}
| tensorflow.layers.dense | 5,319 |
import tensorflow as tf
pos = beam_search.resize_like(pos, symbol)
max_pos = beam_search.resize_like(max_pos, symbol)
pos += tf.to_float(is_not_ins)
if max_pos is not None:
pos = tf.minimum(pos, tf.to_float(max_pos))
return pos
| tensorflow.to_float | 5,320 |
import tensorflow as tf
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
q_values = q_func(observations_ph.get(), num_actions, scope="q_func")
deterministic_actions = tf.argmax(q_values, axis=1)
batch_size = tf.shape(observations_ph.get())[0]
random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
_act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],
outputs=output_actions,
givens={update_eps_ph: -1.0, stochastic_ph: True},
updates=[update_eps_expr])
| tensorflow.where | 5,321 |
import tensorflow as tf
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(
shape=(self.config.batch_size,),
minval=0,
maxval=self.config.n_classes,
dtype=tf.int32)
global_step = tf.Variable(0., trainable=False)
model = revnet.RevNet(config=config)
| tensorflow.random_uniform | 5,322 |
import tensorflow as tf
config.gpu_options.per_process_gpu_memory_fraction = 0.4
sess_limited = tf.Session(config=config)
# How to set placements on multiple devices.
# Here, assume we have three devies CPU:0, GPU:0, and GPU:1
if tf.test.is_built_with_cuda():
with tf.device('/cpu:0'):
a = tf.constant([1.0, 3.0, 5.0], shape=[1, 3])
b = tf.constant([2.0, 4.0, 6.0], shape=[3, 1])
with tf.device('/gpu:1'):
c = tf.matmul(a,b)
c = tf.reshape(c, [-1])
with tf.device('/gpu:2'):
| tensorflow.constant | 5,323 |
import tensorflow as tf
if pt[0] > box_limits_x[1]:
box_limits_x[1] = pt[0]
if pt[1] < box_limits_z[0]:
box_limits_z[0] = pt[1]
if pt[1] > box_limits_z[1]:
box_limits_z[1] = pt[1]
mean_x = tf.reduce_mean(box_limits_x)
mean_z = tf.reduce_mean(box_limits_z)
else:
mean_x = tf.reduce_mean(labeled_translations[:, 0])
mean_z = tf.reduce_mean(labeled_translations[:, 2])
samples_world = grid.generate(
(mean_x - 0.5, 0.0, mean_z - 0.5), (mean_x + 0.5, 1.0, mean_z + 0.5),
[self.resolution, self.resolution, self.resolution])
# samples_world = grid.generate(
# (box_limits_x[0][0], box_limits_y[0], box_limits_z[0][0]),
# (box_limits_x[1][0], box_limits_y[1], box_limits_z[1][0]),
# [self.resolution, self.resolution, self.resolution])
# samples_world = grid.generate(
# (-5.0, -5.0, -5.0),
# (5.0, 5.0, 5.0),
# [self.resolution, self.resolution, self.resolution])
samples_world = tf.reshape(samples_world, [-1, 3])
| tensorflow.reduce_mean | 5,324 |
import tensorflow as tf
# if our output includes timesteps we need to reshape
return tf.reshape(res, tf.shape(output)[:-1])
| tensorflow.shape | 5,325 |
import tensorflow as tf
model_name = tu.get_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel", input_dtype,
output0_dtype, output1_dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
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")
output1_tensor = tf.get_default_graph().get_tensor_by_name(
"TENSOR_OUTPUT1:0")
tf.saved_model.simple_save(sess,
| tensorflow.Session | 5,326 |
import tensorflow as tf
def _decode_masks(self, parsed_tensors):
"""Decode a set of PNG masks to the tf.float32 tensors."""
def _decode_png_mask(png_bytes):
mask = tf.squeeze(
tf.io.decode_png(png_bytes, channels=1, dtype=tf.uint8), axis=-1)
mask = tf.cast(mask, dtype=tf.float32)
mask.set_shape([None, None])
return mask
height = parsed_tensors['image/height']
width = parsed_tensors['image/width']
| tensorflow.cast | 5,327 |
import tensorflow as tf
# Do random crop + horizontal flip for each train image
image = tf.pad(image, [[4, 4], [4, 4], [0, 0]])
| tensorflow.pad | 5,328 |
import tensorflow as tf
pass
else:
# add a skip connection
lstm_cell = tf.nn.rnn_cell.ResidualWrapper(lstm_cell)
# collect the input state, run the dynamic rnn, collect
| tensorflow.nn.rnn_cell.ResidualWrapper | 5,329 |
import tensorflow as tf
# loss = None
with tf.name_scope(name, "click_loglikelihood"):
ob_prob=tf.nn.softmax(propensity)
rel_prob=tf.nn.softmax(train_output)
click_prob=ob_prob*rel_prob
click_prob_norm=click_prob/tf.reduce_sum(click_prob,axis=1,keep_dims=True)
label_dis = labels/ tf.reduce_sum(labels, 1, keep_dims=True)
entropy = tf.reduce_sum(tf.math.log(click_prob_norm)*label_dis,1)
return tf.reduce_mean(entropy)
def click_weighted_pairwise_loss(self, output, labels, propensity_weights, name=None):
"""Computes pairwise entropy loss with propensity weighting.
Args:
output: (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
| tensorflow.math.log | 5,330 |
from tensorflow.python.ops import math_ops
`predictions`, or if either `metrics_collections` or `updates_collections`
are not a list or tuple.
"""
top_k_idx = math_ops.to_int64(top_k_idx)
weights = _mask_weights(ignore_mask, weights)
tp, tp_update = _streaming_sparse_true_positive_at_k(
| tensorflow.python.ops.math_ops.to_int64 | 5,331 |
import tensorflow as tf
outputs_bw, (hidden_bw, output_bw) = lstm_cell_bw(t, dtype=tf.float32, sequence_length=nwords)
outputs = tf.concat([outputs_fw, outputs_bw], axis=-1)
hidden = tf.concat([hidden_fw, hidden_bw], axis=-1)
output = tf.concat([output_fw, output_bw], axis=-1)
else:
outputs = outputs_fw
| tensorflow.concat | 5,332 |
import tensorflow as tf
writer.write(json.dumps(sentences, indent=2, ensure_ascii=False))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
| tensorflow.logging.set_verbosity | 5,333 |
import tensorflow as tf
classes,
min_score_thresh=0.65,
min_iou_thresh=0.5,
is_class_agnostic=False)
nms_masks_expected2 = tf.stack([mask0, mask1, mask4, mask2])
nms_scores_expected2 = tf.constant([1.0, 0.9, 0.85, 0.8], dtype=tf.float32)
nms_classes_expected2 = tf.constant([1, 2, 2, 3], dtype=tf.int32)
self.assertAllEqual(nms_masks1.numpy(), nms_masks_expected1.numpy())
| tensorflow.stack | 5,334 |
import tensorflow as tf
operation = tf.assign(perturbed_var,
var + tf.random_normal(shape=tf.shape(var), mean=0.,
stddev=param_noise_scale))
else:
# Do not perturb, just assign.
operation = tf.assign(perturbed_var, var)
perturb_ops.append(operation)
assert len(perturb_ops) == len(all_vars)
return tf.group(*perturb_ops)
| tensorflow.assign | 5,335 |
import tensorflow as tf
w = tf.get_variable('w', [self.H, self.D], initializer=self.weight_initializer)
b = tf.get_variable('b', [self.D], initializer=self.const_initializer)
w_att = tf.get_variable('w_att', [self.D, 1], initializer=self.weight_initializer)
h_att = tf.nn.relu(features_proj + tf.expand_dims(tf.matmul(h, w), 1) + b) # (N, L, D)
out_att = tf.reshape(tf.matmul(tf.reshape(h_att, [-1, self.D]), w_att), [-1, self.L]) # (N, L)
alpha = tf.nn.softmax(out_att)
context = tf.reduce_sum(features * tf.expand_dims(alpha, 2), 1, name='context') #(N, D)
return context, alpha
def _selector(self, context, h, reuse=False):
with tf.variable_scope('selector', reuse=reuse):
w = tf.get_variable('w', [self.H, 1], initializer=self.weight_initializer)
b = tf.get_variable('b', [1], initializer=self.const_initializer)
beta = tf.nn.sigmoid(tf.matmul(h, w) + b, 'beta') # (N, 1)
context = tf.multiply(beta, context, name='selected_context')
return context, beta
def _decode_lstm(self, x, h, context, dropout=False, reuse=False):
with tf.variable_scope('logits', reuse=reuse):
w_h = tf.get_variable('w_h', [self.H, self.M], initializer=self.weight_initializer)
b_h = tf.get_variable('b_h', [self.M], initializer=self.const_initializer)
w_out = tf.get_variable('w_out', [self.M, self.V], initializer=self.weight_initializer)
b_out = tf.get_variable('b_out', [self.V], initializer=self.const_initializer)
if dropout:
h = tf.nn.dropout(h, 0.5)
h_logits = tf.matmul(h, w_h) + b_h
if self.ctx2out:
| tensorflow.multiply | 5,336 |
from tensorflow.contrib.learn.python.learn.estimators import tensor_signature
def _check_inputs(self, features, targets):
if self._features_info is not None:
if not tensor_signature.tensors_compatible(features, self._features_info):
raise ValueError('Features are incompatible with given information. '
'Given features: %s, required signatures: %s.' %
(str(features), str(self._features_info)))
else:
self._features_info = tensor_signature.create_signatures(features)
if self._targets_info is not None:
if not tensor_signature.tensors_compatible(targets, self._targets_info):
raise ValueError('Targets are incompatible with given information. '
'Given targets: %s, required signatures: %s.' %
(str(targets), str(self._targets_info)))
else:
| tensorflow.contrib.learn.python.learn.estimators.tensor_signature.create_signatures | 5,337 |
import tensorflow as tf
def instance_norm(x,name='instance_norm'):
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
| tensorflow.get_variable_scope | 5,338 |
import tensorflow as tf
NotImplementedError, "log_prob is not implemented"):
terrible_distribution.log_prob(1.)
with self.assertRaisesRegexp(
NotImplementedError, "cdf is not implemented"):
terrible_distribution.cdf(1.)
with self.assertRaisesRegexp(
NotImplementedError, "log_cdf is not implemented"):
terrible_distribution.log_cdf(1.)
if __name__ == "__main__":
tf.test.main()
| tensorflow.test.main | 5,339 |
import tensorflow as tf
constraint['sigmean'] = (5.20, 5.30)
constraint['sigwidth'] = (0.001, 1.)
constraint['argpar'] = (-100., -1.)
constraint['nsig'] = (0., 10000)
constraint['nbkg'] = (0., 10000)
constraint['mes'] = (5.20, 5.30)
# keep a variable dictionary for easy key-based access compatible with constraints
vdict = {}
pi = tf.constant(np.pi, dtype=tf.float64, name="pi")
sqrt2pi = tf.constant(np.sqrt(2 * np.pi), dtype=tf.float64, name="sqrt2pi")
two = tf.constant(2, dtype=tf.float64, name="two")
one = tf.constant(1, dtype=tf.float64, name="one")
zero = tf.constant(0, dtype=tf.float64, name="zero")
def gradsafe_sqrt(x, clip_low=1e-18, name=None):
with tf.name_scope(name, "gradsafe_sqrt"):
return tf.sqrt(tf.clip_by_value(x, clip_low, x))
| tensorflow.constant | 5,340 |
import tensorflow.contrib.layers as layers
with tf.variable_scope(scope, reuse=reuse):
out = img_in
with tf.variable_scope("convnet"):
# original architecture
out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu)
out = layers.flatten(out)
with tf.variable_scope("action_value"):
out = layers.fully_connected(out, num_outputs=512, activation_fn=tf.nn.relu)
out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None)
return out
| tensorflow.contrib.layers.flatten | 5,341 |
from tensorflow.python.framework import ops
if event_ndims is None: raise ValueError("event_ndims cannot be None")
self._batch_ndims = batch_ndims
self._event_ndims = event_ndims
self._validate_args = validate_args
with ops.name_scope(name) as ns:
self._name = ns
with ops.name_scope("init"):
self._batch_ndims = self._assert_non_negative_int32_scalar(
ops.convert_to_tensor(
batch_ndims, name="batch_ndims"))
self._batch_ndims_static, self._batch_ndims_is_0 = (
self._introspect_ndims(self._batch_ndims))
self._event_ndims = self._assert_non_negative_int32_scalar(
ops.convert_to_tensor(
event_ndims, name="event_ndims"))
self._event_ndims_static, self._event_ndims_is_0 = (
self._introspect_ndims(self._event_ndims))
@property
def name(self):
"""Name given to ops created by this class."""
return self._name
@property
def batch_ndims(self):
"""Returns number of dimensions corresponding to non-identical draws."""
| tensorflow.python.framework.ops.convert_to_tensor | 5,342 |
import tensorflow as tf
idx2 = cell_arch[bi][2]
block_use = tf.one_hot(idx1, ni, dtype=tf.int32) + tf.one_hot(idx2, ni, dtype=tf.int32)
block_uses.append(block_use)
block_uses = tf.add_n(block_uses)
unused_indices = tf.reshape(tf.cast(tf.where(tf.equal(block_uses, 0)), tf.int32), [-1])
num_out_blocks = tf.size(unused_indices)
# Select only unused blocks
with tf.variable_scope('select'):
stacked_blocks = tf.stack(cell_inputs + blocks)
out_blocks = tf.gather(stacked_blocks, unused_indices, axis=0)
out_blocks = tf.transpose(out_blocks, (1, 2, 3, 0, 4))
# Combine to constant channels
with tf.variable_scope('combine'):
W = self._make_var('W', (ni, block_ch * block_ch))
W = tf.gather(W, unused_indices, axis=0)
W = tf.reshape(W, (1, 1, num_out_blocks * block_ch, block_ch))
| tensorflow.stack | 5,343 |
import tensorflow as tf
config = copy.deepcopy(config)
if not is_training:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
input_shape = get_shape_list(input_ids, expected_rank=2)
batch_size = input_shape[0]
seq_length = input_shape[1]
if input_mask is None:
input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)
if token_type_ids is None:
token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)
with tf.variable_scope(scope, default_name="bert"):
with tf.variable_scope("embeddings"):
# Perform embedding lookup on the word ids.
(self.word_embedding_output,
self.output_embedding_table) = embedding_lookup(
input_ids=input_ids,
vocab_size=config.vocab_size,
embedding_size=config.embedding_size,
initializer_range=config.initializer_range,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=use_one_hot_embeddings)
| tensorflow.zeros | 5,344 |
import tensorflow as tf
obs_shape[:1] + [num_target_frames] + [action_space.n]),
"target_value": tf.zeros(
obs_shape[:1] + target_value_shape_suffix)
}
model.distributional_value_size = max(distributional_size, 1)
model.use_epochs = hparams.use_epochs
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
t2t_model.create_dummy_vars()
(targets, _) = model(features)
target_values = targets["target_value"][:, 0]
if distributional_size > 1:
target_values = targets["target_value"][:, :]
| tensorflow.get_variable_scope | 5,345 |
import tensorflow as tf
rgb = tf.floor(rgb/(2**(8-n_bits)))
rgb = rgb/(n_bins) - 0.5
return rgb
def post_process(self, rgb, add_dequantization_noise=True):
n_bits = config.model.data.n_bits
n_bins = 2**n_bits
rgb_out = rgb
# discretization noise
if add_dequantization_noise:
shape = tf.shape(rgb_out)
rgb_out += tf.random_uniform(shape=shape)*(1/n_bins)
return rgb_out
| tensorflow.random_uniform | 5,346 |
import tensorflow as tf
def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings):
"""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(label_ids.shape[0], dtype=tf.float32)
| tensorflow.logging.info | 5,347 |
import tensorflow as tf
if output_dtype is None:
raise TypeError('Tensor type %r is not supported' % x.dtype)
with tf.compat.v1.name_scope('tukey_parameters'):
x = tf.cast(x, output_dtype)
(count_l1, l1, count_l2, l2, count_l3, l3, count_l4, l4) = (
tf_utils.reduce_batch_count_l_moments(x, reduce_instance_dims))
| tensorflow.cast | 5,348 |
from tensorflow.python.ops import gen_nn_ops
name: A name for the operation (optional).
Returns:
A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
softmax cross entropy loss.
"""
# The second output tensor contains the gradients. We use it in
# _CrossEntropyGrad() in nn_grad but not here.
cost, unused_backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
logits, labels, name=name)
return cost
def sparse_softmax_cross_entropy_with_logits(logits, labels, name=None):
"""Computes sparse softmax cross entropy between `logits` and `labels`.
| tensorflow.python.ops.gen_nn_ops._softmax_cross_entropy_with_logits | 5,349 |
import tensorflow as tf
facts = tf.concat(facts, 2)
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_trans_shine' + 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, facts_size, activation=tf.nn.sigmoid, name='f1_shine_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, facts_size, activation=tf.nn.sigmoid, name='f2_shine_att' + stag)
d_layer_2_all = tf.reshape(d_layer_2_all, tf.shape(facts))
output = d_layer_2_all
return output
| tensorflow.shape | 5,350 |
import tensorflow as tf
def validation_mapper(byte):
image = tf.image.decode_jpeg(
tf.reshape(byte, shape=[]), 3, **JPEG_OPT)
image = resize_shortest_edge(image, tf.shape(image), 256)
image = center_crop(image, 224)
image = tf.reverse(image, axis=[2]) # to BGR
return image
def training_mapper(byte):
jpeg_shape = tf.image.extract_jpeg_shape(byte) # hwc
bbox_begin, bbox_size, distort_bbox = tf.image.sample_distorted_bounding_box(
jpeg_shape,
bounding_boxes=tf.zeros(shape=[0, 0, 4]),
min_object_covered=0,
aspect_ratio_range=[0.75, 1.33],
area_range=[0.08, 1.0],
max_attempts=10,
use_image_if_no_bounding_boxes=True)
| tensorflow.image.extract_jpeg_shape | 5,351 |
import tensorflow as tf
As described in https://arxiv.org/pdf/1608.06993v3.pdf (page 5).
Args:
image: a Tensor.
Returns:
Tensor of the same shape as image.
"""
image = tf.image.resize_with_crop_or_pad(image, 40, 40)
image = tf.image.random_crop(image, [32, 32, 3])
image = tf.image.random_flip_left_right(image)
return image
# Makes the function accessible in gin configs, even with all args denylisted.
@gin.configurable(module='trax.data', denylist=['dataset', 'training'])
def cifar10_augmentation_preprocess(dataset, training):
| tensorflow.image.resize_with_crop_or_pad | 5,352 |
import tensorflow as tf
# Optimisation hyperparameters
tf.app.flags.DEFINE_integer('batch-size', 256, 'Number of examples per mini-batch (default: %(default)d)')
tf.app.flags.DEFINE_float('learning-rate', 1e-4, 'Learning rate (default: %(default)d)')
tf.app.flags.DEFINE_integer('img-width', 32, 'Image width (default: %(default)d)')
tf.app.flags.DEFINE_integer('img-height', 32, 'Image height (default: %(default)d)')
tf.app.flags.DEFINE_integer('img-channels', 3, 'Image channels (default: %(default)d)')
tf.app.flags.DEFINE_integer('num-classes', 10, 'Number of classes (default: %(default)d)')
| tensorflow.app.flags.DEFINE_integer | 5,353 |
import tensorflow as tf
logits = tf.nn.xw_plus_b(tweet_embedding, hidden, bias)
self.probs = tf.nn.softmax(logits)
self._xent = tf.nn.softmax_cross_entropy_with_logits(logits, self.y)
self.cost = tf.reduce_mean(self.example_weights * self._xent)
| tensorflow.nn.softmax_cross_entropy_with_logits | 5,354 |
import tensorflow as tf
net_1 = convLinear(inpOp, nIn, nOut, kH, kW, dH, dW, padType, name+'_1', phase_train, use_batch_norm, weight_decay)
net_2 = convLinear(inpOp, nIn, nOut, kH, kW, dH, dW, padType, name+'_2', phase_train, use_batch_norm, weight_decay)
out = tf.maximum(net_1, net_2)
return out
def affine(inpOp, nIn, nOut, name, weight_decay=0.0):
with tf.variable_scope(name):
l2_regularizer = lambda t: l2_loss(t, weight=weight_decay)
weights = tf.get_variable("weights", [nIn, nOut],
initializer=tf.truncated_normal_initializer(stddev=1e-1),
regularizer=l2_regularizer, dtype=inpOp.dtype)
biases = tf.get_variable("biases", [nOut], initializer=tf.constant_initializer(), dtype=inpOp.dtype)
affine1 = tf.nn.relu_layer(inpOp, weights, biases)
return affine1
def l2_loss(tensor, weight=1.0, scope=None):
"""Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for op_scope.
Returns:
the L2 loss op.
"""
with tf.name_scope(scope):
| tensorflow.nn.relu_layer | 5,355 |
import tensorflow as tf
h_pool5 = h_conv5
h_pool5 = tf.reshape(h_pool5, shape=[-1, POOL_X, POOL_Y])
feature_mat = h_pool5
print("----feature_mat-----")
print(feature_mat)
| tensorflow.reshape | 5,356 |
import tensorflow as tf
def _evaluate_legendre_polynomial_loop_body(x, n, l, m, pmm, pmm1):
n_float = tf.cast(n, dtype=x.dtype)
m_float = tf.cast(m, dtype=x.dtype)
pmn = (x * (2.0 * n_float - 1.0) * pmm1 - (n_float + m_float - 1) * pmm) / (
n_float - m_float)
pmm = tf.where(tf.less_equal(n, l), pmm1, pmm)
pmm1 = tf.where(tf.less_equal(n, l), pmn, pmm1)
n += 1
return x, n, l, m, pmm, pmm1
def _evaluate_legendre_polynomial_loop(x, m, l, pmm, pmm1):
| tensorflow.less_equal | 5,357 |
from tensorflow.python.ops import variable_scope as vs
[inputs, state],
2 * self._num_units,
True,
bias_initializer=bias_ones,
kernel_initializer=self._kernel_initializer)
value = math_ops.sigmoid(self._gate_linear([inputs, state]))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
r_state = r * state
if self._candidate_linear is None:
with vs.variable_scope("candidate"):
self._candidate_linear = _Linear(
[inputs, r_state],
self._num_units,
True,
bias_initializer=self._bias_initializer,
kernel_initializer=self._kernel_initializer)
c = self._activation(self._candidate_linear([inputs, r_state]))
new_h = (1. - att_score) * state + att_score * c
return new_h, new_h
| tensorflow.python.ops.variable_scope.variable_scope | 5,358 |
import tensorflow as tf
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
probabilities = tf.nn.softmax(logits, axis=-1)
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, logits, probabilities)
| tensorflow.nn.bias_add | 5,359 |
import tensorflow as tf
class A2C(Base):
def __init__(self, env, summary_dir='./', gpu=False):
self.LR = 1e-4
self.MINIBATCH = 32
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')
| tensorflow.ConfigProto | 5,360 |
import tensorflow as tf
)
# Masking tensor.
left_mask = _left_mask(
batch_shaped(
band_features["before_padding"]),
window_size)
right_mask = _right_mask(
batch_shaped(band_features["after_padding"]), window_size
)
self.mask = batch_2win_shaped(
tf.logical_and(
tf.concat([left_mask, right_mask], axis=1), tile_to_2win(self.in_window)
)
)
def dflux_dt(self, clip_magnitude: typing.Optional[float]) -> tf.Tensor:
"""Computes dflux/dt.
:param clip_magnitude: Option for clipping the magnitude, if dt might be very small.
:return: <float>[batch_size, 2 * window_size] dflux/dt tensor.
"""
result = self.dflux / self.dtime
if clip_magnitude is not None:
result = tf.clip_by_value(
| tensorflow.concat | 5,361 |
import tensorflow as tf
w_shape = [int(w) for w in weights.get_shape()]
if len(w_shape) > 1 and int(w_shape[-2]) > 1:
# Full convolutions
if symmetric_weights:
g = tf.get_default_graph()
with g.gradient_override_map({'Conv3D': 'SymmetricConv3D'}):
activities = tf.nn.conv3d(
data,
| tensorflow.get_default_graph | 5,362 |
import tensorflow as tf
dtype=tf.float32)
mask1 = tf.constant([[1, 1, 1, 1, 1, 1, 1, 1],
| tensorflow.constant | 5,363 |
import tensorflow as tf
feature_emb_list.append(speaker_pair_emb)
tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0), [k, c, 1]) # [k, c, emb]
feature_emb_list.append(tiled_genre_emb)
if self.config["use_features"]:
antecedent_distance_buckets = self.bucket_distance(top_antecedent_offsets) # [k, c]
antecedent_distance_emb = tf.gather(tf.get_variable("antecedent_distance_emb", [10, self.config["feature_size"]]), antecedent_distance_buckets) # [k, c]
feature_emb_list.append(antecedent_distance_emb)
feature_emb = tf.concat(feature_emb_list, 2) # [k, c, emb]
feature_emb = tf.nn.dropout(feature_emb, self.dropout) # [k, c, emb]
target_emb = tf.expand_dims(top_span_emb, 1) # [k, 1, emb]
similarity_emb = top_antecedent_emb * target_emb # [k, c, emb]
target_emb = tf.tile(target_emb, [1, c, 1]) # [k, c, emb]
pair_emb = tf.concat([target_emb, top_antecedent_emb, similarity_emb, feature_emb], 2) # [k, c, emb]
with tf.variable_scope("slow_antecedent_scores"):
| tensorflow.concat | 5,364 |
import tensorflow as tf
"""
def mlp_actor_critic(x, a, hidden_sizes=(400,300), activation=tf.nn.relu,
output_activation=tf.tanh, action_space=None,
dropout_rate=0, nn_type='mlp_variational'):
act_dim = a.shape.as_list()[-1]
act_limit = action_space.high[0]
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)
| tensorflow.variable_scope | 5,365 |
import tensorflow as tf
batch_size = params["batch_size"]
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
| tensorflow.data.TFRecordDataset | 5,366 |
import tensorflow as tf
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)
q1_dropout_mask_phs = q1_dropout_mask_generator.generate_dropout_mask_placeholders()
q1, q1_reg = mlp_variational(q1_in_ph, q1_dropout_mask_phs, list(hidden_sizes) + [1],
activation, None, dropout_rate)
q1 = tf.squeeze(q1, axis=2)
with tf.variable_scope('q1', reuse=True):
q1_pi, q1_pi_reg = mlp_variational(tf.concat([x, pi[0]], axis=-1), q1_dropout_mask_phs, list(hidden_sizes) + [1],
activation, None, dropout_rate)
q1_pi = tf.squeeze(q1_pi, axis=2)
with tf.variable_scope('q2'):
q2_in_ph = tf.concat([x, a], axis=-1)
q2_in_dim = q2_in_ph.shape.as_list()[1]
q2_dropout_mask_generator = DropoutMaskGenerator(q2_in_dim, hidden_sizes, model_prob=1.0 - dropout_rate)
q2_dropout_mask_phs = q2_dropout_mask_generator.generate_dropout_mask_placeholders()
q2, q2_reg = mlp_variational(q2_in_ph, q2_dropout_mask_phs, list(hidden_sizes) + [1],
activation, None, dropout_rate)
q2 = tf.squeeze(q2, axis=2)
else:
raise ValueError('Please choose a proper nn_type!')
return pi, pi_reg, pi_dropout_mask_generator, pi_dropout_mask_phs,\
q1, q1_reg, q1_dropout_mask_generator, q1_dropout_mask_phs, q1_pi, q1_pi_reg,\
q2, q2_reg, q2_dropout_mask_generator, q2_dropout_mask_phs
| tensorflow.concat | 5,367 |
import tensorflow as tf
raw_data = reader.ptb_raw_data(FLAGS.data_path)
train_data, valid_data, test_data, _ = raw_data
config = get_config()
eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1
train_graph = tf.Graph()
eval_graph = tf.Graph()
infer_graph = tf.Graph()
with train_graph.as_default():
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
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)
| tensorflow.Graph | 5,368 |
import tensorflow as tf
return loss
def contra_step_lossV3(pred, tgt, margin=1.0):
# Step-wise contrastive loss
pred1, pred2 = tf.split(pred, 2, axis=0)
tgt1, tgt2 = tf.split(tgt, 2, axis=0)
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) + margin)
loss = tf.reduce_mean(loss)
return loss
def contra_step_lossV4(pred, tgt):
# 50*50
# Step-wise contrastive loss
| tensorflow.where | 5,369 |
import tensorflow as tf
LayersConfig.set_keep[name] = tf.placeholder(tf.float32)
self.outputs = tf.nn.dropout(self.inputs, LayersConfig.set_keep[name], seed=seed, name=name) # 1.2
| tensorflow.nn.dropout | 5,370 |
import tensorflow as tf
tf.flags.DEFINE_string(
| tensorflow.flags.DEFINE_string | 5,371 |
import tensorflow as tf
with tf.name_scope('preprocess'):
A = tf.transpose(A / 128.0 - 1.0, [0, 3, 1, 2])
B = tf.transpose(B / 128.0 - 1.0, [0, 3, 1, 2])
def viz3(name, a, b, c):
with tf.name_scope(name):
im = tf.concat([a, b, c], axis=3)
im = tf.transpose(im, [0, 2, 3, 1])
im = (im + 1.0) * 128
im = tf.clip_by_value(im, 0, 255)
im = tf.cast(im, tf.uint8, name='viz')
tf.summary.image(name, im, max_outputs=50)
| tensorflow.concat | 5,372 |
import tensorflow as tf
if attention_mask is not None:
attention_mask = tf.reshape(
| tensorflow.reshape | 5,373 |
import tensorflow as tf
self.loss = self.loss1 + self.loss2
self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.loss)
# In[3]:
tf.reset_default_graph()
sess = tf.InteractiveSession()
size_layers = 128
learning_rate = 1e-3
num_layers = 2
| tensorflow.reset_default_graph | 5,374 |
import tensorflow as tf
for grad, var in grads_and_vars:
if grad is not None:
if isinstance(grad, tf.IndexedSlices):
grad_values = grad.values
else:
grad_values = grad
summaries.append(tf.histogram_summary(var.op.name + ':gradient',
grad_values))
summaries.append(tf.histogram_summary(var.op.name + ':gradient_norm',
tf.global_norm([grad_values])))
else:
tf.logging.info('Var %s has no gradient', var.op.name)
return summaries
class DeploymentConfig(object):
"""Configuration for deploying a model with `deploy()`.
You can pass an instance of this class to `deploy()` to specify exactly
how to deploy the model to build. If you do not pass one, an instance built
from the default deployment_hparams will be used.
"""
| tensorflow.logging.info | 5,375 |
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)
| tensorflow.cast | 5,376 |
import tensorflow as tf
Args:
private_samples: a tensor of shape [num_samples, num_features].
shared_samples: a tensor of shape [num_samples, num_features].
weight: the weight of the incoherence loss.
name: the name of the tf summary.
"""
with tf.name_scope(name):
private_samples -= tf.reduce_mean(private_samples, 0)
shared_samples -= tf.reduce_mean(shared_samples, 0)
private_samples = tf.nn.l2_normalize(private_samples, 1)
shared_samples = tf.nn.l2_normalize(shared_samples, 1)
correlation_matrix = tf.matmul(private_samples, shared_samples, transpose_a=True)
cost = tf.reduce_mean(tf.square(correlation_matrix)) * weight
cost = tf.where(cost > 0, cost, 0, name='value')
assert_op = tf.Assert(tf.is_finite(cost), [cost])
with tf.control_dependencies([assert_op]):
barrier = tf.no_op(name)
| tensorflow.nn.l2_normalize | 5,377 |
import tensorflow as tf
attn_dist = add_first_word_prob_to_atten_dists(self.in_passage_words, self.phrase_starts,
vocab_dist, attn_dist)
# match attn_dist[batch_size, passage_length] to sparse one-hot representation [batch_size, passage_length, extended_vsize]
batch_nums = tf.range(0, limit=batch_size) # shape (batch_size)
batch_nums = tf.expand_dims(batch_nums, axis=1) # shape (batch_size, 1)
batch_nums = tf.tile(batch_nums, [1, passage_length]) # shape (batch_size, passage_length)
step_nums = tf.range(0, limit=passage_length) # [passage_length]
step_nums = tf.expand_dims(step_nums, axis=0) # shape (1, passage_length)
step_nums = tf.tile(step_nums, [batch_size, 1]) # shape (batch_size, passage_length)
indices = tf.stack((batch_nums, step_nums, passage_word_idx), axis=2) # shape (batch_size, passage_length, 3)
indices = tf.reshape(indices, [-1, 3]) #[batch_size * passage_length, 3]
indices = tf.cast(indices, tf.int64)
| tensorflow.range | 5,378 |
import tensorflow as tf
optimizer = tf.train.AdadeltaOptimizer(learning_rate)
elif params['optimizer'] == 'adagrad':
optimizer = tf.train.AdagradOptimizer(learning_rate)
elif params['optimizer'] == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate, momentum=params['momentum'])
elif params['optimizer'] == 'lars':
optimizer = tf.contrib.opt.LARSOptimizer(
| tensorflow.train.RMSPropOptimizer | 5,379 |
import tensorflow as tf
@property
def summary_added(self):
return self._summary_added
return Layer
@layer
def identity_layer(tensor, **opts):
out = tf.identity(tensor)
return out
@layer
def embedding_layer(tensor, vocab_size=None, embedding_dim=None, embedding_matrix=None, **opts):
if embedding_matrix is None:
initializer = tf.contrib.layers.xavier_initializer(uniform=True)
embedding_matrix = tf.get_variable("embedding_matrix", initializer=initializer(shape=(vocab_size, embedding_dim)))
| tensorflow.identity | 5,380 |
import tensorflow as tf
aggregated_lm_emb = tf.reshape(flattened_aggregated_lm_emb, [num_sentences, max_sentence_length, lm_emb_size])
aggregated_lm_emb *= self.lm_scaling
context_emb_list.append(aggregated_lm_emb)
context_emb = tf.concat(context_emb_list, 2) # [num_sentences, max_sentence_length, emb]
head_emb = tf.concat(head_emb_list, 2) # [num_sentences, max_sentence_length, emb]
context_emb = tf.nn.dropout(context_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
head_emb = tf.nn.dropout(head_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
| tensorflow.concat | 5,381 |
import tensorflow as tf
"""
:description: Placeholders
"""
def _setup_placeholders(self):
if self.demo:
self.c = tf.placeholder(tf.int32, [None, self.config.max_p_len], "context")
self.q = tf.placeholder(tf.int32, [None, self.config.max_q_len], "question")
self.ch = tf.placeholder(tf.int32, [None, self.config.max_p_len, self.config.max_ch_len], "context_char")
self.qh = tf.placeholder(tf.int32, [None, self.config.max_q_len, self.config.max_ch_len], "question_char")
self.start_label = tf.placeholder(tf.int32, [None], "answer_label1")
self.end_label = tf.placeholder(tf.int32, [None], "answer_label2")
else:
self.c = tf.placeholder(tf.int32, [self.config.batch_size * self.max_p_num, self.config.max_p_len],
| tensorflow.placeholder | 5,382 |
import tensorflow as tf
bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
file_based_convert_examples_to_features(
| tensorflow.contrib.tpu.TPUEstimator | 5,383 |
import tensorflow as tf
summaries = {
'regularization_loss/KL/PriorMean/Mean':
tf.math.reduce_mean(tf.constant(prior_mean)),
'regularization_loss/KL/PriorVar/Mean':
tf.math.reduce_mean(tf.constant(prior_stddev)**2),
'regularization_loss/KL/Loss/Original':
loss,
'regularization_loss/KL/Loss/Weighted':
| tensorflow.constant | 5,384 |
import tensorflow as tf
summed = tf.scan(lambda a, x: x[0] + x[1] * a,
sequences,
initializer=tf.convert_to_tensor(initial_value),
parallel_iterations=1,
| tensorflow.convert_to_tensor | 5,385 |
import tensorflow as tf
"""Name of dataset: imagenet or flowers.
If not specified, it is automatically guessed
based on --data_dir.""")
tf.flags.DEFINE_string('resize_method', 'bilinear',
"""Method for resizing input images:
crop,nearest,bilinear,bicubic or area.
The 'crop' mode requires source images to be at least
as large as the network input size,
while the other modes support any sizes and apply
random bbox distortions
before resizing (even with --nodistortions).""")
tf.flags.DEFINE_boolean('distortions', True,
"""Enable/disable distortions during
image preprocessing. These include bbox and color
distortions.""")
tf.flags.DEFINE_string('local_parameter_device', 'gpu',
"""Device to use as parameter server: cpu or gpu.
For distributed training, it can affect where caching
of variables happens.""")
tf.flags.DEFINE_string('device', 'gpu',
"""Device to use for computation: cpu or gpu""")
#tf.flags.DEFINE_string('data_format', 'NCHW',
tf.flags.DEFINE_string('data_format', 'NHWC',
"""Data layout to use: NHWC (TF native)
or NCHW (cuDNN native).""")
tf.flags.DEFINE_integer('num_intra_threads', 1,
"""Number of threads to use for intra-op
parallelism. If set to 0, the system will pick
an appropriate number.""")
tf.flags.DEFINE_integer('num_inter_threads', 0,
| tensorflow.flags.DEFINE_string | 5,386 |
import tensorflow as tf
self.assign_vars = []
for var in tf.global_variables():
v = self.var_ema.average(var)
| tensorflow.global_variables | 5,387 |
import tensorflow as tf
def nin(x, num_units, **kwargs):
s = tf.shape(x)
sh = x.get_shape().as_list()
x = tf.reshape(x, [tf.reduce_prod(s[:-1]), sh[-1]])
x = dense(x, num_units, **kwargs)
return tf.reshape(x, [-1] + sh[1:-1] + [num_units])
def dense(x, num_units, scope="dense", training=True, ema=None, init=False, bias_initializer=tf.constant_initializer(0.)):
with tf.variable_scope(scope):
V = tf.get_variable('V', shape=[int(x.get_shape()[1]), num_units], dtype=tf.float32,
initializer=tf.random_normal_initializer(0, 0.05), trainable=True)
g = tf.get_variable('g', shape=[num_units], dtype=tf.float32,
initializer=tf.constant_initializer(1.), trainable=True)
b = tf.get_variable('b', shape=[num_units], dtype=tf.float32,
initializer=bias_initializer, trainable=True)
def maybe_avg(v):
if ema is not None and not init:
v = tf.cond(training, lambda: v, lambda: ema.average(v))
| tensorflow.variable_scope | 5,388 |
import tensorflow as tf
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)
| tensorflow.concat | 5,389 |
from tensorflow.python.framework import ops
# gradients and the attrs, but if we do not know orig_input_shape
# statically, then we are unlikely to know the shape of the
# gradients either.
return [tensor_shape.unknown_shape(ndims=4)]
@ops.RegisterShape("Conv2DBackpropFilter")
def _Conv2DBackpropFilterShape(op):
"""Shape function for the Conv2DBackpropFilter op."""
filter_shape = tensor_util.constant_value(op.inputs[1])
if filter_shape is not None:
return [tensor_shape.TensorShape(filter_shape.tolist())]
| tensorflow.python.framework.ops.RegisterShape | 5,390 |
from tensorflow.python.ops import math_ops
indices_at_minval = math_ops.equal(
math_ops.abs(sensitivities - sensitivity), min_val)
indices_at_minval = math_ops.to_int64(indices_at_minval)
indices_at_minval = math_ops.cumsum(indices_at_minval)
tf_index = math_ops.argmax(indices_at_minval, 0)
tf_index = math_ops.cast(tf_index, dtypes.int32)
| tensorflow.python.ops.math_ops.cumsum | 5,391 |
import tensorflow as tf
k_width,
d_height=1,
d_width=1,
mode='SAME',
input_layer=None,
num_channels_in=None,
batch_norm=None,
activation='relu'):
if input_layer is None:
input_layer = self.top_layer
if num_channels_in is None:
num_channels_in = self.top_size
name = 'conv' + str(self.counts['conv'])
self.counts['conv'] += 1
with tf.variable_scope(name):
strides = [1, d_height, d_width, 1]
if self.data_format == 'NCHW':
strides = [strides[0], strides[3], strides[1], strides[2]]
if mode != 'SAME_RESNET':
conv = conv_layers.conv2d(
input_layer,
num_out_channels, [k_height, k_width],
strides=[d_height, d_width],
padding=mode,
data_format=self.channel_pos,
use_bias=False)
else: # Special padding mode for ResNet models
if d_height == 1 and d_width == 1:
| tensorflow.variable_scope | 5,392 |
import tensorflow as tf
deconv_shape2 = image_net["pool3"].get_shape()
W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")
b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))
fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")
shape = tf.shape(image)
deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS])
W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")
b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)
annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")
return tf.expand_dims(annotation_pred, dim=3), conv_t3
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def main(argv=None):
| tensorflow.expand_dims | 5,393 |
import tensorflow as tf
marginalized_gold_scores = tf.reduce_logsumexp(gold_scores, [1]) # [k]
log_norm = tf.reduce_logsumexp(antecedent_scores, [1]) # [k]
return log_norm - marginalized_gold_scores # [k]
def bucket_distance(self, distances):
"""
Places the given values (designed for distances) into 10 semi-logscale buckets:
[0, 1, 2, 3, 4, 5-7, 8-15, 16-31, 32-63, 64+].
"""
logspace_idx = tf.to_int32(tf.floor(tf.log(tf.to_float(distances))/math.log(2))) + 3
use_identity = tf.to_int32(distances <= 4)
combined_idx = use_identity * distances + (1 - use_identity) * logspace_idx
return tf.clip_by_value(combined_idx, 0, 9)
def get_slow_antecedent_scores(self, top_span_emb, top_antecedents, top_antecedent_emb, top_antecedent_offsets, top_span_speaker_ids, genre_emb):
k = util.shape(top_span_emb, 0)
c = util.shape(top_antecedents, 1)
| tensorflow.to_float | 5,394 |
import tensorflow as tf
pc_lr = pc_lr_decay_fn(learning_rate=1., global_step=global_step)
pc_lr_true = tf.train.piecewise_constant(
| tensorflow.train.piecewise_constant | 5,395 |
import tensorflow as tf
self.features = tf.placeholder(tf.float32, [None, self.L, self.D])
self.captions = tf.placeholder(tf.int32, [None, self.T + 1])
def _get_initial_lstm(self, features):
with tf.variable_scope('initial_lstm'):
features_mean = tf.reduce_mean(features, 1)
w_h = tf.get_variable('w_h', [self.D, self.H], initializer=self.weight_initializer)
| tensorflow.variable_scope | 5,396 |
from tensorflow.contrib.eager.python.examples.linear_regression import linear_regression
def testSyntheticDataset(self):
true_w = tf.random_uniform([3, 1])
true_b = [1.0]
batch_size = 10
num_batches = 2
noise_level = 0.
dataset = linear_regression.synthetic_dataset(true_w, true_b, noise_level,
batch_size, num_batches)
it = tfe.Iterator(dataset)
for _ in range(2):
(xs, ys) = it.next()
self.assertEqual((batch_size, 3), xs.shape)
| tensorflow.contrib.eager.python.examples.linear_regression.linear_regression.synthetic_dataset | 5,397 |
from tensorflow.python.framework import ops
@ops.RegisterShape("Relu6Grad")
@ops.RegisterShape("EluGrad")
@ops.RegisterShape("SoftplusGrad")
@ops.RegisterShape("SoftsignGrad")
def _BinaryElementwiseShape(op):
"""Returns same shape as both inputs to op.
| tensorflow.python.framework.ops.RegisterShape | 5,398 |
from tensorflow.python.ops import array_ops
"""Gated recurrent unit (GRU) with nunits cells."""
if self._gate_linear is None:
bias_ones = self._bias_initializer
if self._bias_initializer is None:
bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype)
with vs.variable_scope("gates"): # Reset gate and update gate.
self._gate_linear = _Linear(
[inputs, state],
2 * self._num_units,
True,
bias_initializer=bias_ones,
kernel_initializer=self._kernel_initializer)
value = math_ops.sigmoid(self._gate_linear([inputs, state]))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
r_state = r * state
if self._candidate_linear is None:
with vs.variable_scope("candidate"):
self._candidate_linear = _Linear(
[inputs, r_state],
self._num_units,
True,
bias_initializer=self._bias_initializer,
kernel_initializer=self._kernel_initializer)
c = self._activation(self._candidate_linear([inputs, r_state]))
new_h = (1. - att_score) * state + att_score * c
return new_h, new_h
| tensorflow.python.ops.array_ops.split | 5,399 |
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