seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
e_params += tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name + '/mixing_net' + '/eval_hyper')
t_params += tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name + '/mixing_net' + '/target_hyper')
|
tensorflow.get_collection
| 9,000 |
import tensorflow as tf
self.num_bilstm=num_bilstm
self.lstm_size=lstm_size
self.bilstm_dropout_rate=bilstm_dropout_rate
@classmethod
def from_dict(cls, json_object):
"""Constructs a `Config` from a Python dictionary of parameters."""
config = Config(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
return config
@classmethod
def from_json_file(cls, json_file):
"""Constructs a `Config` from a json file of parameters."""
with tf.gfile.GFile(json_file, "r") as reader:
text = reader.read()
return cls.from_dict(json.loads(text))
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
def create_model(config, is_training, input_ids, input_mask, segment_ids,
|
tensorflow.gfile.GFile
| 9,001 |
import tensorflow as tf
param_eta = tf.placeholder(dtype=tf.float32, shape=[], name="param_eta")
param_omega = tf.placeholder(dtype=tf.float32, shape=[], name="param_omega")
old_entropy = tf.placeholder(dtype=tf.float32, shape=[], name="old_entropy")
varphis = tf.placeholder(dtype=tf.float32, shape=[None, None], name="varphis")
Kt = tf.placeholder(dtype=tf.float32, shape=[None, None], name="Kt")
prec = tf.placeholder(dtype=tf.float32, shape=[None, None], name="prec")
Waa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="Waa")
Wsa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="Wsa")
wa = tf.placeholder(dtype=tf.float32, shape=[None, None], name="wa")
|
tensorflow.placeholder
| 9,002 |
import tensorflow as tf
def get_valid_batch(image,label,batch_size):
images,labels=tf.train.batch([image,label],batch_size=batch_size)
|
tensorflow.train.batch
| 9,003 |
import tensorflow as tf
if self.inputs.get_shape().ndims != 2:
raise Exception("The input dimension must be rank 2, please reshape or flatten it")
n_in = int(self.inputs.get_shape()[-1])
with tf.variable_scope(name):
W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **W_init_args)
if b_init is not None:
try:
b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=LayersConfig.tf_dtype, **b_init_args)
|
tensorflow.variable_scope
| 9,004 |
import tensorflow as tf
"""
metric_names = list(monitor_dict.keys())
def host_call_fn(global_step, *args):
"""actual host call function."""
step = global_step[0]
with tf.contrib.summary.create_file_writer(
logdir=model_dir, filename_suffix=".host_call").as_default():
with tf.contrib.summary.always_record_summaries():
for i, name in enumerate(metric_names):
if reduce_fn is None:
scalar = args[i][0]
else:
|
tensorflow.contrib.summary.create_file_writer
| 9,005 |
from tensorflow.python.ops import math_ops
# Tile the predictions after thresholding them across different thresholds.
pred_is_pos = math_ops.greater(
array_ops.tile(array_ops.transpose(predictions_2d), [num_thresholds, 1]),
thresh_tiled)
pred_is_neg = math_ops.logical_not(pred_is_pos)
# Tile labels by number of thresholds
label_is_pos = array_ops.tile(labels_2d, [num_thresholds, 1])
label_is_neg = math_ops.logical_not(label_is_pos)
|
tensorflow.python.ops.math_ops.logical_not
| 9,006 |
import tensorflow as tf
def _create_dummy_vars():
"""Dummy vars for restore to work when not using TPU codepath."""
var_names = set([v.name for v in tf.global_variables()])
if "losses_avg/problem_0/total_loss:0" in var_names:
return
with tf.variable_scope("losses_avg"):
with tf.variable_scope("problem_0"):
for var_name in ["total", "extra", "training"]:
tf.get_variable(
"%s_loss" % var_name, initializer=100.0, trainable=False)
with tf.variable_scope("train_stats"):
|
tensorflow.variable_scope
| 9,007 |
import tensorflow as tf
with tf.variable_scope("temp_conv") as scope:
filter_shape = [3, embedding_size, 4, 64]
W = tf.get_variable(name='W_1', shape=filter_shape,
initializer=he_normal,
regularizer=regularizer)
paddings = [[0, 0], [1, 1], [0, 0], [0, 0]]
cnn_inputs = tf.pad(cnn_inputs, paddings, "CONSTANT")
#print("cnn_inputs shape:", cnn_inputs.shape)
inputs = tf.nn.conv2d(cnn_inputs, W, strides=[1, 1, 1, 1], padding="VALID", name="first_conv")
inputs = tf.layers.batch_normalization(inputs, axis=-1, training=self.is_training)
inputs = tf.nn.relu(inputs, name="first_relu")
#print("temp cnn output shape:", inputs.shape)
inputs = tf.squeeze(inputs, axis=2)
#print("squeeze shape", inputs.shape)
#inputs = tf.nn.relu(inputs)
print("Temp Conv", inputs.get_shape())
self.layers.append(inputs)
# Conv Block 64
for i in range(num_layers[0]):
|
tensorflow.nn.relu
| 9,008 |
import tensorflow as tf
# Set up functionality to re-compute `param_noise_scale`. This perturbs yet another copy
# of the network and measures the effect of that perturbation in action space. If the perturbation
# is too big, reduce scale of perturbation, otherwise increase.
q_values_adaptive = q_func(observations_ph.get(), num_actions, scope="adaptive_q_func")
perturb_for_adaption = perturb_vars(original_scope="q_func", perturbed_scope="adaptive_q_func")
kl = tf.reduce_sum(tf.nn.softmax(q_values) * (tf.log(tf.nn.softmax(q_values)) - tf.log(tf.nn.softmax(q_values_adaptive))), axis=-1)
mean_kl = tf.reduce_mean(kl)
def update_scale():
with tf.control_dependencies([perturb_for_adaption]):
update_scale_expr = tf.cond(mean_kl < param_noise_threshold,
|
tensorflow.nn.softmax
| 9,009 |
import tensorflow as tf
with tf.control_dependencies([tf.assert_less_equal(num_instances, self.capacity)]):
indices = tf.range(self.memory_index, self.memory_index + num_instances) % self.capacity
|
tensorflow.range
| 9,010 |
from tensorflow.contrib.rnn.python.ops import core_rnn
]
initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127)
cell = rnn_cell.LSTMCell(
num_units=num_units, initializer=initializer, state_is_tuple=True)
multi_cell = rnn_cell.MultiRNNCell(
[cell() for _ in range(num_layers)])
outputs, final_state = core_rnn.static_rnn(
multi_cell, inputs, dtype=dtypes.float32)
trainable_variables = ops.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES)
gradients = gradients_impl.gradients([outputs, final_state],
trainable_variables)
training_op = control_flow_ops.group(*gradients)
|
tensorflow.contrib.rnn.python.ops.core_rnn.static_rnn
| 9,011 |
from tensorflow.python.ops import array_ops
features: features dict.
Returns:
Loss tensor.
"""
target = target[self.name] if isinstance(target, dict) else target
loss_unweighted = self._loss_fn(logits, target)
weight_tensor = self.get_weight_tensor(features)
if weight_tensor is None:
return math_ops.reduce_mean(loss_unweighted, name="loss")
else:
loss_unweighted = array_ops.reshape(loss_unweighted, shape=(-1,))
loss_weighted = math_ops.mul(
loss_unweighted,
array_ops.reshape(weight_tensor, shape=(-1,)))
return math_ops.div(
math_ops.reduce_sum(loss_weighted),
math_ops.to_float(math_ops.reduce_sum(weight_tensor)),
name="loss")
class _RegressionTargetColumn(_TargetColumn):
"""_TargetColumn for regression."""
def __init__(self, loss_fn, label_name, weight_column_name, target_dimension):
super(_RegressionTargetColumn, self).__init__(
loss_fn=loss_fn,
num_label_columns=target_dimension,
label_name=label_name,
|
tensorflow.python.ops.array_ops.reshape
| 9,012 |
import tensorflow as tf
# Assuming we have one shard for logits.
logits = tf.concat([recent_logits, logits[:, -1:]], 1)
loss = sum([l for l in losses.values() if l is not None])
return samples, logits, loss
# Create an initial output tensor. This will be passed
# to the infer_step, which adds one timestep at every iteration.
if "partial_targets" in features:
initial_output = tf.to_int64(features["partial_targets"])
while len(initial_output.get_shape().as_list()) < 4:
initial_output = tf.expand_dims(initial_output, 2)
batch_size = common_layers.shape_list(initial_output)[0]
else:
batch_size = common_layers.shape_list(features["inputs"])[0]
initial_output = tf.zeros((batch_size, 0, 1, 1), dtype=tf.int64)
# Hack: foldl complains when the output shape is less specified than the
# input shape, so we confuse it about the input shape.
|
tensorflow.to_int64
| 9,013 |
import tensorflow as tf
#
# You need to modify the below cost function and optimizer so as to
# implement your own pre-train method.
#
# =====================================================================
lambda_l2_w = 0.004
learning_rate = 0.0001
logging.info(" lambda_l2_w: %f" % lambda_l2_w)
logging.info(" learning_rate: %f" % learning_rate)
# Mean-square-error i.e. quadratic-cost
mse = tf.reduce_sum(tf.squared_difference(y, x_recon), 1)
mse = tf.reduce_mean(mse) # in theano: mse = ((y - x) ** 2 ).sum(axis=1).mean()
# mse = tf.reduce_mean(tf.reduce_sum(tf.square(tf.sub(y, x_recon)), 1))
# mse = tf.reduce_mean(tf.squared_difference(y, x_recon)) # <haodong>: Error
# mse = tf.sqrt(tf.reduce_mean(tf.square(y - x_recon))) # <haodong>: Error
# Cross-entropy
# ce = cost.cross_entropy(y, x_recon) # <haodong>: list , list , Error (only be used for softmax output)
# ce = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, x_recon)) # <haodong>: list , list , Error (only be used for softmax output)
# ce = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y, x_recon)) # <haodong>: list , index , Error (only be used for softmax output)
L2_w = tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[0]) \
+ tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[2]) # faster than the code below
# L2_w = lambda_l2_w * tf.reduce_mean(tf.square(self.train_params[0])) + lambda_l2_w * tf.reduce_mean( tf.square(self.train_params[2]))
|
tensorflow.reduce_mean
| 9,014 |
import tensorflow as tf
biases_regularizer=biases_regularizer,
biases_initializer=tf.constant_initializer(0.0)):
|
tensorflow.constant_initializer
| 9,015 |
import tensorflow as tf
References:
Kolda, Tamara G., and Brett W. Bader. "Tensor decompositions and
applications." SIAM review 51.3 (2009): 455-500.
'''
def _create_model(self, train_triples):
# Count unique items to determine embedding matrix sizes
head_cnt = len(set(train_triples[:,0]))
rel_cnt = len(set(train_triples[:,1]))
tail_cnt = len(set(train_triples[:,2]))
init_sd = 1.0 / np.sqrt(self.embedding_size)
# Embedding matrices for entities and relationship types
head_init = tf.truncated_normal([head_cnt, self.embedding_size], stddev=init_sd)
rel_init = tf.truncated_normal([rel_cnt, self.embedding_size], stddev=init_sd)
tail_init = tf.truncated_normal([tail_cnt, self.embedding_size], stddev=init_sd)
if self.maxnorm is not None:
# Ensure maxnorm constraints are initially satisfied
head_init = dense_maxnorm(head_init, self.maxnorm)
rel_init = dense_maxnorm(rel_init, self.maxnorm)
tail_init = dense_maxnorm(tail_init, self.maxnorm)
self.head_embedding_vars = tf.Variable(head_init)
self.rel_embedding_vars = tf.Variable(rel_init)
self.tail_embedding_vars = tf.Variable(tail_init)
# Embedding layer for each (head, rel, tail) triple being fed in as input
head_embed = tf.nn.embedding_lookup(self.head_embedding_vars, self.head_input)
rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input)
tail_embed = tf.nn.embedding_lookup(self.tail_embedding_vars, self.tail_input)
# Model output
|
tensorflow.truncated_normal
| 9,016 |
import tensorflow as tf
# Initialize hidden layer activities
if self.hidden_init == 'identity':
l1_h2 = tf.identity(x)
l2_h2 = tf.zeros(l2_shape, dtype=self.dtype)
l3_h2 = tf.zeros(l3_shape, dtype=self.dtype)
elif self.hidden_init == 'random':
l1_h2 = tf.random_normal(x_shape, dtype=self.dtype)
l2_h2 = tf.random_normal(l2_shape, dtype=self.dtype)
l3_h2 = tf.random_normal(l3_shape, dtype=self.dtype)
elif self.hidden_init == 'zeros':
l1_h2 = tf.zeros(x_shape, dtype=self.dtype)
l2_h2 = tf.zeros(l2_shape, dtype=self.dtype)
|
tensorflow.random_normal
| 9,017 |
import tensorflow as tf
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length, tokenizer,
predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
|
tensorflow.logging.info
| 9,018 |
import tensorflow as tf
mdl = p.Instantiate()
mdl.FPropDefaultTheta()
decoder_theta = mdl._MakeDecoderTheta(theta=mdl.theta, input_batch=None)
mdl.BProp()
self.assertEqual(decoder_theta, mdl.theta.decoder)
def testFProp(self):
with self.session(use_gpu=False):
tf.set_random_seed(93820985)
p = self._testParams()
mdl = p.Instantiate()
mdl.FPropDefaultTheta()
tf.global_variables_initializer().run()
test_utils.CompareToGoldenSingleFloat(self, 4.472597, mdl.loss.eval())
actual_var_names = [_.name for _ in tf.all_variables()]
|
tensorflow.set_random_seed
| 9,019 |
import tensorflow as tf
def testCollectVarHistogram(self):
with self.session(use_gpu=False, graph=tf.Graph()):
p = self._testParams()
mdl = p.Instantiate()
mdl.FPropDefaultTheta()
var_grads = py_utils.ComputeGradients(mdl.loss, mdl.vars)
summary_utils.CollectVarHistogram(var_grads)
def testGradientMult(self):
with self.session(use_gpu=False, graph=tf.Graph()):
p = self._testParams()
mdl = p.Instantiate()
mdl.FPropDefaultTheta()
var_grads = py_utils.ComputeGradients(mdl.loss, mdl.vars)
py_utils.ApplyGradMultiplier(var_grads, -1.1)
def testLRDecay(self):
with self.session(use_gpu=False, graph=tf.Graph()) as sess:
p = self._testParams()
|
tensorflow.Graph
| 9,020 |
import tensorflow as tf
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))
|
tensorflow.nn.moments
| 9,021 |
import tensorflow as tf
else:
direct_mask = tf.less(head_idxs, dep_idxs) # [bs, slh, sld]
# [bs, slh, slh]
rep_mask_tile = tf.logical_and(tf.expand_dims(rep_dep_mask, 1), tf.expand_dims(rep_head_mask, 2))
attn_mask = tf.logical_and(direct_mask, rep_mask_tile) # [bs, slh, sld]
# tensor tile
rep_map_tile = tf.tile(tf.expand_dims(rep_dep_tensor, 1), [1, sl_head, 1, 1]) # bs,slh,sld,vec
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias', [ivec], tf.float32, tf.constant_initializer(0.))
dependent = linear(rep_dep_tensor_dp, ivec, False, scope='linear_dependent') # bs,sld,vec
dependent_etd = tf.expand_dims(dependent, 1) # bs,1,sld,vec
head = linear(rep_head_tensor_dp, ivec, False, scope='linear_head') # bs,slh,vec
head_etd = tf.expand_dims(head, 2) # bs,slh,1,vec
|
tensorflow.expand_dims
| 9,022 |
import tensorflow as tf
config=run_config,
params={
"batch_size": FLAGS.train_batch_size
if FLAGS.do_train
else FLAGS.eval_batch_size,
},
)
if FLAGS.do_train and FLAGS.do_eval:
tf.logging.info("***** Running training *****")
tf.logging.info(" Training 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,
)
eval_input_fn = input_fn_builder(
input_files=input_files,
|
tensorflow.logging.info
| 9,023 |
import tensorflow as tf
# Open session and restore checkpoint
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
tf.train.start_queue_runners(sess)
sess.run(tf.global_variables_initializer())
|
tensorflow.train.start_queue_runners
| 9,024 |
import tensorflow as tf
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op, update_learning_rate = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, adjust_lr, use_hvd,
use_compression, use_fp16, clip, cos_decay, use_lamb, previous_train_steps, post_train_steps)
logging_hook = tf.train.LoggingTensorHook({"loss": total_loss, "learning_rate": update_learning_rate}, every_n_iter=FLAGS.hooking_frequence)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
training_hooks=[logging_hook])
elif mode == tf.estimator.ModeKeys.EVAL:
|
tensorflow.train.LoggingTensorHook
| 9,025 |
import tensorflow as tf
def _unique_chars(filename):
"""Returns the used alphabet as an array of strings."""
counts = collections.Counter()
with tf.gfile.Open(filename) as file_:
for line in file_:
counts.update(_split_string(line))
alphabet = [k for (k, _) in counts.most_common(max_size)]
alphabet.sort()
return np.asarray(alphabet, dtype=np.object)
chars, = tf.py_func(_unique_chars, [filename], [tf.string])
char_to_id = tf.contrib.lookup.index_table_from_tensor(
chars, num_oov_buckets=num_oov_buckets)
id_to_char = tf.contrib.lookup.index_to_string_table_from_tensor(chars, " ")
return char_to_id, id_to_char
def characters(filename, batch_size, sequence_size):
"""Returns a dataset of characters from the given file."""
def _to_chars(line):
"""string scalar -> Dataset of characters (string scalars)."""
chars, = tf.py_func(_split_string, [line + "\n"], [tf.string])
chars.set_shape([None])
return tf.data.Dataset.from_tensor_slices(chars)
|
tensorflow.contrib.lookup.index_to_string_table_from_tensor
| 9,026 |
import tensorflow as tf
generated_adversarial_task.append(task.goal_velocity)
logger.info('Tasks dump!')
assert (task_generator == 'fixed')
test_summary['task'].append(task.goal_velocity)
if FLAGS.task.reset_policy:
# NOTE: reset policy and valuefunc
logger.info("Resetting Policy")
pol_params = tf.get_default_session().run([nn.utils.parameters_to_vector(policy.parameters())])
tf.get_default_session().run(tf.variables_initializer(policy.parameters()))
pol_params_after = tf.get_default_session().run([nn.utils.parameters_to_vector(policy.parameters())])
print ("pol_params:", np.linalg.norm(pol_params), "pol_params_after_reset:", np.linalg.norm(pol_params_after))
logger.info("Resetting Valuefunc")
tf.get_default_session().run(tf.variables_initializer(vfn.parameters()))
tf.get_default_session().run(tf.variables_initializer(warmup_policy.parameters()))
tf.get_default_session().run(tf.variables_initializer(warmup_vfn.parameters()))
for p in warmup_policy.parameters(): p.invalidate()
for p in warmup_vfn.parameters(): p.invalidate()
for p in policy.parameters(): p.invalidate()
|
tensorflow.get_default_session
| 9,027 |
import tensorflow as tf
if layout != 'NCHW':
need_transpose = False
in_shapes = []
for layer in keras_model._input_layers:
if tf.executing_eagerly():
in_shapes.append(tuple(dim if dim is not None else 1 for dim in layer.input.shape))
else:
in_shapes.append(tuple(dim.value if dim.value is not None else 1 for dim in layer.input.shape))
|
tensorflow.executing_eagerly
| 9,028 |
import tensorflow as tf
Returns:
A Tensor of size 1, denoting the mean error between batches of quaternions.
"""
with tf.name_scope(name):
logcost = log_quaternion_loss_batch(predictions, labels)
logcost = tf.reduce_sum(logcost, [0])
|
tensorflow.name_scope
| 9,029 |
import tensorflow as tf
if encoder.position_bias and input_length is not None and time is not None:
src_pos = tf.tile(tf.expand_dims(tf.range(time_steps), axis=0), [batch_size, 1])
trg_pos = tf.tile(tf.reshape(time, [1, 1]), [batch_size, time_steps])
src_len = tf.tile(tf.expand_dims(input_length, axis=1), [1, time_steps]) # - 1
pos_feats = tf.to_float(tf.stack([src_pos, trg_pos, src_len], axis=2))
pos_feats = tf.log(1 + pos_feats)
y += dense(pos_feats, encoder.attn_size, use_bias=False, name='P_a')
if encoder.attn_filters:
filter_shape = [encoder.attn_filter_length * 2 + 1, 1, 1, encoder.attn_filters]
|
tensorflow.log
| 9,030 |
import tensorflow as tf
extra_zeros = tf.zeros((batch_size, self.max_phrase_size))
vocab_dist = tf.concat(values=[vocab_dist, extra_zeros], axis=1) # [batch_size, extended_vsize]
if self.options.add_first_word_prob_for_phrase: # add prob of the first word to each phrase
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)
shape = [batch_size, passage_length, extended_vsize]
|
tensorflow.tile
| 9,031 |
import tensorflow as tf
Args:
variables: List of variables.
deltas: List of deltas of same length.
Returns:
The step-applied operation. A tf.group of tf.assign_add ops.
"""
if len(variables) != len(deltas):
raise TensorforceError("Invalid variables and deltas lists.")
assignments = list()
for variable, delta in zip(variables, deltas):
assignments.append(tf.assign_add(ref=variable, value=delta))
with tf.control_dependencies(control_inputs=assignments):
return util.no_operation()
def tf_minimize(self, variables, **kwargs):
"""
Performs an optimization step.
Args:
variables: List of variables to optimize.
**kwargs: Additional optimizer-specific arguments. The following arguments are used
by some optimizers:
- arguments: Dict of arguments for callables, like fn_loss.
- fn_loss: A callable returning the loss of the current model.
- fn_reference: A callable returning the reference values, in case of a comparative
|
tensorflow.control_dependencies
| 9,032 |
import tensorflow as tf
image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
annotation = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="annotation")
z = tf.placeholder(tf.float32, shape=[None, 4, 4, 128], name="z")
# pred_annotation, logits = inference(image, keep_probability,z)
# tf.summary.image("input_image", image, max_outputs=2)
# tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
# tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
# loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
# labels=tf.squeeze(annotation, squeeze_dims=[3]),
# name="entropy")))
mask_ = tf.ones([FLAGS.batch_size,64,64,3])
mask = tf.pad(mask_, [[0,0],[32,32],[32,32],[0,0]])
mask2__ = tf.ones([FLAGS.batch_size,78,78,3])
mask2_ = tf.pad(mask2__, [[0,0],[25,25],[25,25],[0,0]])
mask2 = mask2_ - mask
pred_annotation, logits = inference((1-mask)*image + mask*255, keep_probability,z)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
# loss0 = tf.reduce_mean(tf.abs(z))
loss = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square((image - logits)),[1,2,3])))
|
tensorflow.pad
| 9,033 |
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]
|
tensorflow.concat
| 9,034 |
import tensorflow as tf
rot = tf.reshape(tf.gather(labeled_rotations[i], [0, 2, 6, 8]), [2, 2])
min_x = tf.cast(0.0 - labeled_sizes[i][0] / 2.0, dtype=tf.float32)
max_x = tf.cast(0.0 + labeled_sizes[i][0] / 2.0, dtype=tf.float32)
|
tensorflow.cast
| 9,035 |
import tensorflow as tf
train_op = tf.group(apply_gradient_op, variables_averages_op)
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
# Build the summary operation from the last tower summaries.
summary_op = tf.summary.merge(summaries)
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
|
tensorflow.ConfigProto
| 9,036 |
import tensorflow as tf
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
eval_metrics = (metric_fn,
[per_example_loss, label_ids, logits, is_real_example])
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=eval_metrics,
scaffold=scaffold_fn)
else:
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
predictions={"probabilities": probabilities},
scaffold=scaffold_fn
)
return output_spec
return model_fn
# This function is not used by this file but is still used by the Colab and
# people who depend on it.
def input_fn_builder(features, seq_length, is_training, drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
|
tensorflow.estimator.EstimatorSpec
| 9,037 |
import tensorflow as tf
parameters = dict(encoders=encoders[:1], decoder=decoder, encoder_inputs=encoder_inputs[:1],
other_inputs=other_inputs, training=training)
attention_states, encoder_state, encoder_input_length[:1] = multi_encoder(
encoder_input_length=encoder_input_length[:1], **parameters)
if chaining_stop_gradient:
attns = tf.stop_gradient(attns)
states = tf.stop_gradient(states)
decoder_outputs = tf.stop_gradient(decoder_outputs)
if chaining_strategy == 'concat_attns':
attention_states[0] = tf.concat([attention_states[0], attns], axis=2)
elif chaining_strategy == 'concat_states':
attention_states[0] = tf.concat([attention_states[0], states], axis=2)
elif chaining_strategy == 'sum_attns':
attention_states[0] += attns
|
tensorflow.stop_gradient
| 9,038 |
import tensorflow as tf
add = tf.add(in0, in1, "ADD")
sub = tf.subtract(in0, in1, "SUB")
# Cast or convert result to the output dtype.
if tf_output0_dtype == tf.string:
cast0 = tf.dtypes.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.dtypes.as_string(sub if not swap else add, name="TOSTR1")
|
tensorflow.dtypes.as_string
| 9,039 |
import tensorflow as tf
"""Build the inference graph using CUDNN cell."""
inputs = tf.transpose(inputs, [1, 0, 2])
|
tensorflow.transpose
| 9,040 |
import tensorflow as tf
shapes = {movielens.USER_COLUMN: tf.TensorShape([batch_size]),
movielens.ITEM_COLUMN: tf.TensorShape([batch_size])}
|
tensorflow.TensorShape
| 9,041 |
import tensorflow as tf
x = tf.layers.dense(x, size, activation=tf.nn.relu)
value = tf.layers.dense(x, 1)[..., 0]
mean = tf.check_numerics(mean, "mean")
logstd = tf.check_numerics(logstd, "logstd")
value = tf.check_numerics(value, "value")
policy = tfp.distributions.MultivariateNormalDiag(mean, tf.exp(logstd))
return NetworkOutput(policy, value, lambda a: tf.clip_by_value(a, -2., 2))
def clip_logits(logits, config):
|
tensorflow.exp
| 9,042 |
import tensorflow as tf
have_data = True
load_data_during_test()
logger.info(f'Load the task{snapshot} saver!')
saver.load_state_dict(np.load(f'./{setting}/{taskname}.task{snapshot}.saver.npy', allow_pickle=True)[()])
logger.info('Update all copies! (lazymodel, normalizers_copy)')
tf.get_default_session().run(sync_model_to_lazymodel)
tf.get_default_session().run(copy_normalizers)
logger.info('Loaded normalizers:')
load_norm = tf.get_default_session().run(normalizers_parameters)
logger.info(load_norm)
TASK_NUM = train_tasknum
TEST_TASK_NUM = 0
########################## debug #########################
|
tensorflow.get_default_session
| 9,043 |
import tensorflow as tf
output: (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
the ranking score of the corresponding example.
labels: (tf.Tensor) A tensor of the same shape as `output`. A value >= 1 means a
relevant example.
propensity_weights: (tf.Tensor) A tensor of the same shape as `output` containing the weight of each element.
name: A string used as the name for this variable scope.
Returns:
(tf.Tensor) A single value tensor containing the loss.
"""
loss = None
with tf.name_scope(name, "click_softmax_cross_entropy",[output]):
label_dis = labels*propensity_weights / tf.reduce_sum(labels*propensity_weights, 1, keep_dims=True)
loss = tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=label_dis) * tf.reduce_sum(labels*propensity_weights, 1)
return tf.reduce_sum(loss) / tf.reduce_sum(labels*propensity_weights)
def click_loglikelihood(self, labels, propensity,train_output, name=None):
"""Computes listwise softmax loss with propensity weighting.
Args:
output: (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
the ranking score of the corresponding example.
labels: (tf.Tensor) A tensor of the same shape as `output`. A value >= 1 means a
relevant example.
propensity_weights: (tf.Tensor) A tensor of the same shape as `output` containing the weight of each element.
name: A string used as the name for this variable scope.
|
tensorflow.nn.softmax_cross_entropy_with_logits
| 9,044 |
import tensorflow as tf
self.MINIBATCH = 64
self.EPOCHS = 8
self.EPSILON = 0.2
self.EPS_LEN = 100000
# GPU setup
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, device_count={'GPU': gpu})
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
# Placeholders
self.sess = tf.Session(config=config)
self.s_dim, self.a_dim = env.observation_space.shape, env.action_space.shape[0]
self.a_bound = (env.action_space.high - env.action_space.low) / 2
self.actions = tf.placeholder(tf.float32, [None, self.a_dim], 'action')
self.state = tf.placeholder(tf.float32, [None, self.s_dim[0]], 'state')
self.advantage = tf.placeholder(tf.float32, [None, 1], 'advantage')
self.rewards = tf.placeholder(tf.float32, [None, 1], 'discounted_r')
# Dateset with experiennce replay
self.dataset = tf.data.Dataset.from_tensor_slices({'state': self.state, 'actions': self.actions,
'rewards': self.rewards, 'advantage': self.advantage})
self.dataset = self.dataset.shuffle(buffer_size=10000)
self.dataset = self.dataset.batch(self.MINIBATCH)
self.dataset = self.dataset.cache()
self.dataset = self.dataset.repeat(self.EPOCHS)
self.data_iter = self.dataset.make_initializable_iterator()
batch = self.data_iter.get_next()
# Call ppo net
pi_old, pi_old_params = self.build_anet(batch['state'], 'oldpi')
pi, pi_params = self.build_anet(batch['state'], 'pi')
pi_eval, _ = self.build_anet(self.state, 'pi', reuse=True)
|
tensorflow.placeholder
| 9,045 |
import tensorflow as tf
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
|
tensorflow.logging.info
| 9,046 |
from tensorflow.python.framework import ops
in which case a different quantized type may be used.
data_format: A string. 'NHWC' and 'NCHW" are supported.
name: A name for the operation (optional).
Returns:
A `Tensor` with the same type as `value`.
"""
with ops.op_scope([value, bias], name, "BiasAdd") as name:
value = ops.convert_to_tensor(value, name="input")
bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
return gen_nn_ops._bias_add(value, bias, data_format=data_format, name=name)
ops.RegisterShape("BiasAdd")(common_shapes.bias_add_shape)
|
tensorflow.python.framework.ops.op_scope
| 9,047 |
import tensorflow as tf
def main(job_dir, **args):
##Setting up the path for saving logs
logs_dir = job_dir + 'logs/'
data_dir = "gs://deeplearningteam11/data"
print("Current Directory: " + os.path.dirname(__file__))
print("Lets copy the data to: " + os.path.dirname(__file__))
os.system("gsutil -m cp -r " + data_dir + " " + os.path.dirname(__file__) + " > /dev/null 2>&1 " )
#exit(0)
with tf.device('/device:GPU:0'):
# 1: Build the Keras model.
K.clear_session() # Clear previous models from memory.
model = ssd_300(image_size=(img_height, img_width, img_channels),
n_classes=n_classes,
mode='training',
l2_regularization=0.0005,
scales=scales,
aspect_ratios_per_layer=aspect_ratios,
two_boxes_for_ar1=two_boxes_for_ar1,
|
tensorflow.device
| 9,048 |
import tensorflow as tf
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)
|
tensorflow.shape
| 9,049 |
import tensorflow as tf
|
tensorflow.reduce_sum
| 9,050 |
import tensorflow as tf
self.loss_func = self.click_weighted_softmax_cross_entropy_loss
elif self.hparams.loss_func == 'click_weighted_log_loss':
self.loss_func = self.click_weighted_log_loss
elif self.hparams.loss_func == 'click_weighted_pairwise_loss':
self.loss_func = self.click_weighted_pairwise_loss
else: # softmax loss without weighting
self.loss_func = self.softmax_loss
# Compute rank loss
reshaped_train_labels = tf.transpose(tf.convert_to_tensor(train_labels)) # reshape from [rank_list_size, ?] to [?, rank_list_size]
self.propensity_weights = self.get_normalized_weights(self.logits_to_prob(self.propensity))
self.rank_loss = self.loss_func(train_output, reshaped_train_labels, self.propensity_weights)
pw_list = tf.unstack(self.propensity_weights, axis=1) # Compute propensity weights
self.click_metrics=self.click_loglikelihood(reshaped_train_labels,\
self.propensity,train_output)
tf.summary.scalar('click_metrics',self.click_metrics,collections=['train'])
for i in range(len(pw_list)):
tf.summary.scalar('Inverse Propensity weights %d' % i, tf.reduce_mean(pw_list[i]), collections=['train'])
tf.summary.scalar('Rank Loss', tf.reduce_mean(self.rank_loss), collections=['train'])
# Compute examination loss
self.relevance_weights = self.get_normalized_weights(self.logits_to_prob(train_output))
self.exam_loss = self.loss_func(self.propensity, reshaped_train_labels, self.relevance_weights)
rw_list = tf.unstack(self.relevance_weights, axis=1) # Compute propensity weights
|
tensorflow.unstack
| 9,051 |
import tensorflow as tf
if use_bfloat16:
preprocessed_resized_image = tf.cast(
preprocessed_resized_image, tf.bfloat16)
tensor_dict[fields.InputDataFields.image] = tf.squeeze(
preprocessed_resized_image, axis=0)
tensor_dict[fields.InputDataFields.true_image_shape] = tf.squeeze(
|
tensorflow.squeeze
| 9,052 |
from tensorflow.python.framework import tensor_shape
return_dims.append(dim_x)
elif dim_x.value == dim_y.value:
# The dimensions are compatible, so output is the same size in that
# dimension.
return_dims.append(dim_x.merge_with(dim_y))
else:
raise ValueError("Incompatible shapes for broadcasting: %s and %s"
% (shape_x, shape_y))
return [tensor_shape.TensorShape(return_dims)]
@ops.RegisterShape("AddN")
def _AddNShape(op):
merged_shape = tensor_shape.unknown_shape()
for input_ in op.inputs:
merged_shape = merged_shape.merge_with(input_.get_shape())
return [merged_shape]
@ops.RegisterShape("Select")
def _SelectShape(op):
# All three inputs must have the same shape.
return [op.inputs[0].get_shape()
.merge_with(op.inputs[1].get_shape())
.merge_with(op.inputs[2].get_shape())]
|
tensorflow.python.framework.tensor_shape.unknown_shape
| 9,053 |
import tensorflow as tf
cost = rewards ** 2
cost = tf.reduce_sum(cost * weights, axis=1)
|
tensorflow.reduce_sum
| 9,054 |
import tensorflow as tf
[0, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 1]],
dtype=tf.float32)
masks = tf.stack([mask0, mask1, mask2])
return masks
def test_map_labels_to_0_to_n1(self):
labels = tf.constant([[-1, 2, 5],
[0, 9, 1]], dtype=tf.int32)
labels_0_n = isu.map_labels_to_0_to_n(labels)
expected_labels_0_n = tf.constant([[-1, 2, 3],
[0, 4, 1]], dtype=tf.int32)
self.assertAllEqual(labels_0_n.numpy(), expected_labels_0_n.numpy())
def test_map_labels_to_0_to_n2(self):
labels = tf.constant([[-1, 1, 2],
[1, 1, 2]], dtype=tf.int32)
labels_0_n = isu.map_labels_to_0_to_n(labels)
expected_labels_0_n = tf.constant([[-1, 0, 1],
[0, 0, 1]], dtype=tf.int32)
|
tensorflow.constant
| 9,055 |
import tensorflow as tf
#rf = 1,nx=emb,nf=3*emb
w = tf.get_variable("w", [rf, nx, nf], initializer=w_init)
b = tf.get_variable("b", [nf], initializer=b_init)
if rf == 1: #faster 1x1 conv
c = tf.reshape(tf.matmul(tf.reshape(x, [-1, nx]), tf.reshape(w, [-1, nf]))+b, shape_list(x)[:-1]+[nf])
else: #was used to train LM
c = tf.nn.conv1d(x, w, stride=1, padding=pad)+b
return c
def attn(x, scope, n_state, n_head, train=False, scale=False):
assert n_state%n_head==0
with tf.variable_scope(scope):
#c [-1,n_ctx,3*emb]
c = conv1d(x, 'c_attn', n_state*3, 1, train=train)
#q,k,v [-1,n_ctx,emb]
q, k, v = tf.split(c, 3, 2)
#q [-1,head,n_ctx,emb] v [-1,head,emb,n_ctx] v [-1,head,n_ctx,emb]
q = split_heads(q, n_head)
k = split_heads(k, n_head, k=True)
v = split_heads(v, n_head)
#a [-1,head,n_ctx,emb]
a = _attn(q, k, v, train=train, scale=scale)
#a [-1,n_ctx,head,emb]
a = merge_heads(a)
#a [-1,n_ctx,emb]
a = conv1d(a, 'c_proj', n_state, 1, train=train)
a = dropout(a, resid_pdrop, train)
return a
def mlp(x, scope, n_state, train=False):
|
tensorflow.split
| 9,056 |
import tensorflow as tf
import numpy as np
import tensorflow as tf
from collections import deque
def sample(logits):
noise = tf.random_uniform(tf.shape(logits))
return tf.argmax(logits - tf.log(-tf.log(noise)), 1)
def cat_entropy(logits):
a0 = logits - tf.reduce_max(logits, 1, keepdims=True)
ea0 = tf.exp(a0)
z0 = tf.reduce_sum(ea0, 1, keepdims=True)
p0 = ea0 / z0
return tf.reduce_sum(p0 * (tf.log(z0) - a0), 1)
def cat_entropy_softmax(p0):
return - tf.reduce_sum(p0 * tf.log(p0 + 1e-6), axis = 1)
|
tensorflow.reduce_max
| 9,057 |
from tensorflow.python.framework import constant_op
@property
def beta(self):
"""Scale parameter."""
return self._beta
def _batch_shape(self):
return array_ops.broadcast_dynamic_shape(
array_ops.shape(self.alpha), array_ops.shape(self.beta))
def _get_batch_shape(self):
return array_ops.broadcast_static_shape(
self.alpha.get_shape(), self.beta.get_shape())
def _event_shape(self):
return constant_op.constant([], dtype=dtypes.int32)
def _get_event_shape(self):
return tensor_shape.scalar()
def _sample_n(self, n, seed=None):
"""See the documentation for tf.random_gamma for more details."""
return 1. / random_ops.random_gamma([n], self.alpha, beta=self.beta,
dtype=self.dtype, seed=seed)
def _log_prob(self, x):
x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
self.validate_args else [], x)
return (self.alpha * math_ops.log(self.beta) -
math_ops.lgamma(self.alpha) -
|
tensorflow.python.framework.constant_op.constant
| 9,058 |
import tensorflow as tf
# ratio decay when mode = "scheduled"
gradient_norms = [tf.norm(grad) for grad in model.gradients]
tf.summary.histogram("gradient_norm", gradient_norms)
tf.summary.scalar("max_gradient_norm", tf.reduce_max(gradient_norms)) # visualize
# gradients (in case of explosion)
return tf.summary.merge_all()
|
tensorflow.reduce_max
| 9,059 |
import tensorflow as tf
else:
return tf.assert_equal(shape_a, shape_b)
|
tensorflow.assert_equal
| 9,060 |
import tensorflow as tf
def get_conv_dimension(filter_list):
with tf.Graph().as_default():
with tf.Session() as sess:
|
tensorflow.Graph
| 9,061 |
from tensorflow.python.ops import array_ops
self._target_column.num_label_columns)],
array_ops.reshape(centered_bias, [-1]))
|
tensorflow.python.ops.array_ops.reshape
| 9,062 |
import tensorflow as tf
sess.run(zero_similar.initializer)
print(sess.run(ones_similar))
print(sess.run(zero_similar))
fill_var = tf.Variable(tf.fill([row_dim, col_dim], -1))
sess.run(fill_var.initializer)
print(sess.run(fill_var))
const_var = tf.Variable(tf.constant([8, 6, 7, 5, 3, 0, 9]))
const_fill_var = tf.Variable(tf.constant(-1, shape=[row_dim, col_dim]))
sess.run(const_var.initializer)
sess.run(const_fill_var.initializer)
print(sess.run(const_var))
print(sess.run(const_fill_var))
linear_var = tf.Variable(tf.linspace(start=0.0, stop=1.0, num=3)) # Generates [0.0, 0.5, 1.0] includes the end
sequence_var = tf.Variable(tf.range(start=6, limit=15, delta=3)) # Generates [6, 9, 12] doesn't include the end
sess.run(linear_var.initializer)
sess.run(sequence_var.initializer)
print(sess.run(linear_var))
print(sess.run(sequence_var))
rnorm_var = tf.random_normal([row_dim, col_dim], mean=0.0, stddev=1.0)
runif_var = tf.random_uniform([row_dim, col_dim], minval=0, maxval=4)
print(sess.run(rnorm_var))
print(sess.run(runif_var))
ops.reset_default_graph()
sess = tf.Session()
my_var = tf.Variable(tf.zeros([1,20]))
|
tensorflow.linspace
| 9,063 |
import tensorflow as tf
y1_f = tf.to_float(y1)
z0_f = tf.to_float(z0)
z1_f = tf.to_float(z1)
# Check the out-of-boundary case.
x0_valid = tf.to_float(
tf.less_equal(x0, max_x) & tf.greater_equal(x0, 0))
x1_valid = tf.to_float(
tf.less_equal(x1, max_x) & tf.greater_equal(x1, 0))
y0_valid = tf.to_float(
tf.less_equal(y0, max_y) & tf.greater_equal(y0, 0))
y1_valid = tf.to_float(
tf.less_equal(y1, max_y) & tf.greater_equal(y1, 0))
z0_valid = tf.to_float(
tf.less_equal(z0, max_z) & tf.greater_equal(z0, 0))
z1_valid = tf.to_float(
tf.less_equal(z1, max_z) & tf.greater_equal(z1, 0))
w_z0_y0_x0 = tf.expand_dims(((x1_f - x) * (y1_f - y) *
|
tensorflow.less_equal
| 9,064 |
import tensorflow as tf
self.char_mat = tf.get_variable(
"char_mat", initializer=tf.constant(char_mat, dtype=tf.float32))
self.c_mask = tf.cast(self.c, tf.bool)
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
if opt:
# we have to hardcode the max batch size here! use the batch size from the generator as this will be used for PG
N, CL = config.batch_size if not self.demo else config.batch_size, config.char_limit
self.c_maxlen = tf.reduce_max(self.c_len)
self.q_maxlen = tf.reduce_max(self.q_len)
self.c = tf.slice(self.c, [0, 0], [N, self.c_maxlen])
self.q = tf.slice(self.q, [0, 0], [N, self.q_maxlen])
self.c_mask = tf.slice(self.c_mask, [0, 0], [N, self.c_maxlen])
self.q_mask = tf.slice(self.q_mask, [0, 0], [N, self.q_maxlen])
self.ch = tf.slice(self.ch, [0, 0, 0], [N, self.c_maxlen, CL])
self.qh = tf.slice(self.qh, [0, 0, 0], [N, self.q_maxlen, CL])
self.y1 = tf.argmax(tf.slice(self.y1, [0, 0], [N, self.c_maxlen]),axis=-1)
self.y2 = tf.argmax(tf.slice(self.y2, [0, 0], [N, self.c_maxlen]),axis=-1)
else:
self.c_maxlen, self.q_maxlen = config.para_limit, config.ques_limit
|
tensorflow.reduce_max
| 9,065 |
import tensorflow as tf
use_xavier=use_xavier,
stddev=stddev,
wd=weight_decay)
stride_h, stride_w = stride
# from slim.convolution2d_transpose
def get_deconv_dim(dim_size, stride_size, kernel_size, padding):
dim_size *= stride_size
if padding == 'VALID' and dim_size is not None:
dim_size += max(kernel_size - stride_size, 0)
return dim_size
# caculate output shape
batch_size = tf.shape(inputs)[0]
height = tf.shape(inputs)[1]
width = tf.shape(inputs)[2]
out_height = get_deconv_dim(height, stride_h, kernel_h, padding)
out_width = get_deconv_dim(width, stride_w, kernel_w, padding)
output_shape = tf.stack([batch_size, out_height, out_width, num_output_channels], axis=0)
outputs = tf.nn.conv2d_transpose(inputs, kernel, output_shape,
[1, stride_h, stride_w, 1],
padding=padding)
biases = _variable_on_cpu('biases', [num_output_channels],
tf.constant_initializer(0.0))
outputs = tf.nn.bias_add(outputs, biases)
|
tensorflow.shape
| 9,066 |
import tensorflow as tf
"eval_accuracy": accuracy,
"eval_loss": loss,
}
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:
|
tensorflow.contrib.tpu.TPUEstimatorSpec
| 9,067 |
import tensorflow as tf
logcost = log_quaternion_loss_batch(predictions, labels)
logcost = tf.reduce_sum(logcost, [0])
logcost = tf.multiply(logcost, 1.0 / batch_size, name='log_quaternion_loss')
return logcost
|
tensorflow.multiply
| 9,068 |
import tensorflow as tf
with tf.variable_scope(scope, reuse=reuse):
observations_ph = make_obs_ph("observation")
stochastic_ph = tf.placeholder(tf.bool, (), name="stochastic")
update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
update_param_noise_threshold_ph = tf.placeholder(tf.float32, (), name="update_param_noise_threshold")
update_param_noise_scale_ph = tf.placeholder(tf.bool, (), name="update_param_noise_scale")
reset_ph = tf.placeholder(tf.bool, (), name="reset")
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
param_noise_scale = tf.get_variable("param_noise_scale", (), initializer=tf.constant_initializer(0.01), trainable=False)
param_noise_threshold = tf.get_variable("param_noise_threshold", (), initializer=tf.constant_initializer(0.05), trainable=False)
# Unmodified Q.
|
tensorflow.placeholder
| 9,069 |
import tensorflow as tf
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_train_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
tf.gfile.MakeDirs(FLAGS.output_dir)
if FLAGS.recover_dir is not None:
if FLAGS.use_hvd:
FLAGS.recover_dir = FLAGS.recover_dir if hvd.rank() == 0 else os.path.join(FLAGS.recover_dir, str(hvd.rank()))
path_ckpt = os.path.join(FLAGS.output_dir, "checkpoint")
path_ckpt_input = os.path.join(FLAGS.output_dir, "checkpoint_input")
if FLAGS.ckpt_no is not None and not tf.gfile.Exists(path_ckpt):
with tf.gfile.GFile(path_ckpt, "w") as writer:
writer.write('model_checkpoint_path: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "model.ckpt"), str(FLAGS.ckpt_no)))
writer.write('all_model_checkpoint_paths: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "model.ckpt"), str(FLAGS.ckpt_no)))
if FLAGS.ckpt_no_input is not None and not tf.gfile.Exists(path_ckpt_input):
with tf.gfile.GFile(path_ckpt_input, "w") as writer:
writer.write('model_checkpoint_path: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "input.ckpt"), str(FLAGS.ckpt_no_input)))
writer.write('all_model_checkpoint_paths: "%s-%s"\n' % (os.path.join(FLAGS.recover_dir, "input.ckpt"), str(FLAGS.ckpt_no_input)))
if FLAGS.use_hvd and hvd.rank() == 0 and (FLAGS.do_train or FLAGS.do_train_eval):
(cpath, cname) = os.path.split(FLAGS.bert_config_file)
tf.gfile.Copy(FLAGS.bert_config_file, os.path.join(FLAGS.output_dir, cname), True)
|
tensorflow.gfile.Exists
| 9,070 |
import tensorflow as tf
rotations_y = tf.reshape(rotations_y, [-1, 1])
labeled_boxes = tf.concat([sample['translations_3d'],
sample['sizes_3d'],
rotations_y], axis=1)
# Get predicted boxes
predicted_boxes = detections['detection_boxes']
if metric.threed:
rotations_y = tf.concat([tf_utils.euler_from_rotation_matrix(
tf.reshape(detections['rotations_3d'][i], [3, 3]),
1) for i in range(num_boxes)], axis=0)
rotations_y = tf.reshape(rotations_y, [-1, 1])
predicted_boxes = tf.concat([detections['translations_3d'],
detections['sizes_3d'],
rotations_y], axis=1)
labeled_classes = tf.cast(sample['groundtruth_valid_classes'], tf.int64)
predicted_classes = tf.cast(detections['detection_classes'], tf.int64)
|
tensorflow.reshape
| 9,071 |
import tensorflow as tf
# pylint: disable=g-long-lambda
@test_case.named_parameters(
dict(
testcase_name='fixed_len_int',
make_tensors_fn=lambda:
{'x': tf.compat.v1.placeholder(tf.int64, (None,))},
feature_spec={'x': tf.io.FixedLenFeature([], tf.int64)}),
dict(
testcase_name='fixed_len_string',
make_tensors_fn=lambda:
{'x': tf.compat.v1.placeholder(tf.string, (None,))},
feature_spec={'x': tf.io.FixedLenFeature([], tf.string)}),
dict(
testcase_name='fixed_len_float',
make_tensors_fn=lambda:
{'x': tf.compat.v1.placeholder(tf.float32, (None,))},
feature_spec={'x': tf.io.FixedLenFeature([], tf.float32)}),
dict(
testcase_name='override',
make_tensors_fn=_make_tensors_with_override,
feature_spec={'x': tf.io.FixedLenFeature([], tf.int64)},
domains={'x': schema_pb2.IntDomain(is_categorical=True)}),
|
tensorflow.io.FixedLenFeature
| 9,072 |
import tensorflow as tf
rep_tensor_comp = tf.concat([rep_tensor, tf.zeros([bs, comp_len, input_dim], tf.float32)], 1)
rep_mask_comp = tf.concat([rep_mask, tf.cast(tf.zeros([bs, comp_len], tf.int32), tf.bool)], 1)
rep_tensor_split = tf.reshape(rep_tensor_comp, [bs, block_num, block_len, input_dim]) # bs,bn,bl,d
rep_mask_split = tf.reshape(rep_mask_comp, [bs, block_num, block_len]) # bs,bn,bl
# non-linear
rep_map = bn_dense_layer(rep_tensor_split, ivec, True, 0., 'bn_dense_map', activation,
False, wd, keep_prob, is_train) # bs,bn,bl,vec
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 2), [1, 1, block_len, 1, 1]) # bs,bn,bl,bl,vec
# rep_map_dp = dropout(rep_map, keep_prob, is_train)
bn = block_num
bl = block_len
with tf.variable_scope('self_attention'):
# @2.self-attention in block
# mask generation
sl_indices = tf.range(block_len, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col) # bl,bl
else:
direct_mask = tf.greater(sl_col, sl_row) # bl,bl
direct_mask_tile = tf.tile(
tf.expand_dims(tf.expand_dims(direct_mask, 0), 0), [bs, bn, 1, 1]) # bs,bn,bl,bl
rep_mask_tile_1 = tf.tile(tf.expand_dims(rep_mask_split, 2), [1, 1, bl, 1]) # bs,bn,bl,bl
rep_mask_tile_2 = tf.tile(tf.expand_dims(rep_mask_split, 3), [1, 1, 1, bl]) # bs,bn,bl,bl
rep_mask_tile = tf.logical_and(rep_mask_tile_1, rep_mask_tile_2)
|
tensorflow.variable_scope
| 9,073 |
import tensorflow as tf
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)
c = f*c + i*u
h = o*tf.tanh(c)
xs[idx] = h
s = tf.concat(axis=1, values=[c, h])
return xs, s
def _ln(x, g, b, e=1e-5, axes=[1]):
u, s = tf.nn.moments(x, axes=axes, keep_dims=True)
x = (x-u)/tf.sqrt(s+e)
x = x*g+b
return x
|
tensorflow.nn.sigmoid
| 9,074 |
import tensorflow as tf
s_h0, s_h1, s_h2, s_h3 = \
int(s_h/ns0), int(s_h/ns0/ns1), int(s_h/ns0/ns1/ns2), int(s_h/ns0/ns1/ns2/ns3)
s_w0, s_w1, s_w2, s_w3 = \
int(s_w/ns0), int(s_w/ns0/ns1), int(s_w/ns0/ns1/ns2), int(s_w/ns0/ns1/ns2/ns3)
def decode(z, skip_h3, skip_h2, skip_h1, skip_h0):
z_ = lrelu(linear(tf.nn.dropout(z, keep_prob), nf3*s_h3*s_w3, 'd_h0_lin'))
h0 = tf.nn.dropout(tf.reshape(z_, [-1, s_h3, s_w3, nf3]), keep_prob)
import IPython
IPython.embed()
h1 = lrelu(deconv2d(tf.concat([h0, skip_h3], 3),
[self.batch_size, s_h2, s_w2, nf2], name='d_h1', d_h=ns3, d_w=ns3))
h2 = lrelu(deconv2d(tf.concat([h1, skip_h2], 3),
[self.batch_size, s_h1, s_w1, nf1], name='d_h2', d_h=ns2, d_w=ns2))
|
tensorflow.reshape
| 9,075 |
import tensorflow as tf
x2, ldj = log_gaussianize(z2, mus, log_sigmas, inverse=tf.constant(True))
x2 = tf.where(z2 > self.epsilon, x2, z2)
ldj = tf.where(z2 > self.epsilon, ldj, tf.zeros_like(ldj))
return x2, tf.math.reduce_sum(ldj, axis=[1,2,3])
|
tensorflow.zeros_like
| 9,076 |
import tensorflow as tf
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
if clip:
log_probs = tf.log(tf.clip_by_value(tf.nn.softmax(logits, axis=-1), 1e-6, 1.0 - 1e-6))
else:
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
|
tensorflow.reshape
| 9,077 |
import tensorflow as tf
# Create the policy
# first return value corresponds to deterministic actions
# policy_out corresponds to stochastic actions, used for training
# logp_pi is the log probability of actions taken by the policy
self.deterministic_action, policy_out, logp_pi = self.policy_tf.make_actor(self.processed_obs_ph)
# Monitor the entropy of the policy,
# this is not used for training
self.entropy = tf.reduce_mean(self.policy_tf.entropy)
self.obs_ph, self.actions_ph, self.deterministic_actions_ph = self._get_pretrain_placeholders()
# Use two Q-functions to improve performance by reducing overestimation bias.
qf1, qf2, value_fn = self.policy_tf.make_critics(self.processed_obs_ph, self.actions_ph,
create_qf=True, create_vf=True)
qf1_pi, qf2_pi, _ = self.policy_tf.make_critics(self.processed_obs_ph,
|
tensorflow.reduce_mean
| 9,078 |
import tensorflow as tf
eps = 1e-6
# Notation: (var) = batch variable, (var)s = sequence variable,
# (var)_i = variable index by action at step i
# shape is [n_envs * (n_steps + 1)]
if continuous:
value = train_model.value_flat
else:
value = tf.reduce_sum(train_model.policy_proba * train_model.q_value, axis=-1)
rho, rho_i_ = None, None
if continuous:
action_ = strip(train_model.proba_distribution.sample(), self.n_envs, self.n_steps)
distribution_f = tf.contrib.distributions.MultivariateNormalDiag(
loc=strip(train_model.proba_distribution.mean, self.n_envs, self.n_steps),
scale_diag=strip(train_model.proba_distribution.logstd, self.n_envs, self.n_steps))
|
tensorflow.reduce_sum
| 9,079 |
import tensorflow as tf
def state_update_aggregation_modes(self):
"""See base class."""
return {
self.NORM_STATE_UPDATE_KEY: encoding_stage.StateAggregationMode.STACK
}
def initial_state(self):
"""See base class."""
return {self.FACTOR_STATE_KEY: tf.constant(1.0)}
# pylint: disable=g-doc-args,g-doc-return-or-yield
def update_state(self, state, state_update_tensors):
"""Updates the state (see base class).
This method illustrates how the implementation can handle state update based
on a single encoding, or based on a multiple encodings collectively.
|
tensorflow.constant
| 9,080 |
import tensorflow as tf
>>> samples.dtype
dtype('float64')
>>> m.dtype = tf.float32
>>> samples = m.compute_prior_samples(test_points, 1, 2)
>>> samples.dtype
dtype('float32')
"""
mu, var = self.build_prior_mean_var(test_points, num_latent, True)
jitter = tfhacks.eye(tf.shape(mu)[0], var.dtype) * 1e-06
L = tf.batch_cholesky(tf.transpose(var, (2, 0, 1)) + jitter)
V_shape = [tf.shape(L)[0], tf.shape(L)[1], num_samples]
V = tf.random_normal(V_shape, dtype=L.dtype)
samples = tf.expand_dims(tf.transpose(mu), -1) + tf.batch_matmul(L, V)
return tf.transpose(samples)
@autoflow((tf.float64, [None, None]), (tf.float64, [None, None]),
(tf.float64, [None, None]))
def compute_posterior_mean_var(self, X, Y, test_points):
"""Computes the means and variances of the posterior(s).
This is just an autoflowed version of
|
tensorflow.shape
| 9,081 |
from tensorflow.python.framework import tensor_shape
filter_shape = tensor_util.constant_value(op.inputs[1])
if filter_shape is not None:
return [tensor_shape.TensorShape(filter_shape.tolist())]
else:
return [tensor_shape.unknown_shape(ndims=4)]
@ops.RegisterShape("DepthwiseConv2dNativeBackpropInput")
def _DepthwiseConv2dNativeBackpropInputShape(op):
"""Shape function for the DepthwiseConv2dNativeBackpropInput op."""
input_shape = tensor_util.constant_value(op.inputs[0])
if input_shape is not None:
return [tensor_shape.TensorShape(input_shape.tolist())]
else:
return [tensor_shape.unknown_shape(ndims=4)]
@ops.RegisterShape("MaxPoolGrad")
@ops.RegisterShape("MaxPoolGradWithArgmax")
def _MaxPoolGradShape(op):
"""Shape function for the MaxPoolGrad op."""
orig_input_shape = op.inputs[0].get_shape().with_rank(4)
return [orig_input_shape]
@ops.RegisterStatistics("Conv2D", "flops")
def _calc_conv_flops(graph, node):
"""Calculates the compute resources needed for Conv2D."""
|
tensorflow.python.framework.tensor_shape.unknown_shape
| 9,082 |
import tensorflow as tf
n_lstm_layers = self.options['lstm'].get('n_layers', 1)
cell_clip = self.options['lstm'].get('cell_clip')
proj_clip = self.options['lstm'].get('proj_clip')
use_skip_connections = self.options['lstm']['use_skip_connections']
if use_skip_connections:
print("USING SKIP CONNECTIONS")
else:
print("NOT USING SKIP CONNECTIONS")
# the sequence lengths from input mask
if self.use_character_inputs:
mask = tf.reduce_any(self.ids_placeholder > 0, axis=2)
else:
mask = self.ids_placeholder > 0
sequence_lengths = tf.reduce_sum(tf.cast(mask, tf.int32), axis=1)
batch_size = tf.shape(sequence_lengths)[0]
# for each direction, we'll store tensors for each layer
self.lstm_outputs = {'forward': [], 'backward': []}
self.lstm_state_sizes = {'forward': [], 'backward': []}
self.lstm_init_states = {'forward': [], 'backward': []}
self.lstm_final_states = {'forward': [], 'backward': []}
update_ops = []
for direction in ['forward', 'backward']:
if direction == 'forward':
layer_input = self.embedding
else:
|
tensorflow.cast
| 9,083 |
import tensorflow as tf
surrogate_type: Either 'xent' or 'hinge', specifying which upper bound
should be used for indicator functions.
Returns:
A `Tensor` of shape [num_labels] or [num_labels, num_anchors].
"""
maybe_log2 = tf.log(2.0) if surrogate_type == 'xent' else 1.0
maybe_log2 = tf.cast(maybe_log2, logits.dtype.base_dtype)
loss_on_negatives = losses_utils.weighted_surrogate_loss(
labels, logits, surrogate_type, positive_weights=0.0) / maybe_log2
return tf.reduce_sum(weights * loss_on_negatives, 0)
|
tensorflow.cast
| 9,084 |
import tensorflow as tf
images = tf.reshape(images, shape=images_shape)
gpu_compute_stage_ops.append(gpu_compute_stage_op)
else:
# Minor hack to avoid H2D copy when using synthetic data
images = tf.truncated_normal(
host_images.get_shape(),
dtype=input_data_type,
stddev=1e-1,
name='synthetic_images')
images = tf.contrib.framework.local_variable(
images, name='gpu_cached_images')
labels = host_labels
with tf.device(self.devices[device_num]):
# Rescale to [0, 1)
images *= 1. / 256
# Rescale to [-1,1] instead of [0, 1)
images = tf.subtract(images, 0.5)
images = tf.multiply(images, 2.0)
if self.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
if input_data_type != data_type:
images = tf.cast(images, data_type)
network = ConvNetBuilder(
images, input_nchan, phase_train, self.data_format, data_type)
|
tensorflow.device
| 9,085 |
import tensorflow as tf
self.conv3 = tf.layers.conv2d(self.pool2,
self.config.cifar10_cnn["num_filters"],
self.config.cifar10_cnn["filter_size"],
padding='same', activation=tf.nn.relu)
self.pool3 = tf.layers.max_pooling2d(self.conv3, 2, 2)
self.conv4 = tf.layers.conv2d(self.pool3,
self.config.cifar10_cnn["num_filters"],
self.config.cifar10_cnn["filter_size"],
padding='same', activation=tf.nn.relu)
self.drop3 = tf.layers.dropout(self.conv4, self.config.cifar10_cnn["keep_prob"], training=self.train)
|
tensorflow.layers.conv2d
| 9,086 |
import tensorflow as tf
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state('./model_pretrain')
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("loading checkpoint...")
saver.restore(sess, ckpt.model_checkpoint_path)
|
tensorflow.train.get_checkpoint_state
| 9,087 |
import tensorflow as tf
if rf == 1: #faster 1x1 conv
c = tf.reshape(tf.matmul(tf.reshape(x, [-1, nx]), tf.reshape(w, [-1, nf]))+b, shape_list(x)[:-1]+[nf])
else: #was used to train LM
c = tf.nn.conv1d(x, w, stride=1, padding=pad)+b
return c
def attn(x, scope, n_state, n_head, train=False, scale=False):
assert n_state%n_head==0
with tf.variable_scope(scope):
#c [-1,n_ctx,3*emb]
c = conv1d(x, 'c_attn', n_state*3, 1, train=train)
#q,k,v [-1,n_ctx,emb]
q, k, v = tf.split(c, 3, 2)
#q [-1,head,n_ctx,emb] v [-1,head,emb,n_ctx] v [-1,head,n_ctx,emb]
q = split_heads(q, n_head)
k = split_heads(k, n_head, k=True)
|
tensorflow.variable_scope
| 9,088 |
import tensorflow as tf
'near_surface_samples': _float_feature(d['near_surface_samples']),
'grid': _float_feature(d['grid']),
'world2grid': _float_feature(d['world2grid']),
'surface_point_samples': _float_feature(d['surface_point_samples'])
}
example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
return example_proto.SerializeToString()
def full_featurespec():
return {
'bounding_box_samples': tf.io.FixedLenFeature([100000, 4], tf.float32),
'depth_renders': tf.io.FixedLenFeature([20, 224, 224, 1], tf.float32),
'mesh_name': tf.io.FixedLenFeature([], tf.string),
'near_surface_samples': tf.io.FixedLenFeature([100000, 4], tf.float32),
'grid': tf.io.FixedLenFeature([32, 32, 32], tf.float32),
'world2grid': tf.io.FixedLenFeature([4, 4], tf.float32),
'surface_point_samples': tf.io.FixedLenFeature([10000, 6], tf.float32)
}
def parse_tf_example(example_proto):
d = tf.io.parse_single_example(example_proto, full_featurespec())
return (d['bounding_box_samples'], d['depth_renders'], d['mesh_name'],
d['near_surface_samples'], d['grid'], d['world2grid'],
d['surface_point_samples'])
def _example_dict_tf_func_wrapper(mesh_orig_path):
mesh_orig_path = mesh_orig_path.decode(sys.getdefaultencoding())
|
tensorflow.io.FixedLenFeature
| 9,089 |
import tensorflow as tf
out=tf.matmul(l3, self.w4)+self.b4
return out
def valid_inference(self,images):
images=tf.cast(images,tf.float32)/255.0
l1 = tf.matmul(images, self.w1)+self.b1
l1=tf.nn.relu(l1)
l2 = tf.matmul(l1, self.w2)+self.b2
l2=tf.nn.relu(l2)
l3=tf.matmul(l2, self.w3)+self.b3
l3=tf.nn.relu(l3)
out=tf.matmul(l3, self.w4)+self.b4
return out
def softmax_loss(self,predicts,labels):
predicts=tf.nn.softmax(predicts)
|
tensorflow.nn.relu
| 9,090 |
import tensorflow as tf
self.action = tf.squeeze(self.normal_dist.sample(1),axis=0);
self.action = tf.clip_by_value(self.action, action_bound[0], action_bound[1])
|
tensorflow.clip_by_value
| 9,091 |
import tensorflow as tf
y = analysis_transform(x_coori,x)
return tf.squeeze(y,axis=0)
|
tensorflow.squeeze
| 9,092 |
import tensorflow as tf
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
|
tensorflow.gfile.MakeDirs
| 9,093 |
import tensorflow as tf
"""
matrices = [tf.convert_to_tensor(matrix, dtype=dtype) for matrix in matrices]
blocked_rows = tf.Dimension(0)
blocked_cols = tf.Dimension(0)
batch_shape = tf.TensorShape(None)
for matrix in matrices:
full_matrix_shape = matrix.get_shape().with_rank_at_least(2)
batch_shape = batch_shape.merge_with(full_matrix_shape[:-2])
blocked_rows += full_matrix_shape[-2]
blocked_cols += full_matrix_shape[-1]
ret_columns_list = []
for matrix in matrices:
matrix_shape = tf.shape(matrix)
ret_columns_list.append(matrix_shape[-1])
ret_columns = tf.add_n(ret_columns_list)
row_blocks = []
current_column = 0
for matrix in matrices:
matrix_shape = tf.shape(matrix)
row_before_length = current_column
current_column += matrix_shape[-1]
row_after_length = ret_columns - current_column
row_blocks.append(tf.pad(
tensor=matrix,
paddings=tf.concat(
[tf.zeros([tf.rank(matrix) - 1, 2], dtype=tf.int32),
[(row_before_length, row_after_length)]],
axis=0)))
blocked = tf.concat(row_blocks, -2)
|
tensorflow.add_n
| 9,094 |
import tensorflow as tf
parallel_iterations=32)
final_loss = tf.reduce_mean(loss)
return final_loss
def contra_traj_lossV1(pred, tgt, temp=10.0):
# Trajectory-wise contrastive loss
traj_pred = tf.reduce_mean(pred, axis=1)
traj_tgt = tf.reduce_mean(tgt, axis=1)
p1, p2 = tf.split(traj_pred, 2, axis=0)
t1, t2 = tf.split(traj_tgt, 2, axis=0)
soft_sign = tf.tanh((t1 - t2) * temp)
loss = tf.maximum(0.0, soft_sign * ((t1 - t2) - (p1 - p2)))
loss = tf.reduce_mean(loss)
return loss
def horizon_sumV1(input, horizon=12):
bs, epi_len = input.shape[:2]
new_w = epi_len - horizon + 1
weights = np.zeros([epi_len, new_w])
for i in range(new_w):
weights[i:i + horizon, i] = 1.0
|
tensorflow.tanh
| 9,095 |
from tensorflow.python.ops import variable_scope
"""
with variable_scope.variable_scope(name, 'mean_iou', [predictions, labels]):
|
tensorflow.python.ops.variable_scope.variable_scope
| 9,096 |
import tensorflow as tf
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.PREDICT:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, predictions=masked_lm_example_loss, scaffold_fn=scaffold_fn) # 输出mask_word的score
return output_spec
return model_fn
|
tensorflow.logging.info
| 9,097 |
import tensorflow as tf
self.assertEqual(len(grads), len(vars_))
for grad, var in zip(grads, vars_):
if grad is not None:
self.assertEqual(grad.shape, var.shape)
def test_training_graph(self):
"""Test model training in graph mode."""
with tf.Graph().as_default():
config = config_.get_hparams_cifar_38()
config.add_hparam("n_classes", 10)
config.add_hparam("dataset", "cifar-10")
x = tf.random_normal(
shape=(self.config.batch_size,) + self.config.input_shape)
|
tensorflow.Graph
| 9,098 |
import tensorflow as tf
return tf.reduce_mean(tf.square(uP))
def conv2d(img, w, b, strides=[1, 1, 1, 1], is_dilated=False):
if is_dilated:
layer = tf.nn.atrous_conv2d(img, w, rate=2, padding='SAME') + b
else:
layer = tf.nn.conv2d(img, w, strides=strides, padding='SAME') + b
return layer
def dropout(layer, keep_prob=0.9, is_training=True, name=None, selu=False):
if selu:
return dropout_selu(layer, 1.0 - keep_prob, name=name, training=is_training)
if is_training:
return tf.nn.dropout(layer, keep_prob=keep_prob, name=name)
else:
return tf.add(layer, 0, name=name)
def norm(layer, norm_type='batch_norm', decay=0.9, id=0, is_training=True, activation_fn=tf.nn.relu, prefix='conv_'):
if norm_type != 'batch_norm' and norm_type != 'layer_norm':
return tf.nn.relu(layer)
with tf.variable_scope('norm_layer_%s%d' % (prefix, id)) as vs:
if norm_type == 'batch_norm':
if is_training:
try:
layer = tf.contrib.layers.batch_norm(layer, is_training=True, center=True,
scale=False, decay=decay, activation_fn=activation_fn, updates_collections=None, scope=vs) # updates_collections=None
|
tensorflow.nn.dropout
| 9,099 |
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