seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
maxval=30521, dtype=tf.int32)
# case 1
hparams = {
"pretrained_model_name": None,
}
regressor = XLNetRegressor(hparams=hparams)
logits = regressor(inputs)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
logits_ = sess.run(logits)
self.assertEqual(logits_.shape, (batch_size,))
# case 2
hparams = {
"pretrained_model_name": None,
"regr_strategy": "cls_time"
}
regressor = XLNetRegressor(hparams=hparams)
| tensorflow.global_variables_initializer | 7,100 |
import tensorflow as tf
break
print('\rTrained in %.3fs. Global step %i' % (time() - start, step+1))
return summary
class PPO_HC(PPO):
def build_anet(self, state_in, name, reuse=False):
reg = tf.contrib.layers.l2_regularizer(1e-3)
with tf.variable_scope(name, reuse=reuse):
layer_a1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
layer_a2 = tf.layers.dense(layer_a1, 256, tf.nn.relu, kernel_regularizer=reg)
mu = tf.layers.dense(layer_a2, self.a_dim, tf.nn.tanh, kernel_regularizer=reg)
sigma = tf.layers.dense(layer_a2, self.a_dim, tf.nn.softplus, kernel_regularizer=reg)
# sigma = tf.get_variable(name='pi_sigma', shape=self.a_dim, initializer=tf.constant_initializer(0.5))
sigma = tf.clip_by_value(sigma, 0.0, 1.0)
norm_dist = tf.distributions.Normal(loc=mu * self.a_bound, scale=sigma)
| tensorflow.variable_scope | 7,101 |
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))
def argus_integral_phalf(m_low, m_high, m0, c):
"""
Only valid for argus_pdf with p=0.5! Otherwise need to do numerical
integral.
"""
| tensorflow.constant | 7,102 |
import tensorflow as tf
n_batch_train = n_batch*n_gpu
n_updates_total = (n_train//n_batch_train)*n_iter
X_train = tf.placeholder(tf.int32, [n_batch_train, 2, n_ctx, 2])
M_train = tf.placeholder(tf.float32, [n_batch_train, 2, n_ctx])
X = tf.placeholder(tf.int32, [None, 2, n_ctx, 2])
M = tf.placeholder(tf.float32, [None, 2, n_ctx])
Y_train = tf.placeholder(tf.int32, [n_batch_train])
Y = tf.placeholder(tf.int32, [None])
| tensorflow.placeholder | 7,103 |
import tensorflow as tf
step=global_step)
tf.contrib.summary.scalar(
'rpn_score_loss', tf.reduce_mean(rpn_score_loss),
step=global_step)
tf.contrib.summary.scalar(
'rpn_box_loss', tf.reduce_mean(rpn_box_loss), step=global_step)
tf.contrib.summary.scalar(
'total_fast_rcnn_loss', tf.reduce_mean(total_fast_rcnn_loss),
step=global_step)
tf.contrib.summary.scalar(
'fast_rcnn_class_loss', tf.reduce_mean(fast_rcnn_class_loss),
step=global_step)
tf.contrib.summary.scalar(
'fast_rcnn_box_loss', tf.reduce_mean(fast_rcnn_box_loss),
step=global_step)
if params['include_mask']:
tf.contrib.summary.scalar(
'mask_loss', tf.reduce_mean(mask_loss), step=global_step)
tf.contrib.summary.scalar(
'learning_rate', tf.reduce_mean(learning_rate),
| tensorflow.reduce_mean | 7,104 |
import tensorflow as tf
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
return output_tensor
def input_fn_builder(input_files,
max_seq_length,
max_predictions_per_seq,
| tensorflow.gather | 7,105 |
from tensorflow.python.framework import ops
elif 0 <= dimension and dimension < input_shape.ndims:
returned_shape = []
for i, dim in enumerate(input_shape.dims):
if i != dimension:
returned_shape.append(dim)
return [tensor_shape.TensorShape(returned_shape)]
else:
raise ValueError(
"dimension (%d) must be in the range [0, %d), where %d is the number "
"of dimensions in the input"
% (dimension, input_shape.ndims, input_shape.ndims))
@ops.RegisterShape("All")
@ops.RegisterShape("Any")
@ops.RegisterShape("Max")
@ops.RegisterShape("Mean")
@ops.RegisterShape("Min")
@ops.RegisterShape("Prod")
@ops.RegisterShape("Sum")
def _ReductionShape(op):
"""Common shape function for reduction ops."""
input_shape = op.inputs[0].get_shape()
reduction_indices = tensor_util.ConstantValue(op.inputs[1])
keep_dims = op.get_attr("keep_dims")
if reduction_indices is None or input_shape.ndims is None:
if keep_dims:
return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
else:
return [tensor_shape.unknown_shape()]
| tensorflow.python.framework.ops.RegisterShape | 7,106 |
import tensorflow as tf
It is probably a good idea since the whole session must be later be restored by the ChiefSession
"""
os.makedirs(self._checkpoint_dir, exist_ok=True)
#variables = tf.trainable_variables()
self._saver = tf.train.Saver(variables, max_to_keep=max_to_keep, save_relative_paths=True)
def _save_graph(self):
writer = tf.summary.FileWriter(logdir=self._checkpoint_dir,
# graph=self.sess.graph,
graph=tf.get_default_graph(),
filename_suffix="-graph"
)
writer.flush()
def _assemble_checkpoint_name(self, checkpoint_dir):
path = os.path.join(checkpoint_dir, "model.ckpt")
return path
| tensorflow.get_default_graph | 7,107 |
import tensorflow as tf
mask = tf.equal(mask, tf.ones_like(mask))
hidden_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
input_size = query.get_shape().as_list()[-1]
# Trainable parameters
w1 = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1))
w2 = tf.Variable(tf.random_normal([input_size, attention_size], stddev=0.1))
b = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
v = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
with tf.name_scope('v'):
# Applying fully connected layer with non-linear activation to each of the B*T timestamps;
# the shape of `tmp` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
| tensorflow.random_normal | 7,108 |
import tensorflow as tf
if use_scale:
gamma = tf.get_variable('gamma', [channnel], initializer=tf.constant_initializer(1.0))
| tensorflow.constant_initializer | 7,109 |
from tensorflow.python.platform import gfile
import numpy as np
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.python.platform import gfile
DBPEDIA_URL = 'https://github.com/le-scientifique/torchDatasets/blob/master/dbpedia_csv.tar.gz'
def maybe_download_dbpedia(data_dir):
"""Download if DBpedia data is not present."""
train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv')
test_path = os.path.join(data_dir, 'dbpedia_csv/test.csv')
if not (gfile.Exists(train_path) and gfile.Exists(test_path)):
archive_path = base.maybe_download(
'dbpedia_csv.tar.gz', data_dir, DBPEDIA_URL)
tfile = tarfile.open(archive_path, 'r:*')
tfile.extractall(data_dir)
def load_dbpedia(size='small', test_with_fake_data=False):
"""Get DBpedia datasets from CSV files."""
if not test_with_fake_data:
data_dir = os.path.join(os.getenv('TF_EXP_BASE_DIR', ''), 'dbpedia_data')
maybe_download_dbpedia(data_dir)
| tensorflow.python.platform.gfile.Exists | 7,110 |
import tensorflow as tf
self.S = tf.placeholder(tf.float32, [None, self.num_global_s], name='S') # input Global State
self.s = tf.placeholder(tf.float32, [None, self.num_s], name='s1') # input state for agent1
self.S_ = tf.placeholder(tf.float32, [None, self.num_global_s], name='S_') # input Next Global State
self.s_ = tf.placeholder(tf.float32, [None, self.num_s], name='s1_') # input next state for agent1
| tensorflow.placeholder | 7,111 |
import tensorflow as tf
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_input_fn = input_fn_builder(
input_files=validation_input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False,
batch_size=FLAGS.eval_batch_size,
use_hvd=FLAGS.use_hvd)
result = estimator.evaluate(
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if FLAGS.do_train_eval:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True,
| tensorflow.gfile.GFile | 7,112 |
import tensorflow as tf
labels,
logits,
surrogate_type=surrogate_type,
positive_weights=1.0,
negative_weights=lambdas)
maybe_log2 = tf.log(2.0) if surrogate_type == 'xent' else 1.0
maybe_log2 = tf.cast(maybe_log2, logits.dtype.base_dtype)
lambda_term = lambdas * target_rate * (1.0 - label_priors) * maybe_log2
loss = tf.reshape(weighted_loss - lambda_term, original_shape)
other_outputs = {
'lambdas':
lambdas_variable,
| tensorflow.cast | 7,113 |
import tensorflow as tf
tt_cores = [None] * num_dims
for i in range(num_dims):
curr_core_shape = (1, shape[0][i], shape[1][i], 1)
tt_cores[i] = tf.zeros(curr_core_shape, dtype=dtype)
return TensorTrain(tt_cores, shape, tt_rank)
| tensorflow.zeros | 7,114 |
import tensorflow as tf
other_grads = strategy.reduce("sum", other_grads)
other_train_op = dense_opt.apply_gradients(zip(other_grads, other_var))
with tf.control_dependencies([emb_train_op, other_train_op]):
total_loss = strategy.reduce("sum", loss)
total_loss = tf.identity(total_loss)
| tensorflow.control_dependencies | 7,115 |
import tensorflow as tf
if config.decay is not None:
self.var_ema = tf.train.ExponentialMovingAverage(config.decay)
| tensorflow.train.ExponentialMovingAverage | 7,116 |
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")
| tensorflow.shape | 7,117 |
import tensorflow as tf
@pytest.fixture
def sqrt_diag(session_tf):
return tf.convert_to_tensor(Datum.sqrt_diag_data)
@pytest.fixture
def K(session_tf):
return tf.convert_to_tensor(Datum.K_data)
@pytest.fixture
def K_batch(session_tf):
return tf.convert_to_tensor(Datum.K_batch_data)
| tensorflow.convert_to_tensor | 7,118 |
import tensorflow as tf
fake_distribution = self._GetFakeDistribution()
# Make a new session since we're playing with static shapes. [And below.]
x = tf.placeholder_with_default(
input=np.ones((6, 7, 2, 3, 5), dtype=np.float32), shape=None)
dist = fake_distribution(batch_shape=[2, 3], event_shape=[5])
sample_shape = tf.convert_to_tensor([6, 7], dtype=tf.int32)
y = dist._set_sample_static_shape(x, sample_shape)
# We use as_list since TensorShape comparison does not work correctly for
# unknown values, ie, Dimension(None).
self.assertAllEqual([6, 7, 2, 3, 5], y.shape.as_list())
x = tf.placeholder_with_default(
| tensorflow.convert_to_tensor | 7,119 |
import tensorflow as tf
average_across_timesteps=False,
average_across_batch=True)
self._cost = tf.reduce_sum(loss)
self._final_state = state
if not is_training:
return
self._lr = tf.Variable(0., trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self._cost, tvars),
config.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self._lr)
self._train_op = optimizer.apply_gradients(
zip(grads, tvars),
global_step=tf.train.get_or_create_global_step())
self._new_lr = tf.placeholder(
tf.float32, shape=[], name='new_learning_rate')
self._lr_update = tf.assign(self._lr, self._new_lr)
self.saver = tf.train.Saver(tf.global_variables())
def _get_lstm_cell(self, config, is_training):
if config.rnn_mode == BASIC:
| tensorflow.train.GradientDescentOptimizer | 7,120 |
import tensorflow as tf
def __init__(self, sess, model, data, config, logger):
self.model = model
self.config = config
self.sess = sess
self.data = data
self.logger = logger
if not self.config.pretrain: # If not pretrain then initialize variables.
self.init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
self.sess.run(self.init)
def train(self):
"""Train the model for the number of epochs in config.num_epochs.
Calls validate_epoch if config.use_val is set to true and per config.val_per_epoch.
Returns:
| tensorflow.global_variables_initializer | 7,121 |
import tensorflow as tf
for d in variable.get_shape()[1:].as_list():
dim *= d
return tf.reshape(variable, shape=[-1, dim], name=name)
| tensorflow.reshape | 7,122 |
import tensorflow as tf
bh = tf.get_variable("bh", [nh*4], initializer=tf.constant_initializer(0.0))
b = tf.get_variable("b", [nh*4], initializer=tf.constant_initializer(0.0))
gc = tf.get_variable("gc", [nh], initializer=tf.constant_initializer(1.0))
bc = tf.get_variable("bc", [nh], initializer=tf.constant_initializer(0.0))
c, h = tf.split(axis=1, num_or_size_splits=2, value=s)
for idx, (x, m) in enumerate(zip(xs, ms)):
c = c*(1-m)
h = h*(1-m)
z = _ln(tf.matmul(x, wx), gx, bx) + _ln(tf.matmul(h, wh), gh, bh) + b
i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
i = tf.nn.sigmoid(i)
f = tf.nn.sigmoid(f)
o = tf.nn.sigmoid(o)
u = tf.tanh(u)
c = f*c + i*u
h = o*tf.tanh(_ln(c, gc, bc))
xs[idx] = h
s = tf.concat(axis=1, values=[c, h])
| tensorflow.matmul | 7,123 |
import tensorflow as tf
@gin.configurable(module='trax.data', denylist=['dataset', 'training'])
def add_eos_to_output_features(dataset,
training,
output_features='targets',
eos=1):
"""Adds `EOS` to all features in `output_features`."""
del training
if not isinstance(output_features, (list, tuple)):
output_features = [output_features]
def add_eos(x):
for output_feature in output_features:
x[output_feature] = tf.concat([x[output_feature], [eos]], axis=0)
return x
return dataset.map(add_eos)
@gin.configurable(module='trax.data', denylist=['dataset', 'training'])
def generic_text_dataset_preprocess_fn(dataset,
training=True,
text_preprocess_fns=None,
token_preprocess_fns=None,
spm_path=None,
| tensorflow.concat | 7,124 |
import tensorflow as tf
x = tf.keras.layers.AveragePooling2D(pool_size=7, strides=1,
padding="valid", name="pool")(x)
x = tf.reshape(x, shape=(-1, 1024))
self.logits = self.__fully_connected(name="fc_nsfw",
| tensorflow.reshape | 7,125 |
import tensorflow as tf
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def main(argv=None):
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
#debug
annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation")
# annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="annotation")
pred_annotation, logits = inference(image, keep_probability)
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)
#debug
| tensorflow.placeholder | 7,126 |
from tensorflow.python.framework import ops
ops.RegisterShape("Elu")(common_shapes.unchanged_shape)
ops.RegisterShape("Softplus")(common_shapes.unchanged_shape)
ops.RegisterShape("Softsign")(common_shapes.unchanged_shape)
@ops.RegisterShape("ReluGrad")
@ops.RegisterShape("Relu6Grad")
@ops.RegisterShape("EluGrad")
@ops.RegisterShape("SoftplusGrad")
@ops.RegisterShape("SoftsignGrad")
def _BinaryElementwiseShape(op):
"""Returns same shape as both inputs to op.
Args:
op: Input operation.
| tensorflow.python.framework.ops.RegisterShape | 7,127 |
import tensorflow as tf
return tf.to_float(res)
def embed(self, x):
"""Embedding function that takes discrete latent and returns embedding.
Args:
x: Input to the discretization bottleneck.
Returns:
Continuous embedding to be passed on to the decoder.
Raises:
ValueError: For unknown or missing arguments.
"""
shape_x = common_layers.shape_list(x)
x_flat = tf.reshape(x, [-1, 1])
c = self.int_to_bit(x_flat, num_bits=self.hparams.z_size, base=2)
shape = common_layers.shape_list(c)
new_shape = shape
new_shape.append(self.hparams.num_blocks)
new_shape.append(int(self.hparams.z_size / self.hparams.num_blocks))
c = tf.to_int32(tf.reshape(c, shape=new_shape))
h1_shape = shape_x
h1_shape.append(self.hparams.hidden_size)
h1 = tf.zeros(dtype=tf.float32, shape=h1_shape)
c_int = self.bit_to_int(
c, num_bits=int(self.hparams.z_size / self.hparams.num_blocks), base=2)
c_hot = tf.one_hot(c_int, depth=self.hparams.block_v_size, axis=-1)
c_hot_flat = tf.reshape(
| tensorflow.reshape | 7,128 |
import tensorflow as tf
cls = tf.sigmoid(cls)
anchors = anchors.astype(np.float32)
grid_shape = x_shape[1:3]
# print(grid_shape)
grid_h, grid_w = grid_shape[0], grid_shape[1]
# print(grid_h,tf.range(grid_h))
grid = tf.meshgrid(tf.range(grid_w), tf.range(grid_h))
grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2) # [gx, gy, 1, 2]
box_xy = (box_xy + tf.cast(grid, dtype)) * stride
box_wh = tf.exp(box_wh) * anchors
box_x1y1 = box_xy - box_wh / 2.
box_x2y2 = box_xy + box_wh / 2.
box = tf.concat([box_x1y1, box_x2y2], axis=-1)
boxes.append(tf.reshape(box, (x_shape[0], -1, 1, 4)))
objects.append(tf.reshape(obj, (x_shape[0], -1, 1)))
classes.append(tf.reshape(cls, (x_shape[0], -1, num_classes)))
boxes = tf.concat(boxes, axis=1)
objects = tf.concat(objects, axis=1)
| tensorflow.exp | 7,129 |
import tensorflow as tf
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
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)
def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
| tensorflow.matmul | 7,130 |
import tensorflow as tf
a = tf.minimum(m_bound, m0)
x = 1 - tf.pow(a / m0, 2)
| tensorflow.pow | 7,131 |
import tensorflow as tf
# segment.
#
# If you want to use the token-level output, use model_bak.get_sequence_output()
# instead.
output_layer = model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
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)
| tensorflow.variable_scope | 7,132 |
from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
_ops.RegisterShape("TestStringOutput")(None)
def _InitOpDefLibrary():
op_list = _op_def_pb2.OpList()
_text_format.Merge(_InitOpDefLibrary.op_list_ascii, op_list)
_op_def_registry.register_op_list(op_list)
op_def_lib = _op_def_library.OpDefLibrary()
| tensorflow.core.framework.op_def_pb2.OpList | 7,133 |
from tensorflow.python.platform import gfile
self.assertFalse(gfile.Exists(s1))
self.assertFalse(gfile.Exists(save._MetaGraphFilename(s1)))
self.assertTrue(gfile.Exists(s3))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s3)))
| tensorflow.python.platform.gfile.Exists | 7,134 |
import tensorflow as tf
# This is not implemented as this feature is only used for plotting purposes.
raise NotImplementedError
pXnew = Gaussian(Xnew_mu, Xnew_var)
num_data = tf.shape(Xnew_mu)[0] # number of new inputs (N)
num_ind = tf.shape(q_mu)[0] # number of inducing points (M)
num_func = tf.shape(q_mu)[1] # output dimension (D)
q_sqrt_r = tf.matrix_band_part(q_sqrt, -1, 0) # D x M x M
eKuf = tf.transpose(expectation(pXnew, (kern, feat))) # M x N (psi1)
| tensorflow.shape | 7,135 |
import tensorflow as tf
class PyOpTest(tf.test.TestCase):
def testBasic(self):
def my_func(x, y):
return np.sinh(x) + np.cosh(y)
# scalar
with self.test_session():
x = tf.constant(1.0, tf.float32)
y = tf.constant(2.0, tf.float32)
z = tf.py_func(my_func, [x, y], [tf.float32])
self.assertEqual(z[0].eval(), my_func(1.0, 2.0).astype(np.float32))
# array
with self.test_session():
x = tf.constant([1.0, 2.0], tf.float64)
y = tf.constant([2.0, 3.0], tf.float64)
z = tf.py_func(my_func, [x, y], [tf.float64])
self.assertAllEqual(
z[0].eval(),
| tensorflow.constant | 7,136 |
import tensorflow as tf
def get_sorted_full_neighbor(nodes, edge_types):
"""
Args:
nodes: A `Tensor` of `int64`.
edge_types: A 1-D `Tensor` of int32. Specify edge types to filter outgoing
edges.
Return:
A tuple of `SparseTensor` (neibors, weights).
neighbors: A `SparseTensor` of `int64`.
weights: A `SparseTensor` of `float`.
types: A `SparseTensor` of `int32`
"""
sp_returns = base._LIB_OP.get_sorted_full_neighbor(nodes, edge_types)
return tf.SparseTensor(*sp_returns[:3]), tf.SparseTensor(*sp_returns[3:6]), \
tf.SparseTensor(*sp_returns[6:])
def sample_fanout(nodes, edge_types, counts, default_node=-1):
"""
Sample multi-hop neighbors of nodes according to weight in graph.
Args:
nodes: A 1-D `Tensor` of `int64`.
edge_types: A list of 1-D `Tensor` of int32. Specify edge types to filter
outgoing edges in each hop.
counts: A list of `int`. Specify the number of sampling for each node in
each hop.
| tensorflow.SparseTensor | 7,137 |
from tensorflow.python.client import graph_util
output_count = np.prod(output_shape.as_list())
return ops.OpStats("flops", (output_count * filter_in_depth * filter_height *
filter_width * 2))
@ops.RegisterStatistics("Conv2D", "weight_parameters")
def _calc_conv_weight_params(graph, node):
"""Calculates the on-disk size of the weights for Conv2D."""
input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
input_shape.assert_is_fully_defined()
filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
node.input[1])
filter_shape.assert_is_fully_defined()
output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
output_shape.assert_is_fully_defined()
filter_height = int(filter_shape[0])
filter_width = int(filter_shape[1])
filter_in_depth = int(filter_shape[2])
filter_out_depth = int(filter_shape[3])
return ops.OpStats("weight_parameters", (filter_height * filter_width *
| tensorflow.python.client.graph_util.tensor_shape_from_node_def_name | 7,138 |
import tensorflow as tf
queue_ops = []
# For each other worker, add an entry in a queue, signaling that it can
# finish this step.
token = tf.constant(False)
with tf.control_dependencies(enqueue_after_list):
for i, q in enumerate(sync_queues):
if i == self.task_index:
queue_ops.append(tf.no_op())
else:
queue_ops.append(q.enqueue(token))
# Drain tokens off queue for this worker, one for each other worker.
queue_ops.append(
sync_queues[self.task_index].dequeue_many(len(sync_queues) - 1))
| tensorflow.no_op | 7,139 |
import tensorflow as tf
w_init = tf.random_normal_initializer(0.,.1);
with tf.variable_scope(scope+"_"+str(par_idx)):
# state and target
self.state = tf.placeholder(tf.float32, [None,num_state], "state")
self.target = tf.placeholder(tf.float32, [None,1], name="target")
# layers
l_c = tf.layers.dense(self.state, 100, tf.nn.relu6, kernel_initializer=w_init, name='lc')
self.value_estimate = tf.layers.dense(l_c, 1, kernel_initializer=w_init, name='v') # estimated value for state
# loss and optimizer
self.loss = tf.reduce_mean(tf.square(tf.subtract(self.value_estimate, self.target)))
self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
self.train_op = self.optimizer.minimize(
| tensorflow.layers.dense | 7,140 |
import tensorflow as tf
# multiple devices (executors) in TensorFlow.
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()
# To find out where placement occurs, set 'log_device_placement'
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Runs the op.
print(sess.run(c))
# If we load a graph and want device placement to be forgotten,
# we set a parameter in our session:
config = tf.ConfigProto()
| tensorflow.constant | 7,141 |
import tensorflow as tf
mode=mode,
loss=total_loss,
train_op=train_op,
training_hooks=[logging_hook])
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights, next_sentence_example_loss,
next_sentence_log_probs, next_sentence_labels):
"""Computes the loss and accuracy of the model."""
masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
[-1, masked_lm_log_probs.shape[-1]])
masked_lm_predictions = tf.argmax(
masked_lm_log_probs, axis=-1, output_type=tf.int32)
masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
masked_lm_accuracy = tf.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
| tensorflow.argmax | 7,142 |
import tensorflow as tf
def __init__(self,name,input_dim,out_dim,
k_h=4,k_w=4,d_h=2,d_w=2,stddev=0.02,data_format='NCHW') :
with tf.variable_scope(name) :
self.w = tf.get_variable('w', [k_h, k_w, out_dim, input_dim],
| tensorflow.variable_scope | 7,143 |
import tensorflow as tf
return relu
def avg_pool(self, bottom, kernal_size = 2, stride = 2, name = "avg"):
return tf.nn.avg_pool(bottom, ksize=[1, kernal_size, kernal_size, 1], strides=[1, stride, stride, 1], padding='VALID', name=name)
def max_pool(self, bottom, kernal_size = 2, stride = 2, name = "max"):
return tf.nn.max_pool(bottom, ksize=[1, kernal_size, kernal_size, 1], strides=[1, stride, stride, 1], padding='SAME', name=name)
def conv_layer(self, bottom, kernal_size, in_channels, out_channels, stride, name):
with tf.variable_scope(name):
filt, conv_biases = self.get_conv_var(kernal_size, in_channels, out_channels, name)
conv = tf.nn.conv2d(bottom, filt, [1,stride,stride,1], padding='SAME')
bias = tf.nn.bias_add(conv, conv_biases)
tf.summary.histogram('weight', filt)
tf.summary.histogram('bias', conv_biases)
return bias
def conv_bn_relu(self, bottom,name, kernel_size, output_channels, initializer,stride=1, bn=False,training=False,relu=True):
input_channels = bottom.get_shape().as_list()[-1]
with tf.variable_scope(name) as scope:
kernel = self.variable('weights', [kernel_size, kernel_size, input_channels, output_channels], initializer, regularizer=tf.contrib.layers.l2_regularizer(0.0005))
conv = tf.nn.conv2d(bottom, kernel, [1, stride, stride, 1], padding='SAME')
biases = self.variable('biases', [output_channels], tf.constant_initializer(0.0))
conv_layer = tf.nn.bias_add(conv, biases)
| tensorflow.nn.bias_add | 7,144 |
from tensorflow.python.framework import ops
@ops.RegisterShape("Div")
@ops.RegisterShape("Equal")
@ops.RegisterShape("Greater")
@ops.RegisterShape("GreaterEqual")
@ops.RegisterShape("Less")
@ops.RegisterShape("LessEqual")
@ops.RegisterShape("LogicalAnd")
@ops.RegisterShape("LogicalOr")
@ops.RegisterShape("Maximum")
@ops.RegisterShape("Minimum")
@ops.RegisterShape("Mod")
@ops.RegisterShape("Mul")
| tensorflow.python.framework.ops.RegisterShape | 7,145 |
import tensorflow as tf
def conv(inp, name, size, out_channels, strides=[1, 1, 1, 1],
dilation=None, padding='SAME', apply_relu=True, alpha=0.0,bias=True,
initializer=tf.contrib.layers.xavier_initializer_conv2d()):
batch_size = inp.get_shape().as_list()[0]
res1 = inp.get_shape().as_list()[1]
res2 = inp.get_shape().as_list()[1]
in_channels = inp.get_shape().as_list()[3]
with tf.variable_scope(name):
W = get_variable("W", shape=[size, size, in_channels, out_channels], dtype=tf.float32,
initializer=initializer, regularizer=tf.nn.l2_loss)
b = get_variable("b", shape=[1, 1, 1, out_channels], dtype=tf.float32,
initializer=tf.zeros_initializer(),trainable=bias)
if dilation:
assert(strides == [1, 1, 1, 1])
out = tf.add(tf.nn.atrous_conv2d(inp, W, rate=dilation, padding=padding), b, name='convolution')
out.set_shape([batch_size, res1, res2, out_channels])
else:
out = tf.add(tf.nn.conv2d(inp, W, strides=strides, padding=padding), b, name='convolution')
if apply_relu:
out = relu(out, alpha=alpha, name='relu')
return out
| tensorflow.zeros_initializer | 7,146 |
import tensorflow as tf
eval_config.batch_size = 1
eval_config.num_steps = 1
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
with tf.name_scope("Train"):
train_input = PTBInput(config=config, data=train_data, name="TrainInput")
with tf.variable_scope("Model", reuse=None, initializer=initializer):
m = PTBModel(is_training=True, config=config, input_=train_input)
tf.summary.scalar("Training Loss", m.cost)
tf.summary.scalar("Learning Rate", m.lr)
with tf.name_scope("Valid"):
valid_input = PTBInput(config=config, data=valid_data, name="ValidInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mvalid = PTBModel(is_training=False, config=config, input_=valid_input)
tf.summary.scalar("Validation Loss", mvalid.cost)
with tf.name_scope("Test"):
test_input = PTBInput(
config=eval_config, data=test_data, name="TestInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mtest = PTBModel(is_training=False, config=eval_config,
input_=test_input)
models = {"Train": m, "Valid": mvalid, "Test": mtest}
| tensorflow.name_scope | 7,147 |
import tensorflow as tf
assert (len(stride_dims) == 2) # stride height and width
filter_h, filter_w = filter_dims
stride_h, stride_w = stride_dims
with tf.variable_scope(scope):
pool = tf.nn.avg_pool(input, ksize=[1, filter_h, filter_w, 1], strides=[1, stride_h, stride_w, 1],
padding=padding)
return pool
| tensorflow.variable_scope | 7,148 |
import tensorflow as tf
# Some preprocessing
adj_norm = preprocess_graph(adj)
# Define placeholders
placeholders = {
'features': tf.sparse_placeholder(tf.float32),
'adj': tf.sparse_placeholder(tf.float32),
'adj_orig': tf.sparse_placeholder(tf.float32),
'dropout': tf.placeholder_with_default(0., shape=())
}
| tensorflow.sparse_placeholder | 7,149 |
import tensorflow as tf
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
if FLAGS.contrast_norm == 'areafactor':
image = tf.decode_raw(features['image_raw'], tf.float32)
else:
image = tf.decode_raw(features['image_raw'], tf.uint8)
image = tf.cast(image, tf.float32) * (1. / 255)
image.set_shape(np.prod([FLAGS.num_scales, FLAGS.crop_size, FLAGS.crop_size]))
image = tf.reshape(image, [FLAGS.num_scales, FLAGS.crop_size, FLAGS.crop_size, 1])
image = image - 0.5
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.int32)
return image, label
def inputs(name, batch_size, num_epochs):
"""Reads input data num_epochs times.
| tensorflow.reshape | 7,150 |
import tensorflow as tf
state[st_idx]=featurize_state(state[st_idx]);
feed_dict = { self.state: state, self.target: target }
_, loss = sess.run([self.train_op, self.loss], feed_dict)
return loss
"""
For Pendulum-v0
"""
class PolicyEstimator_Pendulum():
def __init__(self, entropy_beta=0.01, learning_rate=0.01, par_idx=0,scope="policy_estimator"):
w_init = tf.random_normal_initializer(0.,.1);
with tf.variable_scope(scope+"_"+str(par_idx)):
# state, target and action
self.state = tf.placeholder(tf.float32, [None,num_state], name="state")
self.target = tf.placeholder(tf.float32,[None,1], name="target")
self.a_his = tf.placeholder(tf.float32, [None, num_action], name="action_hist")
# layers
l_a = tf.layers.dense(self.state, 200, tf.nn.relu6, kernel_initializer=w_init, name='la')
self.mu = tf.layers.dense(l_a, num_action, tf.nn.tanh, kernel_initializer=w_init, name='mu') # estimated action value
self.sigma = tf.layers.dense(l_a, num_action, tf.nn.softplus, kernel_initializer=w_init, name='sigma') # estimated variance
# wrap output
self.mu = self.mu * action_bound[1];
self.sigma = self.sigma + 1e-4
# get action from distribution
self.normal_dist = tf.contrib.distributions.Normal(self.mu, self.sigma)
self.action = tf.squeeze(self.normal_dist.sample(1),axis=0);
| tensorflow.placeholder | 7,151 |
import tensorflow as tf
# 1. Configure the RNN
lstm_cell = rnn.BasicLSTMCell(LSTM_SIZE, forget_bias = 1.0)
outputs, _ = rnn.static_rnn(lstm_cell, x, dtype = tf.float32)
# Slice to keep only the last cell of the RNN
outputs = outputs[-1]
#print('last outputs={}'.format(outputs))
# Output is result of linear activation of last layer of RNN
weight = tf.Variable(tf.random_normal([LSTM_SIZE, N_OUTPUTS]))
bias = tf.Variable(tf.random_normal([N_OUTPUTS]))
predictions = tf.matmul(outputs, weight) + bias
# 2. Loss function, training/eval ops
if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL:
loss = tf.losses.mean_squared_error(labels, predictions)
train_op = tf.contrib.layers.optimize_loss(
loss = loss,
global_step = tf.train.get_global_step(),
learning_rate = 0.01,
optimizer = "SGD")
| tensorflow.random_normal | 7,152 |
import tensorflow as tf
self.loss_db_sum = tf.summary.scalar("db_loss", self.D_B_loss)
self.loss_da_sum = tf.summary.scalar("da_loss", self.D_A_loss)
self.loss_d_sum = tf.summary.scalar("d_loss",self.discriminator_loss)
self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.D_B_loss_real)
self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.D_B_loss_fake)
self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.D_A_loss_real)
self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.D_A_loss_fake)
self.d_sum = tf.summary.merge(
[self.loss_da_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
self.loss_db_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
self.loss_d_sum]
)
| tensorflow.summary.scalar | 7,153 |
import tensorflow as tf
b,name=name)
def get_variables(self):
return {'w':self.w,'b':self.b}
class DilatedConv3D(object) :
def __init__(self,name,input_dim,output_dim,k_t=2,k_h=3,k_w=3,d_t=2,d_h=1,d_w=1,
stddev=0.02, data_format='NDHWC') :
with tf.variable_scope(name) :
assert(data_format == 'NDHWC')
self.w = tf.get_variable('w', [k_t, k_h, k_w, input_dim, output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
self.b = tf.get_variable('b',[output_dim], initializer=tf.constant_initializer(0.0))
self.strides = [1,1,1]
self.dilates = [d_t, d_h, d_w]
def __call__(self,input_var,name=None) :
k_t,k_h,k_w,_,_ = self.w.get_shape().as_list()
_t = tf.pad(input_var, [[0,0],[0,0],[k_h//2,k_h//2],[k_w//2,k_w//2],[0,0]], "SYMMETRIC")
return tf.nn.bias_add(
tf.nn.convolution(_t, self.w,
strides=self.strides, dilation_rate=self.dilates,
padding='VALID'),
self.b,name=name)
| tensorflow.constant_initializer | 7,154 |
import tensorflow as tf
def squared_loss(y_pred,labels):
return tf.reduce_mean((y_pred - labels)**2)
def abs_loss(y_pred,labels):
return tf.reduce_mean(tf.abs(y_pred - labels))
def binary_cross_entropy_loss(logits,labels):
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,logits=logits))
self.images_real = tf.placeholder(tf.float32,[None,self.image_size,self.image_size,self.input_dim + self.output_dim])
self.image_real_A = self.images_real[:,:,:,:self.input_dim]
self.image_real_B = self.images_real[:,:,:,self.input_dim:self.input_dim + self.output_dim]
self.images_fake_B = self.build_generator(self.image_real_A,reuse=False,name='generator_AB')
self.images_fake_A = self.build_generator(self.images_fake_B,reuse=False,name='generator_BA')
self.images_fake_A_ = self.build_generator(self.image_real_B,reuse=True,name='generator_BA')
| tensorflow.nn.sigmoid_cross_entropy_with_logits | 7,155 |
import tensorflow as tf
with tf.variable_scope("attention"):
inputs_ = tf.nn.relu(
dense(d_inputs, hidden, use_bias=False, scope="inputs"))
memory_ = tf.nn.relu(
dense(d_memory, hidden, use_bias=False, scope="memory"))
outputs = tf.matmul(inputs_, tf.transpose(
memory_, [0, 2, 1])) / (hidden ** 0.5)
mask = tf.tile(tf.expand_dims(mask, axis=1), [1, JX, 1])
logits = tf.nn.softmax(softmax_mask(outputs, mask))
outputs = tf.matmul(logits, memory)
| tensorflow.transpose | 7,156 |
import tensorflow as tf
# not use Dataset.from_generator() because that uses tf.py_func which is
# not TPU compatible. The right way to load data is with TFRecordReader.
d = tf.data.Dataset.from_tensor_slices({
"input_ids":
tf.constant(
all_input_ids, shape=[num_examples, seq_length],
dtype=tf.int32),
"input_mask":
| tensorflow.constant | 7,157 |
import tensorflow as tf
gen_optimizer = tf.train.RMSPropOptimizer(learning_rate=0.05)
dis_optimizer = tf.train.RMSPropOptimizer(learning_rate=0.05)
| tensorflow.train.RMSPropOptimizer | 7,158 |
import tensorflow as tf
del dynamics
if __name__ == "__main__":
tf.enable_eager_execution()
tf.test.main()
| tensorflow.enable_eager_execution | 7,159 |
import tensorflow as tf
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
# Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph)
| tensorflow.Session | 7,160 |
import tensorflow as tf
size=0,
dynamic_size=True,
element_shape=(facts[:, 0, :].get_shape()))
_, output_op, _ = tf.while_loop(cond, body, [facts, output_ta, 0])
self_attention = output_op.stack()
self_attention = tf.transpose(self_attention, perm = [1, 0, 2])
return self_attention
def self_all_attention(facts, ATTENTION_SIZE, mask, stag='null'):
if len(facts.get_shape().as_list()) == 2:
facts = tf.expand_dims(facts, 1)
def cond(batch, output, i):
return tf.less(i, tf.shape(batch)[1])
def body(batch, output, i):
self_attention_tmp = din_fcn_attention(batch[:, i, :], batch,
ATTENTION_SIZE, mask, softmax_stag=1, stag=stag,
mode='LIST')
self_attention_tmp = tf.reduce_sum(self_attention_tmp, 1)
| tensorflow.expand_dims | 7,161 |
import tensorflow as tf
if targets in [0., 1.]:
entropy = 0.
else:
entropy = - targets*tf.log(targets) - (1. - targets)*tf.log(1. - targets)
return sigmoid_ce_with_logits(logits, tf.ones_like(logits)*targets) - entropy
def gradient_difference_loss(x, y):
x_h_diff = x[:, 1:] - x[:, :-1]
| tensorflow.ones_like | 7,162 |
import tensorflow as tf
"""
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
| tensorflow.constant_initializer | 7,163 |
import tensorflow as tf
self.variable_mgr.append_apply_gradients_ops(
gradient_state, opt, clipped_grads, training_ops)
train_op = tf.group(*(training_ops + update_ops + extra_nccl_ops))
with tf.device(self.cpu_device):
if self.task_index == 0 and FLAGS.summary_verbosity > 0:
tf.summary.scalar('learning_rate', learning_rate)
tf.summary.scalar('total_loss', total_loss)
for grad, var in avg_grads:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
fetches = [train_op, total_loss] + enqueue_ops
return (enqueue_ops, fetches)
def add_forward_pass_and_gradients(
self, host_images, host_labels, nclass, phase_train, device_num,
input_data_type, data_type, input_nchan, use_synthetic_gpu_images,
gpu_copy_stage_ops, gpu_compute_stage_ops, gpu_grad_stage_ops):
"""Add ops for forward-pass and gradient computations."""
| tensorflow.summary.histogram | 7,164 |
import tensorflow as tf
W = tf.get_variable("W",
shape=[total_filters, nb_classes],
initializer=tf.contrib.layers.xavier_initializer())
# add final layer bias
| tensorflow.contrib.layers.xavier_initializer | 7,165 |
import tensorflow as tf
tf.data.experimental.map_and_batch(
lambda fname, label: (mapper(tf.read_file(fname)), label),
| tensorflow.read_file | 7,166 |
import tensorflow as tf
end_logits = tf.contrib.layers.layer_norm(
end_logits, begin_norm_axis=-1)
end_logits = tf.layers.dense(
end_logits, 1,
kernel_initializer=initializer,
| tensorflow.layers.dense | 7,167 |
import tensorflow as tf
padding_shape = [max_num_boxes, shape_utils.get_dim_as_int(tensor_shape[1])]
padding_shapes[fields.InputDataFields.
groundtruth_keypoint_visibilities] = padding_shape
padded_tensor_dict = {}
for tensor_name in tensor_dict:
padded_tensor_dict[tensor_name] = shape_utils.pad_or_clip_nd(
tensor_dict[tensor_name], padding_shapes[tensor_name])
# Make sure that the number of groundtruth boxes now reflects the
# padded/clipped tensors.
if fields.InputDataFields.num_groundtruth_boxes in padded_tensor_dict:
padded_tensor_dict[fields.InputDataFields.num_groundtruth_boxes] = (
tf.minimum(
padded_tensor_dict[fields.InputDataFields.num_groundtruth_boxes],
max_num_boxes))
return padded_tensor_dict
def augment_input_data(tensor_dict, data_augmentation_options):
"""Applies data augmentation ops to input tensors.
Args:
tensor_dict: A dictionary of input tensors keyed by fields.InputDataFields.
data_augmentation_options: A list of tuples, where each tuple contains a
function and a dictionary that contains arguments and their values.
| tensorflow.minimum | 7,168 |
import tensorflow as tf
from sklearn.preprocessing import normalize
import cv2
import numpy as np
import glob
import json
from keras.layers import merge
from keras.layers.core import Lambda
from keras.models import Model
import tensorflow as tf
def make_parallel(model, gpu_count):
def get_slice(data, idx, parts):
shape = tf.shape(data)
size = tf.concat(0, [shape[:1] // parts, shape[1:]])
stride = tf.concat(0, [shape[:1] // parts, shape[1:] * 0])
start = stride * idx
return tf.slice(data, start, size)
outputs_all = []
for i in range(len(model.outputs)):
outputs_all.append([])
# Place a copy of the model on each GPU, each getting a slice of the batch
for i in range(gpu_count):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i) as scope:
| tensorflow.shape | 7,169 |
import tensorflow as tf
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes,
global_pool=True)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])
def testUnknowBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
| tensorflow.global_variables_initializer | 7,170 |
import tensorflow as tf
return {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
}
eval_metrics = (metric_fn, [
masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights, next_sentence_example_loss,
next_sentence_log_probs, next_sentence_labels
])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
return output_spec
return model_fn
| tensorflow.contrib.tpu.TPUEstimatorSpec | 7,171 |
import tensorflow as tf
x = tf.reshape(scale_init, [1, num_units]) * (x - tf.reshape(m_init, [1, num_units]))
else:
V = maybe_avg(V)
g = maybe_avg(g)
b = maybe_avg(b)
x = tf.matmul(x, V)
scaler = g / tf.sqrt(tf.reduce_sum(tf.square(V), [0]))
x = tf.reshape(scaler, [1, num_units]) * x + tf.reshape(b, [1, num_units])
return x
def sample_from_discretized_mix_logistic(l, nr_mix):
"""
This function is copied from https://github.com/openai/pixel-cnn/blob/master/pixel_cnn_pp/nn.py in reference to:
| tensorflow.reshape | 7,172 |
from tensorflow.python.ops import variable_scope as vs
self._outer_graph = ops.get_default_graph()
self._vscope = vs.get_variable_scope()
| tensorflow.python.ops.variable_scope.get_variable_scope | 7,173 |
import tensorflow as tf
"targets": tf.FixedLenFeature([1], tf.int64),
}
data_items_to_decoders = None
return (data_fields, data_items_to_decoders)
def txt_line_iterator(txt_path):
"""Iterate through lines of file."""
with tf.gfile.Open(txt_path) as f:
for line in f:
yield line.strip()
def text2text_txt_iterator(source_txt_path, target_txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of files."""
for inputs, targets in zip(
| tensorflow.gfile.Open | 7,174 |
import tensorflow as tf
activation=tf.nn.relu, padding="same")
x = tf.layers.conv2d(x, 32, (5, 5), strides=(2, 2),
activation=tf.nn.relu, padding="same")
flat_x = tf.layers.flatten(x)
if self.use_epochs:
epoch = features["epoch"] + tf.zeros([x_shape[0]], dtype=tf.int32)
# Randomly set epoch to 0 in some cases as that's the inference value.
| tensorflow.layers.flatten | 7,175 |
import tensorflow as tf
"""
words = tf.sparse.to_dense(tf.strings.split([text]), default_value='')[0]
num_words = tf.size(words)
ids_ta = tf.TensorArray(tf.int32, 0, dynamic_size=True)
| tensorflow.size | 7,176 |
import tensorflow as tf
self._score_summaries.update(self._anchor_targets)
return rpn_labels
def _proposal_target_layer(self, rois, roi_scores, name):
with tf.variable_scope(name):
# 这里的index是对于cfg.FLAGS.batch_size=256 而言
# rois (0, x1, y1, x2, y2)，coming from RPN 然后再减少至256个
# bbox_target (ndarray): N x 4K blob of regression targets
# bbox_inside_weights (ndarray): N x 4K blob of loss weights
| tensorflow.variable_scope | 7,177 |
import tensorflow as tf
tf.flags.DEFINE_string(
| tensorflow.flags.DEFINE_string | 7,178 |
from tensorflow.python.framework import ops
@ops.RegisterShape("SegmentMax")
@ops.RegisterShape("SegmentMean")
@ops.RegisterShape("SegmentMin")
@ops.RegisterShape("SegmentProd")
@ops.RegisterShape("SegmentSum")
def _SegmentReductionShape(op):
"""Common shape function for segment reduction ops."""
data_shape = op.inputs[0].get_shape()
segment_ids_shape = op.inputs[1].get_shape()
segment_ids_shape.assert_has_rank(1)
return [tensor_shape.TensorShape([None]).concatenate(data_shape[1:])]
@ops.RegisterShape("SparseSegmentMean")
@ops.RegisterShape("SparseSegmentSum")
def _SparseSegmentReductionShape(op):
"""Common shape function for sparse segment reduction ops."""
data_shape = op.inputs[0].get_shape()
indices_shape = op.inputs[1].get_shape()
indices_shape.assert_has_rank(1)
segment_ids_shape = op.inputs[2].get_shape()
segment_ids_shape.assert_has_rank(1)
indices_shape.assert_is_compatible_with(segment_ids_shape)
return [tensor_shape.TensorShape([None]).concatenate(data_shape[1:])]
@ops.RegisterShape("SparseSegmentMeanGrad")
def _SparseSegmentMeanGradShape(op):
"""Shape function for the SparseSegmentMeanGrad op."""
| tensorflow.python.framework.ops.RegisterShape | 7,179 |
from tensorflow.python.framework import ops
for m in self._monitors:
if "session" in inspect.getargspec(m.end).args:
m.end(session=session)
else:
m.end()
def _as_graph_element(obj):
"""Retrieves Graph element."""
graph = ops.get_default_graph()
if not isinstance(obj, six.string_types):
if not hasattr(obj, "graph") or obj.graph != graph:
raise ValueError("Passed %s should have graph attribute that is equal "
"to current graph %s." % (obj, graph))
return obj
if ":" in obj:
element = graph.as_graph_element(obj)
else:
| tensorflow.python.framework.ops.get_default_graph | 7,180 |
import tensorflow as tf
metrics.append(tf.summary.scalar('training/pred_dist', mean_pred_error))
metrics.append(tf.summary.scalar('training/improvement', improvement))
metrics.append(tf.summary.scalar('training/improvement_abs', tf.nn.relu(improvement)))
metrics.append(tf.summary.histogram('training/improvement_abs_hist', nut.nan_to_zero(improvement)))
metrics.append(tf.summary.scalar('training/improvement_pairwise', tf.reduce_mean(pairwise_improvement_bool)))
metrics.append(tf.summary.histogram('training/improvement_pairwise_hist', pairwise_improvement_bool))
self.eval_summs = tf.summary.merge(metrics)
def _build_embedding_saver(self, sess):
| tensorflow.summary.histogram | 7,181 |
import tensorflow as tf
tf.summary.image('input', images)
tf.summary.image('score_map', score_maps)
tf.summary.image('score_map_pred', f_score * 255)
tf.summary.image('geo_map_0', geo_maps[:, :, :, 0:1])
tf.summary.image('geo_map_0_pred', f_geometry[:, :, :, 0:1])
tf.summary.image('training_masks', training_masks)
tf.summary.scalar('model_loss', model_loss)
tf.summary.scalar('total_loss', total_loss)
return total_loss, model_loss
| tensorflow.summary.scalar | 7,182 |
import tensorflow as tf
def mgpu_train(*xs):
gpu_ops = []
gpu_grads = []
xs = (tf.split(x, n_gpu, 0) for x in xs)
for i, xs in enumerate(zip(*xs)):
do_reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, "/gpu:0")), tf.variable_scope(tf.get_variable_scope(), reuse=do_reuse):
clf_logits, clf_losses, lm_losses = model(*xs, train=True, reuse=do_reuse)
if lm_coef > 0:
train_loss = tf.reduce_mean(clf_losses) + lm_coef*tf.reduce_mean(lm_losses)
else:
train_loss = tf.reduce_mean(clf_losses)
| tensorflow.get_variable_scope | 7,183 |
import tensorflow as tf
small_constant_for_finite_diff: a `float`, Small constant for finite difference method
perturb_norm_length: a `float`, Norm length of adversarial perturbation
to be optimized with validatio
Returns:
a `float` `scalar`, KL divergence.
"""
logits = tf.stop_gradient(logits)
weights = _end_of_seq_mask(labels, vocab_size)
perturbs = [_mask_by_length(tf.random_normal(shape=tf.shape(emb)), length) for emb in embedded]
for _ in range(num_power_iteration):
perturbs = [_scale_l2(d, small_constant_for_finite_diff) for d in perturbs]
d_logits = logits_from_embedding_fn([emb + d for (emb, d) in zip(embedded, perturbs)])
| tensorflow.stop_gradient | 7,184 |
import tensorflow as tf
class FeedForwardCategoricalPolicy(PolicyBase):
"""Feed-forward categorical."""
def body(self, features):
observations = features["inputs_raw"]
observations = tf.cast(observations, tf.float32)
flat_observations = tf.layers.flatten(observations)
with tf.variable_scope("policy"):
x = flat_observations
for size in self.hparams.policy_layers:
x = tf.layers.dense(x, size, activation=tf.nn.relu)
logits = tf.layers.dense(x, self.hparams.problem.num_actions)
logits = tf.expand_dims(logits, axis=1)
with tf.variable_scope("value"):
x = flat_observations
for size in self.hparams.value_layers:
x = tf.layers.dense(x, size, activation=tf.nn.relu)
value = tf.layers.dense(x, 1)
logits = clip_logits(logits, self.hparams)
return {"target_policy": logits, "target_value": value}
@registry.register_model
class FeedForwardCnnSmallCategoricalPolicy(PolicyBase):
"""Small cnn network with categorical output."""
| tensorflow.variable_scope | 7,185 |
import tensorflow as tf
# a_fc2_ = tf.layers.dense(a_fc1_, 128, tf.nn.relu, kernel_initializer=w_initializer,
# bias_initializer=b_initializer, name='agent_fc2_t')
# a_fc3_ = tf.layers.dense(a_fc2_, 64, tf.nn.relu, kernel_initializer=w_initializer,
# bias_initializer=b_initializer, name='agent_fc3_t')
self.q_next = tf.layers.dense(a_fc1_, self.num_a, kernel_initializer=w_initializer,
bias_initializer=b_initializer, name='q_t')
# [batch*n_agents, 1]
self.q_selected = tf.reduce_sum(tf.multiply(self.q_eval, self.a), axis=1)
# ------------------ build mixing_net ------------------
with tf.variable_scope('mixing_net'):
# [batch, n_agents]
self.q_concat = tf.reshape(self.q_selected, [-1, self.n_agents])
self.q_concat_ =tf.reshape(self.q_m_, [-1, self.n_agents])
with tf.variable_scope('eval_hyper'):
self.Q_tot = Qmix_mixer(self.q_concat, self.S, self.num_global_s, self.n_agents, 32)
with tf.variable_scope('target_hyper'):
self.Q_tot_ = Qmix_mixer(self.q_concat_, self.S_, self.num_global_s, self.n_agents, 32)
# with tf.variable_scope('layer_mix_eval'):
# lin1 = tf.matmul(tf.reshape(self.q_concat, shape=[-1, 1, self.n_agents]), self.w1) + tf.reshape(self.b1, shape=[-1, 1, 32])
# a1 = tf.nn.elu(lin1, name='a1')
# self.Q_tot = tf.reshape(tf.matmul(a1, self.w2), shape=[-1, 1]) + self.b2
# with tf.variable_scope('layer_mix_target'):
| tensorflow.reshape | 7,186 |
import tensorflow as tf
def _bn(self, name, x):
with tf.variable_scope(name):
moving_average_decay = 0.9
decay = moving_average_decay
batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2])
mu = tf.get_variable('mu', batch_mean.shape, dtype=tf.float32,
initializer=tf.zeros_initializer(), trainable=False)
tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, mu)
tf.add_to_collection('mu_sigma_bn', mu)
sigma = tf.get_variable('sigma', batch_var.shape, dtype=tf.float32,
initializer=tf.ones_initializer(), trainable=False)
tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, sigma)
tf.add_to_collection('mu_sigma_bn', sigma)
beta = tf.get_variable('beta', batch_mean.shape, dtype=tf.float32,
initializer=tf.zeros_initializer())
gamma = tf.get_variable('gamma', batch_var.shape, dtype=tf.float32,
initializer=tf.ones_initializer())
# BN when training
update = 1.0 - decay
update_mu = mu.assign_sub(update * (mu - batch_mean))
update_sigma = sigma.assign_sub(update * (sigma - batch_var))
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_mu)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_sigma)
mean, var = tf.cond(self.train_flag, lambda: (batch_mean, batch_var), lambda: (mu, sigma))
| tensorflow.add_to_collection | 7,187 |
import tensorflow as tf
self.inputs = inputs
with tf.variable_scope(name):
self.embeddings = tf.get_variable(
name='embeddings',
| tensorflow.variable_scope | 7,188 |
import tensorflow as tf
unconnected_gradients=tf.UnconnectedGradients.NONE)
train_op = optimizer.apply_gradients(zip(grads, tf_sparse_demo.trainable_variables))
with tf.control_dependencies([train_op]):
loss = tf.identity(loss)
return loss, embedding_vector
dataset = utils.tf_dataset(*random_samples, batchsize=args.global_batch_size,
to_sparse_tensor=True, repeat=1)
train_iterator = dataset.make_initializable_iterator()
iterator_init = train_iterator.initializer
inputs, labels = train_iterator.get_next()
graph_results = _train_step(inputs, labels, training=True)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
restore_op = list()
for i, embedding_weight in enumerate(tf_sparse_demo.embedding_weights):
restore_op.append(embedding_weight.assign(tf.concat(init_tensors[i], axis=0)))
emb_values = list()
for embedding_weight in tf_sparse_demo.embedding_weights:
if args.save_params:
filepath = r"./embedding_variables/"
utils.try_make_dirs(filepath)
emb_values.append(embedding_weight.read_value())
else:
emb_values = tf.constant(1.0)
| tensorflow.global_variables_initializer | 7,189 |
import tensorflow as tf
# Target for value fn regression
# We update the vf towards the min of two Q-functions in order to
# reduce overestimation bias from function approximation error.
v_backup = tf.stop_gradient(min_qf_pi - self.ent_coef * logp_pi)
value_loss = 0.5 * tf.reduce_mean(((value_fn - v_backup) ** 2)*self.weight_ph)
#value_for_priority = tf.reduce_mean((value_fn - v_backup) ** 2,1)
regularizervf = tf.contrib.layers.l1_l2_regularizer(scale_l1=0.0, scale_l2=1e-5, scope='model/values_fn')
| tensorflow.stop_gradient | 7,190 |
from tensorflow.python.ops import math_ops
def moving_average(name, value, decay):
moving_average_variable = vs.get_variable(
name,
shape=value.get_shape(),
dtype=value.dtype,
initializer=init_ops.zeros_initializer(),
trainable=False)
return moving_averages.assign_moving_average(
moving_average_variable, value, decay, zero_debias=False)
# quicker adaptation at the beginning
if global_step is not None:
n = math_ops.cast(global_step, dtypes.float32)
decay = math_ops.minimum(decay, n / (n + 1.))
# update averages
mean = moving_average("mean", log_norm, decay)
sq_mean = moving_average("sq_mean", math_ops.square(log_norm), decay)
variance = sq_mean - math_ops.square(mean)
std = math_ops.sqrt(math_ops.maximum(epsilon, variance))
max_norms = math_ops.exp(mean + std_factor * std)
return max_norms, mean
def adaptive_clipping_fn(std_factor=2.,
decay=0.95,
| tensorflow.python.ops.math_ops.minimum | 7,191 |
import tensorflow as tf
test_pre = tf.reshape(test_inf, [testnum, classnum])
correct_prediction=tf.equal(tf.argmax(test_inf,1),tf.argmax(test_labels,1))
| tensorflow.argmax | 7,192 |
import tensorflow as tf
if is_training and config.keep_prob < 1:
inputs = tf.nn.dropout(inputs, config.keep_prob)
output, state = self._build_rnn_graph(inputs, config, is_training)
softmax_w = tf.get_variable(
'softmax_w', [hidden_size, vocab_size], dtype=tf.float32)
softmax_b = tf.get_variable('softmax_b', [vocab_size], dtype=tf.float32)
logits = tf.nn.xw_plus_b(output, softmax_w, softmax_b)
logits = tf.reshape(logits, [self.batch_size, self.num_steps, vocab_size])
loss = tf.contrib.seq2seq.sequence_loss(
logits,
input_.targets,
tf.ones([self.batch_size, self.num_steps], dtype=tf.float32),
average_across_timesteps=False,
| tensorflow.nn.xw_plus_b | 7,193 |
import tensorflow as tf
'resnet_size', 50,
'The size of the ResNet model to use.')
tf.app.flags.DEFINE_integer(
'train_epochs', None,
'The number of epochs to use for training.')
tf.app.flags.DEFINE_integer(
'batch_size', 12,
'Batch size for training and evaluation.')
tf.app.flags.DEFINE_string(
'data_format', 'channels_first', # 'channels_first' or 'channels_last'
'A flag to override the data format used in the model. channels_first '
'provides a performance boost on GPU but is not always compatible '
'with CPU. If left unspecified, the data format will be chosen '
'automatically based on whether TensorFlow was built for CPU or GPU.')
tf.app.flags.DEFINE_float(
'negative_ratio', 3., 'Negative ratio in the loss function.')
tf.app.flags.DEFINE_float(
'match_threshold', 0.56, 'Matching threshold in the loss function.')
tf.app.flags.DEFINE_float(
'neg_threshold', 0.4, 'Matching threshold for the negtive examples in the loss function.')
# optimizer related configuration
tf.app.flags.DEFINE_float(
'weight_decay', 0.0005, 'The weight decay on the model weights.')
tf.app.flags.DEFINE_float(
'momentum', 0.9,
'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
tf.app.flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
tf.app.flags.DEFINE_float(
| tensorflow.app.flags.DEFINE_float | 7,194 |
import tensorflow as tf
#Get conditioning status
self.local_condition, self.global_condition = self._check_conditions()
with tf.device('/cpu:0'):
# Create placeholders for inputs and targets. Don't specify batch size because we want
# to be able to feed different batch sizes at eval time.
if is_scalar_input(hparams.input_type):
input_placeholder = tf.placeholder(tf.float32, shape=(None, 1, None), name='audio_inputs')
target_placeholder = tf.placeholder(tf.float32, shape=(None, None, 1), name='audio_targets')
target_type = tf.float32
else:
input_placeholder = tf.placeholder(tf.float32, shape=(None, hparams.quantize_channels, None), name='audio_inputs')
target_placeholder = tf.placeholder(tf.int32, shape=(None, None, 1), name='audio_targets')
target_type = tf.int32
| tensorflow.placeholder | 7,195 |
import tensorflow as tf
offset: (height, width)
Returns:
"""
with tf.variable_scope('anchor_generator'):
if offset is None:
offset = [stride[0]/2, stride[1]/2]
features_width = tf.cast(features_width, tf.int32)
features_height = tf.cast(features_height, tf.int32)
scales = tf.convert_to_tensor(scales, dtype=tf.float32)
ratios = tf.convert_to_tensor(ratios, dtype=tf.float32)
offset = tf.convert_to_tensor(offset, dtype=tf.float32)
scales_grid, ratios_grid = tf.meshgrid(scales,
ratios)
scales_grid = tf.reshape(scales_grid, [-1, 1])
ratios_grid = tf.reshape(ratios_grid, [-1, 1])
ratio_sqrts = tf.sqrt(ratios_grid)
heights = scales_grid / ratio_sqrts * base_size[1]
widths = scales_grid * ratio_sqrts * base_size[0]
x_centers = tf.cast(tf.range(features_width), tf.float32)
| tensorflow.convert_to_tensor | 7,196 |
import tensorflow as tf
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
loss = tf.maximum(0.0, (tgt_larg - tgt_small) - (pred_larg - pred_small))
# loss = tf.maximum(0.0, tf.math.abs(tgt_larg - pred_larg) - tf.math.abs(tgt_small - pred_small))
loss = tf.reduce_mean(loss)
| tensorflow.where | 7,197 |
import tensorflow as tf
input_tensor_dict = {
fields.InputDataFields.image:
tf.placeholder(tf.float32, [None, None, 3]),
fields.InputDataFields.image_additional_channels:
| tensorflow.placeholder | 7,198 |
import tensorflow as tf
# X shape (p_s * b_s * i_s)
net_input = tf.einsum('pbi,pio->pbo', X, self.model_DMW) + self.model_tiled_b
| tensorflow.einsum | 7,199 |
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