seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
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)
classes = tf.concat(classes, axis=1)
scores = objects * classes
boxes, scores, classes, valid = tf.image.combined_non_max_suppression(
boxes=boxes,
scores=scores,
max_output_size_per_class=max_outputs,
max_total_size=max_outputs,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
clip_boxes=False
)
| tensorflow.image.combined_non_max_suppression | 12,100 |
import tensorflow as tf
lstm=tf.nn.rnn_cell.LSTMCell(num_units=state_size, activation = tf.nn.relu, state_is_tuple=True)
cell_drop=tf.contrib.rnn.DropoutWrapper(lstm,variational_recurrent=True,dtype=tf.float32, input_size=num_input,input_keep_prob=input_prob,state_keep_prob=state_prob)
| tensorflow.contrib.rnn.DropoutWrapper | 12,101 |
import tensorflow as tf
losses[loss_name] = mean_loss
return losses
def summarize_features(features, num_shards=1):
with tf.name_scope("input_stats"):
for (k, v) in six.iteritems(features):
if isinstance(v, tf.Tensor) and v.get_shape().ndims > 1:
tf.summary.scalar("%s_batch" % k, tf.shape(v)[0] // num_shards)
tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
nonpadding = tf.to_float(tf.not_equal(v, 0))
nonpadding_tokens = tf.reduce_sum(nonpadding)
tf.summary.scalar("%s_nonpadding_tokens" % k, nonpadding_tokens)
tf.summary.scalar("%s_nonpadding_fraction" % k,
tf.reduce_mean(nonpadding))
| tensorflow.shape | 12,102 |
from tensorflow.python.framework import ops
@ops.RegisterShape("TopK")
| tensorflow.python.framework.ops.RegisterShape | 12,103 |
import tensorflow as tf
sigmean = tf.Variable(5.28, name="sigmean", dtype=tf.float64)
sigwidth = tf.Variable(0.0027, name="sigwidth", dtype=tf.float64)
vdict['sigmean'] = sigmean
| tensorflow.Variable | 12,104 |
import tensorflow as tf
# add final layer bias
b = tf.Variable(tf.constant(0.1, shape=[nb_classes]), name="b")
# calc l2 losses
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
# do logit = W*X+b
logit = tf.nn.xw_plus_b(H_drop, W, b, name="scores")
| tensorflow.nn.l2_loss | 12,105 |
import tensorflow as tf
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
| tensorflow.cond | 12,106 |
import tensorflow as tf
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
| tensorflow.logging.info | 12,107 |
from tensorflow.python.feature_column import feature_column_lib as core_feature_column
learner_config.constraints.max_tree_depth = 1
model_dir = tempfile.mkdtemp()
config = run_config.RunConfig()
est = estimator.CoreGradientBoostedDecisionTreeEstimator(
head=head_fn,
learner_config=learner_config,
num_trees=1,
examples_per_layer=3,
model_dir=model_dir,
config=config,
feature_columns=[core_feature_column.numeric_column("x")])
# Train for a few steps.
est.train(input_fn=_train_input_fn, steps=1000)
est.evaluate(input_fn=_eval_input_fn, steps=1)
est.predict(input_fn=_eval_input_fn)
if __name__ == "__main__":
googletest.main()
| tensorflow.python.feature_column.feature_column_lib.numeric_column | 12,108 |
import tensorflow as tf
return (layer_id + 1, inputs, next_c, next_h, anchors, anchors_w_1,
arc_seq, entropy, log_prob)
loop_vars = [
tf.constant(2, dtype=tf.int32, name="layer_id"),
inputs,
prev_c,
prev_h,
anchors,
anchors_w_1,
arc_seq,
tf.constant([0.0], dtype=tf.float32, name="entropy"),
tf.constant([0.0], dtype=tf.float32, name="log_prob"),
]
loop_outputs = tf.while_loop(_condition, _body, loop_vars,
parallel_iterations=1)
arc_seq = loop_outputs[-3].stack()
arc_seq = tf.reshape(arc_seq, [-1])
entropy = tf.reduce_sum(loop_outputs[-2])
log_prob = tf.reduce_sum(loop_outputs[-1])
last_c = loop_outputs[-7]
| tensorflow.constant | 12,109 |
import tensorflow as tf
return forward_fn(inputs, is_train=False, data_format=self.data_format)
def calc_loss(self, labels, outputs, trainable_vars):
"""Calculate loss (and some extra evaluation metrics)."""
loss = tf.losses.softmax_cross_entropy(labels, outputs)
loss_filter = lambda var: 'batch_normalization' not in var.name
loss += FLAGS.loss_w_dcy \
* tf.add_n([tf.nn.l2_loss(var) for var in trainable_vars if loss_filter(var)])
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(labels, axis=1), tf.argmax(outputs, axis=1)), tf.float32))
metrics = {'accuracy': accuracy}
return loss, metrics
def setup_lrn_rate(self, global_step):
"""Setup the learning rate (and number of training iterations)."""
nb_epochs = 250
idxs_epoch = [100, 150, 200]
decay_rates = [1.0, 0.1, 0.01, 0.001]
| tensorflow.argmax | 12,110 |
import tensorflow as tf
def _get_initial_lstm(self, features):
with tf.variable_scope('initial_lstm'):
features_mean = tf.reduce_mean(features, 1)
w_h = tf.get_variable('w_h', [self.D, self.H], initializer=self.weight_initializer)
b_h = tf.get_variable('b_h', [self.H], initializer=self.const_initializer)
h = tf.nn.tanh(tf.matmul(features_mean, w_h) + b_h)
w_c = tf.get_variable('w_c', [self.D, self.H], initializer=self.weight_initializer)
b_c = tf.get_variable('b_c', [self.H], initializer=self.const_initializer)
c = tf.nn.tanh(tf.matmul(features_mean, w_c) + b_c)
| tensorflow.get_variable | 12,111 |
import tensorflow as tf
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)
| tensorflow.constant_initializer | 12,112 |
import tensorflow as tf
with tf.name_scope('Train'):
train_input = DataInput(config=config, data=train_data, name='TrainInput')
with tf.variable_scope('Model', reuse=None, initializer=initializer):
m = Model(is_training=True, config=config, input_=train_input, graph=train_graph)
tf.summary.scalar('Training Loss', m.cost)
| tensorflow.variable_scope | 12,113 |
import tensorflow as tf
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.metrics.mean | 12,114 |
import tensorflow as tf
image_height, image_width = tf.shape(images)[1], tf.shape(images)[2]
cutout_center_height = tf.random.uniform(
shape=[], minval=0, maxval=image_height,
dtype=tf.int32)
cutout_center_width = tf.random.uniform(
shape=[], minval=0, maxval=image_width,
dtype=tf.int32)
lower_pad = tf.maximum(0, cutout_center_height - length // 2)
upper_pad = tf.maximum(0, image_height - cutout_center_height - length // 2)
left_pad = tf.maximum(0, cutout_center_width - length // 2)
right_pad = tf.maximum(0, image_width - cutout_center_width - length // 2)
cutout_shape = [image_height - (lower_pad + upper_pad),
image_width - (left_pad + right_pad)]
padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]]
mask = tf.pad(
| tensorflow.maximum | 12,115 |
import tensorflow as tf
self.b1 = tf.get_variable('b1', [2048],initializer=tf.constant_initializer(0.0))
self.b2 = tf.get_variable('b2', [3072],initializer=tf.constant_initializer(0.0))
self.b3 = tf.get_variable('b3', [512],initializer=tf.constant_initializer(0.0))
self.b4 = tf.get_variable('b4', [classnum],initializer=tf.constant_initializer(0.0))
def inference(self,images):
| tensorflow.constant_initializer | 12,116 |
import tensorflow as tf
placeholders = {
'features': tf.sparse_placeholder(tf.float32),
'adj': tf.sparse_placeholder(tf.float32),
'adj_orig': tf.sparse_placeholder(tf.float32),
| tensorflow.sparse_placeholder | 12,117 |
import tensorflow as tf
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
def get_next_sentence_output(bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with tf.variable_scope("cls/seq_relationship"):
output_weights = tf.get_variable(
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range),
)
output_bias = tf.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer()
)
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
| tensorflow.variable_scope | 12,118 |
import tensorflow as tf
kernel_size=size, kernel_initializer=tf.constant_initializer(1.0),
strides=stride, padding="SAME", use_bias=False, trainable=False,
dilation_rate=(dilation, dilation))
mask_ratio = slide_window / (update_mask + 1e-8)
update_mask = tf.clip_by_value(update_mask, 0.0, 1.0)
mask_ratio = mask_ratio * update_mask
with tf.variable_scope('parconv'):
x = tf.nn.conv2d(input, filters, strides=[1, stride, stride, 1], padding="SAME", name='zero-conv_' + id,
dilations=(1, dilation, dilation, 1))
x = x * mask_ratio
if use_bias:
bias = tf.get_variable("bias" + id, [channels], initializer=tf.constant_initializer(0.0))
x = tf.nn.bias_add(x, bias)
return x * update_mask
| tensorflow.nn.conv2d | 12,119 |
import tensorflow as tf
def testInitRequiredAssignAdd(self):
with self.test_session():
p = tf.Variable(tf.fill([1024, 1024], 1),
tf.int32)
a = tf.assign_add(p, tf.fill([1024, 1024], 0))
with self.assertRaisesOpError("use uninitialized"):
a.op.run()
def testInitRequiredAssignSub(self):
with self.test_session():
p = tf.Variable(tf.fill([1024, 1024], 1),
tf.int32)
a = tf.assign_sub(p, tf.fill([1024, 1024], 0))
with self.assertRaisesOpError("use uninitialized"):
a.op.run()
# NOTE(mrry): See also
# dense_update_ops_no_tsan_test.AssignOpTest, which contains a benign
# data race and must run without TSAN.
def testParallelUpdateWithLocking(self):
with self.test_session() as sess:
zeros_t = tf.fill([1024, 1024], 0.0)
ones_t = tf.fill([1024, 1024], 1.0)
p = tf.Variable(zeros_t)
| tensorflow.fill | 12,120 |
import tensorflow as tf
with tf.Session(config=config) as sess:
t22 = time.time()
expected = sess.run(conv)
t11 = time.time()
for i in range(0, num_trials):
sess.run(conv)
t22 = time.time()
td = abs(t22 - t11) / max(num_trials,1)
print("time dense gpu: ", td)
tf.reset_default_graph()
print("time ratio: ", ts / td)
return [expected, sv3, ts, td]
def do_test(res, f_density, batch_size):
pid = os.getpid()
print(pid)
| tensorflow.reset_default_graph | 12,121 |
import tensorflow as tf
if logits==True:
return tf.nn.softmax(logit), logit
return tf.nn.softmax(logit)
| tensorflow.nn.softmax | 12,122 |
import tensorflow as tf
# For printing layers shape
self.training_end_points = self.end_points_D
self.training_end_points.update(self.end_points_G)
tf.summary.histogram("d", self.end_points_D['D_on_data'])
tf.summary.histogram("d_", self.end_points_D['D_on_G'])
tf.summary.image("G", G)
| tensorflow.summary.histogram | 12,123 |
import tensorflow as tf
pred_flat1, pred_flat2 = tf.reshape(horizon_pred, [-1, 1]), tf.reshape(horizon_pred, [1, -1])
tgt_flat1, tgt_flat2 = tf.reshape(horizon_tgt, [-1, 1]), tf.reshape(horizon_tgt, [1, -1])
tgt_dif = tgt_flat1 - tgt_flat2
| tensorflow.reshape | 12,124 |
import tensorflow as tf
if use_tpu:
def metric_fn(tf_logits, labels):
with tf.device("cpu:0"), mtf.utils.outside_all_rewrites():
eval_metrics = {}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
if metric_name.split("/")[-1] not in t2t_model.TPU_METRIC_BLACKLIST:
eval_metrics[metric_name] = metric_fn(
tf_logits, None, tf.identity(labels))
return eval_metrics
return tpu_estimator.TPUEstimatorSpec(
tf.estimator.ModeKeys.EVAL,
evaluation_hooks=[restore_hook],
loss=loss,
eval_metrics=(metric_fn, [logits, labels]))
| tensorflow.identity | 12,125 |
import tensorflow as tf
x = tf.matmul(
x, weights) # (batch_size * self._num_nodes, output_size)
biases = tf.get_variable("biases", [output_size],
dtype=dtype,
initializer=tf.constant_initializer(
bias_start, dtype=dtype))
x = tf.nn.bias_add(x, biases)
# Reshape res back to: (batch_size, num_node, state_dim)
return tf.reshape(x, [batch_size, self._num_nodes, output_size]) | tensorflow.reshape | 12,126 |
import tensorflow as tf
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint, different_vocabulary=False)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
| tensorflow.train.init_from_checkpoint | 12,127 |
import tensorflow as tf
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,
| tensorflow.expand_dims | 12,128 |
import tensorflow as tf
def _reverse_seq(sequence, sequence_lengths=None):
"""Reverse sequence along dim 0.
Args:
sequence: Tensor of shape [T, B, ...].
sequence_lengths: (optional) tensor of shape [B]. If `None`, only reverse
along dim 0.
Returns:
Tensor of same shape as sequence with dim 0 reversed up to sequence_lengths.
"""
if sequence_lengths is None:
return tf.reverse(sequence, [0])
sequence_lengths = tf.convert_to_tensor(sequence_lengths)
with tf.control_dependencies(
[tf.assert_equal(sequence.shape[1], sequence_lengths.shape[0])]):
return tf.reverse_sequence(
sequence, sequence_lengths, seq_axis=0, batch_axis=1)
def scan_discounted_sum(sequence, decay, initial_value, reverse=False,
sequence_lengths=None, back_prop=True,
name="scan_discounted_sum"):
"""Evaluates a cumulative discounted sum along dimension 0.
```python
| tensorflow.reverse | 12,129 |
import tensorflow as tf
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self._cost, tvars),
| tensorflow.gradients | 12,130 |
import tensorflow as tf
masks out all losses passed the sequence length.
Args:
logits: Logits of shape `[T, B, vocab_size]`
targets: Target classes of shape `[T, B]`
sequence_length: An int32 tensor of shape `[B]` corresponding
to the length of each input
Returns:
A tensor of shape [T, B] that contains the loss per example, per time step.
"""
with tf.name_scope("cross_entropy_sequence_loss"):
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets)
loss_mask = tf.sequence_mask(tf.to_int32(sequence_length), tf.to_int32(tf.shape(targets)[0]))
losses = losses * tf.transpose(tf.to_float(loss_mask), [1, 0])
return losses
def dice_loss(predictions, targets, weights=1., name='dice_loss'):
with tf.name_scope(name):
# predictions = tf.to_float(predictions)
targets = tf.to_float(targets)
intersection = 2 * tf.reduce_sum(predictions * targets) + weights
union = weights + tf.reduce_sum(predictions) + tf.reduce_sum(targets)
loss = -(intersection / (union))
| tensorflow.shape | 12,131 |
import tensorflow as tf
scale = tf.constant([2.] * 5)
concentration = tf.constant([2.] * 5)
| tensorflow.constant | 12,132 |
from tensorflow.python.framework import ops
ops.RegisterShape("Sign")(common_shapes.unchanged_shape)
ops.RegisterShape("Sin")(common_shapes.unchanged_shape)
ops.RegisterShape("Sqrt")(common_shapes.unchanged_shape)
ops.RegisterShape("Square")(common_shapes.unchanged_shape)
ops.RegisterShape("Sigmoid")(common_shapes.unchanged_shape)
ops.RegisterShape("Tanh")(common_shapes.unchanged_shape)
ops.RegisterShape("Cast")(common_shapes.unchanged_shape)
ops.RegisterShape("ComplexAbs")(common_shapes.unchanged_shape)
@ops.RegisterShape("Add")
@ops.RegisterShape("Complex")
@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")
@ops.RegisterShape("NotEqual")
@ops.RegisterShape("Pow")
@ops.RegisterShape("Sub")
def _BroadcastShape(op):
| tensorflow.python.framework.ops.RegisterShape | 12,133 |
import tensorflow as tf
batch_count = batch_count + 1
test_accuracy = test_accuracy + test_accuracy_temp
evaluated_images = evaluated_images + testLabels.shape[0]
test_accuracy = test_accuracy / batch_count
print('test set: accuracy on test set: %0.3f' % test_accuracy)
if __name__ == '__main__':
tf.app.run(main=main)
| tensorflow.app.run | 12,134 |
import tensorflow as tf
mask = self.q_mask,
num_filters = d,
num_heads = nh,
seq_len = self.q_len,
scope = "Encoder_Residual_Block",
reuse = True, # Share the weights between passage and question
bias = False,
dropout = self.dropout)
with tf.variable_scope("Context_to_Query_Attention_Layer"):
# C = tf.tile(tf.expand_dims(c,2),[1,1,self.q_maxlen,1])
# Q = tf.tile(tf.expand_dims(q,1),[1,self.c_maxlen,1,1])
# S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
S = optimized_trilinear_for_attention([c, q], self.c_maxlen, self.q_maxlen, input_keep_prob = 1.0 - self.dropout)
mask_q = tf.expand_dims(self.q_mask, 1)
S_ = tf.nn.softmax(mask_logits(S, mask = mask_q))
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(tf.nn.softmax(mask_logits(S, mask = mask_c), dim = 1),(0,2,1))
| tensorflow.variable_scope | 12,135 |
import tensorflow as tf
moving_averages.assign_moving_average(
self.ema_count,
tf.reduce_sum(
tf.reshape(
x_means_hot,
shape=[-1, self.hparams.num_blocks,
self.hparams.block_v_size]),
axis=0),
self.hparams.decay,
zero_debias=False)
dw = tf.matmul(
tf.transpose(x_means_hot, perm=[1, 2, 0]),
tf.transpose(x_reshaped, perm=[1, 0, 2]))
updated_ema_means = \
moving_averages.assign_moving_average(
self.ema_means, dw, self.hparams.decay,
zero_debias=False)
n = tf.reduce_sum(updated_ema_count, axis=-1, keep_dims=True)
updated_ema_count = ((updated_ema_count + self.hparams.epsilon) / (
n + 2**self.hparams.z_size * self.hparams.epsilon) * n)
updated_ema_means = updated_ema_means / tf.expand_dims(
updated_ema_count, axis=-1)
with tf.control_dependencies([e_loss]):
| tensorflow.transpose | 12,136 |
import tensorflow as tf
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)
x_centers = x_centers * stride[1]
y_centers = tf.cast(tf.range(features_height), tf.float32)
y_centers = y_centers * stride[0]
# x_centers = x_centers + offset[1]
# y_centers = y_centers + offset[0]
x_centers, y_centers = tf.meshgrid(x_centers, y_centers)
| tensorflow.reshape | 12,137 |
import tensorflow as tf
import tensorflow as tf
import numpy as np
def YoloV4Header(num_classes, anchorlist, mask, strides,
max_outputs, iou_threshold, score_threshold,inputs):
boxes, objects, classes = [], [], []
dtype = inputs[0].dtype
for i, logits in enumerate(inputs):
print(i,mask[i])
stride = strides[i]
anchors = anchorlist[mask[i]]
x_shape = tf.shape(logits)
logits = tf.reshape(logits, (x_shape[0], x_shape[1], x_shape[2], len(anchors), num_classes + 5))
box_xy, box_wh, obj, cls = tf.split(logits, (2, 2, 1, num_classes), axis=-1)
box_xy = tf.sigmoid(box_xy)
obj = tf.sigmoid(obj)
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))
| tensorflow.shape | 12,138 |
import tensorflow as tf
x_shape = tuple(x_shape)
y_shape = []
for i, s in zip(int_shape(y), tf.unstack(tf.shape(y))):
if i is not None:
y_shape.append(i)
else:
y_shape.append(s)
y_shape = tuple(y_shape)
y_permute_dim = list(range(get_ndim(y)))
y_permute_dim = [y_permute_dim.pop(-2)] + y_permute_dim
xt = tf.reshape(x, [-1, x_shape[-1]])
yt = tf.reshape(tf.transpose(y, perm=y_permute_dim), [y_shape[-2], -1])
return tf.reshape(
tf.matmul(xt, yt), x_shape[:-1] + y_shape[:-2] + y_shape[-1:])
out = tf.matmul(x, y)
return out
def get_ndim(x):
"""Returns the number of axes in a tensor, as an integer.
Parameters
| tensorflow.transpose | 12,139 |
import tensorflow as tf
"""
logger.debug("base conditional")
# compute kernel stuff
num_func = tf.shape(f)[1] # R
Lm = tf.cholesky(Kmm)
# Compute the projection matrix A
A = tf.matrix_triangular_solve(Lm, Kmn, lower=True)
# compute the covariance due to the conditioning
if full_cov:
fvar = Knn - tf.matmul(A, A, transpose_a=True)
fvar = tf.tile(fvar[None, :, :], [num_func, 1, 1]) # R x N x N
else:
| tensorflow.matrix_triangular_solve | 12,140 |
import tensorflow as tf
Returns:
List of delta tensors corresponding to the updates for each optimized variable.
"""
learning_rate = self.learning_rate.value()
unperturbed_loss = fn_loss(**arguments)
deltas = [tf.zeros_like(tensor=variable) for variable in variables]
previous_perturbations = [tf.zeros_like(tensor=variable) for variable in variables]
if self.unroll_loop:
# Unrolled for loop
for sample in range(self.num_samples):
with tf.control_dependencies(control_inputs=deltas):
perturbations = [
tf.random_normal(shape=util.shape(variable)) * learning_rate
| tensorflow.zeros_like | 12,141 |
import tensorflow as tf
zeros_t = tf.fill([1024, 1024], 0.0)
ones_t = tf.fill([1024, 1024], 1.0)
p = tf.Variable(zeros_t)
assigns = [tf.assign(p, tf.mul(ones_t, float(i)),
| tensorflow.Variable | 12,142 |
import tensorflow as tf
units=depth,
activation=tf.nn.sigmoid,
name='T',
bias_initializer=tf.constant_initializer(-1.0))
return H * T + inputs * (1.0 - T)
def conv1d(inputs, kernel_size, channels, activation, is_training, scope):
with tf.variable_scope(scope):
conv1d_output = tf.layers.conv1d(
inputs,
filters=channels,
kernel_size=kernel_size,
activation=activation,
padding='same')
return tf.layers.batch_normalization(conv1d_output, training=is_training)
| tensorflow.variable_scope | 12,143 |
import tensorflow as tf
# tf.nn.rnn_cell
# lstm_cell1 = tf.nn.rnn_cell.LSTMCell(lstm_hidden_size_layer1, forget_bias=1.0)
# lstm_cell2 = tf.nn.rnn_cell.LSTMCell(lstm_hidden_size_layer2, forget_bias=1.0)
#lstm_cells = tf.nn.rnn_cell.MultiRNNCell(cells=[lstm_cell1, lstm_cell2], state_is_tuple=True)
# initial_state = lstm_cells.zero_state(batch_size, tf.float32)
_, states = tf.nn.dynamic_rnn(lstm_cells, input, dtype=tf.float32, initial_state=None)
# z_sequence_output = states[1].h
# print(z_sequence_output.get_shape())
states_concat = tf.concat([states[0].h, states[1].h], 1)
#def fc(input, scope, out_dim, non_linear_fn=None, initial_value=None, use_bias=True):
z_sequence_output = fc(states_concat, lstm_z_sequence_dim, scope='linear_transform')
| tensorflow.nn.dynamic_rnn | 12,144 |
from tensorflow.python.framework import tensor_shape
logits_shape = op.inputs[0].get_shape()
input_shape = logits_shape.with_rank(2)
batch_size = input_shape[0]
# labels_shape
op.inputs[1].get_shape().merge_with(tensor_shape.vector(batch_size))
return [tensor_shape.vector(batch_size.value), input_shape]
@ops.RegisterShape("SoftmaxCrossEntropyWithLogits")
def _SoftmaxCrossEntropyWithLogitsShape(op):
"""Shape function for SoftmaxCrossEntropyWithLogits op."""
logits_shape = op.inputs[0].get_shape()
labels_shape = op.inputs[1].get_shape()
input_shape = logits_shape.merge_with(labels_shape).with_rank(2)
batch_size = input_shape[0]
return [tensor_shape.vector(batch_size.value), input_shape]
def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
"""Performs the average pooling on the input.
Each entry in `output` is the mean of the corresponding size `ksize`
window in `value`.
Args:
value: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
`float32`, `float64`, `qint8`, `quint8`, or `qint32`.
ksize: A list of ints that has length >= 4.
The size of the window for each dimension of the input tensor.
strides: A list of ints that has length >= 4.
| tensorflow.python.framework.tensor_shape.vector | 12,145 |
import tensorflow as tf
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
| tensorflow.train.init_from_checkpoint | 12,146 |
import tensorflow as tf
# time_steps = tf.shape(decoder_states)[1]
# state_size = decoder_states.get_shape()[2]
# states = tf.reshape(decoder_states, shape=tf.stack([batch_size * time_steps, state_size]))
baseline = dense(tf.stop_gradient(decoder_states), units=1, activation=None, name='reward_baseline',
kernel_initializer=tf.constant_initializer(0.01))
baseline = tf.squeeze(baseline, axis=2)
# baseline = tf.reshape(baseline, shape=tf.stack([batch_size, time_steps]))
return reward - baseline
| tensorflow.squeeze | 12,147 |
import tensorflow as tf
"""
if isinstance(scale, numbers.Integral):
raise ValueError('scale cannot be an integer: %s' % scale)
if isinstance(scale, numbers.Real):
if scale < 0.:
raise ValueError('Setting a scale less than 0 on a regularizer: %g' % scale)
if scale == 0.:
return lambda _: None
def l1(weights, name='l1_regularizer'):
"""Applies L1 regularization to weights."""
with tf.name_scope(name):
my_scale = tf.convert_to_tensor(scale, dtype=weights.dtype.base_dtype, name='scale')
return tf.multiply(my_scale, tf.reduce_sum(tf.abs(weights)), name=name)
return l1
def l2_regularizer(scale, name='l2_regularizer'):
"""Returns a function that can be used to apply L2 regularization to weights.
Small values of L2 can help prevent overfitting the training data.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
name: An optional name/scope name.
| tensorflow.abs | 12,148 |
from tensorflow.contrib import layers
nn_activations = layers.fully_connected(nn_activations,
self.params.layer_size)
return nn_activations
class ManyToOneLayer(hybrid_layer.HybridLayer):
def _define_vars(self, params):
pass
def inference_graph(self, data):
with ops.device(self.device_assigner):
# Compute activations for the neural network.
nn_activations = layers.fully_connected(data, 1)
# There is always one activation per instance by definition, so squeeze
# away the extra dimension.
return array_ops.squeeze(nn_activations, squeeze_dims=[1])
class FlattenedFullyConnectedLayer(hybrid_layer.HybridLayer):
"""A stacked, fully-connected flattened feed-forward neural network layer."""
def _define_vars(self, params):
pass
| tensorflow.contrib.layers.fully_connected | 12,149 |
import tensorflow as tf
else:
raise ValueError('Not defined for this latent dimension')
def cw_sampling(X, y=None):
def phi_sampling(s, D):
return tf.pow(1.0 + 4.0*s/(2.0*D-3), -0.5)
D = tf.cast(tf.shape(X)[1], tf.float32)
N = tf.cast(tf.shape(X)[0], tf.float32)
D_int = tf.cast(D, tf.int32)
N_int = tf.cast(N, tf.int32)
if y is None:
y = silverman_rule_of_thumb(N)
YDistr = tf.contrib.distributions.MultivariateNormalDiag(loc=tf.zeros(D_int, tf.float32),
scale_diag=tf.ones(D_int, tf.float32))
Y = YDistr.sample(N_int)
T = 1.0/(2.0*N*tf.sqrt(m.pi*y))
A0 = euclidean_norm_squared(tf.subtract(tf.expand_dims(X, 0), tf.expand_dims(X, 1)), axis=2)
A = tf.reduce_sum(phi_sampling(A0/(4*y), D))
| tensorflow.cast | 12,150 |
import tensorflow as tf
out = tf.matmul(self.fc1, w) + b
self.fc2 = tf.nn.relu(out)
# fc3
with tf.variable_scope('fc3'):
w = tf.get_variable('w', [self.fc2.get_shape()[1], num_classes], initializer=initializer,
regularizer=regularizer)
b = tf.get_variable('b', [num_classes], initializer=tf.constant_initializer(1.0))
self.fc3 = tf.matmul(self.fc2, w) + b
# Calculate Mean cross-entropy loss
with tf.name_scope("loss"):
self.predictions = tf.argmax(self.fc3, 1, name="predictions")
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.fc3, labels=self.input_y)
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
self.loss = tf.reduce_mean(losses) + sum(regularization_losses)
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
| tensorflow.reduce_mean | 12,151 |
import tensorflow as tf
# (T,B,D) => (B,T,D)
facts = tf.array_ops.transpose(facts, [1, 0, 2])
# Trainable parameters
mask = tf.equal(mask, tf.ones_like(mask))
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
query = tf.layers.dense(query, facts_size, activation=None, name='f1_trans_shine' + stag)
query = prelu(query)
queries = tf.tile(query, [1, tf.shape(facts)[1]])
queries = tf.reshape(queries, tf.shape(facts))
din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = tf.layers.dense(din_all, facts_size, activation=tf.nn.sigmoid, name='f1_shine_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, facts_size, activation=tf.nn.sigmoid, name='f2_shine_att' + stag)
d_layer_2_all = tf.reshape(d_layer_2_all, tf.shape(facts))
output = d_layer_2_all
return output
| tensorflow.shape | 12,152 |
import tensorflow as tf
# segment.
#
# If you want to use the token-level output, use model.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)
| tensorflow.zeros_initializer | 12,153 |
import tensorflow as tf
self.epsilon = epsilon
self.axis = axis
self.center=center
self.scale=scale
with tf.variable_scope(name) as scope:
with tf.variable_scope('bn') :
self.gamma= tf.get_variable('gamma',[dims], initializer=tf.constant_initializer(1.0))
self.beta = tf.get_variable('beta',[dims], initializer=tf.constant_initializer(0.0))
self.moving_mean = tf.get_variable('moving_mean',[dims], initializer=tf.constant_initializer(0.0), trainable=False)
self.moving_variance = tf.get_variable('moving_variance',[dims], initializer=tf.constant_initializer(1.0), trainable=False)
self.scope = scope
def __call__(self,input_var,is_training,**xargs) :
with tf.variable_scope(self.scope) :
return tf.layers.batch_normalization(
input_var,
axis=self.axis,
momentum=self.momentum,
epsilon=self.epsilon,
center=self.center,
scale=self.scale,
training=is_training,
reuse=True,
name='bn')
"""
---Do NOT forget to add update_ops dependencies for your loss function.---
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,tf.get_default_graph().get_name_scope())
#And, do not make any scope inside map_fn, since scope.name will not work...(it is corrupted by map_fn.)
| tensorflow.layers.batch_normalization | 12,154 |
import tensorflow as tf
else:
return -tf.reduce_sum(log_sum_exp(log_probs), [1, 2])
def mse_loss(pred, labels):
try:
batch_size = tf.cast(pred.shape[0], tf.float32)
except Exception as e:
print('Pred is a tf tensor %s' % str(e.message))
batch_size = tf.cast(tf.shape(pred)[0], tf.float32)
loss_val = tf.sqrt(2 * tf.nn.l2_loss(pred - labels)) / batch_size
return loss_val
def pullaway_loss(embeddings, name='pullaway_loss'):
"""Pull Away loss calculation.
Args:
| tensorflow.shape | 12,155 |
import tensorflow as tf
for input_file in input_files:
tf.logging.info(" %s" % input_file)
validation_input_files = []
if FLAGS.validation_input_file is None and FLAGS.validation_input_dir is None:
validation_input_files = input_files
else:
if FLAGS.validation_input_file is not None:
for input_pattern in FLAGS.validation_input_file.split(","):
validation_input_files.extend(tf.gfile.Glob(input_pattern))
if FLAGS.validation_input_dir is not None:
for filename in tf.gfile.ListDirectory(FLAGS.validation_input_dir):
validation_input_files.extend(tf.gfile.Glob(os.path.join(FLAGS.validation_input_dir, filename)))
tf.logging.info("*** Input Validation Files ***")
for input_file in validation_input_files:
tf.logging.info(" %s" % input_file)
config = tf.ConfigProto()
if FLAGS.xla:
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
if FLAGS.use_hvd:
| tensorflow.gfile.ListDirectory | 12,156 |
import tensorflow as tf
if chaining_strategy == 'map_attns':
x = attns
elif chaining_strategy == 'map_outputs':
x = decoder_outputs
else:
x = states
shape = [x.get_shape()[-1], attention_states[0].get_shape()[-1]]
w = tf.get_variable("map_attns/matrix", shape=shape)
b = tf.get_variable("map_attns/bias", shape=shape[-1:])
x = tf.einsum('ijk,kl->ijl', x, w) + b
if chaining_non_linearity:
x = tf.nn.tanh(x)
attention_states[0] += x
outputs, attention_weights, _, _, samples, beam_fun, initial_data = attention_decoder(
attention_states=attention_states, initial_state=encoder_state,
feed_previous=feed_previous, decoder_inputs=targets[:,:-1],
align_encoder_id=align_encoder_id, encoder_input_length=encoder_input_length[:1],
**parameters
)
| tensorflow.einsum | 12,157 |
import tensorflow as tf
Kuu = feat.Kuu(kern, jitter=settings.numerics.jitter_level) # M x M
Luu = tf.cholesky(Kuu) # M x M
| tensorflow.cholesky | 12,158 |
from tensorflow.python.ops.rnn_cell_impl import _Linear
if self._candidate_linear is None:
with vs.variable_scope("candidate"):
self._candidate_linear = _Linear(
[inputs, r_state],
| tensorflow.python.ops.rnn_cell_impl._Linear | 12,159 |
import tensorflow as tf
grads.append(expanded_g)
grad = tf.concat(grads, 0)
grad = tf.reduce_mean(grad, 0)
var = grad_and_vars[0][1]
grad_and_var = (grad, var)
average_grads.append(grad_and_var)
return average_grads
def binary_mask(shape, p=0.7):
samples = tf.random_uniform(shape, minval=0.0, maxval=1.0)
mask = tf.less_equal(samples, p)
return tf.cast(mask, tf.float32)
def weighted_arithmetic_mean(w, x):
numer = tf.reduce_sum(w*x)
denom = tf.reduce_sum(w)
return tf.div(numer, denom)
| tensorflow.reduce_sum | 12,160 |
import tensorflow as tf
Build the RESNET model using loaded weights
"""
print("Building the RESNET..")
# Convert RGB to BGR
with tf.name_scope('Pre_Processing'):
self.x_preprocessed = self.x_input * (1.0 / 255.0)
# self.x_preprocessed= self.x_input
stat= torchfile.load('stat.t7')
self.resnet_mean= stat.transpose(1,2,0)
# self.resnet_mean = tf.constant([0.2869, 0.3251, 0.2839], dtype=tf.float32)
| tensorflow.name_scope | 12,161 |
import tensorflow as tf
gtboxes_and_label_h = tf.reshape(gtboxes_and_label_h, [cfgs.BATCH_SIZE, -1, 5])
gtboxes_and_label_q = tf.reshape(gtboxes_and_label_batch[start:end], [cfgs.BATCH_SIZE, -1, 9])
num_objects = num_objects_batch[start:end]
num_objects = tf.cast(tf.reshape(num_objects, [cfgs.BATCH_SIZE, -1, ]), tf.float32)
img_h = img_h_batch[start:end]
img_w = img_w_batch[start:end]
inputs_list.append([img, gtboxes_and_label_h,
gtboxes_and_label_q, num_objects, img_h, img_w])
tower_grads = []
biases_regularizer = tf.no_regularizer
weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY)
with tf.variable_scope(tf.get_variable_scope()):
for i in range(num_gpu):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i):
with slim.arg_scope(
[slim.model_variable, slim.variable],
device='/device:CPU:0'):
with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane,
slim.conv2d_transpose, slim.separable_conv2d,
slim.fully_connected],
weights_regularizer=weights_regularizer,
biases_regularizer=biases_regularizer,
biases_initializer=tf.constant_initializer(0.0)):
| tensorflow.contrib.layers.l2_regularizer | 12,162 |
from tensorflow.python.ops import array_ops
def __init__(self, label_name, weight_column_name):
def loss_fn(logits, target):
check_shape_op = control_flow_ops.Assert(
math_ops.less_equal(array_ops.rank(target), 2),
["target's shape should be either [batch_size, 1] or [batch_size]"])
with ops.control_dependencies([check_shape_op]):
target = array_ops.reshape(
target, shape=[array_ops.shape(target)[0], 1])
return loss_ops.hinge_loss(logits, target)
super(_BinarySvmTargetColumn, self).__init__(
loss_fn=loss_fn,
n_classes=2,
label_name=label_name,
weight_column_name=weight_column_name)
| tensorflow.python.ops.array_ops.shape | 12,163 |
import tensorflow as tf
else:
# Do not perturb, just assign.
op = tf.assign(perturbed_var, var)
perturb_ops.append(op)
assert len(perturb_ops) == len(all_vars)
return tf.group(*perturb_ops)
# 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,
lambda: param_noise_scale.assign(param_noise_scale * 1.01),
lambda: param_noise_scale.assign(param_noise_scale / 1.01),
)
return update_scale_expr
# Functionality to update the threshold for parameter space noise.
update_param_noise_threshold_expr = param_noise_threshold.assign(tf.cond(update_param_noise_threshold_ph >= 0,
| tensorflow.nn.softmax | 12,164 |
from tensorflow.python.framework import ops
@ops.RegisterShape("ScatterAdd")
@ops.RegisterShape("ScatterSub")
@ops.RegisterShape("ScatterUpdate")
def _ScatterUpdateShape(op):
| tensorflow.python.framework.ops.RegisterShape | 12,165 |
import tensorflow as tf
for grad, var in grads]
# add vanishing gradient regularizer
#out, test = self.dOmega_dWrec()
#clipped_grads[0] = (tf.add(out[0], clipped_grads[0][0]), clipped_grads[0][1])
#clipped_grads[0] = (tf.Print(clipped_grads[0][0], [clipped_grads[0][0]], "gw_rec"), clipped_grads[0][1])
optimize = optimizer.apply_gradients(clipped_grads)
# run session
sess.run(tf.global_variables_initializer())
step = 1
# time training
t1 = time()
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y, output_mask = generator.next()
sess.run(optimize, feed_dict={self.x: batch_x, self.y: batch_y, self.output_mask: output_mask})
if step % display_step == 0:
| tensorflow.global_variables_initializer | 12,166 |
from tensorflow.python.ops import math_ops
return train_tensor
def _clip_gradients_by_norm(grads_and_vars, clip_gradients):
"""Clips gradients by global norm."""
gradients, variables = zip(*grads_and_vars)
clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients)
return list(zip(clipped_gradients, variables))
def _adaptive_max_norm(norm, std_factor, decay, global_step, epsilon, name):
"""Find max_norm given norm and previous average."""
with vs.variable_scope(name, "AdaptiveMaxNorm", [norm]):
log_norm = math_ops.log(norm + epsilon)
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:
| tensorflow.python.ops.math_ops.log | 12,167 |
import tensorflow as tf
images = tf.cast(input_dict[fields.InputDataFields.image], dtype=tf.float32)
images = tf.expand_dims(images, axis=0)
true_image_shape = tf.expand_dims(
input_dict[fields.InputDataFields.true_image_shape], axis=0)
| tensorflow.expand_dims | 12,168 |
import tensorflow as tf
states_tiled = tf.tile(states[:, None], [1, num_tasks, 1]) # B x B x D
states_tiled = tf.reshape(states_tiled,
[batch_size * num_tasks, obs_dim]) # B*B x D
actions_tiled = tf.tile(actions[:, None], [1, num_tasks, 1]) # B x B x D
actions_tiled = tf.reshape(actions_tiled,
[batch_size * num_tasks, action_dim]) # B*B x D
tasks_tiled = tf.tile(tasks[None], [batch_size, 1, 1]) # B x B x D
| tensorflow.tile | 12,169 |
import tensorflow as tf
return tf.maximum(learning_rate * decay, 5e-6)
elif params.learning_rate_decay == "piecewise_constant":
return tf.train.piecewise_constant(tf.to_int32(global_step),
params.learning_rate_boundaries,
params.learning_rate_values)
elif params.learning_rate_decay == "none":
return learning_rate
else:
raise ValueError("Unknown learning_rate_decay")
def session_config(params):
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L1,
do_function_inlining=True)
graph_options = tf.GraphOptions(optimizer_options=optimizer_options)
config = tf.ConfigProto(allow_soft_placement=True,
graph_options=graph_options)
if params.device_list:
device_str = ",".join([str(i) for i in params.device_list])
config.gpu_options.visible_device_list = device_str
config.gpu_options.per_process_gpu_memory_fraction = params.gpu_memory_fraction
config.gpu_options.allow_growth = True
return config
| tensorflow.OptimizerOptions | 12,170 |
import tensorflow as tf
init_fw = tf.tile(tf.Variable(
tf.zeros([1, 1, num_units])), [1, batch_size, 1])
init_bw = tf.tile(tf.Variable(
tf.zeros([1, 1, num_units])), [1, batch_size, 1])
mask_fw = dropout(tf.ones([1, batch_size, input_size_], dtype=tf.float32),
keep_prob=keep_prob, is_train=is_train, mode=None)
mask_bw = dropout(tf.ones([1, batch_size, input_size_], dtype=tf.float32),
keep_prob=keep_prob, is_train=is_train, mode=None)
self.grus.append((gru_fw, gru_bw, ))
self.inits.append((init_fw, init_bw, ))
self.dropout_mask.append((mask_fw, mask_bw, ))
| tensorflow.ones | 12,171 |
import tensorflow as tf
def testNonReshape(self):
save_path = os.path.join(self.get_temp_dir(), "basics")
with self.test_session() as sess:
# Build a graph with 2 parameter nodes, and Save and
# Restore nodes for them.
v0 = tf.Variable(10.0, name="v0")
v1 = tf.Variable(20.0, name="v1")
save = tf.train.Saver({"save_prefix/v0": v0, "save_prefix/v1": v1})
tf.initialize_all_variables().run()
# Check that the parameter nodes have been initialized.
self.assertEqual(10.0, v0.eval())
self.assertEqual(20.0, v1.eval())
# Save the initialized values in the file at "save_path"
# Use a variable name map to set the saved tensor names
val = save.save(sess, save_path)
self.assertTrue(isinstance(val, six.string_types))
| tensorflow.initialize_all_variables | 12,172 |
import tensorflow as tf
if anchor_match_mining_distance_matrix is None:
anchor_match_mining_distance_matrix = anchor_match_distance_matrix
if use_semi_hard:
if anchor_positive_mining_distances is None:
raise ValueError('Positive match embeddings must be specified to compute '
'semi-hard distances.')
anchor_positive_mining_distances = tf.expand_dims(
anchor_positive_mining_distances, axis=-1)
indicators &= (
anchor_match_mining_distance_matrix > anchor_positive_mining_distances)
def find_hard_distances(distance_matrix, indicator_matrix):
distance_matrix = tf.where(
| tensorflow.expand_dims | 12,173 |
import tensorflow as tf
op2 = cell_arch[bi][3]
with tf.variable_scope('X1'):
X1 = self._add_op(cell_inputs, blocks, idx1, op1, w, h, block_ch,
is_reduction=is_reduction, is_train=is_train)
X1 = self._add_drop_path(X1, drop_path_keep_prob)
with tf.variable_scope('X2'):
X2 = self._add_op(cell_inputs, blocks, idx2, op2, w, h, block_ch,
is_reduction=is_reduction, is_train=is_train)
X2 = self._add_drop_path(X2, drop_path_keep_prob)
X = tf.add_n([X1, X2])
| tensorflow.variable_scope | 12,174 |
import tensorflow as tf
nms_scores_expected1 = tf.constant([], dtype=tf.float32)
nms_classes_expected1 = tf.constant([], dtype=tf.int32)
(nms_masks2,
nms_scores2,
nms_classes2,
_) = isu.instance_non_maximum_suppression_1d_scores(
masks,
scores,
classes,
min_score_thresh=0.65,
min_iou_thresh=0.5,
is_class_agnostic=False)
nms_masks_expected2 = tf.constant(1.0, shape=[0, 2, 2], dtype=tf.float32)
nms_scores_expected2 = tf.constant([], dtype=tf.float32)
nms_classes_expected2 = tf.constant([], dtype=tf.int32)
self.assertAllEqual(nms_masks1.numpy(), nms_masks_expected1.numpy())
self.assertAllClose(nms_scores1.numpy(), nms_scores_expected1.numpy())
self.assertAllEqual(nms_classes1.numpy(), nms_classes_expected1.numpy())
self.assertAllEqual(nms_masks2.numpy(), nms_masks_expected2.numpy())
self.assertAllClose(nms_scores2.numpy(), nms_scores_expected2.numpy())
self.assertAllEqual(nms_classes2.numpy(), nms_classes_expected2.numpy())
def test_instance_non_maximum_suppression_2d_scores(self):
mask0 = tf.constant([[1, 0],
| tensorflow.constant | 12,175 |
from tensorflow.python.client import graph_util
@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,
| tensorflow.python.client.graph_util.tensor_shape_from_node_def_name | 12,176 |
from tensorflow.python.training import saver as saver_lib
return self._best_value
def every_n_step_end(self, step, outputs):
super(ValidationMonitor, self).every_n_step_end(step, outputs)
# TODO(mdan): The use of step below is probably misleading.
# The code should probably use the step from the checkpoint, because
# that's what is being evaluated.
if self._estimator is None:
raise ValueError("Missing call to set_estimator.")
# Check that we are not running evaluation on the same checkpoint.
latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
if latest_path is None:
logging.debug("Skipping evaluation since model has not been saved yet "
"at step %d.", step)
return False
if latest_path is not None and latest_path == self._latest_path:
logging.debug("Skipping evaluation due to same checkpoint %s for step %d "
"as for step %d.", latest_path, step,
self._latest_path_step)
return False
| tensorflow.python.training.saver.latest_checkpoint | 12,177 |
import tensorflow as tf
with tf.variable_scope(encoderscope) as scope:
if reuse_encoder: scope.reuse_variables()
with tf.variable_scope('color_encoder'):
X = encoder_conf('eI', I[:, :, :, :-1], 96, 5, 1, norm, reuse_encoder, is_train, self.args.dropout) # 128 > 124
X0 = encoder_conf('d0', X, 96, 2, 2, norm, reuse_encoder, is_train, self.args.dropout) # 124 > 62 @2
X = encoder_conf('e1', X0, 128, 3, 1, norm, reuse_encoder, is_train, self.args.dropout) # 62 > 60
X_EARLY = X
X1 = encoder_conf('d1', X, 128, 2, 2, norm, reuse_encoder, is_train, self.args.dropout) # 60 > 30 @4
X = encoder_conf('e2', X1, 256, 3, 1, norm, reuse_encoder, is_train, self.args.dropout) # 30 > 28
X2 = encoder_conf('d2', X, 256, 2, 2, norm, reuse_encoder, is_train, self.args.dropout) # 28 > 14 @8
X = encoder_conf('e3', X2, 512, 3, 1, norm, reuse_encoder, is_train, self.args.dropout) # 14 > 12
X_MIDDLE = X
# ===============================================================================DECODER
with tf.variable_scope(decoderscope) as scope:
if reuse_decoder: scope.reuse_variables()
# print('vnet scope', is_train, reuse_unet)
# print('VNET Latent:', X.get_shape().as_list())
with tf.variable_scope('decoder'):
X = decoder_conf('d3', X, 512, F, 1, norm, reuse_decoder, is_train, self.args.dropout) # 12 > 14
if self.args.skip_connections: X = tf.concat((X, X2), axis=-1)
X = decoder_conf('u4', X, 256, F, 2, norm, reuse_decoder, is_train, self.args.dropout) # 14 > 28
X = decoder_conf('d4', X, 256, F, 1, norm, reuse_decoder, is_train, self.args.dropout) # 28 > 30
if self.args.skip_connections: X = tf.concat((X, X1), axis=-1)
X = decoder_conf('u5', X, 128, F, 2, norm, reuse_decoder, is_train, self.args.dropout) # 30 > 60
X_LATE = X
X = decoder_conf('d5', X, 128, F, 1, norm, reuse_decoder, is_train, self.args.dropout) # 60 > 62
if self.args.skip_connections: X = tf.concat((X, X0), axis=-1)
| tensorflow.variable_scope | 12,178 |
import tensorflow as tf
FLAGS.checkpoint_dir,
hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def _get_generator_inputs(num_images_per_class, num_classes, noise_dims):
# Since we want a grid of numbers for the conditional generator, manually
# construct the desired class labels.
num_images_generated = num_images_per_class * num_classes
noise = tf.random_normal([num_images_generated, noise_dims])
labels = [lbl for lbl in range(num_classes) for _
in range(num_images_per_class)]
one_hot_labels = tf.one_hot(tf.constant(labels), num_classes)
return noise, one_hot_labels
if __name__ == '__main__':
app.run(main)
| tensorflow.random_normal | 12,179 |
import tensorflow as tf
all_trainable_weights_pi = tf.trainable_variables('model/pi')
regularization_penalty_pi = tf.contrib.layers.apply_regularization(regularizerpi, all_trainable_weights_pi)
| tensorflow.contrib.layers.apply_regularization | 12,180 |
import tensorflow as tf
with tf.variable_scope('C_train'):
self.train_op = tf.train.AdamOptimizer(self.lr).minimize(self.loss, global_step=GLOBAL_STEP)
with tf.variable_scope('a_grad'):
self.a_grads = tf.gradients(self.q, a)[0] # tensor of gradients of each sample (None, a_dim)
def _build_net(self, s, a, scope, trainable):
with tf.variable_scope(scope):
init_w = tf.random_normal_initializer(0., 0.01)
init_b = tf.constant_initializer(0.01)
with tf.variable_scope('l1'):
n_l1 = 700
# combine the action and states together in this way
w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], initializer=init_w, trainable=trainable)
| tensorflow.variable_scope | 12,181 |
from tensorflow.python.platform import gfile
self.assertEqual([s2, s3], save.last_checkpoints)
self.assertEqual(0, len(gfile.Glob(s1)))
self.assertFalse(gfile.Exists(save._MetaGraphFilename(s1)))
self.assertEqual(2, len(gfile.Glob(s2)))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2)))
self.assertEqual(2, len(gfile.Glob(s3)))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s3)))
class KeepCheckpointEveryNHoursTest(tf.test.TestCase):
| tensorflow.python.platform.gfile.Glob | 12,182 |
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
| tensorflow.config.experimental.set_memory_growth | 12,183 |
import tensorflow as tf
with tf.Session(config=config) as sess:
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
tf.train.Saver().restore(sess,path)
#test
test_acc_avg = 0.0
test_true_total=np.array([])
| tensorflow.train.Saver | 12,184 |
import tensorflow as tf
class SaverTest(tf.test.TestCase):
def testBasics(self):
save_path = os.path.join(self.get_temp_dir(), "basics")
with self.test_session() as sess:
# Build a graph with 2 parameter nodes, and Save and
# Restore nodes for them.
v0 = tf.Variable(10.0, name="v0")
v1 = tf.Variable(20.0, name="v1")
save = tf.train.Saver({"v0": v0, "v1": v1}, restore_sequentially=True)
tf.initialize_all_variables().run()
# Check that the parameter nodes have been initialized.
self.assertEqual(10.0, v0.eval())
self.assertEqual(20.0, v1.eval())
# Save the initialized values in the file at "save_path"
val = save.save(sess, save_path)
self.assertTrue(isinstance(val, six.string_types))
| tensorflow.Variable | 12,185 |
import tensorflow as tf
# Convert input R+1 tensor into a feature dictionary of one R+1 tensor
features = {TIMESERIES_COL: inputs}
return features, labels
# Create list of files that match pattern
file_list = tf.gfile.Glob(filename)
# Create dataset from file list
dataset = tf.data.TextLineDataset(file_list).map(decode_csv)
if mode == tf.estimator.ModeKeys.TRAIN:
num_epochs = None # indefinitely
dataset = dataset.shuffle(buffer_size = 10 * batch_size)
else:
num_epochs = 1 # end-of-input after this
dataset = dataset.repeat(num_epochs).batch(batch_size)
| tensorflow.data.TextLineDataset | 12,186 |
import tensorflow as tf
tf.greater_equal(
matched_iou, self._config_dict['background_iou_low_threshold']),
tf.less(
matched_iou, self._config_dict['background_iou_high_threshold']))
| tensorflow.less | 12,187 |
from tensorflow.python.ops import array_ops
max_update = state_ops.assign_add(max_var, batch_max, name='update')
with ops.name_scope(None, 'total', (average_precision,)) as total_scope:
total_var = contrib_variables.local_variable(
array_ops.zeros([], dtype=dtypes.float64), name=total_scope)
batch_total = math_ops.reduce_sum(average_precision, name='batch_total')
total_update = state_ops.assign_add(total_var, batch_total, name='update')
| tensorflow.python.ops.array_ops.zeros | 12,188 |
import tensorflow as tf
if pairwise_reduction == common.DISTANCE_REDUCTION_NEG_LOG_MEAN:
return lambda x: -tf.math.log(tf.math.reduce_mean(x, axis=[-2, -1]))
if pairwise_reduction == common.DISTANCE_REDUCTION_LOWER_HALF_NEG_LOG_MEAN:
def compute_lower_half_negative_log_mean(x):
return -tf.math.log(
data_utils.compute_lower_percentile_means(x, axis=[-2, -1], q=50))
return compute_lower_half_negative_log_mean
if pairwise_reduction == common.DISTANCE_REDUCTION_ONE_MINUS_MEAN:
return lambda x: 1.0 - tf.math.reduce_mean(x, axis=[-2, -1])
return pairwise_reduction
def get_componentwise_distance_reduction_fn():
"""Selects component-wise distance reduction function."""
if componentwise_reduction == common.DISTANCE_REDUCTION_MEAN:
return functools.partial(tf.math.reduce_mean, axis=[-1])
return componentwise_reduction
def sample_distance_fn(lhs, rhs):
| tensorflow.math.reduce_mean | 12,189 |
import tensorflow as tf
tf.app.flags.DEFINE_boolean('use_processed_data', False, 'whether to use processed data')
tf.app.flags.DEFINE_string('processed_data', './processed_dataset/', 'where to save preprocessed datasets')
| tensorflow.app.flags.DEFINE_string | 12,190 |
import tensorflow as tf
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
org_ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or org_ivec
with tf.variable_scope(scope or 'directional_attention_%s' % direction or 'diag'):
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map', activation,
False, wd, keep_prob, is_train)
# ensure the seletion is right
dep_selection = tf.logical_and(rep_mask, dep_selection)
head_selection = tf.logical_and(rep_mask, head_selection)
rep_dep_tensor, rep_dep_mask, dep_org_idx = reduce_data_rep_max_len(rep_map, dep_selection)
rep_head_tensor,rep_head_mask, head_org_idx = reduce_data_rep_max_len(rep_map, head_selection)
sl_dep, sl_head = tf.shape(rep_dep_tensor)[1], tf.shape(rep_head_tensor)[1]
if keep_unselected:
unhead_selection = tf.logical_and(rep_mask, tf.logical_not(head_selection))
rep_unhead_tensor, rep_unhead_mask, unhead_org_idx = reduce_data_rep_max_len(rep_map, unhead_selection)
sl_unhead = tf.shape(rep_unhead_tensor)[1]
attn_result = tf.cond(
tf.equal(sl_head, 0),
lambda: tf.zeros([bs, 0, hn], tf.float32),
lambda: self_attention_for_selected_head(
head_selection, head_org_idx, sl_head, rep_head_mask,
dep_selection, dep_org_idx, sl_dep, rep_dep_mask,
rep_map, rep_dep_tensor, keep_prob, is_train, direction, ivec
| tensorflow.shape | 12,191 |
import tensorflow as tf
self.assertAllEqual(
padded_tensor_dict[fields.InputDataFields.image].shape.as_list(),
[5, 6, 3])
self.assertAllEqual(
padded_tensor_dict[fields.InputDataFields.image_additional_channels]
.shape.as_list(), [5, 6, 2])
def test_keypoints(self):
input_tensor_dict = {
fields.InputDataFields.groundtruth_keypoints:
tf.placeholder(tf.float32, [None, 16, 4]),
fields.InputDataFields.groundtruth_keypoint_visibilities:
tf.placeholder(tf.bool, [None, 16]),
}
padded_tensor_dict = inputs.pad_input_data_to_static_shapes(
tensor_dict=input_tensor_dict,
max_num_boxes=3,
num_classes=3,
spatial_image_shape=[5, 6])
| tensorflow.placeholder | 12,192 |
import tensorflow as tf
# In[2]:
def prenet(inputs, num_units=None, is_training=True, scope="prenet"):
if num_units is None:
num_units = [embed_size, embed_size // 2]
with tf.variable_scope(scope):
outputs = tf.layers.dense(inputs, units=num_units[0], activation=tf.nn.relu, name="dense1")
outputs = tf.layers.dropout(
outputs, rate=dropout_rate, training=is_training, name="dropout1"
)
outputs = tf.layers.dense(outputs, units=num_units[1], activation=tf.nn.relu, name="dense2")
outputs = tf.layers.dropout(
outputs, rate=dropout_rate, training=is_training, name="dropout2"
)
return outputs
def highwaynet(inputs, num_units=None, scope="highwaynet"):
if not num_units:
num_units = inputs.get_shape()[-1]
with tf.variable_scope(scope):
H = tf.layers.dense(inputs, units=num_units, activation=tf.nn.relu, name="dense1")
| tensorflow.layers.dense | 12,193 |
import tensorflow as tf
# self.final_layer = self.conv_layer(bottom = self.deconv_5, kernal_size = 1, in_channels = 64, out_channels = 3, stride = 1, name = 'final_layer')
self.final_layer = self.conv_bn_relu(bottom = self.deconv_5, name = 'final_layer', kernel_size = 1, output_channels = 3, initializer =tf.contrib.layers.variance_scaling_initializer(), bn = False, training = self.is_training, relu=False)
| tensorflow.contrib.layers.variance_scaling_initializer | 12,194 |
import tensorflow as tf
the dual variable is scaled by this factor.
Returns:
dual_value: An op that computes the absolute value of the dual variable
and reverses its gradient.
dual_variable: The underlying variable itself.
"""
# We disable partitioning while constructing dual variables because they will
# be updated with assign, which is not available for partitioned variables.
partitioner = tf.get_variable_scope().partitioner
try:
tf.get_variable_scope().set_partitioner(None)
dual_variable = tf.contrib.framework.model_variable(
name=name,
shape=shape,
dtype=dtype,
initializer=initializer,
| tensorflow.get_variable_scope | 12,195 |
import tensorflow as tf
tf.reset_default_graph()
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1)
session = tf.Session(config=tf_config)
print("AVAILABLE GPUS: ", get_available_gpus())
return session
| tensorflow.Session | 12,196 |
import tensorflow as tf
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.nn.sigmoid | 12,197 |
import tensorflow as tf
x_aug = tf.convert_to_tensor(x_aug)
return tf.concat([x, x_aug],axis=2)
def gaussian_blur(self,x):
#create random gaussian blur filter
mean = 0
std = tf.random.uniform(shape=[],minval=5,maxval=10,dtype=tf.float32) # std [5-10]
size = tf.random.uniform(shape=[],minval=3,maxval=7,dtype=tf.int32) # size [7-15]
self.kernel = self.gaussian_kernel(size,mean,std)
self.kernel = tf.tile(self.kernel[:, :, tf.newaxis, tf.newaxis], [1, 1, 3, 1])
self.paddings = tf.convert_to_tensor([[size,size],[size,size],[0,0]])
x_aug = tf.nn.separable_conv2d(tf.expand_dims(tf.pad(x,self.paddings,'SYMMETRIC'), 0), self.kernel, self.pointwise_filter,strides=[1, 1, 1, 1], padding='VALID')
x_aug = tf.squeeze(x_aug)
return tf.concat([x, x_aug],axis=2)
| tensorflow.random.uniform | 12,198 |
from tensorflow.python.ops import array_ops
undefined statistics will return NaN for this statistic.
name: The name to prepend to all ops created by this distribution.
Raises:
TypeError: if `alpha` and `beta` are different dtypes.
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[alpha, beta]) as ns:
with ops.control_dependencies([
check_ops.assert_positive(alpha),
check_ops.assert_positive(beta),
] if validate_args else []):
self._alpha = array_ops.identity(alpha, name="alpha")
self._beta = array_ops.identity(beta, name="beta")
super(InverseGamma, self).__init__(
dtype=self._alpha.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
is_continuous=True,
is_reparameterized=False,
parameters=parameters,
graph_parents=[self._alpha, self._beta],
name=ns)
@staticmethod
| tensorflow.python.ops.array_ops.identity | 12,199 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.