seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
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)
if task_name != "sts-b":
probabilities = tf.nn.softmax(logits, axis=-1)
predictions = tf.argmax(probabilities, axis=-1, output_type=tf.int32)
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)
else:
probabilities = logits
logits = tf.squeeze(logits, [-1])
predictions = logits
per_example_loss = tf.square(logits - labels)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, probabilities, logits, predictions)
def model_fn_builder(config, num_labels, init_checkpoint, learning_rate,
|
tensorflow.reduce_sum
| 9,200 |
import tensorflow as tf
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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
| 9,201 |
import tensorflow as tf
with tf.variable_scope(self.model_scope):
# loss & extra evaluation metrics
logits = self.forward_train(images)
self.maskable_var_names = [var.name for var in self.maskable_vars]
loss, metrics = self.calc_loss(labels, logits, self.trainable_vars)
if FLAGS.enbl_dst:
loss += self.helper_dst.calc_loss(logits, logits_dst)
tf.summary.scalar('loss', loss)
for key, value in metrics.items():
tf.summary.scalar(key, value)
# learning rate schedule
self.global_step = tf.train.get_or_create_global_step()
lrn_rate, self.nb_iters_train = self.setup_lrn_rate(self.global_step)
# overall pruning ratios of trainable & maskable variables
pr_trainable = calc_prune_ratio(self.trainable_vars)
pr_maskable = calc_prune_ratio(self.maskable_vars)
|
tensorflow.summary.scalar
| 9,202 |
import tensorflow as tf
out = conv(out, [2*dim[0], dim[1], dim[2]], scope="%s_conv_out"%scope, training=training, ema=ema, init=init)
h_stack1, h_stack2 = tf.split(out, 2, 3)
sigmoid_out = tf.sigmoid(h_stack2)
out = (h_stack1 * sigmoid_out)
out_shp = out.get_shape().as_list()
if out_shp[1:-1] < in_shp[1:-1]:
x = tf.nn.avg_pool(x, [1, dim[2][0], dim[2][1], 1], strides=[1, dim[2][0], dim[2][1], 1], padding='SAME')
elif out_shp[1:-1] > in_shp[1:-1]:
warnings.warn("The height and width of the output are larger than the input. There will be no residual connection.")
residual = False
if out_shp[-1] > in_shp[-1]:
x = tf.pad(x, [[0, 0], [0, 0], [0, 0], [0, int(dim[0] - in_shp[-1])]])
|
tensorflow.nn.avg_pool
| 9,203 |
import tensorflow as tf
"input_ids":
tf.constant(
all_input_ids, shape=[num_examples, seq_length],
dtype=tf.int32),
"input_mask":
tf.constant(
all_input_mask,
shape=[num_examples, seq_length],
dtype=tf.int32),
"segment_ids":
|
tensorflow.constant
| 9,204 |
import tensorflow as tf
alpha = tf.nn.softmax(out_att)
context = tf.reduce_sum(features * tf.expand_dims(alpha, 2), 1, name='context') #(N, D)
|
tensorflow.expand_dims
| 9,205 |
import tensorflow as tf
# case: train mode (uses stats of the current batch)
mean = tf.reduce_mean(_x, axis=reduction_axes)
brodcast_mean = tf.reshape(mean, broadcast_shape)
std = tf.reduce_mean(tf.square(_x - brodcast_mean) + epsilon, axis=reduction_axes)
std = tf.sqrt(std)
|
tensorflow.reshape
| 9,206 |
import tensorflow as tf
Batch tensor of the new observations.
"""
if indices is None:
indices = tf.range(len(self._batch_env))
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
observ = tf.py_func(
self._batch_env.reset, [indices], observ_dtype, name='reset')
observ = tf.check_numerics(observ, 'observ')
reward = tf.zeros_like(indices, tf.float32)
done = tf.zeros_like(indices, tf.bool)
with tf.control_dependencies([
tf.scatter_update(self._observ, indices, observ),
tf.scatter_update(self._reward, indices, reward),
tf.scatter_update(self._done, indices, done)]):
return tf.identity(observ)
@property
def observ(self):
"""Access the variable holding the current observation."""
return self._observ
@property
def action(self):
"""Access the variable holding the last received action."""
return self._action
@property
def reward(self):
|
tensorflow.identity
| 9,207 |
import tensorflow as tf
width = tf.shape(inputs)[2]
out_height = get_deconv_dim(height, stride_h, kernel_h, padding)
out_width = get_deconv_dim(width, stride_w, kernel_w, padding)
output_shape = tf.stack([batch_size, out_height, out_width, num_output_channels], axis=0)
outputs = tf.nn.conv2d_transpose(inputs, kernel, output_shape,
[1, stride_h, stride_w, 1],
padding=padding)
biases = _variable_on_cpu('biases', [num_output_channels],
tf.constant_initializer(0.0))
outputs = tf.nn.bias_add(outputs, biases)
if bn:
# outputs = batch_norm_for_conv2d(outputs, is_training,
# bn_decay=bn_decay, scope='bn')
outputs = tf.layers.batch_normalization(outputs, momentum=0.99, epsilon=1e-6, training=is_training)
if activation_fn is not None:
# outputs = activation_fn(outputs)
outputs = tf.nn.leaky_relu(outputs, alpha=0.2)
return outputs
|
tensorflow.nn.bias_add
| 9,208 |
import tensorflow as tf
import warnings
# https://stackoverflow.com/questions/15777951/how-to-suppress-pandas-future-warning
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=Warning)
import tensorflow as tf
tf.get_logger().setLevel('INFO')
tf.autograph.set_verbosity(0)
import logging
tf.get_logger().setLevel(logging.ERROR)
# For ACER
def get_by_index(input_tensor, idx):
"""
Return the input tensor, offset by a certain value
:param input_tensor: (TensorFlow Tensor) The input tensor
|
tensorflow.get_logger
| 9,209 |
import tensorflow as tf
self._beta = None
if self._scale:
self._set_default_initializer(self.GAMMA)
self._gamma = tf.get_variable(
self.GAMMA,
shape=self._mean_shape,
initializer=self._initializers[self.GAMMA])
|
tensorflow.get_variable
| 9,210 |
import tensorflow as tf
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
is_real_example=True)
return feature
def file_based_convert_examples_to_features(
examples, label_list, max_seq_length, tokenizer, output_file):
"""Convert a set of `InputExample`s to a TFRecord file."""
writer = tf.python_io.TFRecordWriter(output_file)
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
feature = convert_single_example(ex_index, example, label_list,
max_seq_length, tokenizer)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
|
tensorflow.python_io.TFRecordWriter
| 9,211 |
import tensorflow as tf
return dataset
@gin.configurable(module='trax.data', denylist=['dataset', 'training'])
def lm_token_preprocessing(dataset, training):
"""Concatenates inputs, 0, targets, with masking only for targets."""
del training
def concat_and_add_mask(x):
inp = x['inputs']
targets = x['targets']
pad = tf.expand_dims(tf.zeros_like(inp[0]), axis=0)
concat = tf.concat([inp, pad, targets], axis=0)
mask = tf.concat([tf.zeros_like(inp), pad, tf.ones_like(targets)], axis=0)
x['inputs'] = concat
x['targets'] = concat
x['mask'] = mask
return x
dataset = dataset.map(concat_and_add_mask)
return dataset
@gin.configurable(module='trax.data', denylist=['hparams'])
def bair_robot_pushing_hparams(hparams=None,
video_num_input_frames=1,
|
tensorflow.zeros_like
| 9,212 |
import tensorflow as tf
scores = tf.reshape(scores, [-1, tf.shape(facts)[1]])
output = facts * tf.expand_dims(scores, -1)
|
tensorflow.expand_dims
| 9,213 |
import tensorflow as tf
cand_features.append(cand_states)
cand_choices.append(cand_choice)
cand_scoress.append(cand_scores)
n_cands_per_sample = [v.shape[0] for v in cand_features]
cand_features = np.concatenate(cand_features, axis=0).astype(np.float32, copy=False)
cand_choices = np.asarray(cand_choices).astype(np.int32, copy=False)
cand_scoress = np.concatenate(cand_scoress, axis=0).astype(np.float32, copy=False)
n_cands_per_sample = np.asarray(n_cands_per_sample).astype(np.int32, copy=False)
return cand_features, n_cands_per_sample, cand_choices, cand_scoress
def padding(output, n_vars_per_sample, fill=-1e8):
n_vars_max = tf.reduce_max(n_vars_per_sample)
output = tf.split(
value=output,
num_or_size_splits=n_vars_per_sample,
axis=1,
)
output = tf.concat([
tf.pad(
x,
paddings=[[0, 0], [0, n_vars_max - tf.shape(x)[1]]],
mode='CONSTANT',
constant_values=fill)
for x in output
], axis=0)
|
tensorflow.reduce_max
| 9,214 |
import tensorflow as tf
}
v_grad_clip_fn = opt.get_gradient_clip_fn(hparams)
v_grads_and_vars = v_grad_clip_fn(grads_and_vars)
v_grads, _ = zip(*v_grads_and_vars)
v_grads_true = tf.clip_by_value(grads,
hparams["kwargs"]["clip_value_min"],
hparams["kwargs"]["clip_value_max"])
|
tensorflow.clip_by_value
| 9,215 |
import tensorflow as tf
end_points[scope] = net
logits = slim.conv2d(net, 1, kernel_size=1, stride=1, padding='VALID',
normalizer_fn=None, activation_fn=None)
logits = tf.reshape(logits, [-1, 1])
end_points['logits'] = logits
return logits, end_points
|
tensorflow.reshape
| 9,216 |
import tensorflow as tf
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["label_ids"] = create_int_feature([feature.label_id])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
def file_based_input_fn_builder(input_file, seq_length, is_training,
drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
|
tensorflow.train.Features
| 9,217 |
import tensorflow as tf
batch_size = shape[0]
height = shape[1]
width = shape[2]
channels = shape[3]
res = tf.split(axis=0, num_or_size_splits=batch_size, value=input_)
res = [elem[0, :, :, :] for elem in res]
res = [tf.image.random_flip_left_right(elem) for elem in res]
res = [tf.reshape(elem, [1, height, width, channels]) for elem in res]
res = tf.concat(axis=0, values=res)
return res
# build a one hot representation corresponding to the integer tensor
# the one-hot dimension is appended to the integer tensor shape
def as_one_hot(input_, n_indices):
|
tensorflow.concat
| 9,218 |
import tensorflow as tf
order_m = tf.abs(order_m)
zeros = tf.zeros_like(order_m)
|
tensorflow.zeros_like
| 9,219 |
import tensorflow as tf
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
|
tensorflow.logging.info
| 9,220 |
import tensorflow as tf
for i in range(hparams.tacotron_num_gpus):
tf.summary.histogram("mel_outputs %d" % i, model.tower_linear_outputs[i])
tf.summary.histogram("mel_targets %d" % i, model.tower_linear_targets[i])
tf.summary.scalar("regularization_loss", model.regularization_loss)
tf.summary.scalar("stop_token_loss", model.stop_token_loss)
tf.summary.scalar("loss", model.loss)
tf.summary.scalar("learning_rate", model.learning_rate) # Control learning rate decay speed
if hparams.tacotron_teacher_forcing_mode == "scheduled":
tf.summary.scalar("teacher_forcing_ratio", model.ratio) # Control teacher forcing
# ratio decay when mode = "scheduled"
gradient_norms = [tf.norm(grad) for grad in model.gradients]
tf.summary.histogram("gradient_norm", gradient_norms)
tf.summary.scalar("max_gradient_norm", tf.reduce_max(gradient_norms)) # visualize
# gradients (in case of explosion)
return tf.summary.merge_all()
def add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss,
loss):
values = [
tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/eval_before_loss",
simple_value=before_loss),
tf.Summary.Value(tag="Tacotron_eval_model/eval_stats/eval_after_loss",
|
tensorflow.summary.histogram
| 9,221 |
import tensorflow as tf
if decoder.old_maxout: # for back-compatibility with old models
output_ = tf.nn.pool(tf.expand_dims(output_, axis=2), window_shape=[2], pooling_type='MAX',
padding='SAME', strides=[2])
output_ = tf.squeeze(output_, axis=2)
else:
output_ = tf.maximum(*tf.split(output_, num_or_size_splits=2, axis=1))
if decoder.pred_embed_proj:
# intermediate projection to embedding size (before projecting to vocabulary size)
# this is useful to reduce the number of parameters, and
|
tensorflow.split
| 9,222 |
import tensorflow as tf
outputs_to_scales_to_logits[output][
'logits_%.2f' % image_scale] = outputs_to_logits[output]
# Merge the logits from all the multi-scale inputs.
for output in sorted(model_options.outputs_to_num_classes):
# Concatenate the multi-scale logits for each output type.
all_logits = [
tf.expand_dims(logits, axis=4)
for logits in outputs_to_scales_to_logits[output].values()
]
all_logits = tf.concat(all_logits, 4)
merge_fn = (
tf.reduce_max
if model_options.merge_method == 'max' else tf.reduce_mean)
|
tensorflow.expand_dims
| 9,223 |
import tensorflow as tf
sparse_ops._take_many_sparse_from_tensors_map)
# pylint: enable=protected-access
class SparseTensorsMapTest(tf.test.TestCase):
def _SparseTensorPlaceholder(self, dtype=None):
if dtype is None: dtype = tf.int32
return tf.SparseTensor(
tf.placeholder(tf.int64),
tf.placeholder(dtype),
tf.placeholder(tf.int64))
def _SparseTensorValue_5x6(self, permutation):
ind = np.array([
[0, 0],
[1, 0], [1, 3], [1, 4],
[3, 2], [3, 3]]).astype(np.int64)
val = np.array([0, 10, 13, 14, 32, 33]).astype(np.int32)
|
tensorflow.placeholder
| 9,224 |
import tensorflow as tf
# Handle BN scope reuse
if self.reuse:
self.scope_reuse = tf.AUTO_REUSE
else:
self.scope_reuse = None
self.param_initializer = {
'moving_mean': tf.constant_initializer(0., dtype=self.dtype),
'moving_variance': tf.constant_initializer(1., dtype=self.dtype),
'gamma': tf.constant_initializer(0.1, dtype=self.dtype)
}
self.param_trainable = {
'moving_mean': False,
|
tensorflow.constant_initializer
| 9,225 |
import tensorflow as tf
horizon_pred, horizon_tgt = horizon_sumV1(pred, horizon), horizon_sumV1(tgt, horizon)
# horizon_pred, horizon_tgt = horizon_sumV2(pred, tgt, horizon)
pred_flat1, pred_flat2 = tf.reshape(horizon_pred, [-1, 1]), tf.reshape(horizon_pred, [1, -1])
tgt_flat1, tgt_flat2 = tf.reshape(horizon_tgt, [-1, 1]), tf.reshape(horizon_tgt, [1, -1])
tgt_dif = tgt_flat1 - tgt_flat2
|
tensorflow.reshape
| 9,226 |
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,
|
tensorflow.reshape
| 9,227 |
import tensorflow as tf
z_t_len = tf.strings.length(z_t)
z_t = tf.string_split([z_t], delimiter='').values
for i in tf.range(start=0, limit=x_t_len - self._p + 1, delta=1, dtype=None, name='range'):
u = tf.string_join(x_t[i:i + self._p], '')
vx_keys, r = tf.cond(
tf.greater(vx.lookup(u), -1),
true_fn=lambda: (vx_keys, tf.add(vx.lookup(u), 1)),
false_fn=lambda: (tf.concat([vx_keys, tf.reshape(u, (-1, 1))], axis=0),
tf.constant(1, dtype=tf.int64, name='constant'))
)
vx.insert(u, r)
for i in tf.range(start=0, limit=z_t_len - self._p + 1, delta=1, dtype=None, name='range'):
u = tf.string_join(z_t[i:i + self._p], '')
vz_keys, r = tf.cond(
tf.greater(vz.lookup(u), -1),
true_fn=lambda: (vz_keys, tf.add(vz.lookup(u), 1)),
false_fn=lambda: (
tf.concat([vz_keys, tf.reshape(u, (-1, 1))], axis=0), tf.constant(1, dtype=tf.int64))
)
vz.insert(u, r)
kk = tf.Variable(0, dtype=tf.int64)
for i in tf.range(start=0, limit=tf.size(vx_keys), delta=1, dtype=None, name='range'):
for j in tf.range(start=0, limit=tf.size(vz_keys), delta=1, dtype=None, name='range'):
to_add = tf.cond(
tf.greater(vz.lookup(vx_keys[i]), -1),
|
tensorflow.string_join
| 9,228 |
import tensorflow as tf
@layer
def sigmoid_cross_entropy_layer(tensor, target, **opts):
out = tf.nn.sigmoid_cross_entropy_with_logits(logits=tensor, labels=target)
return out
@layer
def mean_loss_by_example_layer(tensor, sequence_length, **opts):
loss = tf.div(
tf.reduce_sum(tensor, axis=1),
tf.cast(sequence_length, dtype=tf.float32)
)
out = tf.reduce_mean(loss)
tf.summary.scalar('cost', out)
return out
@layer
|
tensorflow.reduce_sum
| 9,229 |
import tensorflow as tf
Training:
{spacer}Positive count: {train_pos_ct}
{spacer}Batch size: {train_batch_size} {multiplier}
{spacer}Batch count per epoch: {train_batch_ct}
Eval:
{spacer}Positive count: {eval_pos_ct}
{spacer}Batch size: {eval_batch_size} {multiplier}
{spacer}Batch count per epoch: {eval_batch_ct}"""
_TRAIN_FEATURE_MAP = {
movielens.USER_COLUMN: tf.FixedLenFeature([], dtype=tf.string),
movielens.ITEM_COLUMN: tf.FixedLenFeature([], dtype=tf.string),
rconst.MASK_START_INDEX: tf.FixedLenFeature([1], dtype=tf.string),
"labels": tf.FixedLenFeature([], dtype=tf.string),
}
_EVAL_FEATURE_MAP = {
movielens.USER_COLUMN: tf.FixedLenFeature([], dtype=tf.string),
movielens.ITEM_COLUMN: tf.FixedLenFeature([], dtype=tf.string),
rconst.DUPLICATE_MASK: tf.FixedLenFeature([], dtype=tf.string)
}
|
tensorflow.FixedLenFeature
| 9,230 |
import tensorflow as tf
if chaining_stop_gradient:
attns = tf.stop_gradient(attns)
states = tf.stop_gradient(states)
decoder_outputs = tf.stop_gradient(decoder_outputs)
if chaining_strategy == 'concat_attns':
attention_states[0] = tf.concat([attention_states[0], attns], axis=2)
elif chaining_strategy == 'concat_states':
attention_states[0] = tf.concat([attention_states[0], states], axis=2)
elif chaining_strategy == 'sum_attns':
attention_states[0] += attns
elif chaining_strategy in ('map_attns', 'map_states', 'map_outputs'):
if chaining_strategy == 'map_attns':
x = attns
elif chaining_strategy == 'map_outputs':
x = decoder_outputs
else:
|
tensorflow.concat
| 9,231 |
import tensorflow as tf
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."""
def body(self, features):
observations = features["inputs_raw"]
# Axis 0 - Batch.
# Axis 1 - Input Frames, 4 frames.
|
tensorflow.layers.dense
| 9,232 |
import tensorflow as tf
"""Discriminator layer"""
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
|
tensorflow.get_variable_scope
| 9,233 |
import tensorflow as tf
# We use sampled softmax so we keep output projection separate.
w = tf.get_variable("proj_w", [24, classes])
|
tensorflow.get_variable
| 9,234 |
from tensorflow.contrib.learn.python.learn import monitors as monitor_lib
batch_size=None,
monitors=None,
max_steps=None):
"""See trainable.Trainable. Note: Labels must be integer class indices."""
# TODO(roumposg): Remove when deprecated monitors are removed.
hooks = monitor_lib.replace_monitors_with_hooks(monitors, self)
self._estimator.fit(x=x,
y=y,
input_fn=input_fn,
steps=steps,
|
tensorflow.contrib.learn.python.learn.monitors.replace_monitors_with_hooks
| 9,235 |
import tensorflow as tf
#)
# Kim et al 2015, +/- 0.05
w_init = tf.random_uniform_initializer(
minval=-0.05, maxval=0.05)
elif cnn_options['activation'] == 'tanh':
# glorot init
w_init = tf.random_normal_initializer(
mean=0.0,
stddev=np.sqrt(1.0 / (width * char_embed_dim))
)
w = tf.get_variable(
"W_cnn_%s" % i,
[1, width, char_embed_dim, num],
initializer=w_init,
dtype=DTYPE)
b = tf.get_variable(
"b_cnn_%s" % i, [num], dtype=DTYPE,
initializer=tf.constant_initializer(0.0))
conv = tf.nn.conv2d(
inp, w,
|
tensorflow.get_variable
| 9,236 |
import tensorflow as tf
loss_collection=None,#tf.GraphKeys.LOSSES,
# mean all elements of all pixels in all batch
reduction=tf.losses.Reduction.MEAN))# SUM, SUM_OVER_BATCH_SIZE, default mean by all elements
temp_loss = tf.reduce_mean(tf.reshape(tf.losses.mean_squared_error(targets_list[-1], pred_outputs[-1], weights=1.0, loss_collection=None, reduction=tf.losses.Reduction.NONE), [cur_batch_size, config.class_num_joints[(params['model_scope'] if 'all' not in params['model_scope'] else '*')], -1]), axis=-1)
num_topk = config.class_num_joints[(params['model_scope'] if 'all' not in params['model_scope'] else '*')] // 2
gather_col = tf.nn.top_k(temp_loss, k=num_topk, sorted=True)[1]
gather_row = tf.reshape(tf.tile(tf.reshape(tf.range(cur_batch_size), [-1, 1]), [1, num_topk]), [-1, 1])
gather_indcies = tf.stop_gradient(tf.stack([gather_row, tf.reshape(gather_col, [-1, 1])], axis=-1))
select_targets = tf.gather_nd(targets_list[-1], gather_indcies)
select_heatmap = tf.gather_nd(pred_outputs[-1], gather_indcies)
mse_loss_list.append(tf.losses.mean_squared_error(select_targets, select_heatmap,
weights=1.0 / tf.cast(cur_batch_size, tf.float32),
scope='loss_{}'.format(len(pred_outputs) - 1),
loss_collection=None,#tf.GraphKeys.LOSSES,
# mean all elements of all pixels in all batch
reduction=tf.losses.Reduction.MEAN))
else:
for pred_ind in list(range(len(pred_outputs))):
|
tensorflow.gather_nd
| 9,237 |
import tensorflow as tf
bsz_per_core = tf.shape(features["input_ids"])[0]
inp = tf.transpose(features["input_ids"], [1, 0])
seg_id = tf.transpose(features["segment_ids"], [1, 0])
inp_mask = tf.transpose(features["input_mask"], [1, 0])
label = tf.reshape(features["label_ids"], [bsz_per_core])
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)
run_config = xlnet.create_run_config(is_training, True, FLAGS)
xlnet_model = xlnet.XLNetModel(
|
tensorflow.reshape
| 9,238 |
import tensorflow as tf
init = tf.global_variables_initializer()
|
tensorflow.global_variables_initializer
| 9,239 |
import tensorflow as tf
mapping_path = os.path.join(self._transformed_metadata_dir, self.ASSET_MAP)
mapping = {}
if tf.io.gfile.exists(mapping_path):
with tf.io.gfile.GFile(mapping_path) as f:
mapping = json.loads(f.read())
|
tensorflow.io.gfile.exists
| 9,240 |
import tensorflow as tf
self.click_metrics=self.click_loglikelihood(reshaped_train_labels,\
self.propensity,train_output)
tf.summary.scalar('click_metrics',self.click_metrics,collections=['train'])
for i in range(len(pw_list)):
tf.summary.scalar('Inverse Propensity weights %d' % i, tf.reduce_mean(pw_list[i]), collections=['train'])
tf.summary.scalar('Rank Loss', tf.reduce_mean(self.rank_loss), collections=['train'])
# Compute examination loss
self.relevance_weights = self.get_normalized_weights(self.logits_to_prob(train_output))
self.exam_loss = self.loss_func(self.propensity, reshaped_train_labels, self.relevance_weights)
rw_list = tf.unstack(self.relevance_weights, axis=1) # Compute propensity weights
for i in range(len(rw_list)):
tf.summary.scalar('Relevance weights %d' % i, tf.reduce_mean(rw_list[i]), collections=['train'])
tf.summary.scalar('Exam Loss', tf.reduce_mean(self.exam_loss), collections=['train'])
# Gradients and SGD update operation for training the model.
self.loss = self.exam_loss + self.hparams.ranker_loss_weight * self.rank_loss
# Select optimizer
self.optimizer_func = tf.train.AdagradOptimizer
|
tensorflow.unstack
| 9,241 |
import tensorflow as tf
predict_examples = processor.get_test_examples(FLAGS.data_dir)
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length, tokenizer,
predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d", len(predict_examples))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
if FLAGS.use_tpu:
# Warning: According to tpu_estimator.py Prediction on TPU is an
|
tensorflow.logging.info
| 9,242 |
import tensorflow as tf
def weight_decay(penalty_type, penalty):
"""Add weight decay.
Args:
model: TensorflowGraph.
Returns:
A scalar tensor containing the weight decay cost.
Raises:
NotImplementedError: If an unsupported penalty type is requested.
"""
variables = []
# exclude bias variables
for v in tf.trainable_variables():
if v.get_shape().ndims == 2:
variables.append(v)
with tf.name_scope('weight_decay'):
if penalty_type == 'l1':
cost = tf.add_n([tf.reduce_sum(tf.abs(v)) for v in variables])
elif penalty_type == 'l2':
cost = tf.add_n([tf.nn.l2_loss(v) for v in variables])
else:
raise NotImplementedError('Unsupported penalty_type %s' % penalty_type)
cost *= penalty
#tf.scalar_summary('Weight Decay Cost', cost)
return cost
|
tensorflow.trainable_variables
| 9,243 |
from tensorflow.python.framework import ops
the return type is `quint8`.
"""
with ops.op_scope([x], name, "Tanh") as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops._tanh(x, name=name)
ops.RegisterShape("Abs")(common_shapes.unchanged_shape)
ops.RegisterShape("Ceil")(common_shapes.unchanged_shape)
ops.RegisterShape("Conj")(common_shapes.unchanged_shape)
ops.RegisterShape("Cos")(common_shapes.unchanged_shape)
ops.RegisterShape("Exp")(common_shapes.unchanged_shape)
ops.RegisterShape("Floor")(common_shapes.unchanged_shape)
ops.RegisterShape("Imag")(common_shapes.unchanged_shape)
|
tensorflow.python.framework.ops.RegisterShape
| 9,244 |
import tensorflow as tf
return tf.reshape(tf.concat(axis=1, values=h), [-1, nh])
else:
return tf.reshape(tf.stack(values=h, axis=1), [-1])
def lstm(xs, ms, s, scope, nh, init_scale=1.0):
nbatch, nin = [v.value for v in xs[0].get_shape()]
with tf.variable_scope(scope):
wx = tf.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale))
wh = tf.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale))
b = tf.get_variable("b", [nh*4], initializer=tf.constant_initializer(0.0))
c, h = tf.split(axis=1, num_or_size_splits=2, value=s)
for idx, (x, m) in enumerate(zip(xs, ms)):
c = c*(1-m)
h = h*(1-m)
z = tf.matmul(x, wx) + tf.matmul(h, wh) + b
i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
i = tf.nn.sigmoid(i)
f = tf.nn.sigmoid(f)
o = tf.nn.sigmoid(o)
u = tf.tanh(u)
c = f*c + i*u
|
tensorflow.split
| 9,245 |
import tensorflow as tf
# TODO (jk): removed dilations=dilations to accommodate r1.4
else:
activities = tf.nn.conv3d(
data,
|
tensorflow.nn.conv3d
| 9,246 |
import tensorflow as tf
def sussillo_reg(self):
states = self.states
reg = 0
for state in states:
dJr = tf.matmul(tf.nn.relu(state),
tf.matmul(tf.abs(self.W_rec) * self.rec_Connectivity, self.Dale_rec))
reg += tf.reduce_sum(tf.square(dJr))
return reg / (self.N_steps * self.N_batch)
# train the model using Adam
def train(self, sess, generator,
learning_rate=.001, training_iters=50000,
batch_size=64, display_step=10,weight_save_step=100, save_weights_path= None,
generator_function= None, training_weights_path = None):
|
tensorflow.square
| 9,247 |
import tensorflow as tf
sess.run([y[0].op, z[0].op])
def testNoInput(self):
with self.test_session():
x, = tf.py_func(lambda: 42.0, [], [tf.float64])
self.assertAllClose(x.eval(), 42.0)
def testCleanup(self):
for _ in xrange(1000):
g = tf.Graph()
with g.as_default():
c = tf.constant([1.], tf.float32)
_ = tf.py_func(lambda x: x + 1, [c], [tf.float32])
self.assertTrue(script_ops._py_funcs.size() < 100)
def testError(self):
with self.test_session():
def bad1():
# Structured numpy arrays aren't supported.
return np.array([], dtype=[("foo", np.float32)])
|
tensorflow.constant
| 9,248 |
import tensorflow as tf
if embeddings is not None:
flat_inputs = tf.reshape(encoder_inputs_, [tf.multiply(batch_size, time_steps)])
|
tensorflow.multiply
| 9,249 |
from tensorflow.python.framework import tensor_shape
"""Shape function for the SparseSegmentMeanGrad op."""
input_shape = op.inputs[0].get_shape()
indices_shape = op.inputs[1].get_shape().with_rank(1)
unused_segment_ids_shape = op.inputs[2].get_shape().merge_with(indices_shape)
unused_output_dim0_shape = op.inputs[3].get_shape().merge_with(
tensor_shape.scalar())
output_dim0 = tensor_util.ConstantValue(op.inputs[3])
if output_dim0 is not None:
dim0 = output_dim0[0]
else:
|
tensorflow.python.framework.tensor_shape.scalar
| 9,250 |
import tensorflow as tf
states[name] = tf.gather(params=self.states_memory[name], indices=indices)
internals = dict()
for name in sorted(self.internals_memory):
internals[name] = tf.gather(params=self.internals_memory[name], indices=indices)
actions = dict()
for name in sorted(self.actions_memory):
actions[name] = tf.gather(params=self.actions_memory[name], indices=indices)
terminal = tf.gather(params=self.terminal_memory, indices=indices)
reward = tf.gather(params=self.reward_memory, indices=indices)
if self.include_next_states:
assert util.rank(indices) == 1
next_indices = (indices + 1) % self.capacity
next_states = dict()
for name in sorted(self.states_memory):
next_states[name] = tf.gather(params=self.states_memory[name], indices=next_indices)
|
tensorflow.gather
| 9,251 |
import tensorflow as tf
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
|
tensorflow.logging.info
| 9,252 |
import tensorflow as tf
pool_idx = tf.cast(tf.argmax(tf.cast(tf.equal(X[:, :, 0], clf_token), tf.float32), 1), tf.int32)
clf_h = tf.gather(clf_h, tf.range(shape_list(X)[0], dtype=tf.int32)*n_ctx+pool_idx)
clf_h = tf.reshape(clf_h, [-1, 2, n_embd])
if train and clf_pdrop > 0:
shape = shape_list(clf_h)
shape[1] = 1
clf_h = tf.nn.dropout(clf_h, 1-clf_pdrop, shape)
clf_h = tf.reshape(clf_h, [-1, n_embd])
clf_logits = clf(clf_h, 1, train=train)
clf_logits = tf.reshape(clf_logits, [-1, 2])
clf_losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=clf_logits, labels=Y)
return clf_logits, clf_losses, lm_losses
|
tensorflow.nn.dropout
| 9,253 |
import tensorflow as tf
bboxes = tf.placeholder(tf.float32)
bboxes_val = [[10, 10, 20, 22]]
gt_boxes = tf.placeholder(tf.float32)
gt_boxes_val = [[11, 13, 34, 31]]
|
tensorflow.placeholder
| 9,254 |
from tensorflow.python.ops import math_ops
"""Compute the mean intersection-over-union via the confusion matrix."""
sum_over_row = math_ops.to_float(math_ops.reduce_sum(total_cm, 0))
|
tensorflow.python.ops.math_ops.reduce_sum
| 9,255 |
import tensorflow as tf
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
| 9,256 |
import tensorflow as tf
with tf.variable_scope(scope):
batch_size, height, width, num_channels = x.get_shape().as_list()
f = conv(x, scope='f_conv', filter_dims=[1, 1, channels//8], stride_dims=[1, 1], non_linear_fn=act_func)
f = tf.layers.max_pooling2d(f, pool_size=2, strides=2, padding='SAME')
print('attention f dims: ' + str(f.get_shape().as_list()))
|
tensorflow.layers.max_pooling2d
| 9,257 |
import tensorflow as tf
if FLAGS.debug:
utils.add_activation_summary(relu6)
relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)
|
tensorflow.nn.dropout
| 9,258 |
import tensorflow as tf
target_init_op = [
tf.assign(target, source)
for target, source in zip(target_params, source_params)
]
# Control flow is used because sess.run otherwise evaluates in nondeterministic order
# and we first need to compute the policy action before computing q values losses
with tf.control_dependencies([policy_train_op]):
train_values_op = value_optimizer.minimize(values_losses, var_list=values_params)
self.infos_names = ['policy_loss', 'qf1_loss', 'qf2_loss', 'value_loss', 'entropy']
# All ops to call during one training step
self.step_ops = [policy_loss, qf1_loss, qf2_loss,
value_loss, qf1, qf2, value_fn, logp_pi,
|
tensorflow.control_dependencies
| 9,259 |
import tensorflow as tf
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
accuracy = tf.metrics.accuracy(
labels=label_ids, predictions=predictions, weights=is_real_example)
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
eval_metrics = (metric_fn,
[per_example_loss, label_ids, logits, is_real_example])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
|
tensorflow.metrics.mean
| 9,260 |
import tensorflow as tf
mask_landm = tf.logical_and(tf.equal(landm_valid, 1), mask_pos)
# landm loss (smooth L1)
mask_landm_b = tf.broadcast_to(mask_landm, tf.shape(landm_true))
loss_landm = _smooth_l1_loss(tf.boolean_mask(landm_true, mask_landm_b),
tf.boolean_mask(landm_pred, mask_landm_b))
loss_landm = tf.reduce_mean(loss_landm)
# localization loss (smooth L1)
mask_pos_b = tf.broadcast_to(mask_pos, tf.shape(loc_true))
loss_loc = _smooth_l1_loss(tf.boolean_mask(loc_true, mask_pos_b),
tf.boolean_mask(loc_pred, mask_pos_b))
|
tensorflow.reduce_mean
| 9,261 |
from tensorflow.python.ops import math_ops
if weights is not None:
weights = math_ops.to_float(weights)
values = math_ops.mul(values, weights)
|
tensorflow.python.ops.math_ops.to_float
| 9,262 |
from tensorflow.python.ops import array_ops
# logic.
with ops.device(None):
if all(tensor.shape == tensor_shape.scalar() for tensor in tensors):
with ops.device(tensors[0].device):
values = array_ops.stack(tensors)
with ops.device(device):
return array_ops.unstack(values)
else:
with ops.device(tensors[0].device):
sizes = array_ops.stack(
[array_ops.shape(tensor)[0] for tensor in tensors])
values = array_ops.concat(tensors, axis=0)
with ops.device(device):
sizes = array_ops.unstack(sizes)
return list(array_ops.split(values, sizes, axis=0))
def _scheduled_stamp_resource_op_runner(batch, stamp):
"""Runs a batch operation on a stamped resource."""
if not batch:
return
arg_keys = set(batch[0].args.keys())
|
tensorflow.python.ops.array_ops.concat
| 9,263 |
import tensorflow as tf
# initialize filter
W = tf.get_variable(
name='W',
shape=[filter_size, num_feature, 1, num_filter],
initializer=tf.contrib.layers.xavier_initializer_conv2d())
# convolve w and input
conv = tf.nn.conv2d(
|
tensorflow.contrib.layers.xavier_initializer_conv2d
| 9,264 |
import tensorflow as tf
input_.targets,
tf.ones([self.batch_size, self.num_steps], dtype=data_type()),
average_across_timesteps=False,
average_across_batch=True)
# Update the cost
self._cost = tf.reduce_sum(loss)
self._final_state = state
if not is_training:
return
|
tensorflow.reduce_sum
| 9,265 |
import tensorflow as tf
tf.nn.conv2d(input_var, w,
use_cudnn_on_gpu=True,data_format='NCHW',
strides=self.strides, padding=self.padding),
b,data_format='NCHW',name=name)
else :
return tf.nn.bias_add(
tf.nn.conv2d(input_var, w,data_format='NHWC',
strides=self.strides, padding=self.padding),
b,data_format='NHWC',name=name)
def get_variables(self):
return {'w':self.w,'b':self.b}
|
tensorflow.nn.conv2d
| 9,266 |
import tensorflow as tf
else: #tanh by default
lstm=tf.nn.rnn_cell.LSTMCell(num_units=state_size, 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)
elif cell_type == 'GRU':
if activation == 'linear':
gru=tf.nn.rnn_cell.GRUCell(state_size, activation = tf.identity)
cell_drop=tf.contrib.rnn.DropoutWrapper(gru,variational_recurrent=True,dtype=tf.float32, input_size=num_input,input_keep_prob=input_prob,state_keep_prob=state_prob)
elif activation == 'relu':
gru=tf.nn.rnn_cell.GRUCell(state_size, activation = tf.nn.relu)
cell_drop=tf.contrib.rnn.DropoutWrapper(gru,variational_recurrent=True,dtype=tf.float32, input_size=num_input,input_keep_prob=input_prob,state_keep_prob=state_prob)
else:
gru=tf.nn.rnn_cell.GRUCell(state_size)
cell_drop=tf.contrib.rnn.DropoutWrapper(gru,variational_recurrent=True,dtype=tf.float32, input_size=num_input,input_keep_prob=input_prob,state_keep_prob=state_prob)
else:
if activation == 'linear':
cell_basic = tf.contrib.rnn.BasicRNNCell(state_size,activation=tf.identity)
cell_drop=tf.contrib.rnn.DropoutWrapper(cell_basic,variational_recurrent=True,dtype=tf.float32, input_size=num_input,input_keep_prob=input_prob,state_keep_prob=state_prob)
elif activation == 'relu':
cell_basic = tf.contrib.rnn.BasicRNNCell(state_size, activation=tf.nn.relu)
cell_drop = tf.contrib.rnn.DropoutWrapper(cell_basic, variational_recurrent=True, dtype=tf.float32,
input_size=num_input, input_keep_prob=input_prob,
state_keep_prob=state_prob)
else: #tanh by default
|
tensorflow.nn.rnn_cell.GRUCell
| 9,267 |
import tensorflow as tf
# Restore the saved values in the parameter nodes.
save.restore(sess, save_path)
# Check that the parameter nodes have been restored.
self.assertEqual(np.int64(15), v.eval())
def testSomeErrors(self):
with tf.Graph().as_default():
v0 = tf.Variable([10.0], name="v0")
v1 = tf.Variable([20.0], name="v1")
v2 = tf.Variable([20.0], name="v2")
v2._set_save_slice_info(tf.Variable.SaveSliceInfo("v1", [1], [0], [1]))
# By default the name used for "v2" will be "v1" and raise an error.
with self.assertRaisesRegexp(ValueError, "same name: v1"):
tf.train.Saver([v0, v1, v2])
|
tensorflow.Variable
| 9,268 |
import tensorflow as tf
tf.app.flags.DEFINE_string('dataset', '', 'cifar10 or cifar100.')
tf.app.flags.DEFINE_string('mode', 'train', 'train or eval.')
|
tensorflow.app.flags.DEFINE_string
| 9,269 |
import tensorflow as tf
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
|
tensorflow.parse_single_example
| 9,270 |
import tensorflow as tf
# session info
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = False
|
tensorflow.ConfigProto
| 9,271 |
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.argmax
| 9,272 |
import tensorflow as tf
saver = tf.train.Saver()
x, prediction, output_class, new_sess = self.saveAndRestoreModel(
self.buildLstmLayer(), sess, saver, is_dynamic_rnn=False)
test_inputs, expected_output = self.getInferenceResult(
x, output_class, new_sess)
# Test Toco-converted model.
result = self.tfliteInvoke(new_sess, test_inputs, x, output_class, False)
self.assertTrue(np.allclose(expected_output, result, rtol=1e-6, atol=1e-2))
@test_util.enable_control_flow_v2
def testDynamicRnnMultiRnnCell(self):
sess = tf.compat.v1.Session(config=CONFIG)
x, prediction, output_class = self.buildModel(
self.buildLstmLayer(), is_dynamic_rnn=True)
self.trainModel(x, prediction, output_class, sess)
saver = tf.train.Saver()
x, prediction, output_class, new_sess = self.saveAndRestoreModel(
self.buildLstmLayer(), sess, saver, is_dynamic_rnn=True)
test_inputs, expected_output = self.getInferenceResult(
x, output_class, new_sess)
|
tensorflow.compat.v1.Session
| 9,273 |
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)
|
tensorflow.where
| 9,274 |
import tensorflow as tf
"""
# TODO(user): gradient clipping (see Minimize)
if optimizer == 'adagrad':
train_op = tf.train.AdagradOptimizer(learning_rate)
elif optimizer == 'adam':
train_op = tf.train.AdamOptimizer(learning_rate)
elif optimizer == 'momentum':
train_op = tf.train.MomentumOptimizer(learning_rate, momentum)
elif optimizer == 'rmsprop':
train_op = tf.train.RMSPropOptimizer(learning_rate, momentum)
elif optimizer == 'sgd':
train_op = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise NotImplementedError('Unsupported optimizer %s' % optimizer)
return train_op
|
tensorflow.train.RMSPropOptimizer
| 9,275 |
import tensorflow.contrib.graph_editor as ge
- 'speed': checkpoint all outputs of convolutions and matmuls. these ops are usually the most expensive,
so checkpointing them maximizes the running speed
(this is a good option if nonlinearities, concats, batchnorms, etc are taking up a lot of memory)
- 'memory': try to minimize the memory usage
(currently using a very simple strategy that identifies a number of bottleneck tensors in the graph to checkpoint)
- 'collection': look for a tensorflow collection named 'checkpoints', which holds the tensors to checkpoint
'''
# print("Calling memsaving gradients with", checkpoints)
if not isinstance(ys,list):
ys = [ys]
if not isinstance(xs,list):
xs = [xs]
bwd_ops = ge.get_backward_walk_ops([y.op for y in ys],
inclusive=True)
debug_print("bwd_ops: %s", bwd_ops)
# forward ops are all ops that are candidates for recomputation
fwd_ops = ge.get_forward_walk_ops([x.op for x in xs],
inclusive=True,
within_ops=bwd_ops)
debug_print("fwd_ops: %s", fwd_ops)
# exclude ops with no inputs
fwd_ops = [op for op in fwd_ops if op.inputs]
|
tensorflow.contrib.graph_editor.get_backward_walk_ops
| 9,276 |
import tensorflow as tf
# checkpoint related configuration
tf.app.flags.DEFINE_string(
'checkpoint_path', './model/resnet50',#None,
'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
'checkpoint_model_scope', '',
'Model scope in the checkpoint. None if the same as the trained model.')
tf.app.flags.DEFINE_string(
'model_scope', 'xdet_resnet',
'Model scope name used to replace the name_scope in checkpoint.')
tf.app.flags.DEFINE_string(
'checkpoint_exclude_scopes', 'xdet_resnet/xdet_head, xdet_resnet/xdet_multi_path, xdet_resnet/xdet_additional_conv',#None
'Comma-separated list of scopes of variables to exclude when restoring from a checkpoint.')
tf.app.flags.DEFINE_boolean(
'ignore_missing_vars', True,
'When restoring a checkpoint would ignore missing variables.')
tf.app.flags.DEFINE_boolean(
'run_on_cloud', True,
'Wether we will train on cloud (pre-trained model will be placed in the "data_dir/cloud_checkpoint_path").')
tf.app.flags.DEFINE_string(
'cloud_checkpoint_path', 'resnet50/model.ckpt',
'The path to a checkpoint from which to fine-tune.')
FLAGS = tf.app.flags.FLAGS
def input_pipeline():
|
tensorflow.app.flags.DEFINE_boolean
| 9,277 |
import tensorflow as tf
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
# Optimizer
with tf.name_scope('optimizer'):
if model_str == 'gcn_ae':
opt = OptimizerAE(preds=model.reconstructions,
labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'],
validate_indices=False), [-1]),
pos_weight=pos_weight,
norm=norm)
elif model_str == 'gcn_vae':
opt = OptimizerVAE(preds=model.reconstructions,
labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'],
validate_indices=False), [-1]),
|
tensorflow.sparse_tensor_to_dense
| 9,278 |
import tensorflow as tf
logit_w = tf.get_variable('W', shape=[dnn_output_size, 1], initializer=tf.truncated_normal_initializer(stddev=1.0 / dnn_output_size, dtype=dtype), dtype=dtype)
logit_b = tf.get_variable('b', shape=[1], initializer=tf.constant_initializer(0.0), dtype=dtype)
logits = tf.squeeze(tf.nn.bias_add(tf.matmul(dnn_output, logit_w), logit_b), squeeze_dims=[1])
prediction = tf.nn.sigmoid(logits)
prediction_inspect = tf.reshape(prediction, [batch_size, rnn_nunroll])
prediction_final = tf.squeeze(tf.slice(prediction_inspect, [0, rnn_nunroll - 1], [-1, 1]), squeeze_dims=[1])
print('logit: {}'.format(logits.get_shape()))
# Compute loss
if mode != 'gen':
neg_log_lhoods = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=targets)
if target_weight_strategy == 'rect':
avg_neg_log_lhood = tf.reduce_mean(neg_log_lhoods)
else:
neg_log_lhoods = tf.multiply(neg_log_lhoods, target_weights)
# be careful to have at least one weight be nonzero
# should we be taking the mean elem-wise by batch? i think this is a big bug
avg_neg_log_lhood = tf.reduce_sum(neg_log_lhoods) / tf.reduce_sum(target_weights)
neg_log_lhoods_inspect = tf.reshape(neg_log_lhoods, [batch_size, rnn_nunroll])
|
tensorflow.nn.sigmoid_cross_entropy_with_logits
| 9,279 |
import tensorflow as tf
output: []
for output in model_options.outputs_to_num_classes
}
with tf.variable_scope(tf.get_variable_scope(), reuse=None):
outputs_to_scales_to_logits = multi_scale_logits(
images,
model_options=model_options,
|
tensorflow.get_variable_scope
| 9,280 |
from tensorflow.python.layers import convolutional as conv_layers
input_layer,
num_out_channels, [k_height, k_width],
strides=[d_height, d_width],
padding=mode,
data_format=self.channel_pos,
use_bias=False)
else: # Special padding mode for ResNet models
if d_height == 1 and d_width == 1:
conv = conv_layers.conv2d(
input_layer,
num_out_channels, [k_height, k_width],
strides=[d_height, d_width],
padding='SAME',
data_format=self.channel_pos,
use_bias=False)
else:
|
tensorflow.python.layers.convolutional.conv2d
| 9,281 |
import tensorflow as tf
tf.app.flags.DEFINE_float('ws_lrn_rate_ft', 3e-4, 'WS: learning rate for global fine-tuning')
tf.app.flags.DEFINE_integer('ws_nb_iters_ft', 400, 'WS: # of iterations for global fine-tuning')
tf.app.flags.DEFINE_integer('ws_nb_iters_feval', 25, 'WS: # of iterations for fast evaluation')
tf.app.flags.DEFINE_float('ws_prune_ratio_exp', 3.0, 'WS: pruning ratio\'s exponent term')
|
tensorflow.app.flags.DEFINE_integer
| 9,282 |
import tensorflow as tf
# load model.
model = importlib.import_module(model)
analysis_transform = model.AnalysisTransform(latent_points)
hyper_encoder = model.HyperEncoder()
hyper_decoder = model.HyperDecoder()
entropy_bottleneck = EntropyBottleneck()
conditional_entropy_model = SymmetricConditional()
checkpoint = tf.train.Checkpoint(analysis_transform=analysis_transform,
hyper_encoder=hyper_encoder,
hyper_decoder=hyper_decoder,
estimator=entropy_bottleneck)
status = checkpoint.restore(tf.train.latest_checkpoint(ckpt_dir))
x = tf.convert_to_tensor(x_color, "float32")
x_coori = tf.convert_to_tensor(x_coori, "float32")
def loop_analysis(element):
x = tf.expand_dims(element[0], 0)
x_coori = tf.expand_dims(element[1], 0)
y = analysis_transform(x_coori,x)
return tf.squeeze(y,axis=0)
element = [x,x_coori]
|
tensorflow.train.latest_checkpoint
| 9,283 |
import tensorflow as tf
Returns:
out_string: A tf.tensor of dtype string. If string_tensor contains the empty
string, out_string will contain a random integer casted to a string.
Otherwise string_tensor is returned unchanged.
"""
empty_string = tf.constant('', dtype=tf.string, name='EmptyString')
random_source_id = tf.as_string(
tf.random_uniform(shape=[], maxval=2 ** 63 - 1, dtype=tf.int64))
out_string = tf.cond(
tf.equal(string_tensor, empty_string),
true_fn=lambda: random_source_id,
false_fn=lambda: string_tensor)
return out_string
def _get_features_dict(input_dict):
"""Extracts features dict from input dict."""
source_id = _replace_empty_string_with_random_number(
input_dict[fields.InputDataFields.source_id])
hash_from_source_id = tf.string_to_hash_bucket_fast(source_id, HASH_BINS)
|
tensorflow.equal
| 9,284 |
import tensorflow as tf
def contra_step_lossV3(pred, tgt, margin=1.0):
# Step-wise contrastive loss
pred1, pred2 = tf.split(pred, 2, axis=0)
tgt1, tgt2 = tf.split(tgt, 2, axis=0)
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
|
tensorflow.split
| 9,285 |
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
tmp1 = tf.tensordot(facts, w1, axes=1)
tmp2 = tf.tensordot(query, w2, axes=1)
tmp2 = tf.reshape(tmp2, [-1, 1, tf.shape(tmp2)[-1]])
tmp = tf.tanh((tmp1 + tmp2) + b)
# For each of the timestamps its vector of size A from `tmp` is reduced with `v` vector
v_dot_tmp = tf.tensordot(tmp, v, axes=1, name='v_dot_tmp') # (B,T) shape
key_masks = mask # [B, 1, T]
# key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
paddings = tf.ones_like(v_dot_tmp) * (-2 ** 32 + 1)
v_dot_tmp = tf.where(key_masks, v_dot_tmp, paddings) # [B, 1, T]
alphas = tf.nn.softmax(v_dot_tmp, name='alphas') # (B,T) shape
# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
#output = tf.reduce_sum(facts * tf.expand_dims(alphas, -1), 1)
output = facts * tf.expand_dims(alphas, -1)
output = tf.reshape(output, tf.shape(facts))
|
tensorflow.shape
| 9,286 |
import tensorflow as tf
def simple_block_attention(
rep_tensor, rep_mask, block_len=5, scope=None, direction=None,
keep_prob=1., is_train=None, wd=0., activation='elu', hn=None):
assert direction is not None
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1. / scale * x)
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = hn or rep_tensor.get_shape().as_list()[2]
input_dim = rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'block_simple'):
# @1. split sequence
with tf.variable_scope('split_seq'):
block_num = tf.cast(tf.ceil(tf.divide(tf.cast(sl, tf.float32), tf.cast(block_len, tf.float32))), tf.int32)
comp_len = block_num * block_len - sl
rep_tensor_comp = tf.concat([rep_tensor, tf.zeros([bs, comp_len, input_dim], tf.float32)], 1)
rep_mask_comp = tf.concat([rep_mask, tf.cast(tf.zeros([bs, comp_len], tf.int32), tf.bool)], 1)
rep_tensor_split = tf.reshape(rep_tensor_comp, [bs, block_num, block_len, input_dim]) # bs,bn,bl,d
rep_mask_split = tf.reshape(rep_mask_comp, [bs, block_num, block_len]) # bs,bn,bl
# non-linear
|
tensorflow.variable_scope
| 9,287 |
import tensorflow as tf
config.add_hparam("n_classes", 10)
config.add_hparam("dataset", "cifar-10")
# Reconstruction could cause numerical error, use double precision for tests
config.dtype = tf.float64
config.fused = False # Fused batch norm does not support tf.float64
# Reduce the batch size for tests because the OSS version runs
# in constrained GPU environment with 1-2GB of memory.
config.batch_size = 2
shape = (config.batch_size,) + config.input_shape
self.model = revnet.RevNet(config=config)
self.x = tf.random_normal(shape=shape, dtype=tf.float64)
self.t = tf.random_uniform(
shape=[config.batch_size],
minval=0,
maxval=config.n_classes,
dtype=tf.int64)
self.config = config
def tearDown(self):
del self.model
del self.x
del self.t
|
tensorflow.random_uniform
| 9,288 |
import tensorflow as tf
self._saver = None
self.global_step = None
tf.set_random_seed(seed)
#restore checkpoint
|
tensorflow.set_random_seed
| 9,289 |
import tensorflow as tf
def get_gtboxes_and_label(self, gtboxes_and_label_h, gtboxes_and_label_r, num_objects):
return gtboxes_and_label_h[:, :int(max(num_objects)), :].astype(np.float32), \
gtboxes_and_label_r[:, :int(max(num_objects)), :].astype(np.float32)
def main(self):
with tf.Graph().as_default() as graph, tf.device('/cpu:0'):
num_gpu = len(cfgs.GPU_GROUP.strip().split(','))
global_step = slim.get_or_create_global_step()
lr = self.warmup_lr(cfgs.LR, global_step, cfgs.WARM_SETP, num_gpu*cfgs.BATCH_SIZE)
tf.summary.scalar('lr', lr)
|
tensorflow.device
| 9,290 |
import tensorflow as tf
if not isinstance(data, dict):
data = {'data': data}
if 'length' not in data:
example = data[list(data.keys())[0]]
data['length'] = (
tf.zeros((tf.shape(example)[0],), tf.int32) + tf.shape(example)[1])
return data
def train(model_fn, datasets, logdir, config):
|
tensorflow.shape
| 9,291 |
import tensorflow as tf
tf.app.flags.DEFINE_float('test_max', 10000, 'max number of examples in the test set')
tf.app.flags.DEFINE_integer('max_epochs', 0, 'Train for at most this number of epochs')
tf.app.flags.DEFINE_integer('save_every', 250, 'Save model state every INT epochs')
tf.app.flags.DEFINE_integer('eval_every', 25, 'Save encoding and visualizations every')
tf.app.flags.DEFINE_integer('visualiza_max', 10, 'Max pairs to show on visualization')
tf.app.flags.DEFINE_boolean('load_state', True, 'Load state if possible ')
tf.app.flags.DEFINE_boolean('kill_depth', False, 'Ignore depth information')
tf.app.flags.DEFINE_boolean('dev', False, 'Indicate development mode')
tf.app.flags.DEFINE_integer('batch_size', 128, 'Batch size')
tf.app.flags.DEFINE_float('learning_rate', 0.0001, 'Create visualization of ')
tf.app.flags.DEFINE_float('blur', 5.0, 'Max sigma value for Gaussian blur applied to training set')
tf.app.flags.DEFINE_boolean('new_blur', False, 'Use data augmentation as blur info')
tf.app.flags.DEFINE_integer('blur_decrease', 10000, 'Decrease image blur every X steps')
FLAGS = tf.app.flags.FLAGS
slim = tf.contrib.slim
AUTOENCODER = 'ae'
PREDICTIVE = 'pred'
DENOISING = 'noise'
CHECKPOINT_NAME = '-9999.chpt'
EMB_SUFFIX = '_embedding'
def is_stopping_point(current_epoch, epochs_to_train, stop_every=None, stop_x_times=None,
|
tensorflow.app.flags.DEFINE_integer
| 9,292 |
import tensorflow as tf
def _add_losses(self, sigma_rpn=3.0):
with tf.variable_scope('loss_' + self._tag):
# RPN, class loss
rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2])
rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1])
# 得到前景和背景anchor的index
rpn_select = tf.where(tf.not_equal(rpn_label, -1))
rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2])
rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1])
rpn_cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label))
# RPN, bbox loss
|
tensorflow.not_equal
| 9,293 |
import tensorflow as tf
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)
|
tensorflow.get_collection
| 9,294 |
import tensorflow as tf
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
def create_model(config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings, task_name,):
"""Creates a classification model from_scratch."""
_true_length = tf.cast(tf.reduce_sum(input_mask, axis=-1), dtype=tf.int32)
with tf.variable_scope("baseline"):
with tf.variable_scope("embeddings"):
# Perform embedding lookup on the word ids.
(word_embedding_output,
output_embedding_table) = modeling.embedding_lookup(
input_ids=input_ids,
|
tensorflow.reduce_sum
| 9,295 |
import tensorflow as tf
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)
|
tensorflow.one_hot
| 9,296 |
import tensorflow as tf
tf.train.start_queue_runners()
result, batch_size = session.run(output)
self.assertAllEqual([[3, 5]], result)
self.assertAllEqual([1], batch_size)
def test_two(self):
with self.test_session() as session:
@dynamic_batching.batch_fn
def f(a, b):
batch_size = tf.shape(a)[0]
return a + b, tf.tile([batch_size], [batch_size])
output0 = f(tf.constant([1]), tf.constant([2]))
output1 = f(tf.constant([2]), tf.constant([3]))
tp = pool.ThreadPool(2)
f0 = tp.apply_async(session.run, [output0])
f1 = tp.apply_async(session.run, [output1])
# Make sure both inputs are in the batcher before starting it.
time.sleep(_SLEEP_TIME)
tf.train.start_queue_runners()
result0, batch_size0 = f0.get()
result1, batch_size1 = f1.get()
self.assertAllEqual([3], result0)
|
tensorflow.constant
| 9,297 |
import tensorflow as tf
tf.argmax(self.predictions_action, 1)
)
self.accuracy_action = tf.reduce_mean(tf.cast(correct_prediction_action, 'float'))
tf.scalar_summary('accuracy_action', self.accuracy_action)
correct_prediction_arguments = tf.equal(tf.argmax(one_hot_labels_arguments, 2),
tf.argmax(self.predictions_arguments, 2))
self.accuracy_arguments = tf.reduce_mean(tf.cast(correct_prediction_arguments, 'float'))
tf.scalar_summary('accuracy_arguments', self.accuracy_arguments)
|
tensorflow.cast
| 9,298 |
from tensorflow.python.ops import array_ops
# Shape of filled IDs is the same as `ids` with the last dim collapsed to 1.
ids_shape = array_ops.shape(ids, out_type=dtypes.int64)
ids_last_dim = array_ops.size(ids_shape) - 1
|
tensorflow.python.ops.array_ops.shape
| 9,299 |
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