seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
b = tf.get_variable("proj_b", [5])
with tf.variable_scope("proj_seq2seq"):
|
tensorflow.variable_scope
| 8,000 |
import tensorflow as tf
lm_emb = lm_emb[sentence_offset:sentence_offset + max_training_sentences, :, :, :]
char_index = char_index[sentence_offset:sentence_offset + max_training_sentences, :, :]
text_len = text_len[sentence_offset:sentence_offset + max_training_sentences]
speaker_ids = speaker_ids[word_offset: word_offset + num_words]
gold_spans = np.logical_and(gold_ends >= word_offset, gold_starts < word_offset + num_words)
gold_starts = gold_starts[gold_spans] - word_offset
gold_ends = gold_ends[gold_spans] - word_offset
cluster_ids = cluster_ids[gold_spans]
return tokens, context_word_emb, head_word_emb, lm_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids
def get_candidate_labels(self, candidate_starts, candidate_ends, labeled_starts, labeled_ends, labels):
same_start = tf.equal(tf.expand_dims(labeled_starts, 1), tf.expand_dims(candidate_starts, 0)) # [num_labeled, num_candidates]
same_end = tf.equal(tf.expand_dims(labeled_ends, 1), tf.expand_dims(candidate_ends, 0)) # [num_labeled, num_candidates]
same_span = tf.logical_and(same_start, same_end) # [num_labeled, num_candidates]
candidate_labels = tf.matmul(tf.expand_dims(labels, 0), tf.to_int32(same_span)) # [1, num_candidates]
candidate_labels = tf.squeeze(candidate_labels, 0) # [num_candidates]
return candidate_labels
def get_dropout(self, dropout_rate, is_training):
return 1 - (tf.to_float(is_training) * dropout_rate)
def coarse_to_fine_pruning(self, top_span_emb, top_span_mention_scores, c):
k = util.shape(top_span_emb, 0)
top_span_range = tf.range(k) # [k]
|
tensorflow.expand_dims
| 8,001 |
import tensorflow as tf
Train RNN graph for multiple series
"""
def train_rnn_multi(raw_data_x, raw_data_y, val_data_x, val_data_y, timeindex_train, timeindex_val, g, num_epochs, num_steps, batch_size, input_prob, output_prob, state_prob, epoch_before_val = 50, max_checks_without_progress=50,epoch_overlap=None, verbose=True, save=False):
with tf.Session() as sess:
"initialize the variables"
sess.run(tf.global_variables_initializer())
"see the trainable variables"
# print("The trainable variables are:")
variable_names = [v.name for v in tf.trainable_variables()]
variable_shapes = [v.get_shape() for v in tf.trainable_variables()]
parameter_num = 0
for name, shape in zip(variable_names, variable_shapes):
# print('{}\nShape: {}'.format(name, shape))
parameter_num += shape[0]*shape[1] if np.size(shape)>1 else shape[0]
"train the graph"
training_losses = []
val_losses = []
#set early_stopping cretirion
checks_without_progress = 0
best_loss = np.infty
|
tensorflow.trainable_variables
| 8,002 |
import tensorflow as tf
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])
# Train op
if mode == 'train':
lr = tf.Variable(0.0, trainable=False)
|
tensorflow.multiply
| 8,003 |
import tensorflow as tf
# invalid position mask such as query and special symbols (PAD, SEP, CLS)
p_mask = features["p_mask"]
# logit of the start position
with tf.variable_scope("start_logits"):
start_logits = tf.layers.dense(
output,
1,
kernel_initializer=initializer)
start_logits = tf.transpose(tf.squeeze(start_logits, -1), [1, 0])
start_logits_masked = start_logits * (1 - p_mask) - 1e30 * p_mask
start_log_probs = tf.nn.log_softmax(start_logits_masked, -1)
# logit of the end position
with tf.variable_scope("end_logits"):
if is_training:
# during training, compute the end logits based on the
# ground truth of the start position
start_positions = tf.reshape(features["start_positions"], [-1])
start_index = tf.one_hot(start_positions, depth=seq_len, axis=-1,
dtype=tf.float32)
|
tensorflow.nn.log_softmax
| 8,004 |
from tensorflow.contrib.eager.python.examples.l2hmc import l2hmc
hparams = get_default_hparams()
energy_fn, _, _ = l2hmc.get_scg_energy_fn()
dynamics = l2hmc.Dynamics(
x_dim=hparams.x_dim,
|
tensorflow.contrib.eager.python.examples.l2hmc.l2hmc.Dynamics
| 8,005 |
import tensorflow as tf
import tensorflow as tf
_SLEEP_TIME = 1.0
class DynamicBatchingTest(tf.test.TestCase):
def test_one(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])
output = f(tf.constant([[1, 3]]), tf.constant([2]))
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:
|
tensorflow.tile
| 8,006 |
import tensorflow as tf
tf.gfile.DeleteRecursively(tmp_dir)
def run(dataset_dir):
"""Runs the download and conversion operation.
Args:
dataset_dir: The dataset directory where the dataset is stored.
"""
if not tf.gfile.Exists(dataset_dir):
tf.gfile.MakeDirs(dataset_dir)
dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
# First, process the training data:
#with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
filenames = []
for i in range(_NUM_TRAIN_FILES):
filenames.append(os.path.join(dataset_dir,
'cifar-10-batches-py',
|
tensorflow.gfile.MakeDirs
| 8,007 |
import tensorflow as tf
resize_side_min: The lower bound for the smallest side of the image for
aspect-preserving resizing.
resize_side_max: The upper bound for the smallest side of the image for
aspect-preserving resizing.
Returns:
A preprocessed image.
"""
resize_side = tf.random_uniform(
[], minval=resize_side_min, maxval=resize_side_max+1, dtype=tf.int32)
image = _aspect_preserving_resize(image, resize_side)
image = _random_crop([image], output_height, output_width)[0]
image.set_shape([output_height, output_width, 3])
image = tf.to_float(image)
image = tf.image.random_flip_left_right(image)
return _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
def preprocess_for_eval(image, output_height, output_width, resize_side):
"""Preprocesses the given image for evaluation.
Args:
image: A `Tensor` representing an image of arbitrary size.
output_height: The height of the image after preprocessing.
output_width: The width of the image after preprocessing.
resize_side: The smallest side of the image for aspect-preserving resizing.
|
tensorflow.to_float
| 8,008 |
import tensorflow as tf
precision = precision_score(labels_all,predicted_score)
recall = recall_score(labels_all,predicted_score)
apk_sc = rank_metrics.apk(actual, predicted, k=50)
return roc_sc, auprc_score,accuracy,precision,recall,f ,apk_sc
def construct_placeholders(edge_types):
placeholders = {
'batch': tf.placeholder(tf.int32, name='batch'),
'batch_neg': tf.placeholder(tf.int32, name='batch_neg'),
'batch_node':tf.placeholder(tf.int32,name = 'batch_node'),
'adj_min_batch': tf.placeholder(tf.float32,name='adj_min_batch'),
'sim_min_batch': tf.placeholder(tf.float32,name='sim_min_batch'),
'batch_edge_type_idx': tf.placeholder(tf.int32, shape=(), name='batch_edge_type_idx'),
'batch_row_edge_type': tf.placeholder(tf.int32, shape=(), name='batch_row_edge_type'),
'batch_col_edge_type': tf.placeholder(tf.int32, shape=(), name='batch_col_edge_type'),
'degrees': tf.placeholder(tf.int32),
'dropout': tf.placeholder_with_default(0., shape=()),
}
placeholders.update({
'adj_mats_%d,%d,%d' % (i, j, k): tf.sparse_placeholder(tf.float32)
for i, j in edge_types for k in range(edge_types[i,j])})
placeholders.update({
'feat_%d' % i: tf.sparse_placeholder(tf.float32)
for i, _ in edge_types})
return placeholders
|
tensorflow.placeholder
| 8,009 |
import tensorflow as tf
def _evaluate_spherical_harmonics_branch(degree,
order,
theta,
phi,
sign_order,
var_type=tf.float64):
sqrt_2 = tf.constant(1.41421356237, dtype=var_type)
order_float = tf.cast(order, dtype=var_type)
tmp = sqrt_2 * _spherical_harmonics_normalization(
degree, order, var_type) * evaluate_legendre_polynomial(
degree, order, tf.cos(theta))
positive = tmp * tf.cos(order_float * phi)
negative = tmp * tf.sin(order_float * phi)
return tf.where(tf.greater(sign_order, 0), positive, negative)
def evaluate_spherical_harmonics(
degree_l: TensorLike,
order_m: TensorLike,
theta: TensorLike,
phi: TensorLike,
name: str = "spherical_harmonics_evaluate_spherical_harmonics") -> TensorLike: # pylint: disable=line-too-long
|
tensorflow.cos
| 8,010 |
import tensorflow as tf
if padding == "PARTIAL":
with tf.variable_scope('mask'):
_, h, w, _ = input.get_shape().as_list()
slide_window = size[0] * size[1]
mask = tf.ones(shape=[1, h, w, 1])
update_mask = tf.layers.conv2d(mask, filters=1, dilation_rate=(dilation, dilation), name='mask' + id,
kernel_size=size, kernel_initializer=tf.constant_initializer(1.0),
strides=stride, padding="SAME", use_bias=False, trainable=False)
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.layers.conv2d(input, filters=channels, name='conv' + id, kernel_size=size, kernel_initializer=init,
strides=stride, padding="SAME", use_bias=False)
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
if padding == "REFLECT":
assert size[0] % 2 == 1 and size[1] % 2 == 1, "REFLECTION PAD ONLY WORKING FOR ODD FILTER SIZE.. " + str(size)
pad_x = size[0] // 2
pad_y = size[1] // 2
input = tf.pad(input, [[0, 0], [pad_x, pad_x], [pad_y, pad_y], [0, 0]], "REFLECT")
padding = "VALID"
|
tensorflow.layers.conv2d
| 8,011 |
import tensorflow as tf
def add_grad_to_graph(self, a_grads):
with tf.variable_scope('policy_grads'):
|
tensorflow.variable_scope
| 8,012 |
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten
# Block 1
conv1a = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[8, 8], strides=4, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(self.inputs)
conv1b = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1a)
conv1c = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1b)
pool1 = MaxPool2D(pool_size=[2,2])(conv1c)
# Block 2
conv2a = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool1)
conv2b = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2a)
conv2c = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv2b)
pool2 = MaxPool2D(pool_size=[2,2])(conv2c)
# Block 3
conv3a = Conv2D(padding="same", filters=RNN_SIZE//2, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool2)
|
tensorflow.keras.layers.Conv2D
| 8,013 |
from tensorflow import keras
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)
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
return batch_features, batch_labels
return _input_fn
# Create inference model using Keras
# The model here is a dnn regressor
def make_keras_estimator(output_dir):
from tensorflow import keras
model = keras.models.Sequential()
model.add(keras.layers.Dense(32, input_shape=(N_INPUTS,), name=TIMESERIES_INPUT_LAYER))
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dense(1))
model.compile(loss = 'mean_squared_error',
optimizer = 'adam',
metrics = ['mae', 'mape']) # mean absolute [percentage] error
return keras.estimator.model_to_estimator(model, model_dir=output_dir)
# Create the inference model
def simple_rnn(features, labels, mode):
# 0. Reformat input shape to become a sequence
x = tf.split(features[TIMESERIES_COL], N_INPUTS, 1)
# 1. Configure the RNN
|
tensorflow.keras.models.Sequential
| 8,014 |
import tensorflow as tf
'triplet_loss/Anchor/%s/Distance/Mean' % tag:
tf.math.reduce_mean(negative_distances),
'triplet_loss/%s/Loss/All' % tag:
loss,
'triplet_loss/%s/Loss/Active' % tag:
active_loss,
'triplet_loss/%s/ActiveTripletNum' % tag:
num_active_triplets,
'triplet_loss/%s/ActiveTripletRatio' % tag:
active_triplet_ratio,
# Summaries related to triplet mining.
'triplet_mining/Anchor/%s/Distance/Mean' % tag:
tf.math.reduce_mean(negative_mining_distances),
'triplet_mining/%s/Loss/All' % tag:
mining_loss,
'triplet_mining/%s/Loss/Active' % tag:
active_mining_loss,
'triplet_mining/%s/ActiveTripletNum' % tag:
num_active_mining_triplets,
'triplet_mining/%s/ActiveTripletRatio' % tag:
active_mining_triplet_ratio,
}
if summarize_percentiles:
summaries.update({
'triplet_loss/Anchor/%s/Distance/Median' % tag:
|
tensorflow.math.reduce_mean
| 8,015 |
import tensorflow as tf
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess)
sess.run(train_op)
def testMulticlass(self):
FLAGS.num_classes = 10
graphs.VatxtModel().classifier_graph()
def testATMethods(self):
at_methods = [None, 'rp', 'at', 'vat', 'atvat']
for method in at_methods:
FLAGS.adv_training_method = method
with tf.Graph().as_default():
graphs.VatxtModel().classifier_graph()
# Ensure variables have been reused
# Embedding + LSTM layers + hidden layers + logits layer
expected_num_vars = 1 + 2 * FLAGS.rnn_num_layers + 2 * (
FLAGS.cl_num_layers) + 2
self.assertEqual(len(tf.trainable_variables()), expected_num_vars)
def testSyncReplicas(self):
FLAGS.sync_replicas = True
graphs.VatxtModel().language_model_training()
|
tensorflow.Graph
| 8,016 |
import tensorflow as tf
else:
self.inputs = prev_layer.outputs
# The name of placeholder for keep_prob is the same with the name
# of the Layer.
if is_fix:
self.outputs = tf.nn.dropout(self.inputs, keep, seed=seed, name=name)
else:
LayersConfig.set_keep[name] = tf.placeholder(tf.float32)
self.outputs = tf.nn.dropout(self.inputs, LayersConfig.set_keep[name], seed=seed, name=name) # 1.2
# self.all_layers = list(layer.all_layers)
|
tensorflow.nn.dropout
| 8,017 |
import tensorflow as tf
# Add entropy coefficient optimization operation if needed
if ent_coef_loss is not None:
with tf.control_dependencies([train_values_op]):
ent_coef_op = entropy_optimizer.minimize(ent_coef_loss, var_list=self.log_ent_coef)
self.infos_names += ['ent_coef_loss', 'ent_coef']
self.step_ops += [ent_coef_op, ent_coef_loss, self.ent_coef]
# Monitor losses and entropy in tensorboard
tf.summary.scalar('policy_loss', policy_loss)
tf.summary.scalar('qf1_loss', qf1_loss)
tf.summary.scalar('qf2_loss', qf2_loss)
tf.summary.scalar('value_loss', value_loss)
tf.summary.scalar("Imitation_loss",self.actor_loss_di)
tf.summary.scalar('entropy', self.entropy)
tf.summary.scalar('importance weight',tf.reduce_mean(self.weight_ph))
if ent_coef_loss is not None:
tf.summary.scalar('ent_coef_loss', ent_coef_loss)
|
tensorflow.summary.scalar
| 8,018 |
import tensorflow as tf
with self.graph.as_default():
with tf.device('/cpu:0'):
embedding = tf.get_variable(
'embedding', [vocab_size, hidden_size], dtype=tf.float32)
|
tensorflow.get_variable
| 8,019 |
import tensorflow as tf
self.D_B_loss = (self.D_B_loss_real + self.D_B_loss_fake) / 2.0
self.D_A_loss_real = binary_cross_entropy_loss(tf.ones_like(self.D_A_real),self.D_A_real)
self.D_A_loss_fake = binary_cross_entropy_loss(tf.zeros_like(self.D_A_fake),self.D_A_fake)
self.D_A_loss = (self.D_A_loss_real + self.D_A_loss_fake) / 2.0
self.discriminator_loss = self.D_B_loss + self.D_A_loss
self.loss_GABA_sum = tf.summary.scalar("g_loss_a2b", self.loss_GABA)
self.loss_GBAB_sum = tf.summary.scalar("g_loss_b2a", self.loss_GBAB)
self.g_total_loss_sum = tf.summary.scalar("g_loss", self.generator_loss)
self.g_sum = tf.summary.merge([self.loss_GABA_sum,self.loss_GBAB_sum,self.g_total_loss_sum])
self.loss_db_sum = tf.summary.scalar("db_loss", self.D_B_loss)
self.loss_da_sum = tf.summary.scalar("da_loss", self.D_A_loss)
self.loss_d_sum = tf.summary.scalar("d_loss",self.discriminator_loss)
self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.D_B_loss_real)
self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.D_B_loss_fake)
self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.D_A_loss_real)
self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.D_A_loss_fake)
self.d_sum = tf.summary.merge(
[self.loss_da_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
self.loss_db_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
self.loss_d_sum]
)
|
tensorflow.summary.scalar
| 8,020 |
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten
# FC layers to find next location
loc_layer1 = Dense(units=loc_layer_size)(prev_loc)
loc_layer2 = Dense(units=loc_layer_size)(loc_layer1)
|
tensorflow.keras.layers.Dense
| 8,021 |
import tensorflow as tf
return (tf.data.TextLineDataset([filename])
.flat_map(_to_chars)
.repeat()
.batch(tf.to_int64(sequence_size))
.shuffle(1000)
.batch(tf.to_int64(batch_size)))
|
tensorflow.to_int64
| 8,022 |
import tensorflow as tf
heatmap_pred = tf.squeeze(heatmap_pred_list[i])
heatmap_true = tf.squeeze(heatmap_true_list[i])
loss = 0.5 * tf.losses.mean_squared_error(y_pred=heatmap_pred * true_weight[:, i],
y_true=heatmap_true * true_weight[:, i])
|
tensorflow.losses.mean_squared_error
| 8,023 |
import tensorflow as tf
return learning_rate * decay
elif params.learning_rate_decay == "rnnplus_warmup_decay":
step = tf.to_float(global_step)
n = float(len(params.device_list))
warmup_steps = tf.to_float(params.warmup_steps)
decay = tf.minimum(1 + step * (n - 1) / (n * warmup_steps), tf.minimum(n, n * ((2*n) ** ((params.s - n * step) / (params.e - params.s)))))
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:
|
tensorflow.maximum
| 8,024 |
import tensorflow as tf
if min_negative_keypoint_distance >= 0.0:
anchor_match_negative_indicator_matrix = (
compute_negative_indicator_matrix(
anchor_points=anchor_keypoints,
match_points=match_keypoints,
distance_fn=keypoint_distance_fn,
min_negative_distance=min_negative_keypoint_distance,
anchor_point_masks=anchor_keypoint_masks,
match_point_masks=match_keypoint_masks))
else:
num_anchors = tf.shape(anchor_keypoints)[0]
anchor_match_negative_indicator_matrix = tf.math.logical_not(
tf.eye(num_anchors, dtype=tf.bool))
anchor_positive_distances = embedding_sample_distance_fn(
anchor_embeddings, positive_embeddings)
if anchor_mining_embeddings is None and positive_mining_embeddings is None:
anchor_positive_mining_distances = anchor_positive_distances
else:
anchor_positive_mining_distances = embedding_sample_distance_fn(
|
tensorflow.shape
| 8,025 |
import tensorflow as tf
tf.app.flags.DEFINE_float(
'gpu_memory_fraction', 1., 'GPU memory fraction to use.')
# scaffold related configuration
tf.app.flags.DEFINE_string(
'data_dir', '../PASCAL/VOC_TF/VOC0712TF/',
'The directory where the dataset input data is stored.')
tf.app.flags.DEFINE_string(
'dataset_name', 'pascalvoc_0712', 'The name of the dataset to load.')
tf.app.flags.DEFINE_integer(
'num_classes', 21, 'Number of classes to use in the dataset.')
tf.app.flags.DEFINE_string(
'dataset_split_name', 'train', 'The name of the train/test split.')
tf.app.flags.DEFINE_string(
'model_dir', './logs_v3/',
'The directory where the model will be stored.')
tf.app.flags.DEFINE_integer(
'log_every_n_steps', 10,
'The frequency with which logs are print.')
tf.app.flags.DEFINE_integer(
'save_summary_steps', 500,
'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer(
'save_checkpoints_secs', 7200,
'The frequency with which the model is saved, in seconds.')
|
tensorflow.app.flags.DEFINE_string
| 8,026 |
import tensorflow as tf
TensorFlow's every time we want to use a std function that handles bounding
boxes.
Args:
bboxes: A Tensor of shape (total_bboxes, 4)
Returns:
bboxes: A Tensor of shape (total_bboxes, 4) with the order swaped.
"""
with tf.name_scope('BoundingBoxTransform/change_order'):
first_min, second_min, first_max, second_max = tf.unstack(
bboxes, axis=1
)
bboxes = tf.stack(
[second_min, first_min, second_max, first_max], axis=1
)
return bboxes
if __name__ == '__main__':
import numpy as np
bboxes = tf.placeholder(tf.float32)
bboxes_val = [[10, 10, 20, 22]]
gt_boxes = tf.placeholder(tf.float32)
|
tensorflow.stack
| 8,027 |
import tensorflow as tf
NUM_EVAL_EXAMPLES = 5000
def build_training_graph(x, y, ul_x, ul_u, lr, mom):
global_step = tf.get_variable(
name="global_step",
shape=[],
dtype=tf.float32,
initializer=tf.constant_initializer(0.0),
trainable=False,
)
logit = vat.forward(x)
nll_loss = L.ce_loss(logit, y)
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
if FLAGS.method == 'vat':
ul_logit = vat.forward(ul_x, is_training=True, update_batch_stats=False)
vat_loss, ul_u_updated = vat.virtual_adversarial_loss(ul_x, ul_u, ul_logit)
additional_loss = vat_loss
|
tensorflow.constant_initializer
| 8,028 |
from tensorflow.python.ops import array_ops
target = array_ops.expand_dims(target, dim=[1])
loss_vec = nn.sigmoid_cross_entropy_with_logits(logits,
math_ops.to_float(target))
return loss_vec
def _softmax_cross_entropy_loss(logits, target):
# sigmoid_cross_entropy_with_logits requires [batch_size, 1] target.
# Check that we got int32/int64 for classification.
if (not target.dtype.is_compatible_with(dtypes.int64) and
not target.dtype.is_compatible_with(dtypes.int32)):
raise ValueError("Target's dtype should be int32, int64 or compatible. "
"Instead got %s." % target.dtype)
# sparse_softmax_cross_entropy_with_logits requires [batch_size] target.
if len(target.get_shape()) == 2:
target = array_ops.squeeze(target, squeeze_dims=[1])
loss_vec = nn.sparse_softmax_cross_entropy_with_logits(logits, target)
return loss_vec
def _run_metrics(predictions, targets, metrics, weights):
result = {}
targets = math_ops.cast(targets, predictions.dtype)
for name, metric in six.iteritems(metrics or {}):
if "weights" in inspect.getargspec(metric)[0]:
result[name] = metric(predictions, targets, weights=weights)
else:
result[name] = metric(predictions, targets)
return result
|
tensorflow.python.ops.array_ops.squeeze
| 8,029 |
import tensorflow as tf
return video_num_input_frames, video_num_target_frames
@gin.configurable(module='trax.data', denylist=['dataset', 'training'])
def bair_robot_pushing_preprocess(dataset, training):
"""Pre-processing function that concatenates input and target frames."""
del training
def concat_and_add_mask(features, targets):
"""Concatenate input and output frames to form a language modeling setup."""
inp = features['inputs']
concat = tf.concat([inp, targets], axis=0)
mask = tf.concat([tf.zeros_like(inp), tf.ones_like(targets)], axis=0)
concat = tf.reshape(concat, (-1,))
mask = tf.reshape(mask, (-1,))
concat = tf.cast(concat, tf.int32)
mask = tf.cast(mask, tf.float32)
features['inputs'] = features['targets'] = concat
features['mask'] = mask
return features, concat
dataset = dataset.map(concat_and_add_mask)
return dataset
def sentencepiece_tokenize(stream, spm_path=None, extra_ids=0):
"""Sentencepiece tokenization."""
spm_path = spm_path or t5_data().DEFAULT_SPM_PATH
|
tensorflow.reshape
| 8,030 |
import tensorflow as tf
def _bottleneck_residual(x,
ksize_list,
strides,
padding,
is_training,
data_format='NHWC',
no_activation=False):
with tf.variable_scope('sub1'):
if not no_activation:
x = _batch_norm('bn1', x, is_training, data_format)
x = _relu('relu1', x)
STRIDES_ERR_MSG = 'Strides height and width are not the same.'
if data_format == 'NHWC':
|
tensorflow.variable_scope
| 8,031 |
from tensorflow.contrib.layers.python.layers.layers import _build_variable_getter, _add_variable_to_collections
conv_dims: Optional convolution dimensionality, when set it would use the
corresponding convolution (e.g. 2 for Conv 2D, 3 for Conv 3D, ..). When
leaved to None it would select the convolution dimensionality based on
the input rank (i.e. Conv ND, with N = input_rank - 2).
Returns:
A tensor representing the output of the operation.
Raises:
ValueError: If `data_format` is invalid.
ValueError: Both 'rate' and `stride` are not uniformly 1.
"""
if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC', 'NCDHW']:
raise ValueError('Invalid data_format: %r' % (data_format,))
layer_variable_getter = _build_variable_getter({'bias': 'biases', 'kernel': 'weights'})
with variable_scope.variable_scope(scope, 'Conv', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc:
inputs = ops.convert_to_tensor(inputs)
input_rank = inputs.get_shape().ndims
if conv_dims is not None and conv_dims + 2 != input_rank:
raise ValueError('Convolution expects input with rank %d, got %d' % (conv_dims + 2, input_rank))
if input_rank == 3:
layer_class = convolutional_layers.Convolution1D
elif input_rank == 4:
layer_class = MyConv2D
elif input_rank == 5:
|
tensorflow.contrib.layers.python.layers.layers._build_variable_getter
| 8,032 |
import tensorflow as tf
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)
|
tensorflow.matmul
| 8,033 |
import tensorflow as tf
"""
with tf.name_scope(name):
learning_rate = tf.cast(learning_rate, dtype=tf.float32)
global_step = tf.cast(global_step, dtype=tf.float32)
step_size = tf.cast(step_size, dtype=tf.float32)
max_lr = tf.cast(max_lr, dtype=tf.float32)
if mode == 'tri':
periodic_comp = tf.mod((global_step + step_size / 4) / step_size, 1)
first_factor = tf.abs(periodic_comp - 0.5)
|
tensorflow.cast
| 8,034 |
import tensorflow as tf
cell = cell_fn(rnn_size)
if mode == 'train' and rnn_keep_prob < 1.0:
cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=rnn_keep_prob)
if rnn_nlayers > 1:
|
tensorflow.nn.rnn_cell.DropoutWrapper
| 8,035 |
from tensorflow.python.ops import gen_nn_ops
type `tf.float32`.
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. The stride of the sliding
window for each dimension of the input tensor.
padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
data_format: A string. 'NHWC' and 'NCHW" are supported.
name: Optional name for the operation.
Returns:
A `Tensor` with type `tf.float32`. The max pooled output tensor.
"""
with ops.op_scope([value], name, "MaxPool") as name:
value = ops.convert_to_tensor(value, name="input")
return gen_nn_ops._max_pool(value, ksize=ksize, strides=strides,
padding=padding,
data_format=data_format,
name=name)
ops.RegisterShape("Relu")(common_shapes.unchanged_shape)
ops.RegisterShape("Relu6")(common_shapes.unchanged_shape)
ops.RegisterShape("Elu")(common_shapes.unchanged_shape)
ops.RegisterShape("Softplus")(common_shapes.unchanged_shape)
ops.RegisterShape("Softsign")(common_shapes.unchanged_shape)
@ops.RegisterShape("ReluGrad")
|
tensorflow.python.ops.gen_nn_ops._max_pool
| 8,036 |
import tensorflow as tf
with tf.variable_scope("network_parameters"):
with tf.variable_scope("policy"):
x = flat_observations
for size in config.policy_layers:
x = tf.layers.dense(x, size, activation=tf.nn.relu)
mean = tf.layers.dense(
x, action_space.shape[0], activation=tf.tanh,
kernel_initializer=mean_weights_initializer)
logstd = tf.get_variable(
"logstd", mean.shape[2:], tf.float32, logstd_initializer)
logstd = tf.tile(
logstd[None, None],
[tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
with tf.variable_scope("value"):
x = flat_observations
for size in config.value_layers:
x = tf.layers.dense(x, size, activation=tf.nn.relu)
value = tf.layers.dense(x, 1)[..., 0]
mean = tf.check_numerics(mean, "mean")
logstd = tf.check_numerics(logstd, "logstd")
value = tf.check_numerics(value, "value")
policy = tfp.distributions.MultivariateNormalDiag(mean, tf.exp(logstd))
|
tensorflow.shape
| 8,037 |
import tensorflow as tf
tf.summary.scalar('Loss/Entropy', loss_entropy)
tf.summary.scalar('Loss/Total', loss)
tf.summary.scalar('Var/Epsilon', epsilon_decay)
tf.summary.scalar('Var/Policy Mode', tf.reduce_mean(pi.mode()))
tf.summary.scalar('Var/Policy Sigma', tf.reduce_mean(pi.stddev()))
tf.summary.scalar('Var/Value', tf.reduce_mean(self.vf))
self.summarise = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES))
# AC net
def build_anet(self, state_in, name, reuse=False, batch_size=64):
reg = None
with tf.variable_scope(name, reuse=reuse):
layer_a1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
layer_a2 = tf.layers.dense(layer_a1, 256, tf.nn.relu, kernel_regularizer=reg)
lstm_a = tf.nn.rnn_cell.LSTMCell(num_units=256)
lstm_a = tf.nn.rnn_cell.DropoutWrapper(lstm_a, output_keep_prob=self.keep_prob)
state_init_a = lstm_a.zero_state(batch_size=batch_size, dtype=tf.float32)
lstm_ain = tf.expand_dims(layer_a2, axis=1)
out_a, state_final_a = tf.nn.dynamic_rnn(cell=lstm_a, inputs=lstm_ain, initial_state=state_init_a)
cell_out_a = tf.reshape(out_a, [-1, 256])
mu = tf.layers.dense(cell_out_a, self.a_dim, tf.nn.tanh, kernel_regularizer=reg)
sigma = tf.layers.dense(cell_out_a, self.a_dim, tf.nn.softplus, kernel_regularizer=reg)
# sigma = tf.get_variable(name='pi_sigma', shape=self.a_dim, initializer=tf.constant_initializer(0.5))
sigma = tf.clip_by_value(sigma, 0.0, 1.0)
norm_dist = tf.distributions.Normal(loc=mu * self.a_bound, scale=sigma)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
return norm_dist, params, state_init_a, state_final_a
|
tensorflow.nn.rnn_cell.LSTMCell
| 8,038 |
import tensorflow as tf
# Load IRK weights
tmp = np.float32(np.loadtxt('../../Utilities/IRK_weights/Butcher_IRK%d.txt' % (q), ndmin = 2))
weights = np.reshape(tmp[0:q**2+q], (q+1,q))
self.IRK_alpha = weights[0:-1,:]
self.IRK_beta = weights[-1:,:]
self.IRK_times = tmp[q**2+q:]
# tf placeholders and graph
self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True))
self.x0_tf = tf.placeholder(tf.float32, shape=(None, self.x0.shape[1]))
self.x1_tf = tf.placeholder(tf.float32, shape=(None, self.x1.shape[1]))
self.u0_tf = tf.placeholder(tf.float32, shape=(None, self.u0.shape[1]))
self.u1_tf = tf.placeholder(tf.float32, shape=(None, self.u1.shape[1]))
self.dummy_x0_tf = tf.placeholder(tf.float32, shape=(None, self.q)) # dummy variable for fwd_gradients
self.dummy_x1_tf = tf.placeholder(tf.float32, shape=(None, self.q)) # dummy variable for fwd_gradients
self.U0_pred = self.net_U0(self.x0_tf) # N0 x q
self.U1_pred = self.net_U1(self.x1_tf) # N1 x q
self.loss = tf.reduce_sum(tf.square(self.u0_tf - self.U0_pred)) + \
tf.reduce_sum(tf.square(self.u1_tf - self.U1_pred))
self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss,
|
tensorflow.placeholder
| 8,039 |
import tensorflow as tf
outputs = [inputs]
with tf.variable_scope(self.scope):
for layer in range(self.num_layers):
gru_fw, gru_bw = self.grus[layer]
init_fw, init_bw = self.inits[layer]
mask_fw, mask_bw = self.dropout_mask[layer]
with tf.variable_scope("fw_{}".format(layer)):
out_fw, _ = tf.nn.dynamic_rnn(
gru_fw, outputs[-1] * mask_fw, seq_len, initial_state=init_fw, dtype=tf.float32)
with tf.variable_scope("bw_{}".format(layer)):
inputs_bw = tf.reverse_sequence(
outputs[-1] * mask_bw, seq_lengths=seq_len, seq_dim=1, batch_dim=0)
out_bw, _ = tf.nn.dynamic_rnn(
gru_bw, inputs_bw, seq_len, initial_state=init_bw, dtype=tf.float32)
|
tensorflow.nn.dynamic_rnn
| 8,040 |
import tensorflow as tf
else:
direct_mask = tf.less(head_idxs, dep_idxs) # [bs, slh, sld]
# [bs, slh, slh]
rep_mask_tile = tf.logical_and(tf.expand_dims(rep_dep_mask, 1), tf.expand_dims(rep_head_mask, 2))
attn_mask = tf.logical_and(direct_mask, rep_mask_tile) # [bs, slh, sld]
# tensor tile
rep_map_tile = tf.tile(tf.expand_dims(rep_dep_tensor, 1), [1, sl_head, 1, 1]) # bs,slh,sld,vec
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias', [ivec], tf.float32, tf.constant_initializer(0.))
dependent = linear(rep_dep_tensor_dp, ivec, False, scope='linear_dependent') # bs,sld,vec
dependent_etd = tf.expand_dims(dependent, 1) # bs,1,sld,vec
head = linear(rep_head_tensor_dp, ivec, False, scope='linear_head') # bs,slh,vec
head_etd = tf.expand_dims(head, 2) # bs,slh,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias, 5.0) # bs,slh,sld,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask) # bs,slh,sld,vec
attn_score = tf.nn.softmax(logits_masked, 2) # bs,slh,sld,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_result = tf.reduce_sum(attn_score * rep_map_tile, 2) # bs,slh,vec -> head_org_idx
return attn_result
|
tensorflow.expand_dims
| 8,041 |
import tensorflow as tf
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
def get_test_batch(image,label,batch_size):
images,labels=tf.train.batch([image,label],batch_size=batch_size)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
def get_valid_batch(image,label,batch_size):
images,labels=tf.train.batch([image,label],batch_size=batch_size)
return tf.reshape(images,[batch_size,4096]),tf.reshape(labels,[batch_size])
class trainwork(object):
def __init__(self):
with tf.variable_scope('scop'):
self.w1=tf.get_variable('w1', [4096,1024],initializer=tf.contrib.layers.xavier_initializer_conv2d())
self.w2=tf.get_variable('w2', [1024,classnum],initializer=tf.contrib.layers.xavier_initializer_conv2d())
self.b1 = tf.get_variable('b1', [1024],initializer=tf.constant_initializer(0.0))
self.b2 = tf.get_variable('b2', [classnum],initializer=tf.constant_initializer(0.0))
def inference(self,images):
images=tf.cast(images,tf.float32)/255.0
l1 = tf.matmul(images, self.w1)+self.b1
l1=tf.nn.relu(l1)
out = tf.matmul(l1, self.w2)+self.b2
return out
def test_inference(self,images):
images=tf.cast(images,tf.float32)/255.0
l1 = tf.matmul(images, self.w1)+self.b1
l1=tf.nn.relu(l1)
out = tf.matmul(l1, self.w2)+self.b2
|
tensorflow.constant_initializer
| 8,042 |
import tensorflow as tf
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
|
tensorflow.device
| 8,043 |
import tensorflow as tf
with tf.variable_scope(name) as scope:
kernel = self.variable('weights', [kernel_size, kernel_size, output_channels, input_channels], initializer, regularizer=tf.contrib.layers.l2_regularizer(0.0005))
deconv = tf.nn.conv2d_transpose(bottom, kernel, output_shape, [1, stride, stride, 1], padding='SAME')
biases = self.variable('biases', [output_channels], tf.constant_initializer(0.0))
deconv_layer = tf.nn.bias_add(deconv, biases)
if bn:
|
tensorflow.constant_initializer
| 8,044 |
import tensorflow as tf
self.epsilon = epsilon
self.data_format = data_format
self.name = name
def __call__(self,input_var,**kwargs) :
mean, var = tf.nn.moments(input_var, self.axis, keep_dims=True)
ret = (input_var - mean) / tf.sqrt(var+self.epsilon)
if self.gamma is None :
return ret
|
tensorflow.nn.moments
| 8,045 |
from tensorflow.python.framework import ops as _ops
else:
with _ops.device(self._send_device):
return _XlaSend(
tensor, tensor_name=self._name, name="Send_" + self._name)
def Recv(self):
"""Receives a tensor from the channel."""
if self._send_tpu_core == -1:
return _Recv(self._dtype, self._name,
self._send_device, self._recv_device)
else:
with _ops.device(self._recv_device):
return _XlaRecv(
self._dtype,
tensor_name=self._name,
shape=self._shape,
name="Recv_" + self._name)
|
tensorflow.python.framework.ops.device
| 8,046 |
import tensorflow as tf
tf.summary.scalar('Loss/Policy', loss_pg)
tf.summary.scalar('Loss/Value', loss_vf)
tf.summary.scalar('Loss/Entropy', - 0.01 * tf.reduce_mean(pi.entropy()))
tf.summary.scalar('Var/Policy Mode', tf.reduce_mean(pi.mode()))
tf.summary.scalar('Var/Policy Sigma', tf.reduce_mean(pi.stddev()))
tf.summary.scalar('Var/Value', tf.reduce_mean(self.vf))
self.summarise = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES))
# AC net
def build_anet(self, state_in, name, reuse=False):
reg = tf.contrib.layers.l2_regularizer(1e-3)
with tf.variable_scope(name, reuse=reuse):
layer_a1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
layer_a2 = tf.layers.dense(layer_a1, 256, tf.nn.relu, kernel_regularizer=reg)
mu = tf.layers.dense(layer_a2, self.a_dim, tf.nn.tanh, kernel_regularizer=reg)
# sigma = tf.layers.dense(layer_a2, self.a_dim, tf.nn.softplus, kernel_regularizer=reg)
sigma = tf.get_variable(name='pi_sigma', shape=self.a_dim, initializer=tf.constant_initializer(0.5))
sigma = tf.clip_by_value(sigma, 0.0, 1.0)
norm_dist = tf.distributions.Normal(loc=mu * self.a_bound, scale=sigma)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
return norm_dist, params
def build_cnet(self, state_in, name, reuse=False):
reg = tf.contrib.layers.l2_regularizer(1e-3)
with tf.variable_scope(name, reuse=reuse):
layer_c1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
layer_c2 = tf.layers.dense(layer_c1, 256, tf.nn.relu, kernel_regularizer=reg)
vf = tf.layers.dense(layer_c2, 1, kernel_regularizer=reg)
|
tensorflow.layers.dense
| 8,047 |
import tensorflow as tf
Returns:
Computed features after split separable conv2d.
"""
outputs = slim.separable_conv2d(
inputs,
None,
3,
depth_multiplier=1,
rate=rate,
weights_initializer=tf.truncated_normal_initializer(
stddev=depthwise_weights_initializer_stddev),
weights_regularizer=None,
scope=scope + '_depthwise')
return slim.conv2d(
outputs,
filters,
1,
weights_initializer=tf.truncated_normal_initializer(
|
tensorflow.truncated_normal_initializer
| 8,048 |
import tensorflow as tf
class BenchmarkSparseTensorsMapVsSerialization(tf.test.Benchmark):
def benchmarkVeryLarge2DFloatSparseTensor(self):
np.random.seed(127)
num_elements = 10000
batch_size = 64
indices_batch = np.random.randint(
batch_size, size=num_elements, dtype=np.int64)
indices_value = np.arange(num_elements, dtype=np.int64)
indices = np.asarray(
sorted(zip(indices_batch, indices_value)), dtype=np.int64)
values = ["feature_value_for_embedding_lookup"] * num_elements
shape = np.asarray([batch_size, num_elements], dtype=np.int64)
with tf.Session() as sess:
with tf.device("/cpu:0"):
indices = tf.Variable(indices)
values = tf.Variable(values)
shape = tf.Variable(shape)
st = tf.SparseTensor(indices, values, shape)
st_handles = add_many_sparse_to_tensors_map(st)
st_roundtrip = take_many_sparse_from_tensors_map(
sparse_map_op=st_handles.op, sparse_handles=st_handles)
st_roundtrip_op = st_roundtrip.values.op
st_serialized = tf.serialize_many_sparse(st)
st_deserialized = tf.deserialize_many_sparse(
|
tensorflow.Session
| 8,049 |
import tensorflow as tf
pos = tf.reshape(pos, [-1, 1])
pos = tf.minimum(pos, encoder_input_length - 1)
idx = tf.tile(tf.to_float(tf.range(attn_length)), tf.stack([batch_size]))
idx = tf.reshape(idx, [-1, attn_length])
low = pos - encoder.attn_window_size
high = pos + encoder.attn_window_size
mlow = tf.to_float(idx < low)
mhigh = tf.to_float(idx > high)
m = mlow + mhigh
m += tf.to_float(idx >= encoder_input_length)
mask = tf.to_float(tf.equal(m, 0.0))
e = compute_energy(hidden_states, state, encoder, input_length=encoder_input_length, **kwargs)
weights = softmax(e, mask=mask)
if encoder.attn_window_size > 0:
|
tensorflow.to_float
| 8,050 |
import tensorflow as tf
query = tf.layers.dense(query, facts_size, activation=None, name='f1' + 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, 80, activation=tf.nn.sigmoid, name='f1_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag)
d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag)
d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]])
scores = d_layer_3_all
# Mask
# key_masks = tf.sequence_mask(facts_length, tf.shape(facts)[1]) # [B, T]
key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
paddings = tf.ones_like(scores) * (-2 ** 32 + 1)
|
tensorflow.layers.dense
| 8,051 |
import tensorflow as tf
projection = linear(
input=activation,
input_size=dialogue_state_size,
output_size=action_templates_vocabulary_length,
name='linear_projection_3_predictions_action'
)
self.predictions_action = tf.nn.softmax(projection, name="softmax_output_prediction_action")
# argument prediction
# first encode decoded action template and teh true action template
choice = tf.floor(tf.random_uniform([1], self.use_inputs_prob, 1 + self.use_inputs_prob, tf.float32))
prediction_action_argmax = tf.stop_gradient(tf.argmax(self.predictions_action, 1))
predicted_action_templates_embedding = embedding(
input=prediction_action_argmax,
length=action_templates_vocabulary_length,
size=action_templates_embedding_size,
name='action_templates_embedding'
)
true_action_template_embedding = tf.gather(predicted_action_templates_embedding.embedding_table, actions_template)
predicted_action_templates_embedding = tf.stop_gradient(predicted_action_templates_embedding)
action_templates_embedding = choice * true_action_template_embedding + (1.0 - choice) * predicted_action_templates_embedding
dialogue_state_action_template = tf.concat(
|
tensorflow.argmax
| 8,052 |
import tensorflow as tf
if encoder.binary:
raise NotImplementedError
stride = encoder.maxout_stride
k = tf.to_int32(tf.ceil(time_steps / stride) * stride) - time_steps # TODO: simpler
pad = tf.zeros([batch_size, k, tf.shape(encoder_inputs_)[2]])
encoder_inputs_ = tf.concat([encoder_inputs_, pad], axis=1)
encoder_inputs_ = tf.nn.pool(encoder_inputs_, window_shape=[stride], pooling_type='MAX',
padding='VALID', strides=[stride])
encoder_input_length_ = tf.to_int32(tf.ceil(encoder_input_length_ / stride))
if encoder.highway_layers:
|
tensorflow.concat
| 8,053 |
import tensorflow as tf
W_head = tf.get_variable("embed_W_head", [num_quantized_chars, embedding_size], initializer=initializer)
embedded_head = tf.nn.embedding_lookup(W_head, self.input_head)
embedded_head_expanded = tf.expand_dims(embedded_head, -1)
cnn_inputs = tf.concat(
[embedded_text_expand, embedded_tags_expanded, embedded_deps_expanded, embedded_head_expanded], -1)
print("-" * 20)
|
tensorflow.concat
| 8,054 |
from tensorflow.python.ops import math_ops
predictions, labels = tensor_util.remove_squeezable_dimensions(
predictions, labels)
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
if labels.dtype != predictions.dtype:
predictions = math_ops.cast(predictions, labels.dtype)
is_correct = math_ops.to_float(math_ops.equal(predictions, labels))
return streaming_mean(is_correct, weights, metrics_collections,
updates_collections, name or 'accuracy')
|
tensorflow.python.ops.math_ops.equal
| 8,055 |
from tensorflow.python.ops import math_ops
raise ValueError(
"logits to probabilities is not supported for _BinarySvmTargetColumn")
logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)
return math_ops.argmax(logits, 1)
# TODO(zakaria): use contrib losses.
def _mean_squared_loss(logits, target):
# To prevent broadcasting inside "-".
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
logits.get_shape().assert_is_compatible_with(target.get_shape())
return math_ops.square(logits - math_ops.to_float(target))
def _log_loss_with_two_classes(logits, target):
# sigmoid_cross_entropy_with_logits requires [batch_size, 1] target.
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
loss_vec = nn.sigmoid_cross_entropy_with_logits(
labels=math_ops.to_float(target), logits=logits)
return loss_vec
def _softmax_cross_entropy_loss(logits, target):
# Check that we got integer for classification.
|
tensorflow.python.ops.math_ops.to_float
| 8,056 |
import tensorflow as tf
hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
# Linear
with tf.name_scope("softmax_linear"):
weights = tf.Variable(
tf.truncated_normal([32, 10],
stddev=1.0 / math.sqrt(float(32))),
name="weights")
biases = tf.Variable(tf.zeros([10]),
name="biases")
logits = tf.matmul(hidden2, weights) + biases
tf.add_to_collection("logits", logits)
# Runs to logit.
tf.initialize_all_variables().run()
sess.run(logits)
# Creates a saver.
saver0 = tf.train.Saver()
saver0.save(sess, saver0_ckpt)
# Generates MetaGraphDef.
saver0.export_meta_graph(filename)
|
tensorflow.add_to_collection
| 8,057 |
import tensorflow as tf
self.initialize_tf_vars()
logger.log(self.sess.graph)
self.has_setup = True
self.setup_args = SimpleNamespace(
sampler_cls=sampler_cls, sampler_args=sampler_args)
def initialize_tf_vars(self):
"""Initialize all uninitialized variables in session."""
with tf.name_scope('initialize_tf_vars'):
uninited_set = [
e.decode()
for e in self.sess.run(tf.report_uninitialized_variables())
]
self.sess.run(
tf.variables_initializer([
v for v in tf.global_variables()
if v.name.split(':')[0] in uninited_set
]))
def _start_worker(self):
"""Start Plotter and Sampler workers."""
self.sampler.start_worker()
if self.plot:
from garage.tf.plotter import Plotter
|
tensorflow.report_uninitialized_variables
| 8,058 |
import tensorflow as tf
def __init__(self,name,dims,axis=1,epsilon=1e-3,momentum=0.999,center=True,scale=True) :
self.momentum = momentum
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
|
tensorflow.variable_scope
| 8,059 |
import tensorflow as tf
initializer=tf.contrib.layers.xavier_initializer(), l2_strength=0.0, dilation=1.0, bias=-1):
with tf.variable_scope(name):
stride = [1, stride[0], stride[1], 1]
kernel_shape = [kernel_size[0], kernel_size[1], x.shape[-1], num_filters]
w = variable_with_weight_decay(kernel_shape, initializer, l2_strength)
variable_summaries(w)
if dilation > 1:
conv = tf.nn.atrous_conv2d(x, w, dilation, padding)
else:
if type(padding)==type(''):
conv = tf.nn.conv2d(x, w, stride, padding)
else:
conv = tf.pad(x, padding, "CONSTANT")
conv = tf.nn.conv2d(conv, w, stride, padding='VALID')
if bias != -1:
bias = tf.get_variable('biases', [num_filters], initializer=tf.constant_initializer(bias))
|
tensorflow.nn.atrous_conv2d
| 8,060 |
import tensorflow as tf
if average_across_timesteps:
total_size = tf.reduce_sum(weights, axis=1)
total_size += 1e-12 # just to avoid division by 0 for all-0 weights
log_perp /= total_size
cost = tf.reduce_sum(log_perp)
if average_across_batch:
return cost / tf.to_float(batch_size)
else:
return cost
def reinforce_baseline(decoder_states, reward):
"""
Center the reward by computing a baseline reward over decoder states.
|
tensorflow.to_float
| 8,061 |
import tensorflow as tf
# check that we have some nodes to checkpoint
if not checkpoints:
raise Exception('no checkpoints nodes found or given as input! ')
# disconnect dependencies between checkpointed tensors
checkpoints_disconnected = {}
for x in checkpoints:
if x.op and x.op.name is not None:
grad_node = tf.stop_gradient(x, name=x.op.name+"_sg")
else:
grad_node = tf.stop_gradient(x)
checkpoints_disconnected[x] = grad_node
# partial derivatives to the checkpointed tensors and xs
ops_to_copy = fast_backward_ops(seed_ops=[y.op for y in ys],
stop_at_ts=checkpoints, within_ops=fwd_ops)
debug_print("Found %s ops to copy within fwd_ops %s, seed %s, stop_at %s",
|
tensorflow.stop_gradient
| 8,062 |
import tensorflow as tf
def _init():
v_norm = tf.nn.l2_normalize(self.v,axis=0)
t = tf.matmul(input_var,v_norm)
mu,var = tf.nn.moments(t,axes=[0])
std = tf.sqrt(var+self.epsilon)
return [tf.assign(self.g,1/std),tf.assign(self.b,-1.*mu/std)]
require_init = tf.reduce_any(tf.is_nan(self.g))
init_ops = tf.cond(require_init,_init,lambda : [self.g,self.b])
|
tensorflow.assign
| 8,063 |
import tensorflow as tf
batch_size = tf.shape(observations_ph.get())[0]
random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],
outputs=[output_actions, q_values, s_value, a_values, update_eps_expr],
givens={update_eps_ph: test_epsilon, stochastic_ph: False},
updates=[update_eps_expr])
|
tensorflow.cond
| 8,064 |
import tensorflow as tf
self.config.data["num_channels"]), name="X") # ex. (50000, 32, 32, 3)
self.y = tf.compat.v1.placeholder(tf.int32, shape = (None, self.config.data["num_categories"]), name="y") # ex. (50000, 10)
self.train = tf.compat.v1.placeholder(tf.bool)
# The CNN architecture = conv/poo layers + flatten layer + connected layers
with tf.name_scope("cnn"):
# a. Create convolution/pooling layers = conv + drop + pool + conv + drop + pool + conv + pool + conv + drop
self.conv1 = tf.layers.conv2d(self.X,
self.config.cifar10_cnn["num_filters"],
|
tensorflow.name_scope
| 8,065 |
import tensorflow as tf
Actor-Critics
"""
def mlp_actor_critic(x, a, hidden_sizes=(400,300), activation=tf.nn.relu,
output_activation=tf.tanh, action_space=None,
dropout_rate=0, nn_type='mlp_variational'):
act_dim = a.shape.as_list()[-1]
act_limit = action_space.high[0]
if nn_type == 'mlp':
with tf.variable_scope('pi'):
pi = act_limit * mlp(x, list(hidden_sizes) + [act_dim], activation, output_activation)
with tf.variable_scope('q1'):
q1 = tf.squeeze(mlp(tf.concat([x, a], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
with tf.variable_scope('q2'):
q2 = tf.squeeze(mlp(tf.concat([x, a], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
with tf.variable_scope('q1', reuse=True):
q1_pi = tf.squeeze(mlp(tf.concat([x, pi], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
elif nn_type == 'mlp_dropout':
with tf.variable_scope('pi'):
pi = act_limit * mlp_dropout(x, list(hidden_sizes)+[act_dim], activation, output_activation)
with tf.variable_scope('q'):
|
tensorflow.variable_scope
| 8,066 |
import tensorflow as tf
if FLAGS.mode == 'attack':
if is_carliniL2 == True:
apply_attack_carlini(hps)
else:
apply_attack_loop(hps)
elif FLAGS.mode == 'tSNE_logits':
if is_carliniL2 == True:
tSNE_visual_carliniLi(hps,num_batch)
else:
tSNE_visual(hps,num_batch)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
|
tensorflow.logging.set_verbosity
| 8,067 |
import tensorflow as tf
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if FLAGS.do_predict:
|
tensorflow.logging.info
| 8,068 |
import tensorflow as tf
y, = tf.py_func(bad1, [], [tf.string])
z, = tf.py_func(bad2, [], [tf.float64])
with self.assertRaisesRegexp(errors.UnimplementedError,
"Unsupported numpy type"):
y.eval()
with self.assertRaisesRegexp(errors.UnimplementedError,
"Unsupported object type"):
z.eval()
if __name__ == "__main__":
tf.test.main()
|
tensorflow.test.main
| 8,069 |
import tensorflow as tf
seq_shape = get_shape_list(mask_sequence, expected_rank=2)
seq_len = seq_shape[1]
ones = tf.ones((1, seq_len, seq_len))
a_mask = tf.matrix_band_part(ones, -1, 0)
s_ex12 = tf.expand_dims(tf.expand_dims(mask_sequence, 1), 2)
s_ex13 = tf.expand_dims(tf.expand_dims(mask_sequence, 1), 3)
a_mask = (1 - s_ex13) * (1 - s_ex12) + s_ex13 * a_mask
# generate mask of batch x seq_len x seq_len
a_mask = tf.reshape(a_mask, (-1, seq_len, seq_len))
out_mask = attention_mask * a_mask
else:
ones = tf.ones_like(attention_mask[:1])
mask = (tf.matrix_band_part(ones, -1, 0))
out_mask = attention_mask * mask
else:
out_mask = attention_mask
|
tensorflow.reshape
| 8,070 |
import tensorflow as tf
types: A list of `Tensor` of `int32`, with shapes
`[num_nodes * count1]`, `[num_nodes * count1 * count2]` ...
"""
neighbors_list = [tf.reshape(nodes, [-1])]
weights_list = []
type_list = []
for hop_edge_types, count in zip(edge_types, counts):
neighbors, weights, types = sample_neighbor(
neighbors_list[-1], hop_edge_types, count, default_node=default_node)
neighbors_list.append(tf.reshape(neighbors, [-1]))
weights_list.append(tf.reshape(weights, [-1]))
type_list.append(tf.reshape(types, [-1]))
return neighbors_list, weights_list, type_list
def get_multi_hop_neighbor(nodes, edge_types):
"""
Get multi-hop neighbors with adjacent matrix.
|
tensorflow.reshape
| 8,071 |
import tensorflow as tf
i_z1_y1_x0 = tf.gather(im_flat, idx_z1_y1_x0)
i_z1_y1_x1 = tf.gather(im_flat, idx_z1_y1_x1)
# Finally calculate interpolated values.
x0_f = tf.to_float(x0)
x1_f = tf.to_float(x1)
y0_f = tf.to_float(y0)
y1_f = tf.to_float(y1)
z0_f = tf.to_float(z0)
z1_f = tf.to_float(z1)
# Check the out-of-boundary case.
x0_valid = tf.to_float(
tf.less_equal(x0, max_x) & tf.greater_equal(x0, 0))
x1_valid = tf.to_float(
tf.less_equal(x1, max_x) & tf.greater_equal(x1, 0))
y0_valid = tf.to_float(
tf.less_equal(y0, max_y) & tf.greater_equal(y0, 0))
y1_valid = tf.to_float(
tf.less_equal(y1, max_y) & tf.greater_equal(y1, 0))
z0_valid = tf.to_float(
tf.less_equal(z0, max_z) & tf.greater_equal(z0, 0))
z1_valid = tf.to_float(
tf.less_equal(z1, max_z) & tf.greater_equal(z1, 0))
|
tensorflow.greater_equal
| 8,072 |
import tensorflow as tf
# Target for value fn regression
# We update the vf towards the min of two Q-functions in order to
# reduce overestimation bias from function approximation error.
v_backup = tf.stop_gradient(min_qf_pi - self.ent_coef * logp_pi)
value_loss = 0.5 * tf.reduce_mean(((value_fn - v_backup) ** 2)*self.weight_ph)
#value_for_priority = tf.reduce_mean((value_fn - v_backup) ** 2,1)
regularizervf = tf.contrib.layers.l1_l2_regularizer(scale_l1=0.0, scale_l2=1e-5, scope='model/values_fn')
all_trainable_weights_vf = tf_util.get_trainable_vars('model/values_fn')
regularization_penalty_vf = tf.contrib.layers.apply_regularization(regularizervf, all_trainable_weights_vf)
|
tensorflow.reduce_mean
| 8,073 |
import tensorflow as tf
def testSharded(self):
save_dir = os.path.join(self.get_temp_dir(), "max_to_keep_sharded")
try:
gfile.DeleteRecursively(save_dir)
except OSError:
pass # Ignore
gfile.MakeDirs(save_dir)
with tf.Session(
target="",
config=tf.ConfigProto(device_count={"CPU": 2})) as sess:
with sess.graph.device("/cpu:0"):
v0 = tf.Variable(111, name="v0")
with sess.graph.device("/cpu:1"):
v1 = tf.Variable(222, name="v1")
save = tf.train.Saver({"v0": v0, "v1": v1}, sharded=True, max_to_keep=2)
tf.initialize_all_variables().run()
self.assertEqual([], save.last_checkpoints)
s1 = save.save(sess, os.path.join(save_dir, "s1"))
|
tensorflow.ConfigProto
| 8,074 |
import tensorflow as tf
tf.summary.histogram("mel_targets %d" % i, model.tower_mel_targets[i])
tf.summary.scalar("before_loss", model.before_loss)
tf.summary.scalar("after_loss", model.after_loss)
if hparams.predict_linear:
tf.summary.scalar("linear_loss", model.linear_loss)
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()
|
tensorflow.summary.scalar
| 8,075 |
import tensorflow as tf
maxpool = tf.nn.max_pool(inpOp,
ksize=[1, kH, kW, 1],
strides=[1, dH, dW, 1],
padding=padding)
return maxpool
def apool(inpOp, kH, kW, dH, dW, padding, name):
with tf.variable_scope(name):
avgpool = tf.nn.avg_pool(inpOp,
ksize=[1, kH, kW, 1],
strides=[1, dH, dW, 1],
padding=padding)
return avgpool
# def mfmpool(input1, input2, name):
# with tf.variable_scope(name):
|
tensorflow.nn.avg_pool
| 8,076 |
import tensorflow as tf
gtboxes_in_img_q = self.drawer.draw_boxes_with_categories(
img_batch=tf.expand_dims(img[0, :, :, :], axis=0),
boxes=gtboxes_and_label_q[0, :, :-1],
labels=gtboxes_and_label_q[0, :, -1],
method=2)
tf.summary.image('Compare/gtboxes_q_gpu:%d' % i, gtboxes_in_img_q)
gtboxes_in_img_h = self.drawer.draw_boxes_with_categories(
img_batch=tf.expand_dims(img[0, :, :, :], axis=0),
boxes=gtboxes_and_label_h[0, :, :-1],
|
tensorflow.summary.image
| 8,077 |
import tensorflow as tf
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.range
| 8,078 |
import tensorflow as tf
################################ jupyter-vim #######################################
# https://github.com/qrsforever/vim/blob/master/bundle/.configs/jupyter-vim_conf.vim
# %pylab --no-import-all # noqa
#####################################################################################
# https://github.com/yaroslavvb/memory_util
import sys
import tensorflow as tf
import memory_util
memory_util.vlog(1)
sess = tf.Session()
with sess.as_default():
tensor = tf.range(10)
print_op = tf.print("tensors:", tensor, {'2': tensor * 2}, output_stream=sys.stderr)
with tf.control_dependencies([print_op]):
tripled_tensor = tensor * 3
with memory_util.capture_stderr() as stderr:
print(sess.run(tripled_tensor))
print(stderr.getvalue())
|
tensorflow.Session
| 8,079 |
import tensorflow as tf
# Flatten CNN and concat with other features
zack_hack_div_2 = 0
if cnn_rnn_zack:
zack_hack_div_2 = zack_hack // 2
cnn_output = tf.slice(cnn_output, [0, zack_hack_div_2, 0, 0], [-1, rnn_nunroll, -1, -1])
nfeats_conv = reduce(lambda x, y: x * y, [int(x) for x in cnn_output.get_shape()[-2:]])
else:
nfeats_conv = reduce(lambda x, y: x * y, [int(x) for x in cnn_output.get_shape()[-3:]])
feats_conv = tf.reshape(cnn_output, [batch_size * rnn_nunroll, nfeats_conv])
|
tensorflow.slice
| 8,080 |
import tensorflow as tf
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)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs)
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
|
tensorflow.reshape
| 8,081 |
import tensorflow as tf
name='conv',
input=X,
filter=W,
strides=[1, 1, 1, 1],
padding='VALID')
#add bias of size = out cannels
b = tf.get_variable(
name='b',
shape=[num_filter],
initializer=tf.constant_initializer(0.0))
H = tf.nn.bias_add(
name='H',
value=conv,
bias=b)
# Apply nonlinearity
H = tf.nn.relu(H, name="relu")
# max pool
pooled = tf.nn.max_pool(H,
ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
|
tensorflow.nn.bias_add
| 8,082 |
import tensorflow as tf
# Check that the parameter nodes have been initialized.
self.assertEqual(1000.0, v0_2.eval())
self.assertEqual(2000.0, v1_2.eval())
# Restore the values saved earlier in the parameter nodes.
save2.restore(sess, save_path)
# Check that the parameter nodes have been restored.
self.assertEqual(10.0, v0_2.eval())
self.assertEqual(20.0, v1_2.eval())
def _SaveAndLoad(self, var_name, var_value, other_value, save_path):
with self.test_session() as sess:
var = tf.Variable(var_value, name=var_name)
save = tf.train.Saver({var_name: var})
var.initializer.run()
val = save.save(sess, save_path)
self.assertEqual(save_path, val)
with self.test_session() as sess:
var = tf.Variable(other_value, name=var_name)
save = tf.train.Saver({var_name: var})
save.restore(sess, save_path)
self.assertAllClose(var_value, var.eval())
def testCacheRereadsFile(self):
|
tensorflow.Variable
| 8,083 |
import tensorflow as tf
**self.policy_kwargs)
with tf.variable_scope("loss", reuse=False):
self.done_ph = tf.placeholder(tf.float32, [self.n_batch]) # dones
self.reward_ph = tf.placeholder(tf.float32, [self.n_batch]) # rewards, not returns
self.mu_ph = tf.placeholder(tf.float32, [self.n_batch, self.n_act]) # mu's
self.action_ph = train_model.pdtype.sample_placeholder([self.n_batch])
self.learning_rate_ph = tf.placeholder(tf.float32, [])
eps = 1e-6
|
tensorflow.placeholder
| 8,084 |
import tensorflow as tf
dec, mem = tf.nn.seq2seq.rnn_decoder(dec_inp, enc_state, cell)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testBasicRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
cell = tf.nn.rnn_cell.OutputProjectionWrapper(
tf.nn.rnn_cell.GRUCell(2), 4)
dec, mem = tf.nn.seq2seq.basic_rnn_seq2seq(inp, dec_inp, cell)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testTiedRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
|
tensorflow.nn.rnn_cell.GRUCell
| 8,085 |
import tensorflow as tf
# Structured numpy arrays aren't supported.
return np.array([], dtype=[("foo", np.float32)])
def bad2():
# Non-string python objects aren't supported.
return tf.float32
y, = tf.py_func(bad1, [], [tf.string])
z, = tf.py_func(bad2, [], [tf.float64])
with self.assertRaisesRegexp(errors.UnimplementedError,
"Unsupported numpy type"):
y.eval()
with self.assertRaisesRegexp(errors.UnimplementedError,
"Unsupported object type"):
|
tensorflow.py_func
| 8,086 |
import tensorflow as tf
with tf.variable_scope(scope):
n_state = shape_list(x)[-1]
g = tf.get_variable("g", [n_state], initializer=tf.constant_initializer(1))
b = tf.get_variable("b", [n_state], initializer=tf.constant_initializer(0))
return _norm(x, g, b, axis=axis)
def dropout(x, pdrop, train):
if train and pdrop > 0:
x = tf.nn.dropout(x, 1-pdrop)
return x
def mask_attn_weights(w):
n = shape_list(w)[-1]
b = tf.matrix_band_part(tf.ones([n, n]), -1, 0)
b = tf.reshape(b, [1, 1, n, n])
w = w*b + -1e9*(1-b)
|
tensorflow.nn.dropout
| 8,087 |
from tensorflow.python.ops import array_ops
# Use static shape if known.
num_predictions = predictions_2d.get_shape().as_list()[0]
# Otherwise use dynamic shape.
if num_predictions is None:
num_predictions = array_ops.shape(predictions_2d)[0]
thresh_tiled = array_ops.tile(
array_ops.expand_dims(array_ops.constant(thresholds), [1]),
array_ops.pack([1, num_predictions]))
# Tile the predictions after thresholding them across different thresholds.
pred_is_pos = math_ops.greater(
array_ops.tile(array_ops.transpose(predictions_2d), [num_thresholds, 1]),
thresh_tiled)
pred_is_neg = math_ops.logical_not(pred_is_pos)
# Tile labels by number of thresholds
|
tensorflow.python.ops.array_ops.pack
| 8,088 |
import tensorflow as tf
with tf.device('/gpu:0'):
c = tf.matmul(a,b)
c = tf.reshape(c, [-1])
with tf.device('/gpu:1'):
d = tf.matmul(b, a)
flat_d = tf.reshape(d, [-1])
combined = tf.multiply(c, flat_d)
print(sess.run(combined))
|
tensorflow.multiply
| 8,089 |
import tensorflow as tf
imgs = random_apply(color_drop, imgs, self._aug_color_drop_prob)
return tf.stop_gradient(imgs)
aug_images, aug_generated = augment(images), augment(generated)
# concat all images
all_images = tf.concat([images, generated, aug_images, aug_generated], 0)
if self.conditional:
all_y = tf.concat([y, sampled_y, y, sampled_y], axis=0)
# Compute discriminator output for real and fake images in one batch.
d_all, d_all_logits, d_latents = self.discriminator(
x=all_images, y=all_y, is_training=is_training)
z_projs = self._latent_projections(d_latents)
|
tensorflow.concat
| 8,090 |
import tensorflow as tf
if FLAGS.num_gpus > len(gpus):
raise ValueError('Invalid num_gpus')
raw_data = reader.ptb_raw_data(FLAGS.data_path)
train_data, valid_data, test_data, _ = raw_data
config = get_config()
eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1
train_graph = tf.Graph()
eval_graph = tf.Graph()
infer_graph = tf.Graph()
with train_graph.as_default():
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
with tf.name_scope('Train'):
train_input = DataInput(config=config, data=train_data, name='TrainInput')
with tf.variable_scope('Model', reuse=None, initializer=initializer):
m = Model(is_training=True, config=config, input_=train_input, graph=train_graph)
|
tensorflow.Graph
| 8,091 |
import tensorflow as tf
def test_discriminator_grad_norm_progress(self):
stable_stage_num_images = 2
transition_stage_num_images = 3
current_image_id_ph = tf.placeholder(tf.int32, [])
progress = networks.compute_progress(
current_image_id_ph,
stable_stage_num_images,
|
tensorflow.placeholder
| 8,092 |
import tensorflow as tf
matched_label = tf.gather(groundtruth_labels, matched_gt_indices)
matched_is_foreground = tf.cast(matched_label[:,0] <= 0, tf.float32)
matched_anchor_indices = match.matched_column_indices()
unmatched_ignored_anchor_indices=match.unmatched_or_ignored_column_indices()
unmatched_ignored_reg_weights = tf.gather(reg_weights, unmatched_ignored_anchor_indices)
reg_weights= tf.dynamic_stitch(
[matched_anchor_indices, unmatched_ignored_anchor_indices],
[matched_is_foreground, unmatched_ignored_reg_weights])
|
tensorflow.gather
| 8,093 |
import tensorflow as tf
decoder=decoders[1], training=training, encoders=decoders[:1]
)
target_weights = get_weights(targets[1][:, 1:], utils.EOS_ID, include_first_eos=True)
xent_loss += reconstruction_weight * sequence_loss(logits=reconstructed_outputs, targets=targets[1][:, 1:],
weights=target_weights)
max_src_len = tf.shape(reconstructed_weights)[1]
batch_size = tf.shape(reconstructed_weights)[0]
attn_loss = tf.matmul(reconstructed_weights, attention_weights) - tf.eye(max_src_len)
src_mask = tf.sequence_mask(encoder_input_length[0], maxlen=max_src_len, dtype=tf.float32)
src_mask = tf.einsum('ij,ik->ijk', src_mask, src_mask)
attn_loss *= tf.to_float(src_mask) # don't take padding words into account
attn_loss = tf.norm(attn_loss) / tf.to_float(batch_size)
xent_loss += reconstruction_attn_weight * attn_loss
attention_weights = [attention_weights, reconstructed_weights]
|
tensorflow.matmul
| 8,094 |
import tensorflow.contrib.eager as tfe
if policy['type'] == 'gcnn':
# load model
sys.path.insert(0, os.path.abspath(f"models/{policy['name']}"))
import model
importlib.reload(model)
del sys.path[0]
policy['model'] = model.GCNPolicy()
policy['model'].restore_state(f"trained_models/{args.problem}/{policy['name']}/{seed}/best_params.pkl")
policy['model'].call = tfe.defun(policy['model'].call, input_signature=policy['model'].input_signature)
policy['batch_datatypes'] = [tf.float32, tf.int32, tf.float32,
tf.float32, tf.int32, tf.int32, tf.int32, tf.int32, tf.int32, tf.float32]
policy['batch_fun'] = load_batch_gcnn
else:
# load feature normalization parameters
try:
with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/normalization.pkl", 'rb') as f:
|
tensorflow.contrib.eager.defun
| 8,095 |
import tensorflow as tf
" batch_shape=()"
" event_shape=()"
" dtype=float16>")
chi2 = tfd.Chi2(df=np.float32([1., 2.]), name="silly")
self.assertEqual(
repr(chi2),
"<tfp.distributions.Chi2"
" 'silly/'" # What a silly name that is!
" batch_shape=(2,)"
" event_shape=()"
" dtype=float32>")
# There's no notion of partially known shapes in eager mode, so exit
# early.
if tf.executing_eagerly():
return
exp = tfd.Exponential(rate=tf.placeholder_with_default(
input=1., shape=None))
self.assertEqual(
repr(exp),
"<tfp.distributions.Exponential"
" 'Exponential/'"
" batch_shape=<unknown>"
" event_shape=()"
" dtype=float32>")
def testReprWorksCorrectlyMultivariate(self):
mvn_static = tfd.MultivariateNormalDiag(
|
tensorflow.executing_eagerly
| 8,096 |
import tensorflow as tf
with tf.variable_scope(args.name):
model = HredModel(data, args, embed)
model.print_parameters()
latest_dir = '%s/checkpoint_latest' % args.model_dir
best_dir = '%s/checkpoint_best' % args.model_dir
if tf.train.get_checkpoint_state(latest_dir) and args.restore == "last":
print("Reading model parameters from %s" % latest_dir)
model.latest_saver.restore(sess, tf.train.latest_checkpoint(latest_dir))
else:
if tf.train.get_checkpoint_state(best_dir) and args.restore == "best":
print('Reading model parameters from %s' % best_dir)
model.best_saver.restore(sess, tf.train.latest_checkpoint(best_dir))
else:
print("Created model with fresh parameters.")
global_variable = [gv for gv in tf.global_variables() if args.name in gv.name]
sess.run(tf.variables_initializer(global_variable))
return model
|
tensorflow.train.get_checkpoint_state
| 8,097 |
import tensorflow as tf
def update(self, state, target, sess=None):
sess = sess or tf.get_default_session()
for st_idx in range(len(state)):
state[st_idx]=featurize_state(state[st_idx]);
feed_dict = { self.state: state, self.target: target }
_, loss = sess.run([self.train_op, self.loss], feed_dict)
return loss
"""
For Pendulum-v0
"""
class PolicyEstimator_Pendulum():
def __init__(self, entropy_beta=0.01, learning_rate=0.01, par_idx=0,scope="policy_estimator"):
w_init = tf.random_normal_initializer(0.,.1);
with tf.variable_scope(scope+"_"+str(par_idx)):
# state, target and action
self.state = tf.placeholder(tf.float32, [None,num_state], name="state")
self.target = tf.placeholder(tf.float32,[None,1], name="target")
self.a_his = tf.placeholder(tf.float32, [None, num_action], name="action_hist")
# layers
l_a = tf.layers.dense(self.state, 200, tf.nn.relu6, kernel_initializer=w_init, name='la')
self.mu = tf.layers.dense(l_a, num_action, tf.nn.tanh, kernel_initializer=w_init, name='mu') # estimated action value
self.sigma = tf.layers.dense(l_a, num_action, tf.nn.softplus, kernel_initializer=w_init, name='sigma') # estimated variance
# wrap output
|
tensorflow.random_normal_initializer
| 8,098 |
import tensorflow as tf
**self.policy_kwargs)
# Initialize Placeholders
self.observations_ph = self.policy_tf.obs_ph
# Normalized observation for pixels
self.processed_obs_ph = self.policy_tf.processed_obs
self.next_observations_ph = self.target_policy.obs_ph
self.processed_next_obs_ph = self.target_policy.processed_obs
self.action_target = self.target_policy.action_ph
self.terminals_ph = tf.placeholder(tf.float32, shape=(None, 1), name='terminals')
self.rewards_ph = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.is_demo_ph = tf.placeholder(tf.float32, shape=(None, 1), name='is_demonstrations')
self.weight_ph = tf.placeholder(tf.float32, shape=(None, 1), name='importance_weight')
self.actions_ph = tf.placeholder(tf.float32, shape=(None,) + self.action_space.shape,
name='actions')
self.learning_rate_ph = tf.placeholder(tf.float32, [], name="learning_rate_ph")
if self.n_step:
self.next_observations_ph_n = self.target_policy.obs_ph
self.processed_next_obs_ph_n = self.target_policy.processed_obs
self.rewards_ph_n = tf.placeholder(tf.float32, shape=(None, 1), name='n_step_rewards')
self.terminals_ph_n = tf.placeholder(tf.float32, shape=(None, 1), name='n_step_terminals')
|
tensorflow.placeholder
| 8,099 |
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