seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
self._lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self._cost, tvars),
config.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self._lr)
self._train_op = optimizer.apply_gradients(
zip(grads, tvars),
global_step=tf.contrib.framework.get_or_create_global_step())
self._new_lr = tf.placeholder(
tf.float32, shape=[], name="new_learning_rate")
self._lr_update = tf.assign(self._lr, self._new_lr)
def _build_rnn_graph(self, inputs, config, is_training):
if config.rnn_mode == CUDNN:
return self._build_rnn_graph_cudnn(inputs, config, is_training)
else:
return self._build_rnn_graph_lstm(inputs, config, is_training)
def _build_rnn_graph_cudnn(self, inputs, config, is_training):
"""Build the inference graph using CUDNN cell."""
inputs = tf.transpose(inputs, [1, 0, 2])
|
tensorflow.assign
| 8,200 |
import tensorflow as tf
if not silent:
log_info("Doing %s took %.3f sec." % (msg, time.time() - start_time))
return res
return fn_with_timing
def _create_dummy_vars():
"""Dummy vars for restore to work when not using TPU codepath."""
var_names = set([v.name for v in tf.global_variables()])
if "losses_avg/problem_0/total_loss:0" in var_names:
return
with tf.variable_scope("losses_avg"):
with tf.variable_scope("problem_0"):
for var_name in ["total", "extra", "training"]:
tf.get_variable(
"%s_loss" % var_name, initializer=100.0, trainable=False)
with tf.variable_scope("train_stats"):
tf.get_variable("problem_0_steps", initializer=0, trainable=False)
# These metrics are implemented with py_funcs and therefore do no work with TPU
TPU_METRIC_BLACKLIST = set([
metrics.Metrics.APPROX_BLEU,
metrics.Metrics.ROUGE_2_F,
metrics.Metrics.ROUGE_L_F,
|
tensorflow.variable_scope
| 8,201 |
import tensorflow as tf
'The directory where the dataset input data is stored.')
tf.app.flags.DEFINE_string(
'dataset_name', '{}_????', 'The pattern of the dataset name to load.')
tf.app.flags.DEFINE_string(
'model_dir', './logs_sext_cpn/',
'The parent 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', 100,
'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer(
'save_checkpoints_secs', 3600,
'The frequency with which the model is saved, in seconds.')
# model related configuration
tf.app.flags.DEFINE_integer(
'train_image_size', 384,
|
tensorflow.app.flags.DEFINE_integer
| 8,202 |
from tensorflow.contrib.learn.python.learn.datasets import base
SOURCE_URL + TEST_IMAGES)
test_images = extract_images(local_file)
local_file = base.maybe_download(TEST_LABELS, train_dir,
SOURCE_URL + TEST_LABELS)
test_labels = extract_labels(local_file, one_hot=one_hot)
|
tensorflow.contrib.learn.python.learn.datasets.base.maybe_download
| 8,203 |
import tensorflow as tf
loss_class = tf.reshape(loss_class, [num_batch, num_prior])
loss_class_idx = tf.argsort(loss_class, axis=1, direction='DESCENDING')
loss_class_idx_rank = tf.argsort(loss_class_idx, axis=1)
mask_pos_per_batch = tf.reshape(mask_pos, [num_batch, num_prior])
num_pos_per_batch = tf.reduce_sum(
tf.cast(mask_pos_per_batch, tf.float32), 1, keepdims=True)
num_pos_per_batch = tf.maximum(num_pos_per_batch, 1)
num_neg_per_batch = tf.minimum(neg_pos_ratio * num_pos_per_batch,
tf.cast(num_prior, tf.float32) - 1)
mask_hard_neg = tf.reshape(
tf.cast(loss_class_idx_rank, tf.float32) < num_neg_per_batch,
[num_batch * num_prior, 1])
|
tensorflow.maximum
| 8,204 |
import tensorflow as tf
output_weight = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02)
)
output_bias = tf.get_variable(
|
tensorflow.truncated_normal_initializer
| 8,205 |
import tensorflow as tf
'ftol' : 1.0 * np.finfo(float).eps})
self.optimizer_Adam = tf.compat.v1.train.AdamOptimizer()
self.train_op_Adam = self.optimizer_Adam.minimize(self.loss)
init = tf.global_variables_initializer()
self.sess.run(init)
self.loss_log = []
|
tensorflow.global_variables_initializer
| 8,206 |
import tensorflow as tf
#need to fix
if self.ms_task:
with tf.variable_scope('mixed'):
#add fake pc as a rotation class
num_to_add = int(max(self.batch_size/self.num_angles, 1))
idx = tf.range(0, self.batch_size, 1)
idx = tf.random_shuffle(idx)[0:num_to_add]
self.fake_to_add = tf.gather(self.generator_out, idx)
self.mixed_pc = tf.concat([self.real_pc_rotated, self.fake_to_add], 0)
self.mixed_label = tf.concat([self.rot_label_pl, tf.constant(self.num_angles, shape = (num_to_add,))], axis = 0)
|
tensorflow.range
| 8,207 |
import tensorflow as tf
if len(facts.get_shape().as_list()) == 2:
facts = tf.expand_dims(facts, 1)
|
tensorflow.expand_dims
| 8,208 |
from tensorflow.python.framework import ops
vector_dim = vector_dim.merge_with(beta_shape[0])
return [input_shape] + ([tensor_shape.vector(vector_dim)] * 4)
ops.RegisterShape("Conv2D")(common_shapes.conv2d_shape)
ops.RegisterShape("DepthwiseConv2dNative")(
common_shapes.depthwise_conv2d_native_shape)
ops.RegisterShape("AvgPool")(common_shapes.avg_pool_shape)
ops.RegisterShape("MaxPool")(common_shapes.max_pool_shape)
|
tensorflow.python.framework.ops.RegisterShape
| 8,209 |
import tensorflow as tf
x: The input tensor.
prediction: The prediction class tensor.
output_class: The output tensor.
sess: The graph session.
"""
# input label placeholder
y = tf.placeholder("float", [None, self.n_classes])
# Loss function
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
# Optimization
opt = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).minimize(loss)
# Initialize variables
init = tf.global_variables_initializer()
sess.run(init)
for _ in range(TRAIN_STEPS):
batch_x, batch_y = self.mnist.train.next_batch(
batch_size=self.batch_size, shuffle=False)
batch_x = batch_x.reshape((self.batch_size, self.time_steps,
self.n_input))
sess.run(opt, feed_dict={x: batch_x, y: batch_y})
def saveAndRestoreModel(self, lstm_layer, sess, saver, is_dynamic_rnn):
"""Saves and restores the model to mimic the most common use case.
Args:
lstm_layer: The lstm layer either a single lstm cell or a multi lstm cell.
|
tensorflow.global_variables_initializer
| 8,210 |
import tensorflow as tf
confidence_scores = tf.concat(1, [tf.matmul(o_, confidence_mat)
for o_ in self._inputs])
# dropout on confidence_scores
random_tensor = (1.0 - self._dropout_keep_prob +
tf.random_uniform(tf.shape(confidence_scores)))
binary_tensor = -50.0 * tf.floor(random_tensor)
csshape = confidence_scores.get_shape()
self.cs = tf.nn.softmax(tf.constant(1.0, shape=csshape))
# The final prediction is the average of the predictions for each word
|
tensorflow.floor
| 8,211 |
import tensorflow as tf
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
|
tensorflow.reduce_sum
| 8,212 |
import tensorflow as tf
add_weight_decay(params['weight_decay'])
regularization_loss = tf.losses.get_regularization_loss()
# create localization and classification losses
losses = ssd.loss(labels, params)
tf.losses.add_loss(params['localization_loss_weight'] * losses['localization_loss'])
tf.losses.add_loss(params['classification_loss_weight'] * losses['classification_loss'])
tf.summary.scalar('regularization_loss', regularization_loss)
tf.summary.scalar('localization_loss', losses['localization_loss'])
tf.summary.scalar('classification_loss', losses['classification_loss'])
total_loss = tf.losses.get_total_loss(add_regularization_losses=True)
if mode == tf.estimator.ModeKeys.EVAL:
batch_size = features['images'].shape[0].value
assert batch_size == 1
|
tensorflow.summary.scalar
| 8,213 |
import tensorflow as tf
elif actL == 'esp' or actL == 'relu': #r2 score
norm= tf.reduce_mean( tf.squared_difference(Y,tf.reduce_mean(Y)) )
accuracy = 1 - tf.divide( tf.reduce_mean(tf.squared_difference(an, Y)), norm)
|
tensorflow.reduce_mean
| 8,214 |
import tensorflow as tf
with tf.control_dependencies(update_ops), tf.variable_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(learning_rate)
grads_and_vars = optimizer.compute_gradients(total_loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
for g, v in grads_and_vars:
tf.summary.histogram(v.name[:-2] + '_hist', v)
tf.summary.histogram(v.name[:-2] + '_grad_hist', g)
with tf.control_dependencies([train_op]), tf.name_scope('ema'):
ema = tf.train.ExponentialMovingAverage(decay=MOVING_AVERAGE_DECAY, num_updates=global_step)
train_op = ema.apply(tf.trainable_variables())
return tf.estimator.EstimatorSpec(mode, loss=total_loss, train_op=train_op)
|
tensorflow.summary.histogram
| 8,215 |
import tensorflow as tf
gpu_compute_stage_ops.extend(
[put_op for _, (put_op, _) in six.iteritems(staging_ops)])
enqueue_ops.append(tf.group(*gpu_compute_stage_ops))
if gpu_grad_stage_ops:
|
tensorflow.group
| 8,216 |
import tensorflow as tf
class ACnet(object): # 这个class即可用于生产global net，也可生成 worker net，因为结构相同
def __init__(self, scope, globalAC=None, global_step=None): # scope 用于确定生成什么网络
# global GLOBALE_STEP
# self.global_step = GLOBALE_STEP
if scope == GLOBAL_NET_SCOPE: # 创建中央大脑
with tf.variable_scope(scope):
self.global_step = tf.get_variable("global_step", [], tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
self.obs_space = N_S
self.act_space = N_A
self.k = 16
self.g_dim = 256
self.c = 10
self.vf_hidden_size = 128 # for value function network
self.alpha = 0.5 # for build loss
|
tensorflow.constant_initializer
| 8,217 |
from tensorflow.contrib.metrics.python.ops import set_ops
weights: `Tensor` whose shape is broadcastable to the the first [D1, ... DN]
dimensions of `predictions_idx` and `labels`.
Returns:
A [D1, ... DN] `Tensor` of false positive counts.
"""
with ops.name_scope(None, 'false_positives', (predictions_idx, labels)):
labels, predictions_idx = _maybe_select_class_id(labels,
predictions_idx,
class_id)
fp = set_ops.set_size(set_ops.set_difference(
predictions_idx, labels, aminusb=True))
fp = math_ops.to_double(fp)
if weights is not None:
weights = math_ops.to_double(weights)
fp = math_ops.mul(fp, weights)
return fp
def _streaming_sparse_false_positive_at_k(predictions_idx,
|
tensorflow.contrib.metrics.python.ops.set_ops.set_difference
| 8,218 |
import tensorflow as tf
def look(time, state, input_, prev_weights=None, pos=None, context=None):
prev_weights_ = [prev_weights if i == align_encoder_id else None for i in range(len(encoders))]
pos_ = None
if decoder.pred_edits:
pos_ = [pos if i == align_encoder_id else None for i in range(len(encoders))]
if decoder.attn_prev_word:
state = tf.concat([state, input_], axis=1)
if decoder.attn_prev_attn and context is not None:
state = tf.concat([state, context], axis=1)
if decoder.hidden_state_scaling:
attention_states_ = [states * decoder.hidden_state_scaling for states in attention_states]
else:
attention_states_ = attention_states
parameters = dict(hidden_states=attention_states_, encoder_input_length=encoder_input_length,
encoders=encoders, aggregation_method=decoder.aggregation_method)
|
tensorflow.concat
| 8,219 |
import tensorflow as tf
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(
|
tensorflow.to_float
| 8,220 |
import tensorflow as tf
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
queries = tf.tile(query, [1, tf.shape(facts)[1]])
queries = tf.reshape(queries, tf.shape(facts))
|
tensorflow.shape
| 8,221 |
import tensorflow as tf
if len(episode_rewards) > 0:
mean_episode_reward = np.mean(episode_rewards[-100:])
if len(episode_rewards) > 100:
best_mean_episode_reward = max(best_mean_episode_reward, mean_episode_reward)
if t % DEBUG_LOG_EVERY_N_STEPS == 0:
print('Timestep = {} | Elapsed time = {:.3f}sec'.format(t,time.time() - start))
if t % LOG_EVERY_N_STEPS == 0 and model_initialized:
print("Timestep %d" % (t,))
print("mean reward (100 episodes) %f" % mean_episode_reward)
print("best mean reward %f" % best_mean_episode_reward)
print("episodes %d" % len(episode_rewards))
print("exploration %f" % exploration.value(t))
print("learning_rate %f" % optimizer_spec.lr_schedule.value(t))
mean_rew_summ = tf.Summary(value=[tf.Summary.Value(tag='mean_rew',simple_value=mean_episode_reward)])
best_mean_rew_summ = tf.Summary(value=[tf.Summary.Value(tag='best_mean_rew',simple_value=best_mean_episode_reward)])
writer.add_summary(mean_rew_summ, global_step=t)
writer.add_summary(best_mean_rew_summ, global_step=t)
sys.stdout.flush()
def gather_2d(vectors,indices):
return tf.gather_nd(vectors, tf.stack([tf.range(tf.shape(vectors)[0]), indices], axis=1))
|
tensorflow.Summary.Value
| 8,222 |
import tensorflow as tf
if full_cov:
fvar = Knn - tf.matmul(A, A, transpose_a=True)
fvar = tf.tile(fvar[None, :, :], [num_func, 1, 1]) # R x N x N
else:
fvar = Knn - tf.reduce_sum(tf.square(A), 0)
fvar = tf.tile(fvar[None, :], [num_func, 1]) # R x N
# another backsubstitution in the unwhitened case
if not white:
A = tf.matrix_triangular_solve(tf.transpose(Lm), A, lower=False)
# construct the conditional mean
fmean = tf.matmul(A, f, transpose_a=True)
if q_sqrt is not None:
if q_sqrt.get_shape().ndims == 2:
LTA = A * tf.expand_dims(tf.transpose(q_sqrt), 2) # R x M x N
elif q_sqrt.get_shape().ndims == 3:
|
tensorflow.transpose
| 8,223 |
import tensorflow as tf
else:
raise NotImplementedError(nl_type)
def update_params(self, kwargs):
"""Update the class attributes with kwargs."""
if kwargs is not None:
for k, v in kwargs.iteritems():
setattr(self, k, v)
def symmetric_weights(self, w, name):
"""Apply symmetric weight sharing."""
conv_w_t = tf.transpose(w, (2, 3, 0, 1))
conv_w_symm = 0.5 * (conv_w_t + tf.transpose(conv_w_t, (1, 0, 2, 3)))
conv_w = tf.transpose(conv_w_symm, (2, 3, 0, 1), name=name)
return conv_w
def prepare_tensors(self):
""" Prepare recurrent/forward weight matrices.
(np.prod([h, w, k]) / 2) - k params in the surround filter
"""
# FEEDFORWARD AND FEEDBACK KERNELS
lower_feats = self.in_k
for idx, (higher_feats, ff_dhw, fb_dhw) in enumerate(
zip(self.ff_conv_k,
|
tensorflow.transpose
| 8,224 |
import tensorflow as tf
gamma = tf.Variable(tf.constant(1.0, shape=[n_out], dtype=x.dtype),
name=name+'/gamma', trainable=True, dtype=x.dtype)
batch_mean, batch_var = tf.nn.moments(x, [0,1,2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.9)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = control_flow_ops.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
return normed
|
tensorflow.control_dependencies
| 8,225 |
import tensorflow as tf
with tf.variable_scope("model"):
# ------------------------------------------------
# Random initialization Load weights from weights path
# for Initial state, Weight matrices, and bias weights
# ------------------------------------------------
if self.load_weights_path is None:
# random initializations
init_state_initializer = tf.random_normal_initializer(mean=0.1, stddev=0.01)
W_in_initializer = tf.constant_initializer(
0.1 * np.random.uniform(-1, 1, size=(self.N_rec, self.N_in)))
W_rec_initializer = tf.constant_initializer(self.initial_W())
W_out_initializer = tf.constant_initializer(
0.1 * np.random.uniform(-1, 1, size=(self.N_out, self.N_rec)))
b_rec_initializer = tf.constant_initializer(0.0)
b_out_initializer = tf.constant_initializer(0.0)
else:
print("Loading Weights")
weights = np.load(self.load_weights_path)
init_state_initializer = tf.constant_initializer(weights['init_state'])
W_in_initializer = tf.constant_initializer(weights['W_in'])
W_rec_initializer = tf.constant_initializer(weights['W_rec'])
W_out_initializer = tf.constant_initializer(weights['W_out'])
b_rec_initializer = tf.constant_initializer(weights['b_rec'])
b_out_initializer = tf.constant_initializer(weights['b_out'])
self.input_connectivity_mask = weights['input_Connectivity']
self.recurrent_connectivity_mask = weights['rec_Connectivity']
|
tensorflow.constant_initializer
| 8,226 |
import tensorflow as tf
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))
w_z0_y0_x0 = tf.expand_dims(((x1_f - x) * (y1_f - y) *
(z1_f - z) * x1_valid * y1_valid * z1_valid),
1)
w_z0_y0_x1 = tf.expand_dims(((x - x0_f) * (y1_f - y) *
(z1_f - z) * x0_valid * y1_valid * z1_valid),
1)
w_z0_y1_x0 = tf.expand_dims(((x1_f - x) * (y - y0_f) *
(z1_f - z) * x1_valid * y0_valid * z1_valid),
1)
w_z0_y1_x1 = tf.expand_dims(((x - x0_f) * (y - y0_f) *
(z1_f - z) * x0_valid * y0_valid * z1_valid),
|
tensorflow.less_equal
| 8,227 |
import tensorflow as tf
tf.summary.scalar('location_loss', loc_loss)
tf.losses.add_loss(loc_loss)
# Add weight decay to the loss. We exclude the batch norm variables because
# doing so leads to a small improvement in accuracy.
loss = cross_entropy + loc_loss + params['weight_decay'] * tf.add_n(
[tf.nn.l2_loss(v) for v in tf.trainable_variables()
if 'batch_normalization' not in v.name])
total_loss = tf.identity(loss, name='total_loss')
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
lr_values = [params['learning_rate'] * decay for decay in params['lr_decay_factors']]
learning_rate = tf.train.piecewise_constant(tf.cast(global_step, tf.int32),
[int(_) for _ in params['decay_boundaries']],
|
tensorflow.identity
| 8,228 |
import tensorflow as tf
with arg_scope([slim.conv2d, slim.conv2d_in_plane,
slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected],
weights_regularizer=weights_regularizer,
biases_regularizer=biases_regularizer,
biases_initializer=tf.constant_initializer(0.0)):
rois, cls_prob, bbox_pred = self.build_network(sess, training)
layers_to_output = {'rois': rois}
|
tensorflow.constant_initializer
| 8,229 |
from tensorflow.python.framework import ops
"of at least one of the inputs.")
with ops.op_scope(inputs, name, "AccumulateN") as name:
|
tensorflow.python.framework.ops.op_scope
| 8,230 |
import tensorflow as tf
candidate_start_sentence_indices = tf.gather(flattened_sentence_indices, candidate_starts) # [num_words, max_span_width]
candidate_end_sentence_indices = tf.gather(flattened_sentence_indices, tf.minimum(candidate_ends, num_words - 1)) # [num_words, max_span_width]
candidate_mask = tf.logical_and(candidate_ends < num_words, tf.equal(candidate_start_sentence_indices, candidate_end_sentence_indices)) # [num_words, max_span_width]
flattened_candidate_mask = tf.reshape(candidate_mask, [-1]) # [num_words * max_span_width]
candidate_starts = tf.boolean_mask(tf.reshape(candidate_starts, [-1]), flattened_candidate_mask) # [num_candidates]
candidate_ends = tf.boolean_mask(tf.reshape(candidate_ends, [-1]), flattened_candidate_mask) # [num_candidates]
candidate_sentence_indices = tf.boolean_mask(tf.reshape(candidate_start_sentence_indices, [-1]), flattened_candidate_mask) # [num_candidates]
candidate_cluster_ids = self.get_candidate_labels(candidate_starts, candidate_ends, gold_starts, gold_ends, cluster_ids) # [num_candidates]
candidate_span_emb = self.get_span_emb(flattened_head_emb, context_outputs, candidate_starts, candidate_ends) # [num_candidates, emb]
candidate_mention_scores = self.get_mention_scores(candidate_span_emb) # [k, 1]
candidate_mention_scores = tf.squeeze(candidate_mention_scores, 1) # [k]
k = tf.to_int32(tf.floor(tf.to_float(tf.shape(context_outputs)[0]) * self.config["top_span_ratio"]))
top_span_indices = coref_ops.extract_spans(tf.expand_dims(candidate_mention_scores, 0),
tf.expand_dims(candidate_starts, 0),
tf.expand_dims(candidate_ends, 0),
tf.expand_dims(k, 0),
util.shape(context_outputs, 0),
True) # [1, k]
top_span_indices.set_shape([1, None])
top_span_indices = tf.squeeze(top_span_indices, 0) # [k]
top_span_starts = tf.gather(candidate_starts, top_span_indices) # [k]
top_span_ends = tf.gather(candidate_ends, top_span_indices) # [k]
top_span_emb = tf.gather(candidate_span_emb, top_span_indices) # [k, emb]
top_span_cluster_ids = tf.gather(candidate_cluster_ids, top_span_indices) # [k]
top_span_mention_scores = tf.gather(candidate_mention_scores, top_span_indices) # [k]
top_span_sentence_indices = tf.gather(candidate_sentence_indices, top_span_indices) # [k]
|
tensorflow.expand_dims
| 8,231 |
import tensorflow as tf
pareto = tfd.Pareto(concentration, scale)
self.assertEqual(self.evaluate(pareto.batch_shape_tensor()), (5,))
self.assertEqual(pareto.batch_shape, tf.TensorShape([5]))
self.assertAllEqual(self.evaluate(pareto.event_shape_tensor()), [])
self.assertEqual(pareto.event_shape, tf.TensorShape([]))
def testParetoShapeBroadcast(self):
scale = tf.constant([[3., 2.]])
concentration = tf.constant([[4.], [5.], [6.]])
pareto = tfd.Pareto(concentration, scale)
self.assertAllEqual(self.evaluate(pareto.batch_shape_tensor()), (3, 2))
self.assertAllEqual(pareto.batch_shape, tf.TensorShape([3, 2]))
self.assertAllEqual(self.evaluate(pareto.event_shape_tensor()), [])
self.assertEqual(pareto.event_shape, tf.TensorShape([]))
def testInvalidScale(self):
invalid_scales = [-.01, 0., -2.]
concentration = 3.
for scale in invalid_scales:
with self.assertRaisesOpError("Condition x > 0"):
pareto = tfd.Pareto(concentration, scale, validate_args=True)
self.evaluate(pareto.scale)
def testInvalidConcentration(self):
scale = 1.
invalid_concentrations = [-.01, 0., -2.]
for concentration in invalid_concentrations:
with self.assertRaisesOpError("Condition x > 0"):
|
tensorflow.TensorShape
| 8,232 |
import tensorflow as tf
return losses, [outputs], encoder_state, attention_states, attention_weights, samples, beam_fun, initial_data
def softmax(logits, dim=-1, mask=None):
e = tf.exp(logits)
if mask is not None:
e *= mask
return e / tf.clip_by_value(tf.reduce_sum(e, axis=dim, keep_dims=True), 10e-37, 10e+37)
def sequence_loss(logits, targets, weights, average_across_timesteps=False, average_across_batch=True, rewards=None):
batch_size = tf.shape(targets)[0]
time_steps = tf.shape(targets)[1]
logits_ = tf.reshape(logits, tf.stack([time_steps * batch_size, logits.get_shape()[2].value]))
|
tensorflow.reduce_sum
| 8,233 |
import tensorflow as tf
if tt_rank.size == 1:
tt_rank = tt_rank * np.ones(num_dims - 1)
tt_rank = np.concatenate([[1], tt_rank, [1]])
shape = shape.astype(int)
tt_rank = tt_rank.astype(int)
cr_exponent = -1.0 / (2 * num_dims)
var = np.prod(tt_rank ** cr_exponent)
core_stddev = stddev ** (1.0 / num_dims) * var
with tf.name_scope(name):
tt = matrix_batch_with_random_cores(shape, tt_rank=tt_rank,
stddev=core_stddev,
batch_size=batch_size,
dtype=dtype)
if np.abs(mean) < 1e-8:
return tt
else:
raise NotImplementedError('non-zero mean is not supported yet')
|
tensorflow.name_scope
| 8,234 |
import tensorflow as tf
data_format=self.channel_pos,
use_bias=False)
else:
rate = 1 # Unused (for 'a trous' convolutions)
kernel_size_effective = k_height + (k_width - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
padding = [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]
if self.data_format == 'NCHW':
padding = [padding[0], padding[3], padding[1], padding[2]]
input_layer = tf.pad(input_layer, padding)
conv = conv_layers.conv2d(
input_layer,
num_out_channels, [k_height, k_width],
strides=[d_height, d_width],
padding='VALID',
data_format=self.channel_pos,
use_bias=False)
if batch_norm is None:
batch_norm = self.use_batch_norm
if not batch_norm:
|
tensorflow.pad
| 8,235 |
import tensorflow as tf
initializer=tf.truncated_normal_initializer(stddev=stddev))
self.g = tf.get_variable('g',[output_dim],
initializer=tf.constant_initializer(float('nan')))
self.b = tf.get_variable('b',[output_dim],
initializer=tf.constant_initializer(float('nan')))
self.strides = [1, d_h, d_w, 1]
self.padding = padding
self.epsilon = epsilon
def __call__(self,input_var,name=None,**kwargs) :
def _init():
v_norm = tf.nn.l2_normalize(self.v,axis=[0,1,2])
t = tf.nn.conv2d(input_var,v_norm,self.strides,self.padding,data_format='NHWC')
mu,var = tf.nn.moments(t,axes=[0,1,2])
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])
with tf.control_dependencies(init_ops):
w = tf.reshape(self.g,[1,1,1,tf.shape(self.v)[-1]]) * tf.nn.l2_normalize(self.v,axis=[0,1,2])
return tf.nn.bias_add(
tf.nn.conv2d(input_var, w,data_format='NHWC',
strides=self.strides, padding=self.padding),
self.b,data_format='NHWC',name=name)
def get_variables(self):
|
tensorflow.nn.moments
| 8,236 |
import tensorflow.contrib.graph_editor as ge
fwd_ops = [op for op in fwd_ops if not '/read' in op.name]
ts_all = ge.filter_ts(fwd_ops, True) # get the tensors
|
tensorflow.contrib.graph_editor.filter_ts
| 8,237 |
import tensorflow as tf
:param white: boolean of whether to use the whitened representation as
described above.
:return:
- mean: N x R
- variance: N x R (full_cov = False), R x N x N (full_cov = True)
"""
logger.debug("Conditional: Kernel")
num_data = tf.shape(X)[0] # M
Kmm = kern.K(X) + tf.eye(num_data, dtype=settings.float_type) * settings.numerics.jitter_level
Kmn = kern.K(X, Xnew)
if full_cov:
Knn = kern.K(Xnew)
else:
Knn = kern.Kdiag(Xnew)
mean, var = base_conditional(Kmn, Kmm, Knn, f, full_cov=full_cov, q_sqrt=q_sqrt, white=white)
|
tensorflow.eye
| 8,238 |
import tensorflow as tf
if(h < len(layers_width)-2):
# Perform affine mapping at each layer of the neural network
Z = tf.layers.dense(Z, n_basis//2)
# Define variational parameters
|
tensorflow.layers.dense
| 8,239 |
import tensorflow as tf
batch_size=10, model_params=None, c2v=None,
max_sequence_len=None,
dropout_keep_prob=None,
weights=None):
self.params = model_params
self._out_vocab_size = out_vocab_size # num. of languages
self.weights = tf.constant(weights, dtype=tf.float32, name='class_weights')
with tf.variable_scope("tweetff"):
hidden = tf.get_variable("ff_hidden",
[c2v.embedding_dims, out_vocab_size])
bias = tf.get_variable('ff_bias', [out_vocab_size])
#probably useless. at least I don't want to use it
self.seq_lens = tf.placeholder(tf.int64, [batch_size], name='seq_lens')
self.x = tf.placeholder(tf.int32, [batch_size, max_sequence_len],
name='x')
|
tensorflow.get_variable
| 8,240 |
import tensorflow as tf
loss_class = tf.where(mask_neg,
1 - class_pred[:, 0][..., tf.newaxis], 0)
# 2. hard negative mining
loss_class = tf.reshape(loss_class, [num_batch, num_prior])
loss_class_idx = tf.argsort(loss_class, axis=1, direction='DESCENDING')
loss_class_idx_rank = tf.argsort(loss_class_idx, axis=1)
mask_pos_per_batch = tf.reshape(mask_pos, [num_batch, num_prior])
num_pos_per_batch = tf.reduce_sum(
tf.cast(mask_pos_per_batch, tf.float32), 1, keepdims=True)
|
tensorflow.argsort
| 8,241 |
from tensorflow.python.platform import tf_logging as logging
"""
logging.info("Create CheckpointSaver.")
|
tensorflow.python.platform.tf_logging.info
| 8,242 |
import tensorflow as tf
def _add_dynamic_cell(self, cell_arch, layers, w, h, block_ch, drop_path_keep_prob, is_train=False):
b = CELL_NUM_BLOCKS
# Downsample inputs to have same dimensions as blocks
with tf.variable_scope('layer_-1_calibrate'):
layers[-1] = (self._calibrate(*layers[-1], w, h, block_ch, is_train=is_train), w, h, block_ch)
with tf.variable_scope('layer_-2_calibrate'):
layers[-2] = (self._calibrate(*layers[-2], w, h, block_ch, is_train=is_train), w, h, block_ch)
|
tensorflow.variable_scope
| 8,243 |
from tensorflow.python.framework import ops
"""Pass resource_variable_ops.is_resource_variable check."""
pass
def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
"""Converts a variable to a tensor."""
# pylint: disable=protected-access
if _enclosing_tpu_context() is None:
if hasattr(self._primary_var, '_dense_var_to_tensor'):
return self._primary_var._dense_var_to_tensor(dtype, name, as_ref)
else:
return ops.convert_to_tensor(self._primary_var)
# pylint: enable=protected-access
if dtype is not None and dtype != self.dtype:
return NotImplemented
if as_ref:
return self.handle
else:
return self.read_value()
|
tensorflow.python.framework.ops.convert_to_tensor
| 8,244 |
import tensorflow as tf
log("Tacotron training set to a maximum of {} steps".format(args.tacotron_train_steps))
# Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
|
tensorflow.ConfigProto
| 8,245 |
import tensorflow as tf
'number of batches to run, excluding warmup')
tf.flags.DEFINE_integer('num_warmup_batches', None,
'number of batches to run before timing')
tf.flags.DEFINE_integer('autotune_threshold', None,
'The autotune threshold for the models')
tf.flags.DEFINE_integer('num_gpus', 1, 'the number of GPUs to run on')
tf.flags.DEFINE_integer('display_every', 10,
"""Number of local steps after which progress is printed
out""")
tf.flags.DEFINE_string('data_dir', None, """Path to dataset in TFRecord format
(aka Example protobufs). If not specified,
synthetic data will be used.""")
tf.flags.DEFINE_string('data_name', None,
"""Name of dataset: imagenet or flowers.
If not specified, it is automatically guessed
based on --data_dir.""")
tf.flags.DEFINE_string('resize_method', 'bilinear',
"""Method for resizing input images:
|
tensorflow.flags.DEFINE_string
| 8,246 |
import tensorflow as tf
total_loss = tf.nn.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=False,
average_across_batch=False)
res = sess.run(total_loss)
self.assertAllClose(9.656628, res)
def testSequenceLossByExample(self):
with self.test_session() as sess:
output_classes = 5
logits = [tf.constant(i + 0.5, shape=[2, output_classes])
for i in range(3)]
targets = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
weights = [tf.constant(1.0, shape=[2]) for i in range(3)]
average_loss_per_example = tf.nn.seq2seq.sequence_loss_by_example(
logits, targets, weights,
average_across_timesteps=True)
res = sess.run(average_loss_per_example)
self.assertAllClose(np.asarray([1.609438, 1.609438]), res)
|
tensorflow.constant
| 8,247 |
import tensorflow as tf
tf.app.flags.DEFINE_string('eval_data_path', '',
'Filepattern for eval data')
tf.app.flags.DEFINE_string('train_dir', '',
'Directory to keep training outputs.')
tf.app.flags.DEFINE_string('eval_dir', '',
'Directory to keep eval outputs.')
tf.app.flags.DEFINE_integer('eval_batch_count', 10,
'Number of batches to eval.')
tf.app.flags.DEFINE_bool('eval_once', False,
'Whether evaluate the model only once.')
tf.app.flags.DEFINE_string('log_root', '',
'Directory to keep the checkpoints. Should be a '
'parent directory of FLAGS.train_dir/eval_dir.')
tf.app.flags.DEFINE_integer('num_gpus', 0,
'Number of gpus used for training. (0 or 1)')
tf.app.flags.DEFINE_integer('num_residual_units', 5,
'num of residual units')
tf.app.flags.DEFINE_string('Optimizer', 'mom',
'The optimizer used to train the model.')
tf.app.flags.DEFINE_bool('RCE_train', False,
'Whether use RCE to train the model.')
tf.app.flags.DEFINE_string('attack_method', 'fgsm',
'The attacking method used')
tf.app.flags.DEFINE_float('eps', 0.01,
'The eps in attacking methods.')
tf.app.flags.DEFINE_string('save_pwd', None,
|
tensorflow.app.flags.DEFINE_integer
| 8,248 |
import tensorflow.contrib.layers as layers
out = img_in
with tf.variable_scope("convnet"):
# original architecture
out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu)
|
tensorflow.contrib.layers.convolution2d
| 8,249 |
import tensorflow as tf
def commutes_with_sum(self):
"""See base class."""
return True
@property
def decode_needs_input_shape(self):
"""See base class."""
return False
def get_params(self):
"""See base class."""
params = {self.FACTOR_PARAM_KEY: tf.constant(2.0)}
return params, params
def encode(self, x, encode_params):
"""See base class."""
return {self.ENCODED_VALUES_KEY: x * encode_params[self.FACTOR_PARAM_KEY]}
def decode(self,
encoded_tensors,
decode_params,
num_summands=None,
|
tensorflow.constant
| 8,250 |
import tensorflow as tf
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
next_sentence_log_probs = tf.reshape(
next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
next_sentence_predictions = tf.argmax(
next_sentence_log_probs, axis=-1, output_type=tf.int32)
next_sentence_labels = tf.reshape(next_sentence_labels, [-1])
next_sentence_accuracy = tf.metrics.accuracy(
|
tensorflow.reshape
| 8,251 |
import tensorflow as tf
inputs.append(inputs_)
encoder_inputs_ = tf.concat(inputs, axis=2)
# if encoder.convolution_activation.lower() == 'relu':
encoder_inputs_ = tf.nn.relu(encoder_inputs_)
if encoder.maxout_stride:
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:
x = encoder_inputs_
for j in range(encoder.highway_layers):
size = x.shape[2].value
with tf.variable_scope('highway_{}'.format(j + 1)):
g = tf.layers.dense(x, size, activation=tf.nn.sigmoid, use_bias=True, name='g')
y = tf.layers.dense(x, size, activation=tf.nn.relu, use_bias=True, name='y')
x = g * y + (1 - g) * x
|
tensorflow.nn.pool
| 8,252 |
import tensorflow as tf
# sess.close()
identity_matrix = tf.diag([1.0, 3.0, 1.0])
A = tf.truncated_normal([2, 3])
B = tf.fill([2, 3], 5.0)
C = tf.random_uniform([3, 2], maxval=100)
D = tf.convert_to_tensor(np.array([[1., 2., 3.], [-3., -7., -1.], [0., 5., -2.]]))
sess = tf.Session()
# sess.run(tf.global_variables_initializer())
|
tensorflow.fill
| 8,253 |
import tensorflow as tf
name="biases")
hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
# Hidden 2
with tf.name_scope("hidden2"):
weights = tf.Variable(
tf.truncated_normal([128, 32],
stddev=1.0 / math.sqrt(float(128))),
name="weights")
biases = tf.Variable(tf.zeros([32]),
name="biases")
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))),
|
tensorflow.zeros
| 8,254 |
from tensorflow.python.framework import constant_op
constant_op.constant(indices, dtypes.int64, [len(indices), 2]),
constant_op.constant(values, value_type, [len(indices)]),
constant_op.constant(shape, dtypes.int64))
|
tensorflow.python.framework.constant_op.constant
| 8,255 |
import tensorflow as tf
self.assertEqual((2, 2), res[0].shape)
def testEmbeddingRNNDecoder(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
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
_, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
dec, mem = tf.nn.seq2seq.embedding_rnn_decoder(
dec_inp, enc_state, cell, num_symbols=4, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
|
tensorflow.constant
| 8,256 |
import tensorflow as tf
import numpy as np
import tensorflow as tf
import layers as L
import vat
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('device', '/gpu:0', "device")
tf.app.flags.DEFINE_string('dataset', 'cifar10', "{cifar10, svhn}")
tf.app.flags.DEFINE_string('log_dir', "", "log_dir")
tf.app.flags.DEFINE_integer('seed', 1, "initial random seed")
tf.app.flags.DEFINE_bool('validation', False, "")
tf.app.flags.DEFINE_integer('batch_size', 32, "the number of examples in a batch")
tf.app.flags.DEFINE_integer('ul_batch_size', 128, "the number of unlabeled examples in a batch")
tf.app.flags.DEFINE_integer('eval_batch_size', 100, "the number of eval examples in a batch")
tf.app.flags.DEFINE_integer('eval_freq', 5, "")
tf.app.flags.DEFINE_integer('num_epochs', 120, "the number of epochs for training")
tf.app.flags.DEFINE_integer('epoch_decay_start', 80, "epoch of starting learning rate decay")
tf.app.flags.DEFINE_integer('num_iter_per_epoch', 400, "the number of updates per epoch")
tf.app.flags.DEFINE_float('learning_rate', 0.001, "initial leanring rate")
tf.app.flags.DEFINE_float('mom1', 0.9, "initial momentum rate")
tf.app.flags.DEFINE_float('mom2', 0.5, "momentum rate after epoch_decay_start")
tf.app.flags.DEFINE_string('method', 'vat', "{vat, vatent, baseline}")
|
tensorflow.app.flags.DEFINE_bool
| 8,257 |
import tensorflow as tf
return two_stream_loss(FLAGS, features, labels, mems, is_training)
else:
return two_stream_loss(FLAGS, features, labels, mems, is_training)
def get_classification_loss(
FLAGS, features, n_class, is_training):
"""Loss for downstream classification tasks."""
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(
xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp,
seg_ids=seg_id,
input_mask=inp_mask)
|
tensorflow.transpose
| 8,258 |
import tensorflow as tf
def dice(_x, axis=-1, epsilon=0.000000001, name=''):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
alphas = tf.get_variable('alpha'+name, _x.get_shape()[-1],
initializer=tf.constant_initializer(0.0),
dtype=_x.dtype)
input_shape = list(_x.get_shape())
reduction_axes = list(range(len(input_shape)))
del reduction_axes[axis]
broadcast_shape = [1] * len(input_shape)
broadcast_shape[axis] = input_shape[axis]
# 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)
brodcast_std = tf.reshape(std, broadcast_shape)
x_normed = (_x - brodcast_mean) / (brodcast_std + epsilon)
# x_normed = tf.layers.batch_normalization(_x, center=False, scale=False)
x_p = tf.sigmoid(x_normed)
return alphas * (1.0 - x_p) * _x + x_p * _x
class QAAttGRUCell(RNNCell):
"""Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).
Args:
num_units: int, The number of units in the GRU cell.
activation: Nonlinearity to use. Default: `tanh`.
|
tensorflow.square
| 8,259 |
import tensorflow as tf
self.char_mat = tf.get_variable(
"char_mat", initializer=tf.constant(char_mat, dtype=tf.float32))
self.c_mask = tf.cast(self.c, tf.bool)
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
if opt:
# we have to hardcode the max batch size here! use the batch size from the generator as this will be used for PG
N, CL = config.batch_size if not self.demo else config.batch_size, config.char_limit
self.c_maxlen = tf.reduce_max(self.c_len)
self.q_maxlen = tf.reduce_max(self.q_len)
self.c = tf.slice(self.c, [0, 0], [N, self.c_maxlen])
self.q = tf.slice(self.q, [0, 0], [N, self.q_maxlen])
self.c_mask = tf.slice(self.c_mask, [0, 0], [N, self.c_maxlen])
self.q_mask = tf.slice(self.q_mask, [0, 0], [N, self.q_maxlen])
self.ch = tf.slice(self.ch, [0, 0, 0], [N, self.c_maxlen, CL])
self.qh = tf.slice(self.qh, [0, 0, 0], [N, self.q_maxlen, CL])
self.y1 = tf.argmax(tf.slice(self.y1, [0, 0], [N, self.c_maxlen]),axis=-1)
self.y2 = tf.argmax(tf.slice(self.y2, [0, 0], [N, self.c_maxlen]),axis=-1)
else:
|
tensorflow.reduce_max
| 8,260 |
import tensorflow as tf
def to_one_hot(i, n_classes=None):
a = np.zeros(n_classes, 'uint8')
a[i] = 1
return a
render = False # display the game environment
running_reward = None
tf.reset_default_graph()
## Define Q-network q(a,s) that ouput the rewards of 4 actions by given state, i.e. Action-Value Function.
# 4x4 grid can be represented by one-hot vector with 16 integers.
inputs = tf.placeholder(shape=[1, 16], dtype=tf.float32)
net = InputLayer(inputs, name='observation')
net = DenseLayer(net, n_units=4, act=tf.identity,
W_init=tf.random_uniform_initializer(0, 0.01), b_init=None, name='q_a_s')
y = net.outputs # action-value / rewards of 4 actions
predict = tf.argmax(y, 1) # chose action greedily with reward. in Q-Learning, policy is greedy, so we use "max" to select the next action.
## Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.
nextQ = tf.placeholder(shape=[1, 4], dtype=tf.float32)
loss = tl.cost.mean_squared_error(nextQ, y, is_mean=False) # tf.reduce_sum(tf.square(nextQ - y))
train_op = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)
|
tensorflow.placeholder
| 8,261 |
import tensorflow as tf
generator_loss = generator_c_loss + generator_g_loss
# Supervised Encoder Loss
supervised_encoder_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_input, logits=encoder_output_label_))
all_variables = tf.trainable_variables()
dc_g_var = [var for var in all_variables if 'dc_g_' in var.name]
dc_c_var = [var for var in all_variables if 'dc_c_' in var.name]
en_var = [var for var in all_variables if 'e_' in var.name]
# Optimizers
autoencoder_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(autoencoder_loss)
discriminator_g_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(dc_g_loss, var_list=dc_g_var)
discriminator_c_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(dc_c_loss, var_list=dc_c_var)
generator_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(generator_loss, var_list=en_var)
supervised_encoder_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(supervised_encoder_loss,
var_list=en_var)
|
tensorflow.train.AdamOptimizer
| 8,262 |
import tensorflow as tf
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
def input_fn(params):
"""The actual input function."""
# batch_size = params["batch_size"]
name_to_features = {
"input_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_mask":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"masked_lm_positions":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_ids":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
"next_sentence_labels":
|
tensorflow.FixedLenFeature
| 8,263 |
import tensorflow as tf
# layers
l_a = tf.layers.dense(self.state, 200, tf.nn.relu6, kernel_initializer=w_init, name='la')
self.mu = tf.layers.dense(l_a, num_action, tf.nn.tanh, kernel_initializer=w_init, name='mu') # estimated action value
self.sigma = tf.layers.dense(l_a, num_action, tf.nn.softplus, kernel_initializer=w_init, name='sigma') # estimated variance
# wrap output
self.mu = self.mu * action_bound[1];
self.sigma = self.sigma + 1e-4
# get action from distribution
self.normal_dist = tf.contrib.distributions.Normal(self.mu, self.sigma)
self.action = tf.squeeze(self.normal_dist.sample(1),axis=0);
self.action = tf.clip_by_value(self.action, action_bound[0], action_bound[1])
# Loss and train op
self.loss = -self.normal_dist.log_prob(self.a_his) * self.target
# Add cross entropy cost to encourage exploration
self.loss -= entropy_beta * self.normal_dist.entropy()
self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
self.grads_and_vars = self.optimizer.compute_gradients(self.loss)
self.grads=[];
|
tensorflow.contrib.distributions.Normal
| 8,264 |
import tensorflow.contrib.eager as tfe
examples_per_sec = num_epochs * num_batches * batch_size / wall_time
self.report_benchmark(
name="eager_train_%s" %
("gpu" if tfe.num_gpus() > 0 else "cpu"),
iters=num_epochs * num_batches,
extras={"examples_per_sec": examples_per_sec},
wall_time=wall_time)
|
tensorflow.contrib.eager.num_gpus
| 8,265 |
import tensorflow as tf
'adj_orig': tf.sparse_placeholder(tf.float32),
'dropout': tf.placeholder_with_default(0., shape=())
|
tensorflow.placeholder_with_default
| 8,266 |
import tensorflow as tf
tf.compat.v2.summary.scalar(
name="max_q", data=tf.reduce_mean(max_q), step=global_step)
### End metrics
# For both state-centric and goal-centric relabelling, the implementation of
# mixing is the same: we randomly replace some of the indices with the
# diagonal.
relabelled_tasks = tf.gather(candidate_tasks, tf.squeeze(relabel_indices))
if self._relabel_prob == 0:
relabelled_tasks = orig_tasks
elif 0 < self._relabel_prob < 1:
logits = tf.log([1.0 - self._relabel_prob, self._relabel_prob])
mask = tf.squeeze(
tf.random.categorical(
logits[None], num_samples=self._sample_batch_size))
mask = tf.cast(mask, tf.float32)[:, None]
relabelled_tasks = mask * orig_tasks + (1 - mask) * relabelled_tasks
states_and_tasks = self._task_distribution.combine(states, relabelled_tasks)
next_states_and_tasks = self._task_distribution.combine(
next_states, relabelled_tasks)
new_observation = tf.concat(
[states_and_tasks[:, None], next_states_and_tasks[:, None]], axis=1)
assert new_observation.shape == experience.observation.shape
|
tensorflow.log
| 8,267 |
import tensorflow as tf
y_true = tf.reshape(y_true, shape=(batch_size, -1, num_of_joints))
heatmap_true_list = tf.split(value=y_true, # y_true执行与y_pred相同的操作
num_or_size_splits=num_of_joints,
axis=-1)
losses = [] # 计算每一个关键点的损失值，并累加求平均
for i in range(num_of_joints):
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])
losses.append(loss)
return tf.reduce_mean(loss)
|
tensorflow.squeeze
| 8,268 |
import tensorflow as tf
encoder_output_label, encoder_output_latent = encoder(x_input)
# Concat class label and the encoder output
decoder_input = tf.concat([encoder_output_label, encoder_output_latent], 1)
decoder_output = decoder(decoder_input)
|
tensorflow.concat
| 8,269 |
import tensorflow as tf
return tf.contrib.layers.xavier_initializer()
elif params.initializer == "uniform":
max_val = params.initializer_gain
return tf.random_uniform_initializer(-max_val, max_val)
elif params.initializer == "normal":
return tf.random_normal_initializer(0.0, params.initializer_gain)
|
tensorflow.random_uniform_initializer
| 8,270 |
import tensorflow as tf
- one_hot_labels_arguments * tf.log(tf.clip_by_value(self.predictions_arguments, 1e-10, 1.0)),
name='loss'
)
self.loss = loss_action + loss_arguments
tf.scalar_summary('loss', self.loss)
with tf.name_scope('accuracy'):
correct_prediction_action = tf.equal(
tf.argmax(one_hot_labels_action, 1),
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
| 8,271 |
import tensorflow as tf
max_x = tf.cast(0.0 + labeled_sizes[i][0] / 2.0, dtype=tf.float32)
# min_y = tf.cast(0.0 - labeled_sizes[i][1] / 2.0, dtype=tf.float32)
# max_y = tf.cast(0.0 + labeled_sizes[i][1] / 2.0, dtype=tf.float32)
min_z = tf.cast(0.0 - labeled_sizes[i][2] / 2.0, dtype=tf.float32)
max_z = tf.cast(0.0 + labeled_sizes[i][2] / 2.0, dtype=tf.float32)
|
tensorflow.cast
| 8,272 |
import tensorflow as tf
q_func_results = q_func(observations_ph.get(), num_actions, scope="q_func")
q_values = q_func_results['q']
s_value = q_func_results['s']
a_values = q_func_results['a']
deterministic_actions = tf.argmax(q_values, axis=1)
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},
|
tensorflow.stack
| 8,273 |
import tensorflow as tf
return True
def _infer_raw_shape(tt_cores):
"""Tries to infer the (static) raw shape from the TT-cores."""
num_dims = len(tt_cores)
num_tensor_shapes = len(tt_cores[0].get_shape().as_list()) - 2
raw_shape = [[] for _ in range(num_tensor_shapes)]
for dim in range(num_dims):
curr_core_shape = tt_cores[dim].get_shape()
for i in range(num_tensor_shapes):
raw_shape[i].append(curr_core_shape[i + 1])
for i in range(num_tensor_shapes):
raw_shape[i] = tf.TensorShape(raw_shape[i])
return tuple(raw_shape)
def _infer_tt_ranks(tt_cores):
"""Tries to infer the (static) raw shape from the TT-cores."""
tt_ranks = []
for i in range(len(tt_cores)):
tt_ranks.append(tt_cores[i].get_shape()[0])
tt_ranks.append(tt_cores[-1].get_shape()[-1])
return tf.TensorShape(tt_ranks)
|
tensorflow.TensorShape
| 8,274 |
import tensorflow as tf
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
summary_op = tf.summary.merge_all()
# save moving average
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
# batch norm updates
with tf.control_dependencies([variables_averages_op, apply_gradient_op, batch_norm_updates_op]):
train_op = tf.no_op(name='train_op')
saver = tf.train.Saver(tf.global_variables())
summary_writer = tf.summary.FileWriter(FLAGS.checkpoint_path, tf.get_default_graph())
init = tf.global_variables_initializer()
if FLAGS.pretrained_model_path is not None:
variable_restore_op = slim.assign_from_checkpoint_fn(FLAGS.pretrained_model_path, slim.get_trainable_variables(),
ignore_missing_vars=True)
config = tf.ConfigProto()
custom_op = config.graph_options.rewrite_options.custom_optimizers.add()
custom_op.name = "NpuOptimizer"
custom_op.parameter_map["use_off_line"].b = True # 在昇腾AI处理器执行训练
|
tensorflow.get_default_graph
| 8,275 |
import tensorflow as tf
# Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph)
sess.run(tf.global_variables_initializer())
# saved model restoring
if args.restore:
# Restore saved model if the user requested it, default = True
try:
|
tensorflow.global_variables_initializer
| 8,276 |
import tensorflow as tf
res = tf.cast(res, dtype=tf.float32)
res = tf.multiply(res, loss_weights)
return tf.reduce_sum(res)
|
tensorflow.reduce_sum
| 8,277 |
import tensorflow as tf
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)
|
tensorflow.contrib.rnn.DropoutWrapper
| 8,278 |
from tensorflow.python.ops import math_ops
def _prepare(self):
self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate")
self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1")
self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2")
def _create_slots(self, var_list):
# Create slots for the first and second moments.
for v in var_list:
self._zeros_slot(v, "m", self._name)
self._zeros_slot(v, "v", self._name)
self._zeros_slot(v, "g", self._name)
def _apply_dense(self, grad, var):
lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
if var.dtype.base_dtype == tf.float16:
eps = 1e-7 # Can't use 1e-8 due to underflow -- not sure if it makes a big difference.
else:
eps = 1e-8
v = self.get_slot(var, "v")
v_t = v.assign(beta2_t * v + (1. - beta2_t) * tf.square(grad))
m = self.get_slot(var, "m")
m_t = m.assign(beta1_t * m + (1. - beta1_t) * grad)
v_t_hat = tf.div(v_t, 1. - beta2_t)
m_t_hat = tf.div(m_t, 1. - beta1_t)
g_t = tf.div(m_t_hat, tf.sqrt(v_t_hat) + eps)
g_t_1 = self.get_slot(var, "g")
|
tensorflow.python.ops.math_ops.cast
| 8,279 |
import tensorflow as tf
span_mask = tf.expand_dims(tf.sequence_mask(span_width, self.config["max_span_width"], dtype=tf.float32), 2) # [k, max_span_width, 1]
span_head_scores += tf.log(span_mask) # [k, max_span_width, 1]
|
tensorflow.log
| 8,280 |
import tensorflow as tf
def _create_params(self):
initializer = tf.random_uniform_initializer(minval=-0.1, maxval=0.1)
with tf.variable_scope(self.name, initializer=initializer):
with tf.variable_scope("lstm"):
self.w_lstm = []
for layer_id in range(self.lstm_num_layers):
with tf.variable_scope("layer_{}".format(layer_id)):
w = tf.get_variable("w", [2 * self.lstm_size, 4 * self.lstm_size])
self.w_lstm.append(w)
self.g_emb = tf.get_variable("g_emb", [1, self.lstm_size])
with tf.variable_scope("emb"):
self.w_emb = tf.get_variable("w", [self.num_branches, self.lstm_size])
with tf.variable_scope("softmax"):
self.w_soft = tf.get_variable("w", [self.lstm_size, self.num_branches])
|
tensorflow.get_variable
| 8,281 |
import tensorflow as tf
self.user_id = tf.placeholder(shape=[None, ], dtype=tf.int32, name='user_id')
self.item_id = tf.placeholder(shape=[None, ], dtype=tf.int32, name='item_id')
self.labels = tf.placeholder(shape=[None, 1], dtype=tf.float32, name='labels')
self.interaction = gmf(uid=self.user_id, iid=self.item_id, num_users=self.train_set.num_users,
|
tensorflow.placeholder
| 8,282 |
import tensorflow as tf
"""
context, sequence = tf.parse_single_sequence_example(
|
tensorflow.parse_single_sequence_example
| 8,283 |
from tensorflow.python.ops import variable_scope as vs
2 * self._num_units,
True,
bias_initializer=bias_ones,
kernel_initializer=self._kernel_initializer)
value = math_ops.sigmoid(self._gate_linear([inputs, state]))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
r_state = r * state
if self._candidate_linear is None:
with vs.variable_scope("candidate"):
self._candidate_linear = _Linear(
[inputs, r_state],
self._num_units,
True,
bias_initializer=self._bias_initializer,
kernel_initializer=self._kernel_initializer)
c = self._activation(self._candidate_linear([inputs, r_state]))
new_h = (1. - att_score) * state + att_score * c
return new_h, new_h
|
tensorflow.python.ops.variable_scope.variable_scope
| 8,284 |
import tensorflow as tf
# loss = tf.maximum(0.0, tf.math.abs(tgt_larg - pred_larg) - tf.math.abs(tgt_small - pred_small))
loss = tf.reduce_mean(loss)
return loss
def contra_step_lossV5(pred, tgt, resample=1):
# p = tf.print('begin loss v5', [resample, pred.shape,tgt.shape])
# with tf.control_dependencies([p]):
pred_flat = tf.reshape(pred, [-1])
tgt_flat = tf.reshape(tgt, [-1])
batch = tf.stack([pred_flat, tgt_flat], 1)
num_sam = tools.shape(batch)[0]
index = tf.range(num_sam)
divider = tf.constant(resample, dtype=tf.float32)
def sample_compute(cur_loss, i):
|
tensorflow.reshape
| 8,285 |
import tensorflow as tf
base_z0_y1 = base + z0_clip * dim2 + y1_clip * dim3
base_z1_y0 = base + z1_clip * dim2 + y0_clip * dim3
base_z1_y1 = base + z1_clip * dim2 + y1_clip * dim3
idx_z0_y0_x0 = base_z0_y0 + x0_clip
idx_z0_y0_x1 = base_z0_y0 + x1_clip
idx_z0_y1_x0 = base_z0_y1 + x0_clip
idx_z0_y1_x1 = base_z0_y1 + x1_clip
idx_z1_y0_x0 = base_z1_y0 + x0_clip
idx_z1_y0_x1 = base_z1_y0 + x1_clip
idx_z1_y1_x0 = base_z1_y1 + x0_clip
idx_z1_y1_x1 = base_z1_y1 + x1_clip
# Use indices to lookup pixels in the flat image and restore
# channels dim
im_flat = tf.reshape(im, tf.stack([-1, channels]))
im_flat = tf.to_float(im_flat)
i_z0_y0_x0 = tf.gather(im_flat, idx_z0_y0_x0)
i_z0_y0_x1 = tf.gather(im_flat, idx_z0_y0_x1)
i_z0_y1_x0 = tf.gather(im_flat, idx_z0_y1_x0)
i_z0_y1_x1 = tf.gather(im_flat, idx_z0_y1_x1)
i_z1_y0_x0 = tf.gather(im_flat, idx_z1_y0_x0)
i_z1_y0_x1 = tf.gather(im_flat, idx_z1_y0_x1)
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)
|
tensorflow.stack
| 8,286 |
import tensorflow as tf
scale_init = init_scale / tf.sqrt(v_init + 1e-10)
with tf.control_dependencies([g.assign(g * scale_init), b.assign_add(-m_init * scale_init)]):
x = tf.reshape(scale_init, [1, 1, 1, num_filters]) * (x - tf.reshape(m_init, [1, 1, 1, num_filters]))
|
tensorflow.reshape
| 8,287 |
import tensorflow as tf
sqrt_diag_data = rng.randn(M, N) # M x N
K_batch_data = make_K_batch_data(rng, N, M)
@pytest.fixture
def mu(session_tf):
return tf.convert_to_tensor(Datum.mu_data)
@pytest.fixture
def sqrt_diag(session_tf):
return tf.convert_to_tensor(Datum.sqrt_diag_data)
@pytest.fixture
def K(session_tf):
return tf.convert_to_tensor(Datum.K_data)
@pytest.fixture
def K_batch(session_tf):
return tf.convert_to_tensor(Datum.K_batch_data)
|
tensorflow.convert_to_tensor
| 8,288 |
import tensorflow as tf
range_tiled = tf.tile(range_row, [batch_size, 1])
self.lengths_transposed = lengths_transposed
self.lengths_tiled = lengths_tiled
self.range_row = range_row
self.range_tiled = range_tiled
# Use the logical operations to create a mask
indicator = tf.less(range_tiled, lengths_tiled+1) #i.e. where seq len is less than index
trim = np.ones(indicator.get_shape())
trim[:,0] = 0 #ignore start symbol
indicator = tf.logical_and(indicator, trim.astype(bool))
self.indicator = indicator
sz = [batch_size, max_sequence_len]
self._mask = tf.select(indicator, tf.ones(sz), tf.zeros(sz))
#-------------------------------#
self.weights = tf.constant(weights, dtype=tf.float32, name='class_weights')
hidden_size = model_params['model_hidden_size']
proj_size = model_params['model_proj_size'] # optional, can be None
def GetCell():
"""Creates an LSTM cell with dropout."""
c = tf.nn.rnn_cell.LSTMCell(hidden_size,
use_peepholes=model_params['peepholes'],
num_proj=proj_size)
if dropout_keep_prob is not None:
|
tensorflow.ones
| 8,289 |
import tensorflow as tf
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
|
tensorflow.global_variables_initializer
| 8,290 |
import tensorflow as tf
self.t_replace_iter = t_replace_iter
self.t_replace_counter = 0
with tf.variable_scope('Actor'):
# input s, output a
self.a = self._build_net(S, scope='eval_net', trainable=True)
# input s_, output a, get a_ for critic
self.a_ = self._build_net(S_, scope='target_net', trainable=False)
self.e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/eval_net')
self.t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/target_net')
def _build_net(self, s, scope, trainable):
with tf.variable_scope(scope):
init_w = tf.random_normal_initializer(0., 0.01)
init_b = tf.constant_initializer(0.01)
net = tf.layers.dense(s, 500, activation=tf.nn.relu,
kernel_initializer=init_w, bias_initializer=init_b, name='l1', trainable=trainable)
net = tf.layers.dense(net, 200, activation=tf.nn.relu,
kernel_initializer=init_w, bias_initializer=init_b, name='l2', trainable=trainable)
with tf.variable_scope('a'):
actions = tf.layers.dense(net, self.a_dim, activation=tf.nn.tanh, kernel_initializer=init_w,
bias_initializer=init_b, name='a', trainable=trainable)
scaled_a = tf.multiply(actions, self.action_bound, name='scaled_a') # Scale output to -action_bound to action_bound
return scaled_a
def learn(self, s): # batch update
self.sess.run(self.train_op, feed_dict={S: s})
|
tensorflow.random_normal_initializer
| 8,291 |
import tensorflow as tf
def metric_fn(
masked_lm_example_loss,
masked_lm_log_probs,
masked_lm_ids,
masked_lm_weights,
next_sentence_example_loss,
next_sentence_log_probs,
next_sentence_labels,
):
"""Computes the loss and accuracy of the model."""
masked_lm_log_probs = tf.reshape(
masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]]
)
masked_lm_predictions = tf.argmax(
masked_lm_log_probs, axis=-1, output_type=tf.int32
)
masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
masked_lm_accuracy = tf.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights,
)
masked_lm_mean_loss = tf.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights
)
|
tensorflow.argmax
| 8,292 |
import tensorflow as tf
w = tf.get_variable("w", [nx, ny], initializer=w_init)
b = tf.get_variable("b", [ny], initializer=b_init)
return tf.matmul(x, w)+b
def model(X, M, Y, train=False, reuse=False):
with tf.variable_scope('model', reuse=reuse):
we = tf.get_variable("we", [n_vocab+n_special+n_ctx, n_embd], initializer=tf.random_normal_initializer(stddev=0.02))
we = dropout(we, embd_pdrop, train)
#X:[n_batch_train, 2, n_ctx, 2] -> [n_batch_train*2,n_ctx,2]
|
tensorflow.variable_scope
| 8,293 |
import tensorflow as tf
# 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):
# train with gradient clipping
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
grads = optimizer.compute_gradients(self.loss)
clipped_grads = [(tf.clip_by_norm(grad, 1.0), var)
if grad is not None else (grad, var)
for grad, var in grads]
# add vanishing gradient regularizer
#out, test = self.dOmega_dWrec()
#clipped_grads[0] = (tf.add(out[0], clipped_grads[0][0]), clipped_grads[0][1])
#clipped_grads[0] = (tf.Print(clipped_grads[0][0], [clipped_grads[0][0]], "gw_rec"), clipped_grads[0][1])
optimize = optimizer.apply_gradients(clipped_grads)
|
tensorflow.clip_by_norm
| 8,294 |
import tensorflow as tf
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
|
tensorflow.cast
| 8,295 |
import tensorflow as tf
weight: a scalar weight for the loss.
scope: optional name scope for summary tags.
Returns:
a scalar tensor representing the correlation loss value.
"""
with tf.name_scope(name):
source_samples -= tf.reduce_mean(source_samples, 0)
target_samples -= tf.reduce_mean(target_samples, 0)
source_samples = tf.nn.l2_normalize(source_samples, 1)
target_samples = tf.nn.l2_normalize(target_samples, 1)
source_cov = tf.matmul(tf.transpose(source_samples), source_samples)
target_cov = tf.matmul(tf.transpose(target_samples), target_samples)
corr_loss = tf.reduce_mean(tf.square(source_cov - target_cov)) * weight
assert_op = tf.Assert(tf.is_finite(corr_loss), [corr_loss])
|
tensorflow.reduce_mean
| 8,296 |
import tensorflow as tf
best_trial_info_file = os.path.join(FLAGS.output_dir, "best_trial.txt")
def _best_trial_info():
"""Returns information about which checkpoints have been evaled so far."""
if tf.gfile.Exists(best_trial_info_file):
with tf.gfile.GFile(best_trial_info_file, "r") as best_info:
global_step, best_metric_global_step, metric_value = (
best_info.read().split(":"))
global_step = int(global_step)
best_metric_global_step = int(best_metric_global_step)
|
tensorflow.gfile.GFile
| 8,297 |
import tensorflow as tf
dist_loss1 = self._distance_loss(models[1].encode - models[0].encode)
dist_loss2 = self._distance_loss(models[1].encode - models[2].encode)
# Stitch it all together and train
self.loss_reco = tf.add_n(reco_losses)
self.loss_pred = pred_loss_0 + pred_loss_2
self.loss_dist = dist_loss1 + dist_loss2
self.losses = [self.loss_reco, self.loss_pred]
def _distance_loss(self, distances):
error = tf.nn.relu(l2(distances) - FLAGS.distance ** 2)
return tf.reduce_sum(error)
def _prediction_decode(self, prediction, target, model):
"""Predict encoding t3 by encoding (t2 and t1) and expect a good reconstruction"""
predict_decode = interpreter.build_decoder(prediction, self.model.config, reuse=True, masks=model.mask_list)
predict_loss = 1./2 * interpreter.l2_loss(predict_decode, target, alpha=FLAGS.alpha)
self.models += [predict_decode]
return predict_loss * FLAGS.gamma
def build_denoising_model(self):
|
tensorflow.reduce_sum
| 8,298 |
import tensorflow as tf
scope.reuse_variables()
var = variable_on_cpu(
"var", [dim], tf.constant_initializer(1.), trainable=False)
mean = variable_on_cpu(
"mean", [dim], tf.constant_initializer(0.), trainable=False)
step = variable_on_cpu("step", [], tf.constant_initializer(0.), trainable=False)
# choose the appropriate moments
if train:
used_mean, used_var = tf.nn.moments(input_, axes, name="batch_norm")
cur_mean, cur_var = used_mean, used_var
if bn_lag > 0.:
used_var = stable_var(input_=input_, mean=used_mean, axes=axes)
cur_var = used_var
used_mean -= (1 - bn_lag) * (used_mean - tf.stop_gradient(mean))
used_mean /= (1. - bn_lag**(step + 1))
used_var -= (1 - bn_lag) * (used_var - tf.stop_gradient(var))
used_var /= (1. - bn_lag**(step + 1))
else:
used_mean, used_var = mean, var
cur_mean, cur_var = used_mean, used_var
# update variables
if train:
with tf.name_scope(name, "AssignMovingAvg", [mean, cur_mean, decay]):
with ops.colocate_with(mean):
new_mean = tf.assign_sub(
mean,
tf.check_numerics(
decay * (mean - cur_mean), "NaN in moving mean."))
with tf.name_scope(name, "AssignMovingAvg", [var, cur_var, decay]):
|
tensorflow.stop_gradient
| 8,299 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.