seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
ema_op = self.var_ema.apply(tf.trainable_variables())
with tf.control_dependencies([ema_op]):
| tensorflow.control_dependencies | 4,800 |
import tensorflow as tf
tf.gradients(ys=self.T, xs=self.X),
self.X if self.has_multiple_inputs else [self.X])]
@classmethod
def nonlinearity_grad_override(cls, op, grad):
output = op.outputs[0]
input = op.inputs[0]
return grad * output / (input + eps *
tf.compat.v1.where(input >= 0, tf.ones_like(input), -1 * tf.ones_like(input)))
"""
Integrated Gradients
https://arxiv.org/pdf/1703.01365.pdf
"""
class IntegratedGradients(GradientBasedMethod):
| tensorflow.ones_like | 4,801 |
import tensorflow as tf
avg_norm_grads_f = avg_norm(grads_f) * (self.n_steps * self.n_envs)
norm_grads_q = tf.global_norm(grads_q)
norm_grads_policy = tf.global_norm(grads_policy)
else:
grads = tf.gradients(loss, self.params)
| tensorflow.global_norm | 4,802 |
import tensorflow as tf
name_to_features = {
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
"label_ids": tf.FixedLenFeature([seq_length], tf.int64),
"is_real_example": tf.FixedLenFeature([1], tf.int64),
}
| tensorflow.FixedLenFeature | 4,803 |
import tensorflow as tf
@registry.register_model
class FeedForwardCategoricalPolicy(PolicyBase):
"""Feed-forward categorical."""
def body(self, features):
observations = features["inputs_raw"]
observations = tf.cast(observations, tf.float32)
flat_observations = tf.layers.flatten(observations)
with tf.variable_scope("policy"):
x = flat_observations
for size in self.hparams.policy_layers:
x = tf.layers.dense(x, size, activation=tf.nn.relu)
logits = tf.layers.dense(x, self.hparams.problem.num_actions)
logits = tf.expand_dims(logits, axis=1)
with tf.variable_scope("value"):
x = flat_observations
for size in self.hparams.value_layers:
x = tf.layers.dense(x, size, activation=tf.nn.relu)
value = tf.layers.dense(x, 1)
logits = clip_logits(logits, self.hparams)
return {"target_policy": logits, "target_value": value}
@registry.register_model
class FeedForwardCnnSmallCategoricalPolicy(PolicyBase):
| tensorflow.layers.dense | 4,804 |
import tensorflow as tf
tf.app.flags.DEFINE_integer('ws_nb_rlouts', 200, 'WS: # of roll-outs for the RL agent')
tf.app.flags.DEFINE_integer('ws_nb_rlouts_min', 50,
'WS: minimal # of roll-outs for the RL agent to start training')
tf.app.flags.DEFINE_string('ws_reward_type', 'single-obj',
'WS: reward type (\'single-obj\' OR \'multi-obj\')')
tf.app.flags.DEFINE_float('ws_lrn_rate_rg', 3e-2, 'WS: learning rate for layerwise regression')
tf.app.flags.DEFINE_integer('ws_nb_iters_rg', 20, 'WS: # of iterations for layerwise regression')
tf.app.flags.DEFINE_float('ws_lrn_rate_ft', 3e-4, 'WS: learning rate for global fine-tuning')
tf.app.flags.DEFINE_integer('ws_nb_iters_ft', 400, 'WS: # of iterations for global fine-tuning')
tf.app.flags.DEFINE_integer('ws_nb_iters_feval', 25, 'WS: # of iterations for fast evaluation')
tf.app.flags.DEFINE_float('ws_prune_ratio_exp', 3.0, 'WS: pruning ratio\'s exponent term')
tf.app.flags.DEFINE_float('ws_iter_ratio_beg', 0.1, 'WS: iteration ratio (at starting time)')
tf.app.flags.DEFINE_float('ws_iter_ratio_end', 0.5, 'WS: iteration ratio (at ending time)')
tf.app.flags.DEFINE_float('ws_mask_update_step', 500, 'WS: step size for updating the pruning mask')
def calc_prune_ratio(vars_list):
"""Calculate the overall pruning ratio for the given list of variables.
Args:
* vars_list: list of variables
Returns:
| tensorflow.app.flags.DEFINE_float | 4,805 |
import tensorflow as tf
return 196.0 * 21.0 / 4096.0
else:
rec = tf.cast(kw * kh, tf.float32)
n_max = 7 + tf.math.ceil(tf.math.log(rec) / tf.math.log(2.))
ns = tf.range(0., n_max)
ns_pow = tf.pow(2., ns)
ks = tf.round(ns_pow / rec)
diffs = tf.math.abs(ks / ns_pow - 1 / rec)
n = tf.argmin(diffs)
k = ks[n]
scale = k / tf.pow(2., tf.cast(n, tf.float32))
scale *= rec
return scale
@register_keras_serializable(
| tensorflow.argmin | 4,806 |
import tensorflow as tf
target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/target_q_func")
# q scores for actions which we know were selected in the given state.
q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)
# compute estimate of best possible value starting from state at t + 1
if double_q:
q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", reuse=True)
q_tp1_best_using_online_net = tf.argmax(q_tp1_using_online_net, 1)
q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
else:
q_tp1_best = tf.reduce_max(q_tp1, 1)
q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked
# compute the error (potentially clipped)
td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
errors = U.huber_loss(td_error)
weighted_error = tf.reduce_mean(importance_weights_ph * errors)
| tensorflow.reduce_max | 4,807 |
import tensorflow as tf
if gradient_clip is not None:
clipped_grads = [
(tf.clip_by_value(grad, -gradient_clip, +gradient_clip), var)
for grad, var in avg_grads
]
else:
clipped_grads = avg_grads
if FLAGS.optimizer == 'momentum':
opt = tf.train.MomentumOptimizer(
learning_rate, FLAGS.momentum, use_nesterov=True)
elif FLAGS.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate)
elif FLAGS.optimizer == 'rmsprop':
opt = tf.train.RMSPropOptimizer(learning_rate, FLAGS.rmsprop_decay,
momentum=FLAGS.rmsprop_momentum,
epsilon=FLAGS.rmsprop_epsilon)
else:
raise ValueError('Optimizer "%s" was not recognized', FLAGS.optimizer)
self.variable_mgr.append_apply_gradients_ops(
gradient_state, opt, clipped_grads, training_ops)
train_op = tf.group(*(training_ops + update_ops + extra_nccl_ops))
with tf.device(self.cpu_device):
| tensorflow.train.GradientDescentOptimizer | 4,808 |
import tensorflow as tf
sigma2 = sigma * sigma
inside_mul = tf.multiply(bbox_inside_weights, tf.subtract(bbox_pred, bbox_targets))
smooth_l1_sign = tf.cast(tf.less(tf.abs(inside_mul), 1.0 / sigma2), tf.float32)
smooth_l1_option1 = tf.multiply(tf.multiply(inside_mul, inside_mul), 0.5 * sigma2)
smooth_l1_option2 = tf.subtract(tf.abs(inside_mul), 0.5 / sigma2)
smooth_l1_result = tf.add(tf.multiply(smooth_l1_option1, smooth_l1_sign),
tf.multiply(smooth_l1_option2, tf.abs(tf.subtract(smooth_l1_sign, 1.0))))
outside_mul = tf.multiply(bbox_outside_weights, smooth_l1_result)
return outside_mul
def xdet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
| tensorflow.subtract | 4,809 |
from tensorflow.contrib import tpu as contrib_tpu
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
output_spec = contrib_tpu.TPUEstimatorSpec(
mode=mode,
predictions={
"probabilities": probabilities,
"predictions": predictions
| tensorflow.contrib.tpu.TPUEstimatorSpec | 4,810 |
import tensorflow as tf
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(
| tensorflow.matmul | 4,811 |
import tensorflow as tf
infer_step, [result, logits, loss],
shape_invariants=[
tf.TensorShape([None, None, None, None]),
tf.TensorShape([None, None, None, None, None]),
| tensorflow.TensorShape | 4,812 |
import tensorflow as tf
cropped_image = scipy.misc.imresize(image, [resize_height, resize_width])
return np.array(cropped_image)/127.5 - 1.
def inverse_transform(images):
return (images+1.)/2.
def lrelu(x, leak=0.2, name="lrelu"):
return tf.maximum(x, leak*x)
def conv2d(input_, output_dim,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="conv2d"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
# print("c", w.get_shape())
conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
return conv
class batch_norm(object):
def __init__(self, epsilon=1e-5, momentum = 0.9, name="batch_norm"):
with tf.variable_scope(name):
self.epsilon = epsilon
self.momentum = momentum
self.name = name
def __call__(self, x):
| tensorflow.nn.conv2d | 4,813 |
import tensorflow as tf
max_seq_length, tokenizer)
features.append(feature)
return features
# add by wangxiao
# define the inputs of signature
def serving_input_fn():
label_ids = tf.placeholder(tf.int32, [None], name='label_ids')
input_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_ids')
input_mask = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_mask')
segment_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='segment_ids')
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
'label_ids': label_ids,
'input_ids': input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids,
| tensorflow.placeholder | 4,814 |
import tensorflow as tf
with tf.variable_scope(self.model_scope):
# loss & extra evaluation metrics
logits = self.forward_train(images)
self.maskable_var_names = [var.name for var in self.maskable_vars]
loss, metrics = self.calc_loss(labels, logits, self.trainable_vars)
if FLAGS.enbl_dst:
loss += self.helper_dst.calc_loss(logits, logits_dst)
tf.summary.scalar('loss', loss)
for key, value in metrics.items():
tf.summary.scalar(key, value)
# learning rate schedule
self.global_step = tf.train.get_or_create_global_step()
lrn_rate, self.nb_iters_train = self.setup_lrn_rate(self.global_step)
| tensorflow.summary.scalar | 4,815 |
import tensorflow as tf
"How many steps to make in each estimator call.")
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
tf.flags.DEFINE_string(
"tpu_name", None,
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
| tensorflow.flags.DEFINE_string | 4,816 |
import tensorflow as tf
# Set up our moving statistics. When connecting in parallel, this is shared.
self._moving_mean = tf.get_variable(
| tensorflow.get_variable | 4,817 |
import tensorflow as tf
model.export_ops(name)
metagraph = tf.train.export_meta_graph()
if tf.__version__ < "1.1.0" and FLAGS.num_gpus > 1:
raise ValueError("num_gpus > 1 is not supported for TensorFlow versions "
"below 1.1.0")
soft_placement = False
if FLAGS.num_gpus > 1:
soft_placement = True
util.auto_parallel(metagraph, m)
with tf.Graph().as_default():
tf.train.import_meta_graph(metagraph)
for model in models.values():
model.import_ops()
sv = tf.train.Supervisor(logdir=FLAGS.save_path)
config_proto = tf.ConfigProto(allow_soft_placement=soft_placement)
with sv.managed_session(config=config_proto) as session:
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0)
m.assign_lr(session, config.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
| tensorflow.train.import_meta_graph | 4,818 |
import tensorflow as tf
for i in range(2, 2 + len(expected_extra_dims)):
mask = tf.expand_dims(mask, axis=i)
mask = tf.tile(mask, [1, 1] + expected_extra_dims)
return tf.where(mask, expanded_tensor, if_masked_tensor)
def initial_layer(
window_feature: WindowFeatures, *, clip_magnitude=10.0, include_flux_and_time=False
) -> tf.Tensor:
features = tf.expand_dims(window_feature.dflux_dt(clip_magnitude=clip_magnitude), 2)
if include_flux_and_time:
dflux = tf.expand_dims(window_feature.dflux, 2)
dtime = tf.expand_dims(window_feature.dtime, 2)
features = tf.concat([features, dflux, dtime],
axis=2,
name="initial_layer_concat")
return features
class CutoffData:
def __init__(self, config_json: dict):
self.window_size: int = config_json["window_size"]
self.band_time_diff: int = config_json["band_time_diff"]
self.embedding_size: int = config_json["desired_num_cutoffs"]
| tensorflow.expand_dims | 4,819 |
import tensorflow as tf
if len(env.observation_space.shape) == 1:
# This means we are running on low-dimensional observations (e.g. RAM)
input_shape = env.observation_space.shape
else:
img_h, img_w, img_c = env.observation_space.shape
input_shape = (img_h, img_w, frame_history_len * img_c)
num_actions = env.action_space.n
# set up placeholders
# placeholder for current observation (or state)
obs_t_ph = tf.placeholder(tf.uint8, [None] + list(input_shape))
# placeholder for current action
act_t_ph = tf.placeholder(tf.int32, [None])
# placeholder for current reward
rew_t_ph = tf.placeholder(tf.float32, [None])
# placeholder for next observation (or state)
obs_tp1_ph = tf.placeholder(tf.uint8, [None] + list(input_shape))
# placeholder for end of episode mask
# this value is 1 if the next state corresponds to the end of an episode,
# in which case there is no Q-value at the next state; at the end of an
# episode, only the current state reward contributes to the target, not the
# next state Q-value (i.e. target is just rew_t_ph, not rew_t_ph + gamma * q_tp1)
done_mask_ph = tf.placeholder(tf.float32, [None])
# casting to float on GPU ensures lower data transfer times.
obs_t_float = tf.cast(obs_t_ph, tf.float32) / 255.0
obs_tp1_float = tf.cast(obs_tp1_ph, tf.float32) / 255.0
| tensorflow.placeholder | 4,820 |
import tensorflow as tf
params: dict, user passed parameters.
Returns:
List of tensors from fromRGB 1x1 `Conv2D` layers.
"""
with tf.variable_scope(name_or_scope=self.name, reuse=tf.AUTO_REUSE):
# Get fromRGB layer properties.
from_rgb = [
params["discriminator_from_rgb_layers"][i][0][:]
for i in range(len(params["discriminator_from_rgb_layers"]))
| tensorflow.variable_scope | 4,821 |
import tensorflow as tf
layer["weights"] = tf.get_variable(name + "/weights",
dtype=tf.float32,
initializer=Winit)
layer["bias"] = tf.get_variable(name + "/bias",
dtype=tf.float32,
initializer=binit)
| tensorflow.get_variable | 4,822 |
import tensorflow as tf
last_forward.set_shape([None, cell_output_size])
if encoder.final_state == 'concat_last': # concats last states of all backward layers (full LSTM states)
encoder_state_ = tf.concat(encoder_states_, axis=1)
elif encoder.final_state == 'average':
mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_outputs_)[1], dtype=tf.float32)
mask = tf.expand_dims(mask, axis=2)
encoder_state_ = tf.reduce_sum(mask * encoder_outputs_, axis=1) / tf.reduce_sum(mask, axis=1)
elif encoder.final_state == 'average_inputs':
mask = tf.sequence_mask(encoder_input_length_, maxlen=tf.shape(encoder_inputs_)[1], dtype=tf.float32)
mask = tf.expand_dims(mask, axis=2)
encoder_state_ = tf.reduce_sum(mask * encoder_inputs_, axis=1) / tf.reduce_sum(mask, axis=1)
elif encoder.bidir and encoder.final_state == 'last_both':
encoder_state_ = tf.concat([last_forward, last_backward], axis=1)
| tensorflow.reduce_sum | 4,823 |
import tensorflow as tf
print(image.shape)
print(mask.shape)
input_image = np.concatenate([image, mask], axis=2)
print(input_image.shape)
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
input_image = tf.constant(input_image, dtype=tf.float32)
output = MODEL.build_server_graph(FLAGS, input_image)
output = (output + 1.) * 127.5
output = tf.reverse(output, [-1])
output = tf.saturate_cast(output, tf.uint8)
# load pretrained model
vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = []
for var in vars_list:
vname = var.name
from_name = vname
var_value = tf.contrib.framework.load_variable(MODEL_DIR, from_name)
assign_ops.append(tf.assign(var, var_value))
sess.run(assign_ops)
| tensorflow.reverse | 4,824 |
import tensorflow as tf
saver.export_meta_graph(filename, as_text=True)
with self.test_session(graph=tf.Graph()):
# Imports the text format graph.
tf.train.import_meta_graph(filename)
# Writes wrong contents to the file.
tf.train.write_graph(saver.as_saver_def(), os.path.dirname(filename),
os.path.basename(filename))
with self.test_session(graph=tf.Graph()):
# Import should fail.
with self.assertRaisesWithPredicateMatch(
IOError, lambda e: "Cannot parse file"):
tf.train.import_meta_graph(filename)
# Deletes the file
gfile.Remove(filename)
| tensorflow.Graph | 4,825 |
import tensorflow as tf
grads_q = tf.gradients(loss_q * self.q_coef, self.params)
grads = [gradient_add(g1, g2, param, verbose=self.verbose)
for (g1, g2, param) in zip(grads_policy, grads_q, self.params)]
avg_norm_grads_f = avg_norm(grads_f) * (self.n_steps * self.n_envs)
norm_grads_q = tf.global_norm(grads_q)
norm_grads_policy = tf.global_norm(grads_policy)
else:
grads = tf.gradients(loss, self.params)
| tensorflow.global_norm | 4,826 |
import tensorflow as tf
tf.add_to_collection(name, op)
self._initial_state_name = self.with_prefix(self._name, 'initial')
self._final_state_name = self.with_prefix(self._name, 'final')
for state_tuple in self._initial_state:
tf.add_to_collection(self._initial_state_name, state_tuple.c)
tf.add_to_collection(self._initial_state_name, state_tuple.h)
for state_tuple in self._final_state:
tf.add_to_collection(self._final_state_name, state_tuple.c)
tf.add_to_collection(self._final_state_name, state_tuple.h)
def import_state_tuples(self, state_tuples, name, num_replicas):
restored = []
for i in range(len(state_tuples) * num_replicas):
c = tf.get_collection_ref(name)[2 * i + 0]
h = tf.get_collection_ref(name)[2 * i + 1]
restored.append(tf.contrib.rnn.LSTMStateTuple(c, h))
return tuple(restored)
def import_ops(self):
if self._is_training:
self._train_op = tf.get_collection_ref('train_op')[0]
self._lr = tf.get_collection_ref('lr')[0]
self._new_lr = tf.get_collection_ref('new_lr')[0]
self._lr_update = tf.get_collection_ref('lr_update')[0]
rnn_params = tf.get_collection_ref('rnn_params')
if self._cell and rnn_params:
| tensorflow.get_collection_ref | 4,827 |
from tensorflow.python.framework import tensor_shape
return [tensor_shape.unknown_shape()]
elif input_shape.ndims <= 1:
return [tensor_shape.scalar()]
dimension = tensor_util.ConstantValue(op.inputs[1])
if dimension is None:
return [tensor_shape.unknown_shape(ndims=input_shape.ndims - 1)]
elif 0 <= dimension and dimension < input_shape.ndims:
returned_shape = []
for i, dim in enumerate(input_shape.dims):
if i != dimension:
returned_shape.append(dim)
| tensorflow.python.framework.tensor_shape.unknown_shape | 4,828 |
import tensorflow as tf
input = op.inputs[0]
return tf.zeros_like(input)
"""
Saliency maps
https://arxiv.org/abs/1312.6034
"""
class Saliency(GradientBasedMethod):
def get_symbolic_attribution(self):
return [tf.abs(g) for g in tf.gradients(ys=self.T, xs=self.X)]
"""
Gradient * Input
https://arxiv.org/pdf/1704.02685.pdf - https://arxiv.org/abs/1611.07270
"""
class GradientXInput(GradientBasedMethod):
def get_symbolic_attribution(self):
return [g * x for g, x in zip(
| tensorflow.gradients | 4,829 |
import tensorflow as tf
# 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):
batch1 = tf.gather(batch, tf.random.shuffle(index))
batch2 = tf.gather(batch, tf.random.shuffle(index))
pred1 = tf.slice(batch1, [0, 0], [num_sam, 1])
pred2 = tf.slice(batch2, [0, 0], [num_sam, 1])
tgt1 = tf.slice(batch1, [0, 1], [num_sam, 1])
tgt2 = tf.slice(batch2, [0, 1], [num_sam, 1])
loss = cur_loss + compute_contra_loss(pred1, pred2, tgt1, tgt2)
print(loss)
return (loss, i + 1)
# def sample_compute(i):
# batch1 = tf.gather(batch, tf.random.shuffle(index))
# batch2 = tf.gather(batch, tf.random.shuffle(index))
# pred1 = tf.slice(batch1, [0, 0], [num_sam, 1])
# pred2 = tf.slice(batch2, [0, 0], [num_sam, 1])
| tensorflow.slice | 4,830 |
import tensorflow as tf
# hard negative mining for classification
predictions_for_bg = tf.nn.softmax(cls_pred)[:, 0]
prob_for_negtives = tf.where(negtive_mask,
0. - predictions_for_bg,
# ignore all the positives
0. - tf.ones_like(predictions_for_bg))
topk_prob_for_bg, _ = tf.nn.top_k(prob_for_negtives, k=n_neg_to_select)
selected_neg_mask = prob_for_negtives > topk_prob_for_bg[-1]
# # random select negtive examples for classification
| tensorflow.ones_like | 4,831 |
import tensorflow as tf
loss_summary = tf.summary.scalar('Loss', cross_entropy)
acc_summary = tf.summary.scalar('Accuracy', accuracy)
# summaries for TensorBoard visualisation
validation_summary = tf.summary.merge([img_summary, acc_summary])
training_summary = tf.summary.merge([img_summary, loss_summary])
test_summary = tf.summary.merge([img_summary, acc_summary])
# saver for checkpoints
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
| tensorflow.summary.merge | 4,832 |
import tensorflow as tf
add = tf.add(in0, in1, "ADD")
| tensorflow.add | 4,833 |
import tensorflow as tf
num_actions = self.hparams.problem.num_actions
logits = tf.constant(
1. / float(num_actions),
shape=(obs_shape[:1] + [1, num_actions])
)
value = tf.zeros(obs_shape[:1] + [1])
return {"target_policy": logits, "target_value": value}
| tensorflow.zeros | 4,834 |
import tensorflow as tf
"""
logits, losses = self(features) # pylint: disable=not-callable
if self.hparams.sampling_method == "argmax":
samples = tf.argmax(logits, axis=-1)
else:
assert self.hparams.sampling_method == "random"
| tensorflow.argmax | 4,835 |
import tensorflow as tf
with tf.device(self.devices[device_num]):
# Rescale to [0, 1)
images *= 1. / 256
# Rescale to [-1,1] instead of [0, 1)
images = tf.subtract(images, 0.5)
images = tf.multiply(images, 2.0)
if self.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
| tensorflow.subtract | 4,836 |
import tensorflow as tf
adv_bc *
tf.nn.relu(1.0 - (self.correction_term / (rho + eps))) *
f_i_),
axis=1)
# IMP: This is sum, as expectation wrt f
loss_bc = -tf.reduce_mean(gain_bc)
loss_policy = loss_f + loss_bc
# Value/Q function loss, and explained variance
| tensorflow.reduce_mean | 4,837 |
import tensorflow as tf
# scores = scores / (facts.get_shape().as_list()[-1] ** 0.5)
# Activation
if softmax_stag:
scores = tf.nn.softmax(scores) # [B, 1, T]
# Weighted sum
if mode == 'SUM':
output = tf.matmul(scores, facts) # [B, 1, H]
# output = tf.reshape(output, [-1, tf.shape(facts)[-1]])
else:
scores = tf.reshape(scores, [-1, tf.shape(facts)[1]])
output = facts * tf.expand_dims(scores, -1)
output = tf.reshape(output, tf.shape(facts))
if return_alphas:
return output, scores
return output
def self_attention(facts, ATTENTION_SIZE, mask, stag='null'):
if len(facts.get_shape().as_list()) == 2:
facts = tf.expand_dims(facts, 1)
def cond(batch, output, i):
return tf.less(i, tf.shape(batch)[1])
| tensorflow.expand_dims | 4,838 |
import tensorflow as tf
perturbation loss
"""
noise = [tf.random_normal(shape=tf.shape(emb)) for emb in embedded]
masked = [_mask_by_length(n, length) for n in noise]
| tensorflow.shape | 4,839 |
import tensorflow as tf
"""Build the evaluation graph."""
with tf.Graph().as_default():
# create a TF session for the current graph
| tensorflow.Graph | 4,840 |
import tensorflow as tf
return (probs, corrects)
def _compute_loss(self, logits, aux_logits_list, classes, **knobs):
reg_decay = knobs['reg_decay']
aux_loss_mul = knobs['aux_loss_mul'] # Multiplier for auxiliary loss
# Compute sparse softmax cross entropy loss from logits & labels
log_probs = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=classes)
loss = tf.reduce_mean(log_probs)
self._mark_for_monitoring('loss', loss)
# Add regularization loss
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
reg_loss = reg_decay * tf.add_n(reg_losses)
self._mark_for_monitoring('reg_loss', reg_loss)
| tensorflow.reduce_mean | 4,841 |
import tensorflow as tf
elif activation == "elu":
new_context_act = tf.nn.elu(new_context)
| tensorflow.nn.elu | 4,842 |
import tensorflow as tf
# If the vocabulary is empty add a dummy value with count one so
# the tensorflow index operations don't fail to initialize with empty
# tensors downstream.
dummy_value = (b'49d0cd50-04bb-48c0-bc6f-5b575dce351a'
if tf.dtypes.as_dtype(dtype) == tf.string else b'-1')
return (1, dummy_value)
| tensorflow.dtypes.as_dtype | 4,843 |
import tensorflow as tf
tf.int32)
a = tf.assign_add(p, tf.fill([1024, 1024], 0))
| tensorflow.fill | 4,844 |
from tensorflow.contrib import layers
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
target_dimension: TODO(zakaria): dimension of the target for multilabels.
config: RunConfig object to configure the runtime settings.
Raises:
ValueError: If both linear_feature_columns and dnn_features_columns are
empty at the same time.
"""
target_column = layers.regression_target(
weight_column_name=weight_column_name,
target_dimension=target_dimension)
super(DNNLinearCombinedRegressor, self).__init__(
model_dir=model_dir,
linear_feature_columns=linear_feature_columns,
linear_optimizer=linear_optimizer,
dnn_feature_columns=dnn_feature_columns,
dnn_optimizer=dnn_optimizer,
| tensorflow.contrib.layers.regression_target | 4,845 |
import tensorflow as tf
"""actual host call function."""
step = global_step[0]
with tf.contrib.summary.create_file_writer(
logdir=model_dir, filename_suffix=".host_call").as_default():
with tf.contrib.summary.always_record_summaries():
for i, name in enumerate(metric_names):
if reduce_fn is None:
scalar = args[i][0]
else:
scalar = reduce_fn(args[i])
with tf.contrib.summary.record_summaries_every_n_global_steps(
100, global_step=step):
tf.contrib.summary.scalar(prefix + name, scalar, step=step)
return tf.contrib.summary.all_summary_ops()
global_step_tensor = tf.reshape(tf.train.get_or_create_global_step(), [1])
other_tensors = [tf.reshape(monitor_dict[key], [1]) for key in metric_names]
return host_call_fn, [global_step_tensor] + other_tensors
def two_stream_loss(FLAGS, features, labels, mems, is_training):
"""Pretraining loss with two-stream attention Transformer-XL."""
| tensorflow.contrib.summary.scalar | 4,846 |
import tensorflow as tf
y = tf.nn.softmax(tf.matmul(x, w) + b)
y_ = tf.placeholder(tf.float32, [None, shape[1]])
| tensorflow.placeholder | 4,847 |
import tensorflow as tf
indices.bin_counts,
indices.active_block_indices,
x,
dynamic_bsize=tf.constant(block_params.bsize_out, dtype=tf.int32),
dynamic_bstride=tf.constant(block_params.bsize_out, dtype=tf.int32),
dynamic_boffset=tf.constant([0, 0], dtype=tf.int32),
| tensorflow.constant | 4,848 |
import tensorflow as tf
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
self.assertListEqual(pre_pool.get_shape().as_list(),
[batch_size, 4, 4, 1024])
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
| tensorflow.reset_default_graph | 4,849 |
import tensorflow as tf
# (T,B,D) => (B,T,D)
facts = tf.array_ops.transpose(facts, [1, 0, 2])
# Trainable parameters
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
query = tf.layers.dense(query, facts_size, activation=None, name='f1' + 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)
| tensorflow.layers.dense | 4,850 |
import tensorflow as tf
self._anchor_targets['rpn_bbox_outside_weights'] = rpn_bbox_outside_weights
self._score_summaries.update(self._anchor_targets)
return rpn_labels
def _proposal_target_layer(self, rois, roi_scores, name):
with tf.variable_scope(name):
# 这里的index是对于cfg.FLAGS.batch_size=256 而言
# rois (0, x1, y1, x2, y2)，coming from RPN 然后再减少至256个
# bbox_target (ndarray): N x 4K blob of regression targets
# bbox_inside_weights (ndarray): N x 4K blob of loss weights
rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = tf.py_func(
proposal_target_layer,
[rois, roi_scores, self._gt_boxes, self._num_classes],
[tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32])
rois.set_shape([cfg.FLAGS.batch_size, 5])
roi_scores.set_shape([cfg.FLAGS.batch_size])
labels.set_shape([cfg.FLAGS.batch_size, 1])
bbox_targets.set_shape([cfg.FLAGS.batch_size, self._num_classes * 4])
bbox_inside_weights.set_shape([cfg.FLAGS.batch_size, self._num_classes * 4])
bbox_outside_weights.set_shape([cfg.FLAGS.batch_size, self._num_classes * 4])
| tensorflow.py_func | 4,851 |
from tensorflow.python.training import training
["centered_bias_%d" % cb for cb in range(
self._target_column.num_label_columns)],
array_ops.reshape(centered_bias, [-1]))
return centered_bias
def _centered_bias_step(self, targets, features):
centered_bias = ops.get_collection(self._centered_bias_weight_collection)
batch_size = array_ops.shape(targets)[0]
logits = array_ops.reshape(
array_ops.tile(centered_bias[0], [batch_size]),
[batch_size, self._target_column.num_label_columns])
loss = self._target_column.loss(logits, targets, features)
# Learn central bias by an optimizer. 0.1 is a convervative lr for a single
# variable.
return training.AdagradOptimizer(0.1).minimize(loss, var_list=centered_bias)
def _logits(self, features, is_training=False):
linear_feature_columns = self._get_linear_feature_columns()
dnn_feature_columns = self._get_dnn_feature_columns()
if not (linear_feature_columns or dnn_feature_columns):
raise ValueError("Either linear_feature_columns or dnn_feature_columns "
"should be defined.")
if linear_feature_columns and dnn_feature_columns:
logits = (self._linear_logits(features, is_training) +
self._dnn_logits(features, is_training))
elif dnn_feature_columns:
logits = self._dnn_logits(features, is_training)
| tensorflow.python.training.training.AdagradOptimizer | 4,852 |
import tensorflow as tf
init_op = tf.initialize_all_variables()
| tensorflow.initialize_all_variables | 4,853 |
import tensorflow as tf
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)
widths, x_centers = tf.meshgrid(widths, x_centers)
heights, y_centers = tf.meshgrid(heights, y_centers)
anchor_centers = tf.stack([x_centers, y_centers], axis=2)
anchor_centers = tf.reshape(anchor_centers, [-1, 2])
anchor_sizes = tf.stack([widths, heights], axis=2)
| tensorflow.meshgrid | 4,854 |
import tensorflow as tf
lm_embeddings = tf.concat(
[tf.expand_dims(t, axis=1) for t in layers_without_bos_eos],
axis=1
)
# get the mask op without bos/eos.
# tf doesn't support reversing boolean tensors, so cast
# to int then back
mask_wo_bos_eos = tf.cast(lm_graph.mask[:, 1:], 'int32')
mask_wo_bos_eos = tf.reverse_sequence(
mask_wo_bos_eos,
lm_graph.sequence_lengths - 1,
seq_axis=1,
batch_axis=0,
)
mask_wo_bos_eos = mask_wo_bos_eos[:, 1:]
| tensorflow.cast | 4,855 |
import tensorflow as tf
head_etd = tf.expand_dims(head, 3) # bs,bn,bl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias, 5.0) # bs,bn,bl,bl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 3) # bs,bn,bl,bl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask) # bs,bn,bl,bl,vec
self_attn_result = tf.reduce_sum(attn_score * rep_map_tile, 3) # bs,bn,bl,vec
| tensorflow.nn.softmax | 4,856 |
import tensorflow as tf
self._dropout_keep_prob = dropout_keep_prob
self._out_vocab_size = out_vocab_size
self.x = tf.placeholder(tf.int32, [batch_size, max_sequence_len],
name='x')
self.y = tf.placeholder(tf.float32,
| tensorflow.placeholder | 4,857 |
import tensorflow as tf
Args:
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)
| tensorflow.placeholder | 4,858 |
import tensorflow as tf
opt = tf.train.AdagradOptimizer(1.0)
self.opt = opt
self.sess = None
self.train_step = None
self.post_step = None
self.graph = tf.Graph()
with self.graph.as_default():
self.head_input = tf.placeholder(tf.int32, shape=[None])
self.rel_input = tf.placeholder(tf.int32, shape=[None])
self.tail_input = tf.placeholder(tf.int32, shape=[None])
| tensorflow.Graph | 4,859 |
import tensorflow as tf
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.get_collection | 4,860 |
import tensorflow as tf
outputs = tf.layers.conv1d(inputs, embed_size // 2, 1, padding="SAME")
for k in range(2, K + 1):
with tf.variable_scope("num_{}".format(k)):
output = tf.layers.conv1d(inputs, embed_size // 2, k, padding="SAME")
outputs = tf.concat((outputs, output), -1)
outputs = tf.nn.relu(tf.layers.batch_normalization(outputs, training=is_training))
return outputs
| tensorflow.concat | 4,861 |
from tensorflow.python.platform import tf_logging as logging
def end(self, session=None):
super(CheckpointSaver, self).end(session)
self._save(self._last_begin_step, session)
def _save(self, step, session):
"""Saves the latest checkpoint."""
if step == self._last_saved_step:
return
logging.info("Saving checkpoints for %d into %s.", step, self._save_path)
self._last_saved_time = time.time()
self._last_saved_step = step
if self._saver is None:
self._scaffold.saver.save(session, self._save_path, global_step=step)
else:
self._saver.save(session, self._save_path, global_step=step)
self._summary_writer.add_session_log(
SessionLog(
| tensorflow.python.platform.tf_logging.info | 4,862 |
import tensorflow as tf
def load_image_train(datapoint):
"""Load images for training."""
input_image = tf.image.resize(datapoint['image'], (512, 512))
input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))
if tf.random.uniform(()) > 0.5:
input_image = tf.image.flip_left_right(input_image)
| tensorflow.image.resize | 4,863 |
import tensorflow as tf
Args:
input_batch: Input batch Tensor.
reduction_indices: Indices of `input_batch` to reduce over.
use_batch_stats: Boolean to indicate if batch statistics should be
calculated, otherwise moving averages are returned.
Returns:
Tuple of (mean, variance, second_moment).
"""
# Set up our moving statistics. When connecting in parallel, this is shared.
self._moving_mean = tf.get_variable(
"moving_mean",
shape=self._mean_shape,
collections=[tf.GraphKeys.MOVING_AVERAGE_VARIABLES,
tf.GraphKeys.VARIABLES],
initializer=tf.zeros_initializer,
trainable=False)
self._moving_second_moment = tf.get_variable(
"moving_second_moment",
| tensorflow.get_variable | 4,864 |
import tensorflow as tf
"""
original_shape = tf.shape(image)
rank_assertion = tf.Assert(
tf.equal(tf.rank(image), 3),
['Rank of image must be equal to 3.'])
cropped_shape = control_flow_ops.with_dependencies(
[rank_assertion],
| tensorflow.rank | 4,865 |
import tensorflow as tf
with tf.name_scope(clone.scope):
clone_loss = _gather_clone_loss(clone, len(clones),
regularization_losses)
if clone_loss is not None:
clones_losses.append(clone_loss)
# Only use regularization_losses for the first clone
regularization_losses = None
if clones_losses:
total_loss = tf.add_n(clones_losses, name='total_loss')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
first_clone.scope))
if total_loss is not None:
| tensorflow.add_n | 4,866 |
import tensorflow as tf
current_inputs = text_emb # [num_sentences, max_sentence_length, emb]
for layer in range(self.config["contextualization_layers"]):
with tf.variable_scope("layer_{}".format(layer)):
with tf.variable_scope("fw_cell"):
cell_fw = util.CustomLSTMCell(self.config["contextualization_size"], num_sentences, self.lstm_dropout)
with tf.variable_scope("bw_cell"):
cell_bw = util.CustomLSTMCell(self.config["contextualization_size"], num_sentences, self.lstm_dropout)
state_fw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_fw.initial_state.c, [num_sentences, 1]), tf.tile(cell_fw.initial_state.h, [num_sentences, 1]))
state_bw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_bw.initial_state.c, [num_sentences, 1]), tf.tile(cell_bw.initial_state.h, [num_sentences, 1]))
(fw_outputs, bw_outputs), _ = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=current_inputs,
sequence_length=text_len,
| tensorflow.tile | 4,867 |
import tensorflow as tf
def get_batch(image,label,batch_size,crop_size):
#print(image.shape)
#print(label.shape)
images,labels=tf.train.shuffle_batch([image,label],
batch_size=batch_size,num_threads=10,capacity=10000,min_after_dequeue=200)
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'):
| tensorflow.reshape | 4,868 |
import tensorflow as tf
"chars": tf.shape(features["chars"]),
"source": tf.shape(features["source"]),
| tensorflow.shape | 4,869 |
import tensorflow as tf
def false_positive_rate_at_true_positive_rate_loss(labels,
logits,
target_rate,
weights=1.0,
dual_rate_factor=0.1,
label_priors=None,
surrogate_type='xent',
lambdas_initializer=tf.constant_initializer(1.0),
reuse=None,
variables_collections=None,
trainable=True,
scope=None):
"""Computes false positive rate at true positive rate loss.
Note that `true positive rate` is a synonym for Recall, and that minimizing
| tensorflow.constant_initializer | 4,870 |
import tensorflow as tf
pos = tf.nn.sigmoid(tf.matmul(tf.nn.tanh(tf.matmul(state, wp)), vp))
pos = tf.floor(encoder_input_length * pos)
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)
| tensorflow.reshape | 4,871 |
import tensorflow as tf
with tf.variable_scope(name):
with tf.variable_scope('branch1_1x1'):
if o1s>0:
conv1 = conv(inp, inSize, o1s, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
net.append(conv1)
with tf.variable_scope('branch2_3x3'):
if o2s1>0:
conv3a = conv(inp, inSize, o2s1, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
conv3 = conv(conv3a, o2s1, o2s2, 3, 3, ks, ks, 'SAME', 'conv3x3', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
net.append(conv3)
| tensorflow.variable_scope | 4,872 |
import tensorflow as tf
"""Batch normalization with corresponding log determinant Jacobian."""
if reuse is None:
reuse = not train
# create variables
with tf.variable_scope(name) as scope:
if reuse:
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))
| tensorflow.constant_initializer | 4,873 |
from tensorflow.contrib.framework import tensor_util
name, 'precision', [predictions, labels]):
predictions, labels = tensor_util.remove_squeezable_dimensions(
predictions, labels)
| tensorflow.contrib.framework.tensor_util.remove_squeezable_dimensions | 4,874 |
import tensorflow as tf
if type(a[0]) == tf.Tensor:
return tf.stack(a, 0)
else:
return tf.constant(a, dtype)
else:
print(type(a))
return tf.constant(a, dtype)
return sbnet_module.reduce_mask(
mask,
block_params.bcount,
dynamic_bsize=to_tensor(block_params.bsize, tf.int32),
| tensorflow.constant | 4,875 |
import tensorflow as tf
span_width = 1 + span_ends - span_starts # [k]
if self.config["use_features"]:
span_width_index = span_width - 1 # [k]
span_width_emb = tf.gather(tf.get_variable("span_width_embeddings", [self.config["max_span_width"], self.config["feature_size"]]), span_width_index) # [k, emb]
span_width_emb = tf.nn.dropout(span_width_emb, self.dropout)
span_emb_list.append(span_width_emb)
if self.config["model_heads"]:
span_indices = tf.expand_dims(tf.range(self.config["max_span_width"]), 0) + tf.expand_dims(span_starts, 1) # [k, max_span_width]
span_indices = tf.minimum(util.shape(context_outputs, 0) - 1, span_indices) # [k, max_span_width]
span_text_emb = tf.gather(head_emb, span_indices) # [k, max_span_width, emb]
with tf.variable_scope("head_scores"):
self.head_scores = util.projection(context_outputs, 1) # [num_words, 1]
span_head_scores = tf.gather(self.head_scores, span_indices) # [k, max_span_width, 1]
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]
span_attention = tf.nn.softmax(span_head_scores, 1) # [k, max_span_width, 1]
span_head_emb = tf.reduce_sum(span_attention * span_text_emb, 1) # [k, emb]
span_emb_list.append(span_head_emb)
span_emb = tf.concat(span_emb_list, 1) # [k, emb]
| tensorflow.gather | 4,876 |
from tensorflow.python.framework import ops
var = state_ops.assign(var, array_ops.zeros_like(inputs[0]))
update_ops = []
for input_tensor in inputs:
op = state_ops.assign_add(var, input_tensor, use_locking=True)
update_ops.append(op)
with ops.control_dependencies(update_ops):
return gen_state_ops._destroy_temporary_variable(var,
var_name=var_name,
name=name)
| tensorflow.python.framework.ops.control_dependencies | 4,877 |
from tensorflow.python.framework import ops
Returns:
A Tensor of the same shape of `x`.
Raises:
ValueError: If `keep_prob` is not in `(0, 1]`.
"""
with ops.op_scope([x], name, "dropout") as name:
x = ops.convert_to_tensor(x, name="x")
if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
raise ValueError("keep_prob must be a scalar tensor or a float in the "
"range (0, 1], got %g" % keep_prob)
keep_prob = ops.convert_to_tensor(
keep_prob, dtype=x.dtype, name="keep_prob")
keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())
| tensorflow.python.framework.ops.convert_to_tensor | 4,878 |
from tensorflow.python.framework import ops
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")
@ops.RegisterShape("Relu6Grad")
@ops.RegisterShape("EluGrad")
@ops.RegisterShape("SoftplusGrad")
@ops.RegisterShape("SoftsignGrad")
def _BinaryElementwiseShape(op):
"""Returns same shape as both inputs to op.
Args:
op: Input operation.
Returns:
Shape of both inputs to `op`.
"""
return [op.inputs[0].get_shape().merge_with(op.inputs[1].get_shape())]
| tensorflow.python.framework.ops.RegisterShape | 4,879 |
import tensorflow as tf
grads_and_vars_not_none = [(g, v) for (g, v) in grads_and_vars if g is not None]
grads = [g for (g, v) in grads_and_vars_not_none]
variables = [v for (g, v) in grads_and_vars_not_none]
#print_ops = [tf.print("loss=", self.loss)] + [tf.print("norms_g", tf.norm(g)) for g in grads] + [tf.print("g", g) for g in grads] + [tf.print("p", self.prob)] + [tf.print("invFisher", self.invFisher)] + [tf.print("invA", self.invA)] + [tf.print("invB", self.invB)]
#print_ops = [tf.print("partA =", self.partA, summarize=-1), tf.print("partB =", self.partB, summarize=-1), tf.print("prob_sliced =", self.prob_sliced, summarize=-1), tf.print("natural_gradient_theta =", self.natural_gradient_loss_theta, summarize=-1), tf.print("euclidean gradient =", self.euclidean_gradient, summarize=-1), tf.print("TA =", self.TA, summarize=-1), tf.print("Tup =", self.Tup, summarize=-1) , tf.print("Tdown =", self.Tdown, summarize=-1) , tf.print("TB =", self.TB, summarize=-1)] + [tf.print("norms_g", tf.norm(g), summarize=-1) for g in grads]
#with tf.control_dependencies(print_ops):
clip_value = clipping_kwargs["value"]
clipped_grads, global_norm = tf.clip_by_global_norm(grads, clip_value)
clipped_grads_and_vars = [(clipped_grads[i], variables[i]) for i in range(len(grads))]
elif clipping_method == "clip_by_norm":
grads_and_vars_not_none = [(g, v) for (g, v) in grads_and_vars if g is not None]
grads = [g for (g, v) in grads_and_vars_not_none]
variables = [v for (g, v) in grads_and_vars_not_none]
| tensorflow.clip_by_global_norm | 4,880 |
import tensorflow as tf
weighted_average = []
for offset in range(-encoder.attn_window_size, encoder.attn_window_size + 1):
pos_ = pos + offset
pos_ = tf.minimum(pos_, encoder_input_length - 1)
pos_ = tf.maximum(pos_, 0) # TODO: when pos is < 0, use <S> or </S>
weights_ = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos_, axis=1)), depth=attn_length))
weighted_average_ = tf.reduce_sum(tf.expand_dims(weights_, axis=2) * hidden_states, axis=1)
weighted_average.append(weighted_average_)
weighted_average = tf.concat(weighted_average, axis=1)
weighted_average = dense(weighted_average, encoder.attn_size)
| tensorflow.squeeze | 4,881 |
import tensorflow as tf
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3)
| tensorflow.nn.batch_normalization | 4,882 |
from tensorflow.python.client import graph_util
@ops.RegisterStatistics("Conv2D", "flops")
def _calc_conv_flops(graph, node):
"""Calculates the compute resources needed for Conv2D."""
input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
input_shape.assert_is_fully_defined()
filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
node.input[1])
filter_shape.assert_is_fully_defined()
output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
output_shape.assert_is_fully_defined()
filter_height = int(filter_shape[0])
| tensorflow.python.client.graph_util.tensor_shape_from_node_def_name | 4,883 |
from tensorflow.python.framework import ops
name: Optional name for the operation.
Returns:
A `Tensor` with the same type as `value`. The average pooled output tensor.
"""
with ops.op_scope([value], name, "AvgPool") as name:
value = ops.convert_to_tensor(value, name="input")
return gen_nn_ops._avg_pool(value, ksize=ksize, strides=strides,
padding=padding,
data_format=data_format,
name=name)
| tensorflow.python.framework.ops.convert_to_tensor | 4,884 |
import tensorflow as tf
#### Unpack input
mem_name = "mems"
mems = mems.get(mem_name, None)
inp_k = tf.transpose(features["input_k"], [1, 0])
inp_q = tf.transpose(features["input_q"], [1, 0])
seg_id = tf.transpose(features["seg_id"], [1, 0])
inp_mask = None
perm_mask = tf.transpose(features["perm_mask"], [1, 2, 0])
if FLAGS.num_predict is not None:
# [num_predict x tgt_len x bsz]
target_mapping = tf.transpose(features["target_mapping"], [1, 2, 0])
else:
target_mapping = None
# target for LM loss
tgt = tf.transpose(features["target"], [1, 0])
# target mask for LM loss
tgt_mask = tf.transpose(features["target_mask"], [1, 0])
# construct xlnet config and save to model_dir
xlnet_config = xlnet.XLNetConfig(FLAGS=FLAGS)
xlnet_config.to_json(os.path.join(FLAGS.model_dir, "config.json"))
| tensorflow.transpose | 4,885 |
import tensorflow as tf
def _embed(self):
with tf.variable_scope('word_char_embedding'):
if self.config.fix_pretrained_vector:
self.pretrained_word_mat = tf.get_variable("word_emb_mat",
[self.vocab.word_size() - 2, self.vocab.word_embed_dim],
dtype=tf.float32,
initializer=tf.constant_initializer(
self.vocab.word_embeddings[2:],
dtype=tf.float32),
trainable=False)
self.word_pad_unk_mat = tf.get_variable("word_unk_pad",
[2, self.pretrained_word_mat.get_shape()[1]],
dtype=tf.float32,
initializer=tf.constant_initializer(
self.vocab.word_embeddings[:2],
dtype=tf.float32),
trainable=True)
self.word_mat = tf.concat([self.word_pad_unk_mat, self.pretrained_word_mat], axis=0)
self.pretrained_char_mat = tf.get_variable("char_emb_mat",
[self.vocab.char_size() - 2, self.vocab.char_embed_dim],
dtype=tf.float32,
initializer=tf.constant_initializer(
self.vocab.char_embeddings[2:],
dtype=tf.float32),
trainable=False)
| tensorflow.constant_initializer | 4,886 |
import tensorflow as tf
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)
widths, x_centers = tf.meshgrid(widths, x_centers)
heights, y_centers = tf.meshgrid(heights, y_centers)
anchor_centers = tf.stack([x_centers, y_centers], axis=2)
anchor_centers = tf.reshape(anchor_centers, [-1, 2])
anchor_sizes = tf.stack([widths, heights], axis=2)
anchor_sizes = tf.reshape(anchor_sizes, [-1, 2])
anchors = tf.concat([anchor_centers - .5 * anchor_sizes,
anchor_centers + .5 * anchor_sizes], 1)
# anchors = box_utils.convert_yxyx_to_xyxy_format(anchors)
return anchors
| tensorflow.reshape | 4,887 |
import tensorflow as tf
parsed_tensors[k] = tf.sparse.to_dense(
parsed_tensors[k], default_value='')
else:
parsed_tensors[k] = tf.sparse.to_dense(
parsed_tensors[k], default_value=0)
| tensorflow.sparse.to_dense | 4,888 |
import tensorflow as tf
coord.join(threads)
def predict_time(loop=100):
feed_dict={
testnum:1
}
with tf.Session(config=config) as sess:
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
tf.train.Saver().restore(sess,path)
total=0.0
for i in range(loop):
a = datetime.now()
accuracy_np = sess.run([accuracy],feed_dict=feed_dict)
b = datetime.now()
| tensorflow.train.Coordinator | 4,889 |
import tensorflow as tf
outputs = outputs.stack()
weights = weights.stack() # batch_size, encoders, output time, input time
states = states.stack()
attns = attns.stack()
samples = samples.stack()
# put batch_size as first dimension
outputs = tf.transpose(outputs, perm=(1, 0, 2))
weights = tf.transpose(weights, perm=(1, 0, 2))
states = tf.transpose(states, perm=(1, 0, 2))
attns = tf.transpose(attns, perm=(1, 0, 2))
samples = tf.transpose(samples)
return outputs, weights, states, attns, samples, get_logits, initial_data
| tensorflow.transpose | 4,890 |
import tensorflow as tf
sentence_indices = tf.tile(tf.expand_dims(tf.range(num_sentences), 1), [1, max_sentence_length]) # [num_sentences, max_sentence_length]
flattened_sentence_indices = self.flatten_emb_by_sentence(sentence_indices, text_len_mask) # [num_words]
flattened_head_emb = self.flatten_emb_by_sentence(head_emb, text_len_mask) # [num_words]
candidate_starts = tf.tile(tf.expand_dims(tf.range(num_words), 1), [1, self.max_span_width]) # [num_words, max_span_width]
candidate_ends = candidate_starts + tf.expand_dims(tf.range(self.max_span_width), 0) # [num_words, max_span_width]
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]
| tensorflow.range | 4,891 |
import tensorflow as tf
print("----feature_mat-----")
print(feature_mat)
# exit()
# Temporarily crush the feature_mat's dimensions
feature_mat = tf.reshape(feature_mat, [-1, config.n_inputs]) # 9
# New feature_mat's shape: [time_steps*batch_size, n_inputs] # 128 * batch_size, 9
# Linear activation, reshaping inputs to the LSTM's number of hidden:
hidden = tf.nn.relu(tf.matmul(
feature_mat, config.W['hidden']
) + config.biases['hidden'])
# New feature_mat (hidden) shape: [time_steps*batch_size, n_hidden] [128*batch_size, 32]
print("--n_steps--")
print(config.n_steps)
print("--hidden--")
print(hidden)
| tensorflow.matmul | 4,892 |
import tensorflow as tf
def _selector(self, context, h, reuse=False):
with tf.variable_scope('selector', reuse=reuse):
w = tf.get_variable('w', [self.H, 1], initializer=self.weight_initializer)
b = tf.get_variable('b', [1], initializer=self.const_initializer)
beta = tf.nn.sigmoid(tf.matmul(h, w) + b, 'beta') # (N, 1)
context = tf.multiply(beta, context, name='selected_context')
return context, beta
def _decode_lstm(self, x, h, context, dropout=False, reuse=False):
with tf.variable_scope('logits', reuse=reuse):
w_h = tf.get_variable('w_h', [self.H, self.M], initializer=self.weight_initializer)
b_h = tf.get_variable('b_h', [self.M], initializer=self.const_initializer)
w_out = tf.get_variable('w_out', [self.M, self.V], initializer=self.weight_initializer)
b_out = tf.get_variable('b_out', [self.V], initializer=self.const_initializer)
if dropout:
h = tf.nn.dropout(h, 0.5)
h_logits = tf.matmul(h, w_h) + b_h
| tensorflow.variable_scope | 4,893 |
from tensorflow.python.ops import partitioned_variables
weight_collections=[parent_scope],
scope=scope)
hidden_layer_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=num_ps_replicas))
for layer_id, num_hidden_units in enumerate(hidden_units):
with variable_scope.variable_scope(
| tensorflow.python.ops.partitioned_variables.min_max_variable_partitioner | 4,894 |
import tensorflow as tf
# 返回的rois中多加了一列0在第一列
rois, rpn_scores = tf.py_func(proposal_layer,
[rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors],
[tf.float32, tf.float32])
rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1])
return rois, rpn_scores
def _crop_pool_layer(self, bottom, rois, name):
with tf.variable_scope(name):
# tf.squeeze()返回一个张量，这个张量是将原始input中所有维度中为1的那些维都删掉的结果
batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1])
# Get the normalized coordinates of bboxes
bottom_shape = tf.shape(bottom)
height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0])
width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0])
# rois除以h,w就得到了rois在特征图上的位置
x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width
y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height
x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width
y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height
# Won't be backpropagated to rois anyway, but to save time
bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1))
# 'roi_pooling_size', 7
pre_pool_size = cfg.FLAGS.roi_pooling_size * 2
# 把rois对于的特征图上的部分crop出来，然后resize打破14*14的大小
crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops")
| tensorflow.shape | 4,895 |
import tensorflow as tf
initializer = tf.global_variables_initializer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(graph=graph, config=config)
self.sess.run(initializer)
loop = trange(self.num_epochs, disable=not self.verbose)
for _ in loop:
| tensorflow.Session | 4,896 |
import tensorflow as tf
b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
conv_t1 = utils.conv2d_transpose_strided(concat1, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"]))
fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")
deconv_shape2 = image_net["pool3"].get_shape()
W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")
b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))
fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")
shape = tf.shape(image)
deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], 3])
W_t3 = utils.weight_variable([16, 16, 3, deconv_shape2[3].value], name="W_t3")
b_t3 = utils.bias_variable([3], name="b_t3")
conv_t3 = tf.nn.relu(utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8))
annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")
return tf.expand_dims(annotation_pred, dim=3), conv_t3
| tensorflow.shape | 4,897 |
import tensorflow as tf
val = val[permutation]
shape = np.array([3, 4]).astype(np.int64)
return tf.SparseTensorValue(ind, val, shape)
def _SparseTensorValue_1x1x1(self):
| tensorflow.SparseTensorValue | 4,898 |
import tensorflow as tf
self._build_summaries()
self.epoch_stats = get_stats_template()
self.stats = Bunch(
epoch_accuracy=[],
epoch_reconstructions=[],
permutation=None
)
# if FLAGS.dev:
# plt.ion()
# plt.show()
def _build_summaries(self):
# losses
with tf.name_scope('losses'):
loss_names = ['loss_autoencoder', 'loss_predictive', 'loss_distance', 'loss_denoising']
for i, loss in enumerate(self.losses):
self._add_loss_summary(loss_names[i], loss)
self._add_loss_summary('loss_total', self.loss_total)
self.summs_train = tf.summary.merge_all('train')
# reconstructions
with tf.name_scope('decodings'):
self.image_summaries = {
'orig': self._add_decoding_summary('0_original_input', self.input),
'reco': self._add_decoding_summary('1_reconstruction', self.eval_decode),
'pred': self._add_decoding_summary('2_prediction', self.eval_decode),
'midd': self._add_decoding_summary('3_averaged', self.eval_decode),
'nois': self._add_decoding_summary('4_noisy', self.eval_decode)
| tensorflow.name_scope | 4,899 |
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