seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
y += dense(pos_feats, encoder.attn_size, use_bias=False, name='P_a')
if encoder.attn_filters:
filter_shape = [encoder.attn_filter_length * 2 + 1, 1, 1, encoder.attn_filters]
filter_ = get_variable('filter', filter_shape)
prev_weights = tf.reshape(prev_weights, tf.stack([batch_size, time_steps, 1, 1]))
conv = tf.nn.conv2d(prev_weights, filter_, [1, 1, 1, 1], 'SAME')
conv = tf.squeeze(conv, axis=2)
y += dense(conv, encoder.attn_size, use_bias=False, name='C_a')
v = get_variable('v_a', [encoder.attn_size])
return tf.reduce_sum(v * tf.tanh(y), axis=2)
def global_attention(state, hidden_states, encoder, encoder_input_length, scope=None, context=None, **kwargs):
with tf.variable_scope(scope or 'attention_{}'.format(encoder.name)):
if context is not None and encoder.use_context:
state = tf.concat([state, context], axis=1)
e = compute_energy(hidden_states, state, encoder, input_length=encoder_input_length, **kwargs)
mask = tf.sequence_mask(encoder_input_length, maxlen=tf.shape(hidden_states)[1], dtype=tf.float32)
e *= mask
|
tensorflow.tanh
| 8,500 |
import tensorflow as tf
if verbose:
print("Iteration %d: loss %.4f" % (i, loss))
if logdir:
with summary_writer.as_default():
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar("loss", loss)
class L2hmcTest(tf.test.TestCase):
|
tensorflow.contrib.summary.always_record_summaries
| 8,501 |
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten
# Block 1
conv1a = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[8, 8], strides=4, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(self.inputs)
conv1b = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1a)
conv1c = Conv2D(padding="same", filters=RNN_SIZE//8, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(conv1b)
pool1 = MaxPool2D(pool_size=[2,2])(conv1c)
# Block 2
conv2a = Conv2D(padding="same", filters=RNN_SIZE//4, kernel_size=[3, 3], strides=1, data_format='channels_last', kernel_initializer=w_init,activation=tf.nn.relu)(pool1)
|
tensorflow.keras.layers.MaxPool2D
| 8,502 |
import tensorflow as tf
def _fake_model_preprocessor_fn(image):
return (image, tf.expand_dims(tf.shape(image)[1:], axis=0))
def _fake_image_resizer_fn(image, mask):
return (image, mask, tf.shape(image))
class DataTransformationFnTest(test_case.TestCase):
def test_combine_additional_channels_if_present(self):
image = np.random.rand(4, 4, 3).astype(np.float32)
|
tensorflow.shape
| 8,503 |
import tensorflow as tf
finetune_one_hot_labels, global_step,
loss_weights, inst_weights)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
src_train_op, _ = get_src_train_op(loss)
with tf.control_dependencies([src_train_op]):
target_avg_pool = get_logits(
target_features, mode, FLAGS.target_dataset, reuse=True)
target_logits = do_cls(
target_avg_pool, target_num_classes, name='final_target_dense')
is_prediction_correct = tf.equal(
tf.argmax(tf.identity(target_logits), axis=1),
tf.argmax(target_one_hot_labels, axis=1))
acc = tf.reduce_mean(tf.cast(is_prediction_correct, tf.float32))
entropy = loss_entropy + rl_entropy
log_prob = loss_log_prob + log_prob
train_op, _, _ = meta_train_op(acc, entropy, log_prob, rl_scope, params)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
def rl_label_weights(name=None):
"""Returns the weight for importance."""
with tf.variable_scope(name, 'rl_op_selection'):
num_classes = FLAGS.src_num_classes
num_choices = FLAGS.num_choices
|
tensorflow.cast
| 8,504 |
import tensorflow as tf
x: A tensor or variable.
alpha: A scalar, slope of negative section (default=`0.`).
max_value: Saturation threshold.
Returns
-------
A tensor.
"""
if alpha != 0.:
negative_part = tf.nn.relu(-x)
x = tf.nn.relu(x)
if max_value is not None:
max_value = _to_tensor(max_value, x.dtype.base_dtype)
zero = _to_tensor(0., x.dtype.base_dtype)
x = tf.clip_by_value(x, zero, max_value)
if alpha != 0.:
alpha = _to_tensor(alpha, x.dtype.base_dtype)
x -= alpha * negative_part
|
tensorflow.nn.relu
| 8,505 |
import tensorflow as tf
nas_training_hyper_parameters=nas_training_hyper_parameters)
if model_options.decoder_output_stride:
crop_size = model_options.crop_size
if crop_size is None:
crop_size = [tf.shape(images)[1], tf.shape(images)[2]]
features = refine_by_decoder(
features,
end_points,
crop_size=crop_size,
|
tensorflow.shape
| 8,506 |
import tensorflow as tf
fpositive_mask = tf.cast(positive_mask, tf.float32)
n_positives = tf.reduce_sum(fpositive_mask)
# negtive examples are those max_overlap is still lower than neg_threshold, note that some positive may also has lower jaccard
# note those gscores is 0 is either be ignored during anchors encode or anchors have 0 overlap with all ground truth
#negtive_mask = tf.logical_and(tf.logical_and(tf.logical_not(tf.logical_or(positive_mask, glabels < 0)), gscores < params['neg_threshold']), gscores > 0.)
negtive_mask = tf.logical_and(tf.equal(glabels, 0), gscores > 0.)
#negtive_mask = tf.logical_and(tf.logical_and(tf.logical_not(positive_mask), gscores < params['neg_threshold']), gscores > 0.)
#negtive_mask = tf.logical_and(gscores < params['neg_threshold'], tf.logical_not(positive_mask))
fnegtive_mask = tf.cast(negtive_mask, tf.float32)
n_negtives = tf.reduce_sum(fnegtive_mask)
n_neg_to_select = tf.cast(params['negative_ratio'] * n_positives, tf.int32)
n_neg_to_select = tf.minimum(n_neg_to_select, tf.cast(n_negtives, tf.int32))
# hard negative mining for classification
predictions_for_bg = tf.nn.softmax(cls_pred)[:, 0]
|
tensorflow.cast
| 8,507 |
import tensorflow as tf
def focal_loss(onehot_labels, cls_preds,
alpha=0.25, gamma=2.0, name=None, scope=None):
"""Compute softmax focal loss between logits and onehot labels
logits and onehot_labels must have same shape [batchsize, num_classes] and
the same data type (float16, 32, 64)
Args:
onehot_labels: Each row labels[i] must be a valid probability distribution
cls_preds: Unscaled log probabilities
alpha: The hyperparameter for adjusting biased samples, default is 0.25
gamma: The hyperparameter for penalizing the easy labeled samples
name: A name for the operation (optional)
Returns:
A 1-D tensor of length batch_size of same type as logits with softmax focal loss
"""
with tf.name_scope(scope, 'focal_loss', [cls_preds, onehot_labels]) as sc:
logits = tf.convert_to_tensor(cls_preds)
onehot_labels = tf.convert_to_tensor(onehot_labels)
precise_logits = tf.cast(logits, tf.float32) if (
logits.dtype == tf.float16) else logits
onehot_labels = tf.cast(onehot_labels, precise_logits.dtype)
predictions = tf.nn.sigmoid(logits)
predictions_pt = tf.where(tf.equal(onehot_labels, 1), predictions, 1.-predictions)
# add small value to avoid 0
epsilon = 1e-8
alpha_t = tf.scalar_mul(alpha, tf.ones_like(onehot_labels, dtype=tf.float32))
alpha_t = tf.where(tf.equal(onehot_labels, 1.0), alpha_t, 1-alpha_t)
losses = tf.reduce_sum(-alpha_t * tf.pow(1. - predictions_pt, gamma) * tf.log(predictions_pt+epsilon),
|
tensorflow.name_scope
| 8,508 |
import tensorflow as tf
if __name__ == '__main__':
SESS = tf.Session()
with tf.device('/cpu:0'):
OPT = tf.train.AdamOptimizer(1e-4) # 后续主要是使用该optimizer中的apply—gradients操作
# OPT_C = tf.train.RMSPropOptimizer(LR_C, name='RMSPropC') # 定义critic训练过程
GLOBAL_AC = ACnet(scope=GLOBAL_NET_SCOPE) # 创建中央大脑GLOBALE_AC，只创建结构(A和C的参数)
workers = []
for i in range(N_workers): # N—workers等于cpu数量
|
tensorflow.train.AdamOptimizer
| 8,509 |
import tensorflow as tf
assert isinstance(targets, float)
if targets in [0., 1.]:
entropy = 0.
else:
entropy = - targets * np.log(targets) - \
(1. - targets) * np.log(1. - targets)
return tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(logits) * targets, logits=logits) - entropy
def _setup_model_loss(self, update_ops=None, num_classes=6):
self.learning_rate_d = tf.placeholder(tf.float32, shape=[], name="learning_rate_placeholder")
self.learning_rate_g = tf.placeholder(tf.float32, shape=[], name="learning_rate_placeholder")
d_optimizer = self._optimizer(
|
tensorflow.ones_like
| 8,510 |
import tensorflow as tf
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
|
tensorflow.logging.info
| 8,511 |
import tensorflow as tf
if len(sampled_words)!=0:
sampled_words = tf.stack(sampled_words, axis=1) # [batch_size, max_dec_steps]
vocab_scores = tf.stack(vocab_scores, axis=1) # [batch_size, max_dec_steps, vocab]
# calculating loss
self._loss = None
|
tensorflow.stack
| 8,512 |
import tensorflow as tf
img_summary: a string tensor containing sampled input images.
"""
# Reshape to use within a convolutional neural net. Last dimension is for
# 'features' - it would be 1 one for a grayscale image, 3 for an RGB image,
# 4 for RGBA, etc.
x_image = tf.reshape(x, [-1, FLAGS.img_width, FLAGS.img_height, FLAGS.img_channels])
x_image = tf.cond(train, lambda: tf.map_fn(tf.image.random_flip_left_right, x_image), lambda: x_image)
x_image = tf.cond(train, lambda: tf.map_fn(lambda x: tf.image.random_brightness(x, 0.5), x_image), lambda: x_image)
img_summary = tf.summary.image('Input_images', x_image)
# First convolutional layer - maps one image to 32 feature maps.
with tf.variable_scope('Conv_1'):
conv1 = tf.layers.conv2d(
inputs=x_image,
filters=32,
|
tensorflow.image.random_brightness
| 8,513 |
from tensorflow.python.client import device_lib
def main(_):
if not FLAGS.data_path:
raise ValueError("Must set --data_path to PTB data directory")
gpus = [
x.name for x in device_lib.list_local_devices() if x.device_type == "GPU"
]
if FLAGS.num_gpus > len(gpus):
raise ValueError(
"Your machine has only %d gpus "
"which is less than the requested --num_gpus=%d."
|
tensorflow.python.client.device_lib.list_local_devices
| 8,514 |
from tensorflow.contrib.framework.python.framework import checkpoint_utils
"""Returns cluster centers."""
clusters = checkpoint_utils.load_variable(
self.model_dir, gmm_ops.GmmAlgorithm.CLUSTERS_VARIABLE)
return np.squeeze(clusters, 1)
def covariances(self):
"""Returns the covariances."""
return checkpoint_utils.load_variable(
self.model_dir, gmm_ops.GmmAlgorithm.CLUSTERS_COVS_VARIABLE)
def _parse_tensor_or_dict(self, features):
if isinstance(features, dict):
return array_ops.concat([features[k] for k in sorted(features.keys())],
1)
|
tensorflow.contrib.framework.python.framework.checkpoint_utils.load_variable
| 8,515 |
import tensorflow as tf
biases = _variable_on_cpu('biases', [num_outputs],
tf.constant_initializer(0.0))
outputs = tf.nn.bias_add(outputs, biases)
|
tensorflow.nn.bias_add
| 8,516 |
import tensorflow as tf
import tensorflow as tf
#add_layer 函数里面所有的with都是为了tensorboard添加上去的
def add_layer(inputs, in_size, out_size, activation_function=None,nameScope="layer"):
# add one more layer and return the output of this layer
with tf.name_scope(nameScope):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
|
tensorflow.name_scope
| 8,517 |
import tensorflow as tf
targets_dx = (gt_boxes_urx - bboxes_urx)/(bboxes_width * variances[0])
targets_dy = (gt_boxes_ury - bboxes_ury)/(bboxes_height * variances[0])
targets_dw = tf.log(gt_boxes_width / bboxes_width) / variances[1]
targets_dh = tf.log(gt_boxes_height / bboxes_height) / variances[1]
|
tensorflow.log
| 8,518 |
import tensorflow.contrib.graph_editor as ge
- 'speed': checkpoint all outputs of convolutions and matmuls. these ops are usually the most expensive,
so checkpointing them maximizes the running speed
(this is a good option if nonlinearities, concats, batchnorms, etc are taking up a lot of memory)
- 'memory': try to minimize the memory usage
(currently using a very simple strategy that identifies a number of bottleneck tensors in the graph to checkpoint)
- 'collection': look for a tensorflow collection named 'checkpoints', which holds the tensors to checkpoint
'''
# print("Calling memsaving gradients with", checkpoints)
if not isinstance(ys,list):
ys = [ys]
if not isinstance(xs,list):
xs = [xs]
bwd_ops = ge.get_backward_walk_ops([y.op for y in ys],
inclusive=True)
debug_print("bwd_ops: %s", bwd_ops)
# forward ops are all ops that are candidates for recomputation
fwd_ops = ge.get_forward_walk_ops([x.op for x in xs],
inclusive=True,
within_ops=bwd_ops)
debug_print("fwd_ops: %s", fwd_ops)
# exclude ops with no inputs
fwd_ops = [op for op in fwd_ops if op.inputs]
|
tensorflow.contrib.graph_editor.get_backward_walk_ops
| 8,519 |
import tensorflow as tf
src_features, src_labels = features['src'], tf.cast(labels['src'], tf.int64)
finetune_features = features['finetune']
target_features = features['target']
num_classes = FLAGS.src_num_classes
finetune_one_hot_labels = tf.one_hot(
tf.cast(labels['finetune'], tf.int64), target_num_classes)
target_one_hot_labels = tf.one_hot(
tf.cast(labels['target'], tf.int64), target_num_classes)
with tf.variable_scope('rl_controller') as rl_scope:
# It creates a `rl_scope` which will be used for ops.
pass
rl_entropy, label_weights, log_prob = rl_label_weights(rl_scope)
loss_entropy, loss_weights, loss_log_prob = get_loss_weights(rl_scope)
def gather_init_weights():
|
tensorflow.cast
| 8,520 |
from tensorflow.python.framework import ops
returned_dims = []
if keep_dims:
for i, dim in enumerate(input_shape.dims):
if i in reduction_indices:
returned_dims.append(1)
else:
returned_dims.append(dim)
else:
for i, dim in enumerate(input_shape.dims):
if i not in reduction_indices:
returned_dims.append(dim)
return [tensor_shape.TensorShape(returned_dims)]
@ops.RegisterShape("SegmentMax")
@ops.RegisterShape("SegmentMean")
@ops.RegisterShape("SegmentMin")
@ops.RegisterShape("SegmentProd")
@ops.RegisterShape("SegmentSum")
def _SegmentReductionShape(op):
"""Common shape function for segment reduction ops."""
data_shape = op.inputs[0].get_shape()
segment_ids_shape = op.inputs[1].get_shape()
segment_ids_shape.assert_has_rank(1)
return [tensor_shape.TensorShape([None]).concatenate(data_shape[1:])]
@ops.RegisterShape("SparseSegmentMean")
@ops.RegisterShape("SparseSegmentSum")
|
tensorflow.python.framework.ops.RegisterShape
| 8,521 |
import tensorflow as tf
init_b = tf.constant_initializer(0.01)
with tf.variable_scope('l1'):
n_l1 = 700
# combine the action and states together in this way
w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], initializer=init_w, trainable=trainable)
w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], initializer=init_w, trainable=trainable)
b1 = tf.get_variable('b1', [1, n_l1], initializer=init_b, trainable=trainable)
net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1)
with tf.variable_scope('l2'):
net = tf.layers.dense(net, 20, activation=tf.nn.relu, kernel_initializer=init_w,
bias_initializer=init_b, name='l2', trainable=trainable)
with tf.variable_scope('q'):
q = tf.layers.dense(net, 1, kernel_initializer=init_w, bias_initializer=init_b, trainable=trainable) # Q(s,a)
return q
|
tensorflow.matmul
| 8,522 |
import tensorflow as tf
hparams, tf.estimator.ModeKeys.TRAIN
)
try:
num_target_frames = hparams.video_num_target_frames
except AttributeError:
num_target_frames = 1
target_value_shape_suffix = [num_target_frames]
if distributional_size > 1:
target_value_shape_suffix = [num_target_frames, distributional_size]
features = {
"inputs": observations,
"epoch": tf.constant(epoch + 1),
"input_action": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
"input_reward": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
"targets": tf.zeros(obs_shape[:1] + [num_target_frames] + obs_shape[2:]),
"target_action": tf.zeros(
obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
"target_reward": tf.zeros(
obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
"target_policy": tf.zeros(
obs_shape[:1] + [num_target_frames] + [action_space.n]),
"target_value": tf.zeros(
obs_shape[:1] + target_value_shape_suffix)
}
model.distributional_value_size = max(distributional_size, 1)
model.use_epochs = hparams.use_epochs
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
t2t_model.create_dummy_vars()
(targets, _) = model(features)
target_values = targets["target_value"][:, 0]
|
tensorflow.zeros
| 8,523 |
import tensorflow as tf
if self.n_step:
q_backup_n = tf.stop_gradient(
self.rewards_ph_n +
(1 - self.terminals_ph_n) *( self.gamma**self.n_step_length ) * self.value_target_n)
qf1_loss_n = 0.5 * tf.reduce_mean(((q_backup_n - qf1) ** 2)*self.weight_ph)
qf1_loss_n_col = tf.reduce_mean(((q_backup_n - qf1) ** 2),1)
qf2_loss_n = 0.5 * tf.reduce_mean(((q_backup_n - qf2) ** 2)*self.weight_ph)
if self.n_step:
|
tensorflow.reduce_mean
| 8,524 |
import tensorflow as tf
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(
|
tensorflow.greater_equal
| 8,525 |
import tensorflow as tf
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
|
tensorflow.logging.info
| 8,526 |
import tensorflow as tf
# print('vnet scope T/R', is_train, reuse_unet)
encoderscope = 'unet_enc'
decoderscope = 'unet_dec'
reuse_encoder = reuse_unet
reuse_decoder = reuse_unet
print([encoderscope, ' ', decoderscope])
# ===============================================================================ENCODER
with tf.variable_scope(encoderscope) as scope:
if reuse_encoder: scope.reuse_variables()
with tf.variable_scope('color_encoder'):
X = encoder_conf('eI', I[:, :, :, :-1], 96, 5, 1, norm, reuse_encoder, is_train, self.args.dropout) # 128 > 124
X0 = encoder_conf('d0', X, 96, 2, 2, norm, reuse_encoder, is_train, self.args.dropout) # 124 > 62 @2
X = encoder_conf('e1', X0, 128, 3, 1, norm, reuse_encoder, is_train, self.args.dropout) # 62 > 60
X_EARLY = X
X1 = encoder_conf('d1', X, 128, 2, 2, norm, reuse_encoder, is_train, self.args.dropout) # 60 > 30 @4
|
tensorflow.variable_scope
| 8,527 |
import tensorflow as tf
raise ValueError('split name %s was not recognized.' % split_name)
file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
# Allowing None in the signature so that dataset_factory can use the default.
if reader is None:
reader = tf.TFRecordReader
# Features in Pascal VOC TFRecords.
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/height': tf.FixedLenFeature([1], tf.int64),
'image/width': tf.FixedLenFeature([1], tf.int64),
'image/channels': tf.FixedLenFeature([1], tf.int64),
'image/shape': tf.FixedLenFeature([3], tf.int64),
'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
|
tensorflow.FixedLenFeature
| 8,528 |
from tensorflow.python.framework import ops as _ops
def __init__(self, dtype, shape, send_device, recv_device, name=None):
"""Construct a channel.
Args:
dtype: The dtype of tensors sent through the channel.
shape: The shape of tensors sent through the channel. Must be a fully
defined shape for TPUs.
send_device: A fully-specified tensorflow device.
recv_device: A fully-specified tensorflow device.
name: A name for the channel (optional).
"""
current_graph = _ops.get_default_graph()
assert current_graph, "A channel is scoped within a tf.Graph"
self._dtype = dtype
self._send_device = send_device
self._recv_device = recv_device
self._name = current_graph.unique_name(name if name else "channel")
assert shape is not None
shape = _tensor_shape.TensorShape(shape)
self._shape = shape
|
tensorflow.python.framework.ops.get_default_graph
| 8,529 |
import tensorflow as tf
def decode_needs_input_shape(self):
"""See base class."""
return False
def get_params(self):
"""See base class."""
return {}, {}
def encode(self, x, encode_params):
"""See base class."""
del encode_params # Unused.
return {self.ENCODED_VALUES_KEY: x + tf.sign(tf.random.normal(tf.shape(x)))}
def decode(self,
encoded_tensors,
decode_params,
num_summands=None,
shape=None):
"""See base class."""
del decode_params # Unused.
del num_summands # Unused.
del shape # Unused.
|
tensorflow.shape
| 8,530 |
from tensorflow.python.ops import math_ops
logits, array_ops.reshape(target, [-1]))
if weight_tensor is None:
return math_ops.reduce_mean(loss_vec, name="loss")
else:
loss_vec = array_ops.reshape(loss_vec, shape=(-1,))
loss_vec = math_ops.mul(
loss_vec, array_ops.reshape(weight_tensor, shape=(-1,)))
return math_ops.div(
math_ops.reduce_sum(loss_vec),
math_ops.to_float(math_ops.reduce_sum(weight_tensor)),
name="loss")
def _get_linear_vars(self):
if self._get_linear_feature_columns():
return ops.get_collection(self._linear_weight_collection)
return []
def _get_linear_training_ops(self, linear_grads, linear_vars):
if self._get_linear_feature_columns():
|
tensorflow.python.ops.math_ops.reduce_sum
| 8,531 |
import tensorflow as tf
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, label_ids, logits):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
accuracy = tf.metrics.accuracy(label_ids, predictions)
loss = tf.metrics.mean(per_example_loss)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
|
tensorflow.argmax
| 8,532 |
import tensorflow as tf
data: Data_loader object to interact with dataset.
config: Config object to store data related to training, testing and validation.
logger: Logger object to use tensorboard.
"""
def __init__(self, sess, model, data, config, logger):
self.model = model
self.config = config
self.sess = sess
self.data = data
self.logger = logger
if not self.config.pretrain: # If not pretrain then initialize variables.
self.init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
self.sess.run(self.init)
def train(self):
"""Train the model for the number of epochs in config.num_epochs.
Calls validate_epoch if config.use_val is set to true and per config.val_per_epoch.
Returns:
"""
for cur_epoch in range(self.model.cur_epoch_tensor.eval(self.sess), self.config.num_epochs + 1, 1):
self.data.prepare_new_epoch_data()
self.train_epoch()
|
tensorflow.local_variables_initializer
| 8,533 |
from tensorflow.python.ops import math_ops
# tf.equal and tf.reduce_any?
# Shape of filled IDs is the same as `ids` with the last dim collapsed to 1.
ids_shape = array_ops.shape(ids, out_type=dtypes.int64)
ids_last_dim = array_ops.size(ids_shape) - 1
filled_selected_id_shape = math_ops.reduced_shape(
ids_shape, array_ops.reshape(ids_last_dim, [1]))
# Intersect `ids` with the selected ID.
filled_selected_id = array_ops.fill(
filled_selected_id_shape, math_ops.to_int64(selected_id))
result = set_ops.set_intersection(filled_selected_id, ids)
return ops.SparseTensor(
indices=result.indices, values=result.values, shape=ids_shape)
def _maybe_select_class_id(labels, predictions_idx, selected_id=None):
"""If class ID is specified, filter all other classes.
Args:
|
tensorflow.python.ops.math_ops.to_int64
| 8,534 |
from tensorflow.python.ops import gen_nn_ops
"""
with ops.op_scope([value, bias], name, "BiasAdd") as name:
value = ops.convert_to_tensor(value, name="input")
bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
return gen_nn_ops._bias_add(value, bias, data_format=data_format, name=name)
ops.RegisterShape("BiasAdd")(common_shapes.bias_add_shape)
|
tensorflow.python.ops.gen_nn_ops._bias_add
| 8,535 |
from tensorflow.python.training import training as train
Raises:
ValueError: if:
* `loss` is an invalid type or shape.
* `global_step` is an invalid type or shape.
* `learning_rate` is an invalid type or value.
* `optimizer` has the wrong type.
* `clip_gradients` is neither float nor callable.
* `learning_rate` and `learning_rate_decay_fn` are supplied, but no
`global_step` is available.
* `gradients` is empty.
"""
loss = ops.convert_to_tensor(loss)
contrib_framework.assert_scalar(loss)
if global_step is None:
global_step = train.get_global_step()
else:
train.assert_global_step(global_step)
with vs.variable_scope(name, "OptimizeLoss", [loss, global_step]):
# Update ops take UPDATE_OPS collection if not provided.
if update_ops is None:
update_ops = set(ops.get_collection(ops.GraphKeys.UPDATE_OPS))
# Make sure update ops are ran before computing loss.
if update_ops:
loss = control_flow_ops.with_dependencies(list(update_ops), loss)
# Learning rate variable, with possible decay.
lr = None
if learning_rate is not None:
|
tensorflow.python.training.training.get_global_step
| 8,536 |
import tensorflow as tf
x: Batch of encoder continuous latent states sliced/projected into
shape
[-1, num_blocks, block_dim].
means: Embedding means.
Returns:
The nearest neighbor in one hot form, the nearest neighbor
itself, the
commitment loss, embedding training loss.
"""
x_means_hot = self.nearest_neighbor(x, means)
x_means_hot_flat = tf.reshape(
x_means_hot, [-1, self.hparams.num_blocks, self.hparams.block_v_size])
x_means = tf.matmul(tf.transpose(x_means_hot_flat, perm=[1, 0, 2]), means)
x_means = tf.transpose(x_means, [1, 0, 2])
q_loss = tf.reduce_mean(
tf.squared_difference(tf.stop_gradient(x), x_means))
e_loss = tf.reduce_mean(
tf.squared_difference(x, tf.stop_gradient(x_means)))
return x_means_hot, x_means, q_loss, e_loss
def bit_to_int(self, x_bit, num_bits, base=2):
"""Turn x_bit representing numbers bitwise (lower-endian) to int tensor.
Args:
x_bit: Tensor containing numbers in a particular base to be
|
tensorflow.transpose
| 8,537 |
import tensorflow as tf
bias_initializer=b_initializer, name='q_e')
# ------------------ build target_net ------------------
with tf.variable_scope('target_net'):
a_fc1_ = tf.layers.dense(self.s_, 128, tf.nn.relu, kernel_initializer=w_initializer,
bias_initializer=b_initializer, name='agent_fc1_t')
# a_fc2_ = tf.layers.dense(a_fc1_, 128, tf.nn.relu, kernel_initializer=w_initializer,
# bias_initializer=b_initializer, name='agent_fc2_t')
|
tensorflow.layers.dense
| 8,538 |
import tensorflow as tf
self.assertEqual(
tf.train.latest_checkpoint(self.get_temp_dir()),
os.path.join(self.get_temp_dir(), "sharded-?????-of-00002"))
def testSaverDef(self):
with self.test_session():
v0 = tf.Variable(123, name="v0")
save = tf.train.Saver({"v0": v0}, sharded=True)
sd = save.as_saver_def()
self.assertTrue(sd.sharded)
class MaxToKeepTest(tf.test.TestCase):
|
tensorflow.Variable
| 8,539 |
import tensorflow as tf
#Construct predictions
image = tf.placeholder(tf.float32,shape=[hps.batch_size, image_size, image_size,
num_channel])############MNIST and CIFAR10 are different ar here
adv_image = tf.placeholder(tf.float32,shape=[hps.batch_size, image_size, image_size,
num_channel])############MNIST and CIFAR10 are different ar here
predict = tf.placeholder(tf.float32,shape=[hps.batch_size, 10])
logit_nor,tsne_logit_nor = model_carlini_adv.predict(image,tsne_logits=True)
logit_adv,tsne_logit_adv = model_carlini_adv.predict(adv_image,tsne_logits=True)
predict_nor = tf.nn.softmax(logit_nor)
predict_adv = tf.nn.softmax(logit_adv)
# Calculate entropy
argmax_y_onehot = tf.one_hot(tf.argmax(predict, 1), 10, on_value=0.0, off_value=1.0, axis=-1)
normalized_y_nonmaximal = tf.reduce_sum(predict * argmax_y_onehot, 1)
entropy = tf.reduce_sum(-tf.log(predict) * predict * argmax_y_onehot,1) / normalized_y_nonmaximal + tf.log(normalized_y_nonmaximal)
for k in range(1):
result_dict = loadmat('kernel_para_'+FLAGS.dataset+'/kernel1000_for_attack_' + f1 + '.mat')
result_dict_median = loadmat('kernel_para_'+FLAGS.dataset+'/kernel1000_median_for_attack_' + f1 + '.mat')
|
tensorflow.nn.softmax
| 8,540 |
import tensorflow as tf
initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = tf.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs)
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
|
tensorflow.reduce_sum
| 8,541 |
import tensorflow as tf
cell_drop=tf.contrib.rnn.DropoutWrapper(gru,variational_recurrent=True,dtype=tf.float32, input_size=num_input,input_keep_prob=input_prob,state_keep_prob=state_prob)
else:
if activation == 'linear':
cell_basic = tf.contrib.rnn.BasicRNNCell(state_size,activation=tf.identity)
cell_drop=tf.contrib.rnn.DropoutWrapper(cell_basic,variational_recurrent=True,dtype=tf.float32, input_size=num_input,input_keep_prob=input_prob,state_keep_prob=state_prob)
elif activation == 'relu':
cell_basic = tf.contrib.rnn.BasicRNNCell(state_size, activation=tf.nn.relu)
cell_drop = tf.contrib.rnn.DropoutWrapper(cell_basic, variational_recurrent=True, dtype=tf.float32,
input_size=num_input, input_keep_prob=input_prob,
state_keep_prob=state_prob)
else: #tanh by default
cell_basic = tf.contrib.rnn.BasicRNNCell(state_size)
cell_drop = tf.contrib.rnn.DropoutWrapper(cell_basic, variational_recurrent=True, dtype=tf.float32,
input_size=num_input, input_keep_prob=input_prob,
state_keep_prob=state_prob)
|
tensorflow.contrib.rnn.DropoutWrapper
| 8,542 |
import tensorflow as tf
tt_rank = tt_rank.astype(int)
# Empirically entries of a TT tensor with cores initialized from N(0, 1)
# will have variances np.prod(tt_rank) and mean 0.
# We scale each TT-core to obtain the desired stddev
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 = tensor_with_random_cores(shape, tt_rank=tt_rank, stddev=core_stddev,
dtype=dtype)
if np.abs(mean) < 1e-8:
return tt
else:
raise NotImplementedError('non-zero mean is not supported yet')
|
tensorflow.name_scope
| 8,543 |
import tensorflow as tf
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)
mixed_idx = tf.range(0, self.mixed_label.get_shape().as_list()[0], 1)
mixed_idx = tf.random_shuffle(mixed_idx)[0:self.batch_size]
self.mixed_pc = tf.gather(self.mixed_pc, mixed_idx)
self.mixed_label = tf.gather(self.mixed_label, mixed_idx)
self.mixed_pred, mixed_end_points = self.get_pred(self.mixed_pc)
self.mixed_loss = self.get_loss(self.mixed_pred, self.mixed_label, mixed_end_points)
|
tensorflow.random_shuffle
| 8,544 |
import tensorflow as tf
# Check that the parameter nodes have been restored.
self.assertEqual(10.0, v0_2.eval())
self.assertEqual(20.0, v1_2.eval())
def testInt64(self):
save_path = os.path.join(self.get_temp_dir(), "int64")
with self.test_session() as sess:
# Build a graph with 1 node, and save and restore for them.
v = tf.Variable(np.int64(15), name="v")
save = tf.train.Saver({"v": v}, restore_sequentially=True)
tf.initialize_all_variables().run()
# Save the initialized values in the file at "save_path"
val = save.save(sess, save_path)
self.assertTrue(isinstance(val, six.string_types))
self.assertEqual(save_path, val)
with self.test_session() as sess:
v = tf.Variable(np.int64(-1), name="v")
save = tf.train.Saver({"v": v})
|
tensorflow.initialize_all_variables
| 8,545 |
import tensorflow as tf
w = tf.get_variable("proj_w", [2, 5])
b = tf.get_variable("proj_b", [5])
with tf.variable_scope("proj_seq2seq"):
dec, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, output_projection=(w, b))
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [tf.constant(0, tf.int32, shape=[2]) for _ in range(3)]
with tf.variable_scope("other"):
d3, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2,
feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
tf.get_variable_scope().reuse_variables()
d1, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
d2, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
|
tensorflow.variable_scope
| 8,546 |
import tensorflow as tf
def update(self, labeled_sdfs, labeled_classes, labeled_poses,
predicted_sdfs, predicted_classes, predicted_poses):
"""Update."""
labeled_rotations = labeled_poses[0]
labeled_translations = labeled_poses[1]
labeled_sizes = labeled_poses[2]
status = True
if status:
box_limits_x = [100, -100]
# box_limits_y = [100, -100]
box_limits_z = [100, -100]
for i in range(labeled_translations.shape[0]):
rot = tf.reshape(tf.gather(labeled_rotations[i], [0, 2, 6, 8]), [2, 2])
min_x = tf.cast(0.0 - labeled_sizes[i][0] / 2.0, dtype=tf.float32)
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)
translation = tf.reshape([labeled_translations[i][0],
labeled_translations[i][2]], [2, 1])
pt_0 = rot @ tf.reshape([min_x, min_z], [2, 1]) + translation
|
tensorflow.gather
| 8,547 |
import tensorflow as tf
tgt_dif = tgt_flat1 - tgt_flat2
pred_dif = pred_flat1 - pred_flat2
geq = tf.cast(tgt_dif > 0, tf.bool)
# tgt_posi_dif = tf.where(geq, tgt_dif, -tgt_dif)
pred_posi_dif = tf.where(geq, pred_dif, -pred_dif)
loss = tf.maximum(0., margin-pred_posi_dif)
cstr_pct = tf.math.count_nonzero(loss, dtype=tf.float32) / tf.cast(tf.reduce_prod(tf.shape(loss)), tf.float32)
|
tensorflow.where
| 8,548 |
from tensorflow.contrib.framework import tensor_util
update_op: `Operation` that increments `true_positives` and
`false_negatives` variables appropriately and whose value matches
`recall`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`ignore_mask` is not `None` and its shape doesn't match `predictions`, or
if `weights` is not `None` and its shape doesn't match `predictions`, or
if either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(name, 'recall', [predictions, labels]):
predictions, labels = tensor_util.remove_squeezable_dimensions(
predictions, labels)
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
weights = _mask_weights(ignore_mask, weights)
true_positives, true_positives_update_op = _streaming_true_positives(
predictions, labels, weights, metrics_collections=None,
updates_collections=None, name=None)
false_negatives, false_negatives_update_op = _streaming_false_negatives(
predictions, labels, weights, metrics_collections=None,
updates_collections=None, name=None)
|
tensorflow.contrib.framework.tensor_util.remove_squeezable_dimensions
| 8,549 |
import tensorflow as tf
word_emb = lm_embeddings["word_emb"] # [num_sentences, max_sentence_length, 512]
lm_emb = tf.stack([tf.concat([word_emb, word_emb], -1),
lm_embeddings["lstm_outputs1"],
lm_embeddings["lstm_outputs2"]], -1) # [num_sentences, max_sentence_length, 1024, 3]
lm_emb_size = util.shape(lm_emb, 2)
lm_num_layers = util.shape(lm_emb, 3)
with tf.variable_scope("lm_aggregation"):
self.lm_weights = tf.nn.softmax(tf.get_variable("lm_scores", [lm_num_layers], initializer=tf.constant_initializer(0.0)))
self.lm_scaling = tf.get_variable("lm_scaling", [], initializer=tf.constant_initializer(1.0))
flattened_lm_emb = tf.reshape(lm_emb, [num_sentences * max_sentence_length * lm_emb_size, lm_num_layers])
flattened_aggregated_lm_emb = tf.matmul(flattened_lm_emb, tf.expand_dims(self.lm_weights, 1)) # [num_sentences * max_sentence_length * emb, 1]
aggregated_lm_emb = tf.reshape(flattened_aggregated_lm_emb, [num_sentences, max_sentence_length, lm_emb_size])
|
tensorflow.variable_scope
| 8,550 |
import tensorflow as tf
if mode == tf.estimator.ModeKeys.EVAL:
batch_size = features['images'].shape[0].value
assert batch_size == 1
evaluator = Evaluator(num_classes=params['num_classes'])
eval_metric_ops = evaluator.get_metric_ops(labels, predictions)
return tf.estimator.EstimatorSpec(
mode, loss=total_loss,
eval_metric_ops=eval_metric_ops
)
assert mode == tf.estimator.ModeKeys.TRAIN
with tf.variable_scope('learning_rate'):
global_step = tf.train.get_global_step()
learning_rate = tf.train.cosine_decay(
params['initial_learning_rate'],
global_step, decay_steps=params['num_steps']
)
tf.summary.scalar('learning_rate', learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
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)
|
tensorflow.train.get_global_step
| 8,551 |
import tensorflow as tf
context, alpha = self._attention_layer(features, features_proj, h, reuse=(t!=0))
alpha_list.append(alpha)
if self.selector:
context, beta = self._selector(context, h, reuse=(t!=0))
beta_list.append(beta)
with tf.variable_scope('lstm', reuse=(t!=0)):
_, (c, h) = lstm_cell(inputs=tf.concat(axis=1, values=[x, context]), state=[c, h])
logits = self._decode_lstm(x, h, context, reuse=(t!=0))
sampled_word = tf.argmax(logits, 1)
sampled_word_list.append(sampled_word)
alphas = tf.transpose(tf.stack(alpha_list), (1, 0, 2)) # (N, T, L)
betas = tf.transpose(tf.squeeze(beta_list), (1, 0)) # (N, T)
sampled_captions = tf.transpose(tf.stack(sampled_word_list), (1, 0)) # (N, max_len)
|
tensorflow.concat
| 8,552 |
import tensorflow as tf
flags.DEFINE_float(
"warmup_proportion", 0.1,
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10% of training.")
flags.DEFINE_integer("save_checkpoints_steps", 1000,
"How often to save the model checkpoint.")
flags.DEFINE_integer("iterations_per_loop", 1000,
"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 "
"url.")
tf.flags.DEFINE_string(
"tpu_zone", None,
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string(
|
tensorflow.flags.DEFINE_string
| 8,553 |
import tensorflow as tf
if clone_losses:
clone_loss = tf.add_n(clone_losses, name='clone_loss')
if num_clones > 1:
clone_loss = tf.div(clone_loss, 1.0 * num_clones,
name='scaled_clone_loss')
all_losses.append(clone_loss)
if regularization_losses:
regularization_loss = tf.add_n(regularization_losses,
name='regularization_loss')
all_losses.append(regularization_loss)
if all_losses:
sum_loss = tf.add_n(all_losses)
# Add the summaries out of the clone device block.
if clone_loss is not None:
tf.summary.scalar('clone_loss', clone_loss)
# tf.summary.scalar(clone.scope + '/clone_loss', clone_loss)
if regularization_loss is not None:
tf.summary.scalar('regularization_loss', regularization_loss)
return sum_loss
def _optimize_clone(optimizer, clone, num_clones, regularization_losses,
|
tensorflow.add_n
| 8,554 |
import tensorflow as tf
for i in range(self.config["coref_depth"]):
with tf.variable_scope("coref_layer", reuse=(i > 0)):
top_antecedent_emb = tf.gather(top_span_emb, top_antecedents) # [k, c, emb]
top_antecedent_scores = top_fast_antecedent_scores + self.get_slow_antecedent_scores(top_span_emb, top_antecedents, top_antecedent_emb, top_antecedent_offsets, top_span_speaker_ids, genre_emb) # [k, c]
|
tensorflow.gather
| 8,555 |
import tensorflow as tf
dataset = dataset.map(concat_and_add_mask)
return dataset
@gin.configurable(module='trax.data', denylist=['dataset', 'training'])
def lm_token_preprocessing(dataset, training):
"""Concatenates inputs, 0, targets, with masking only for targets."""
del training
def concat_and_add_mask(x):
inp = x['inputs']
targets = x['targets']
pad = tf.expand_dims(tf.zeros_like(inp[0]), axis=0)
concat = tf.concat([inp, pad, targets], axis=0)
mask = tf.concat([tf.zeros_like(inp), pad, tf.ones_like(targets)], axis=0)
x['inputs'] = concat
x['targets'] = concat
x['mask'] = mask
return x
dataset = dataset.map(concat_and_add_mask)
return dataset
@gin.configurable(module='trax.data', denylist=['hparams'])
def bair_robot_pushing_hparams(hparams=None,
video_num_input_frames=1,
|
tensorflow.concat
| 8,556 |
import tensorflow as tf
:return: does not return anything
"""
# Reconstruction Phase
with tf.variable_scope(tf.get_variable_scope()):
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)
|
tensorflow.get_variable_scope
| 8,557 |
import tensorflow as tf
self.mixed_pred, mixed_end_points = self.get_pred(self.mixed_pc)
self.mixed_loss = self.get_loss(self.mixed_pred, self.mixed_label, mixed_end_points)
with tf.variable_scope('discriminator') as scope:
self.real_prob, self.real_logit = self.discriminator(self.real_pc_rotated, scope=scope, **disc_kwargs)
self.synthetic_prob, self.synthetic_logit = self.discriminator(self.gen_out_rotated, reuse=True, scope=scope, **disc_kwargs)
# Compute WGAN losses
self.loss_d = tf.reduce_mean(self.synthetic_logit) - tf.reduce_mean(self.real_logit) # comparing rotated fake and real images
self.loss_g = -tf.reduce_mean(self.synthetic_logit)
# Add rotation loss
if self.ms_task:
self.g_ms_loss = tf.abs(self.gen_out_rot_loss - self.real_pc_rot_loss, name = 'abs')
self.d_ms_loss = self.mixed_loss
self.loss_d_rot = self.loss_d + self.weight_rotation_loss_d * self.d_ms_loss
self.loss_g_rot = self.loss_g + self.weight_rotation_loss_g * self.g_ms_loss
|
tensorflow.reduce_mean
| 8,558 |
import tensorflow as tf
self._plot_model_hooks_kwargs = {"time_reference" : self._time_reference_str,
"plot_offset": self._plot_offset}
self._n_steps_stats = self._get_steps(config["stats_period"], self._time_reference_str)
# stop hook
tot_steps = int(self._opts['epochs']+1)*self.n_batches_per_epoch
hooks.append(tf.train.StopAtStepHook(last_step=tot_steps))
# general info hook (no average on validation but only on train loop)
hooks.append(self._create_general_info_hook(config))
# regularizers hook (no average on validation but only on train loop)
hooks.append(self._create_regularizers_hook(config))
|
tensorflow.train.StopAtStepHook
| 8,559 |
from tensorflow.python.framework import ops
Dimensions typically: batch, out_units.
"""
with ops.op_scope([x, weights, biases], name, "xw_plus_b") as name:
x = ops.convert_to_tensor(x, name="x")
weights = ops.convert_to_tensor(weights, name="weights")
biases = ops.convert_to_tensor(biases, name="biases")
|
tensorflow.python.framework.ops.convert_to_tensor
| 8,560 |
import tensorflow as tf
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(
|
tensorflow.greater_equal
| 8,561 |
import tensorflow as tf
facts = tf.array_ops.transpose(facts, [1, 0, 2])
mask = tf.equal(mask, tf.ones_like(mask))
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
queries = tf.tile(query, [1, tf.shape(facts)[1]])
queries = tf.reshape(queries, tf.shape(facts))
din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att' + stag)
d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag)
d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag)
d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]])
scores = d_layer_3_all
|
tensorflow.concat
| 8,562 |
import tensorflow as tf
relevant example.
propensity_weights: (tf.Tensor) A tensor of the same shape as `output` containing the weight of each element.
name: A string used as the name for this variable scope.
Returns:
(tf.Tensor) A single value tensor containing the loss.
"""
loss = None
with tf.name_scope(name, "click_softmax_cross_entropy",[output]):
label_dis = labels*propensity_weights / tf.reduce_sum(labels*propensity_weights, 1, keep_dims=True)
loss = tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=label_dis) * tf.reduce_sum(labels*propensity_weights, 1)
return tf.reduce_sum(loss) / tf.reduce_sum(labels*propensity_weights)
def click_loglikelihood(self, labels, propensity,train_output, name=None):
"""Computes listwise softmax loss with propensity weighting.
Args:
output: (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
the ranking score of the corresponding example.
labels: (tf.Tensor) A tensor of the same shape as `output`. A value >= 1 means a
relevant example.
propensity_weights: (tf.Tensor) A tensor of the same shape as `output` containing the weight of each element.
|
tensorflow.reduce_sum
| 8,563 |
import tensorflow as tf
Variable Tensor
"""
if use_xavier:
initializer = tf.contrib.layers.xavier_initializer()
var = _variable_on_cpu(name, shape, initializer)
else:
# initializer = tf.truncated_normal_initializer(stddev=stddev)
with tf.device('/cpu:0'):
var = tf.truncated_normal(shape, stddev=np.sqrt(2 / shape[-1]))
var = tf.round(var * tf.constant(1000, dtype=tf.float32)) / tf.constant(1000, dtype=tf.float32)
var = tf.Variable(var, name='weights')
if wd is not None:
weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def conv1d(inputs,
|
tensorflow.constant
| 8,564 |
from tensorflow.python.framework import ops
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])
filter_width = int(filter_shape[1])
filter_in_depth = int(filter_shape[2])
output_count = np.prod(output_shape.as_list())
return ops.OpStats("flops", (output_count * filter_in_depth * filter_height *
filter_width * 2))
@ops.RegisterStatistics("Conv2D", "weight_parameters")
def _calc_conv_weight_params(graph, node):
"""Calculates the on-disk size of the weights for Conv2D."""
input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
input_shape.assert_is_fully_defined()
filter_shape = graph_util.tensor_shape_from_node_def_name(graph,
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])
filter_width = int(filter_shape[1])
|
tensorflow.python.framework.ops.RegisterStatistics
| 8,565 |
import tensorflow as tf
else:
return 0
# For ACER
def get_by_index(x, idx):
assert(len(x.get_shape()) == 2)
assert(len(idx.get_shape()) == 1)
idx_flattened = tf.range(0, x.shape[0]) * x.shape[1] + idx
y = tf.gather(tf.reshape(x, [-1]), # flatten input
idx_flattened) # use flattened indices
return y
def check_shape(ts,shapes):
i = 0
for (t,shape) in zip(ts,shapes):
assert t.get_shape().as_list()==shape, "id " + str(i) + " shape " + str(t.get_shape()) + str(shape)
i += 1
|
tensorflow.reshape
| 8,566 |
import tensorflow as tf
0: self._add_separable_conv_3x3_op,
1: self._add_separable_conv_5x5_op,
2: self._add_avg_pool_3x3_op,
3: self._add_max_pool_3x3_op,
4: self._add_identity_op,
5: self._add_separable_conv_7x7_op
}
def _add_avg_pool_3x3_op(self, X, input_idx, ni, w, h, ch, is_reduction, is_dynamic, is_train):
filter_size = 3
stride = 2 if is_reduction else 1
with tf.variable_scope('avg_pool_3x3_op'):
X = tf.nn.avg_pool(X, ksize=(1, filter_size, filter_size, 1), strides=[1, stride, stride, 1], padding='SAME')
X = tf.reshape(X, (-1, w // stride, h // stride, ch)) # Sanity shape check
return X
def _add_identity_op(self, X, input_idx, ni, w, h, ch, is_reduction, is_dynamic, is_train):
stride = 2 if is_reduction else 1
with tf.variable_scope('identity_op'):
# If stride > 1, calibrate, else, just return itself
if stride > 1:
X = self._calibrate(X, w, h, ch, w // stride, h // stride, ch, is_train=is_train)
X = tf.reshape(X, (-1, w // stride, h // stride, ch)) # Sanity shape check
return X
|
tensorflow.nn.avg_pool
| 8,567 |
import tensorflow as tf
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]
return span_emb # [k, emb]
def get_mention_scores(self, span_emb):
|
tensorflow.concat
| 8,568 |
import tensorflow as tf
axis=0) # operation of choosing action
# 更新
self.update_old_action_op_op = [
olda.assign(a) for a, olda in zip(
action_op_params, old_action_op_params)]
# 定義輸入變數
self.tfa = tf.placeholder(tf.float32, [None, action_dim], 'action')
self.tfadv = tf.placeholder(tf.float32, [None, 1], 'advantage')
# 機率比較
ratio = action_op.prob(self.tfa) / \
(old_action_op.prob(self.tfa) + 1e-5)
# 替代損失
|
tensorflow.placeholder
| 8,569 |
import tensorflow as tf
img_h = img_h_batch[start:end]
img_w = img_w_batch[start:end]
inputs_list.append([img, gtboxes_and_label_h,
gtboxes_and_label_q, num_objects, img_h, img_w])
tower_grads = []
biases_regularizer = tf.no_regularizer
weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY)
with tf.variable_scope(tf.get_variable_scope()):
for i in range(num_gpu):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i):
with slim.arg_scope(
[slim.model_variable, slim.variable],
device='/device:CPU:0'):
with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane,
slim.conv2d_transpose, slim.separable_conv2d,
slim.fully_connected],
weights_regularizer=weights_regularizer,
biases_regularizer=biases_regularizer,
biases_initializer=tf.constant_initializer(0.0)):
gtboxes_and_label_h, gtboxes_and_label_q = tf.py_func(self.get_gtboxes_and_label,
|
tensorflow.device
| 8,570 |
import tensorflow as tf
if head is not None:
cross_entropy = -tf.reduce_sum(tf.mul(labels * tf.log(softmax), head), axis=[1])
|
tensorflow.log
| 8,571 |
import tensorflow.contrib as contrib
y_1 = tf.placeholder(tf.float32, (None, n_output_1), "y_1")
y_2 = tf.placeholder(tf.float32, (None, n_output_2), "y_2")
is_training = tf.placeholder(tf.bool, (), "is_training")
with tf.variable_scope("network"):
with contrib.framework.arg_scope(
[contrib.layers.fully_connected],
# he initialization
weights_initializer=contrib.layers.variance_scaling_initializer(),
# l2 regularization
weights_regularizer=contrib.layers.l2_regularizer(reg_lambda),
# BN
normalizer_fn=contrib.layers.batch_norm,
normalizer_params={
"is_training": is_training,
"scale": True,
|
tensorflow.contrib.layers.variance_scaling_initializer
| 8,572 |
import tensorflow as tf
self.Y_hat = training_decoder_output.rnn_output
out_decoder2 = tf.reshape(self.Y_hat, [tf.shape(self.Y_hat)[0], -1, n_mels])
|
tensorflow.shape
| 8,573 |
from tensorflow.python.ops import array_ops
return [tensor_shape.vector(None)]
else:
return [tensor_shape.vector((limit_value - start_value + delta_value - 1) //
delta_value)]
# Reduction operations
def _ReductionDims(x, reduction_indices):
"""Returns range(0, rank(x)) if reduction_indices is None."""
if reduction_indices is not None:
return reduction_indices
else:
return range(0, array_ops.rank(x))
def reduce_sum(input_tensor, reduction_indices=None, keep_dims=False,
name=None):
"""Computes the sum of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `reduction_indices`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
|
tensorflow.python.ops.array_ops.rank
| 8,574 |
import tensorflow as tf
trainable=False)
# Loss value
reg_item = tf.contrib.layers.l1_l2_regularizer(L1_reg,
L2_reg)
reg_term = tf.contrib.layers.apply_regularization(reg_item, self.nnweights)
loss_fun = self._negative_log_likelihood(y_, y)
loss = loss_fun + reg_term
# SGD Optimizer
if optimizer == 'sgd':
lr = tf.train.exponential_decay(
learning_rate,
global_step,
1,
learning_rate_decay
)
train_step = tf.train.GradientDescentOptimizer(lr).minimize(loss, global_step=global_step)
elif optimizer == 'adam':
train_step = tf.train.GradientDescentOptimizer(learning_rate).\
minimize(loss, global_step=global_step)
else:
raise NotImplementedError('activation not recognized')
# init op
init_op = tf.global_variables_initializer()
# Save into class members
self.X = X
self.y_ = y_
self.y = y
self.global_step = global_step
self.loss = loss
|
tensorflow.train.GradientDescentOptimizer
| 8,575 |
import tensorflow as tf
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings
)
output_layer = model.get_sequence_output()
hidden_size = output_layer.shape[-1].value
output_weight = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02)
)
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer()
)
with tf.variable_scope("loss"):
if is_training:
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
output_layer = tf.reshape(output_layer, [-1, hidden_size])
logits = tf.matmul(output_layer, output_weight, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, [-1, FLAGS.max_seq_length, 11])
log_probs = tf.nn.log_softmax(logits, axis=-1)
# labels = tf.cast(labels,dtype=tf.float32)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_sum(per_example_loss)
return (loss, per_example_loss, logits)
|
tensorflow.variable_scope
| 8,576 |
import tensorflow as tf
processed_image = utils.process_image(image, mean_pixel)
with tf.variable_scope("inference"):
image_net = vgg_net(weights, processed_image)
conv_final_layer = image_net["conv5_3"]
pool5 = utils.max_pool_2x2(conv_final_layer)
W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")
b6 = utils.bias_variable([4096], name="b6")
conv6 = utils.conv2d_basic(pool5, W6, b6)
relu6 = tf.nn.relu(conv6, name="relu6")
if FLAGS.debug:
utils.add_activation_summary(relu6)
relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)
W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
b7 = utils.bias_variable([4096], name="b7")
conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)
relu7 = tf.nn.relu(conv7, name="relu7")
if FLAGS.debug:
utils.add_activation_summary(relu7)
relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)
|
tensorflow.nn.relu
| 8,577 |
import tensorflow as tf
with tf.Graph().as_default():
|
tensorflow.Graph
| 8,578 |
import tensorflow as tf
var_list = None
if self.params["warm_start_dir"]:
output_vars1 = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="task_dependent")
output_vars2 = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="task_independent/Variable_1")
|
tensorflow.get_collection
| 8,579 |
import tensorflow as tf
losses_eval_test = build_eval_graph(images_eval_test, labels_eval_test, images_eval_test, ul_u_eval_test)
init_op = tf.global_variables_initializer()
|
tensorflow.global_variables_initializer
| 8,580 |
import tensorflow as tf
action_spec = self._actor.output_tensor_spec
states_tiled = tf.tile(states[:, None], [1, num_tasks, 1]) # B x B x D
states_tiled = tf.reshape(states_tiled,
[batch_size * num_tasks, obs_dim]) # B*B x D
actions_tiled = tf.tile(actions[:, None], [1, num_tasks, 1]) # B x B x D
actions_tiled = tf.reshape(actions_tiled,
[batch_size * num_tasks, action_dim]) # B*B x D
tasks_tiled = tf.tile(tasks[None], [batch_size, 1, 1]) # B x B x D
tasks_tiled = tf.reshape(tasks_tiled,
[batch_size * num_tasks, task_dim]) # B*B x D
|
tensorflow.reshape
| 8,581 |
import tensorflow as tf
sc = getOrCreateSparkContext()
train_rdd = callBigDlFunc("float", "createRDDFromTFRecords",
file_path, sc, serialized_graph,
serialized_example.name, output_names)
validation_rdd = None
if validation_file_path is not None:
validation_rdd = callBigDlFunc("float", "createRDDFromTFRecords",
validation_file_path, sc, serialized_graph,
serialized_example.name, output_names)
tensor_structure = nest.pack_sequence_as(results,
[TensorMeta(tf.as_dtype(t.dtype),
shape=t.shape,
name="data_%s" % i)
for i, t in enumerate(nest.flatten(results))])
super(TFRecordDataset, self).__init__(tensor_structure, batch_size,
batch_per_thread, hard_code_batch_size)
self.train_rdd = train_rdd
self.validation_rdd = validation_rdd
def get_prediction_data(self):
|
tensorflow.as_dtype
| 8,582 |
import tensorflow as tf
pop_var = tf.get_variable('pop_var', [shape[-1]], initializer=tf.constant_initializer(1.), trainable=False)
if pop_mean not in tf.moving_average_variables():
tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, pop_mean)
tf.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, pop_var)
def func1():
# execute at training time
|
tensorflow.add_to_collection
| 8,583 |
import tensorflow as tf
def _SaveAndLoad(self, var_name, var_value, other_value, save_path):
with self.test_session() as sess:
var = tf.Variable(var_value, name=var_name)
save = tf.train.Saver({var_name: var})
var.initializer.run()
val = save.save(sess, save_path)
self.assertEqual(save_path, val)
with self.test_session() as sess:
var = tf.Variable(other_value, name=var_name)
save = tf.train.Saver({var_name: var})
save.restore(sess, save_path)
self.assertAllClose(var_value, var.eval())
def testCacheRereadsFile(self):
save_path = os.path.join(self.get_temp_dir(), "cache_rereads")
# Save and reload one Variable named "var0".
self._SaveAndLoad("var0", 0.0, 1.0, save_path)
# Save and reload one Variable named "var1" in the same file.
# The cached readers should know to re-read the file.
|
tensorflow.train.Saver
| 8,584 |
import tensorflow as tf
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map', activation,
False, wd, keep_prob, is_train)
# ensure the seletion is right
dep_selection = tf.logical_and(rep_mask, dep_selection)
head_selection = tf.logical_and(rep_mask, head_selection)
rep_dep_tensor, rep_dep_mask, dep_org_idx = reduce_data_rep_max_len(rep_map, dep_selection)
rep_head_tensor,rep_head_mask, head_org_idx = reduce_data_rep_max_len(rep_map, head_selection)
sl_dep, sl_head = tf.shape(rep_dep_tensor)[1], tf.shape(rep_head_tensor)[1]
if keep_unselected:
unhead_selection = tf.logical_and(rep_mask, tf.logical_not(head_selection))
rep_unhead_tensor, rep_unhead_mask, unhead_org_idx = reduce_data_rep_max_len(rep_map, unhead_selection)
sl_unhead = tf.shape(rep_unhead_tensor)[1]
attn_result = tf.cond(
tf.equal(sl_head, 0),
lambda: tf.zeros([bs, 0, hn], tf.float32),
lambda: self_attention_for_selected_head(
head_selection, head_org_idx, sl_head, rep_head_mask,
dep_selection, dep_org_idx, sl_dep, rep_dep_mask,
rep_map, rep_dep_tensor, keep_prob, is_train, direction, ivec
)
)
if keep_unselected:
|
tensorflow.shape
| 8,585 |
import tensorflow as tf
initializer=tf.constant_initializer(float('nan')))
self.epsilon = epsilon
def __call__(self,input_var,name=None,**kwargs) :
if( input_var.shape.ndims > 2 ) :
dims = tf.reduce_prod(tf.shape(input_var)[1:])
input_var = tf.reshape(input_var,[-1,dims])
def _init():
v_norm = tf.nn.l2_normalize(self.v,axis=0)
|
tensorflow.shape
| 8,586 |
import tensorflow as tf
if __name__ == "__main__":
flags.mark_flag_as_required("data_dir")
flags.mark_flag_as_required("task_name")
flags.mark_flag_as_required("vocab_file")
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("output_dir")
tf.app.run()
|
tensorflow.app.run
| 8,587 |
import tensorflow as tf
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
|
tensorflow.gfile.MakeDirs
| 8,588 |
import tensorflow as tf
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),
add=True,
transpose=transpose)
else:
|
tensorflow.constant
| 8,589 |
import tensorflow as tf
if anchor_match_mining_distance_matrix is None:
anchor_match_mining_distance_matrix = anchor_match_distance_matrix
if use_semi_hard:
if anchor_positive_mining_distances is None:
raise ValueError('Positive match embeddings must be specified to compute '
'semi-hard distances.')
anchor_positive_mining_distances = tf.expand_dims(
anchor_positive_mining_distances, axis=-1)
indicators &= (
anchor_match_mining_distance_matrix > anchor_positive_mining_distances)
def find_hard_distances(distance_matrix, indicator_matrix):
distance_matrix = tf.where(
tf.stop_gradient(indicator_matrix), distance_matrix,
tf.fill(tf.shape(distance_matrix), distance_matrix.dtype.max))
hard_distances = tf.math.reduce_min(distance_matrix, axis=-1)
return hard_distances
hard_negative_mining_distances = find_hard_distances(
anchor_match_mining_distance_matrix, indicators)
indicators &= tf.math.equal(
anchor_match_mining_distance_matrix,
tf.expand_dims(hard_negative_mining_distances, axis=-1))
hard_negative_distances = find_hard_distances(anchor_match_distance_matrix,
indicators)
|
tensorflow.stop_gradient
| 8,590 |
import tensorflow as tf
def _enqueue_loop():
while True:
random.shuffle(train_examples)
for example in train_examples:
tensorized_example = self.tensorize_example(example, is_training=True)
feed_dict = dict(zip(self.queue_input_tensors, tensorized_example))
session.run(self.enqueue_op, feed_dict=feed_dict)
enqueue_thread = threading.Thread(target=_enqueue_loop)
enqueue_thread.daemon = True
enqueue_thread.start()
def restore(self, session):
# Don't try to restore unused variables from the TF-Hub ELMo module.
vars_to_restore = [v for v in tf.global_variables() if "module/" not in v.name]
saver = tf.train.Saver(vars_to_restore)
checkpoint_path = os.path.join(self.config["log_dir"], "model.max.ckpt")
print("Restoring from {}".format(checkpoint_path))
session.run(tf.global_variables_initializer())
saver.restore(session, checkpoint_path)
def load_lm_embeddings(self, doc_key):
if self.lm_file is None:
return np.zeros([0, 0, self.lm_size, self.lm_layers])
file_key = doc_key.replace("/", ":")
group = self.lm_file[file_key]
num_sentences = len(list(group.keys()))
sentences = [group[str(i)][...] for i in range(num_sentences)]
lm_emb = np.zeros([num_sentences, max(s.shape[0] for s in sentences), self.lm_size, self.lm_layers])
|
tensorflow.train.Saver
| 8,591 |
import tensorflow as tf
tf.reshape(poses[0][i], [1, 1, 3, 3]),
tf.reshape(poses[1][i], [1, 1, 3]), inverse=True) * 2.0
samples_object = (samples_object * (29.0/32.0) / 2.0 + 0.5) * 32.0 - 0.5
samples = tf.squeeze(samples_object)
interpolated = trilinear.interpolate(sdf, samples)
occupancy_value = tf.math.sign(tf.nn.relu(interpolated + self.tol))
sdf_values += occupancy_value
intersection = tf.reduce_sum(tf.math.sign(tf.nn.relu(sdf_values - 1)))
if intersection > prev_intersection:
prev_intersection = intersection
num_collisions += 1
status2 = False
if status2:
a = 1
values = interpolated
inter = tf.reshape(values, [self.resolution,
self.resolution,
self.resolution])
inter = tf.transpose(tf.reduce_max(inter, axis=a))
im = axs[fig_obj_count, 0].matshow(inter.numpy())
plt.colorbar(im, ax=axs[fig_obj_count, 0])
values = tf.math.sign(tf.nn.relu(interpolated + self.tol))
inter = tf.reshape(values, [self.resolution,
self.resolution,
self.resolution])
inter = tf.transpose(tf.reduce_max(inter, axis=a))
im = axs[fig_obj_count, 1].matshow(inter.numpy())
plt.colorbar(im, ax=axs[fig_obj_count, 1])
|
tensorflow.reshape
| 8,592 |
from tensorflow.python.ops import gen_nn_ops
value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
`int16`, `int8`, or `complex64`.
bias: A 1-D `Tensor` with size matching the last dimension of `value`.
Must be the same type as `value` unless `value` is a quantized type,
in which case a different quantized type may be used.
name: A name for the operation (optional).
Returns:
A `Tensor` with the same type as `value`.
"""
with ops.op_scope([value, bias], name, "BiasAddV1") as name:
value = ops.convert_to_tensor(value, name="input")
bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
return gen_nn_ops._bias_add_v1(value, bias, name=name)
ops.RegisterShape("BiasAddV1")(common_shapes.bias_add_shape)
ops.RegisterShape("BiasAddGradV1")(common_shapes.bias_add_grad_shape)
def relu6(features, name=None):
"""Computes Rectified Linear 6: `min(max(features, 0), 6)`.
|
tensorflow.python.ops.gen_nn_ops._bias_add_v1
| 8,593 |
import tensorflow as tf
lambda: 0.) # On the first step, prev_log_r = 0.
log_r = tf.cond(
tf.less(t + 1, self.max_seq_len),
lambda: self.tilt(rnn_out, latent_encoded, self.targets_ta.read(t+1)),
lambda: 0.)
# On the last step, log_r = 0.
log_r *= tf.to_float(t < self.seq_lengths - 1)
weights += log_r - prev_log_r
new_state = TrainableVRNNState(rnn_state=next_rnn_state,
rnn_out=rnn_out,
latent_encoded=latent_encoded)
return weights, new_state
_DEFAULT_INITIALIZERS = {"w": tf.contrib.layers.xavier_initializer(),
"b": tf.zeros_initializer()}
def create_vrnn(
data_size,
latent_size,
emission_class,
rnn_hidden_size=None,
fcnet_hidden_sizes=None,
encoded_data_size=None,
encoded_latent_size=None,
sigma_min=0.0,
raw_sigma_bias=0.25,
|
tensorflow.contrib.layers.xavier_initializer
| 8,594 |
import tensorflow as tf
)
# Zero out embeddings of pad value
masks = tf.not_equal(self.inputs, pad_value, name='masks')
word_embeddings *= tf.cast(
|
tensorflow.not_equal
| 8,595 |
import tensorflow as tf
fc_size1 = 384
fc_size2 = 192
# convolutional layers
with tf.variable_scope('conv1'):
layer1, weights1 = new_conv_layer(x, name="conv1", num_input_channels=3, num_filters=64, filter_size=5, ac_fun=tf.nn.relu,
pool_ksize=[1, 3, 3, 1])
with tf.variable_scope('conv2'):
layer2, weights2 = new_conv_layer(input=layer1, name="conv2", num_input_channels=64, num_filters=64, filter_size=5,
ac_fun=tf.nn.relu, pool_ksize=[1, 3, 3, 1])
with tf.name_scope('flatten'):
layer3, num_features = flatten_layer(layer2)
# fully connected layers
with tf.variable_scope('fc1'):
|
tensorflow.variable_scope
| 8,596 |
import tensorflow as tf
# In the demo, we are doing a simple classification task on the entire
# segment.
#
# If you want to use the token-level output, use model.get_sequence_output()
# instead.
output_layer = model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
|
tensorflow.truncated_normal_initializer
| 8,597 |
import tensorflow as tf
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
# Test with state_is_tuple=False.
with tf.variable_scope("no_tuple"):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
dec, mem = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 4), res[0].shape)
# Test externally provided output projection.
w = tf.get_variable("proj_w", [2, 5])
b = tf.get_variable("proj_b", [5])
|
tensorflow.nn.seq2seq.embedding_attention_seq2seq
| 8,598 |
import tensorflow as tf
depth_f = tf.to_float(depth)
height_f = tf.to_float(height)
width_f = tf.to_float(width)
# Number of disparity interpolated.
out_depth = out_size[0]
out_height = out_size[1]
out_width = out_size[2]
zero = tf.zeros([], dtype='int32')
# 0 <= z < depth, 0 <= y < height & 0 <= x < width.
max_z = tf.to_int32(tf.shape(im)[1] - 1)
max_y = tf.to_int32(tf.shape(im)[2] - 1)
max_x = tf.to_int32(tf.shape(im)[3] - 1)
# Converts scale indices from [-1, 1] to [0, width/height/depth].
x = (x + 1.0) * (width_f) / 2.0
y = (y + 1.0) * (height_f) / 2.0
z = (z + 1.0) * (depth_f) / 2.0
|
tensorflow.shape
| 8,599 |
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