seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
from tensorflow.python.framework import ops
ops.RegisterShape("DepthwiseConv2dNative")(
common_shapes.depthwise_conv2d_native_shape)
ops.RegisterShape("AvgPool")(common_shapes.avg_pool_shape)
ops.RegisterShape("MaxPool")(common_shapes.max_pool_shape)
| tensorflow.python.framework.ops.RegisterShape | 3,200 |
import tensorflow as tf
return compute_l2_sigmoid_matching_distances
if distance_kernel == common.DISTANCE_KERNEL_EXPECTED_LIKELIHOOD:
def compute_gaussian_likelihoods(lhs, rhs):
"""Computes sample likelihoods."""
num_lhs_samples = lhs.shape.as_list()[-2] - 2
num_rhs_samples = rhs.shape.as_list()[-2] - 2
lhs_means, lhs_stddevs, lhs_samples = tf.split(
lhs, [1, 1, num_lhs_samples], axis=-2)
rhs_means, rhs_stddevs, rhs_samples = tf.split(
rhs, [1, 1, num_rhs_samples], axis=-2)
rhs_likelihoods = distance_utils.compute_gaussian_likelihoods(
lhs_means,
lhs_stddevs,
rhs_samples,
min_stddev=distance_kernel_kwargs.get(
distance_kernel + '_min_stddev', None),
max_squared_mahalanobis_distance=distance_kernel_kwargs.get(
distance_kernel + '_max_squared_mahalanobis_distance', None),
| tensorflow.split | 3,201 |
import tensorflow as tf
dict(
testcase_name='fixed_len_int',
make_tensors_fn=lambda:
{'x': tf.compat.v1.placeholder(tf.int64, (None,))},
feature_spec={'x': tf.io.FixedLenFeature([], tf.int64)}),
dict(
testcase_name='fixed_len_string',
make_tensors_fn=lambda:
| tensorflow.io.FixedLenFeature | 3,202 |
import tensorflow as tf
with tf.name_scope('prediction_incorrect'):
x = tf.logical_not(tf.nn.in_top_k(logits, label, topk))
| tensorflow.nn.in_top_k | 3,203 |
import tensorflow as tf
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)
| tensorflow.layers.dense | 3,204 |
import tensorflow as tf
features={
'label':tf.FixedLenFeature([], tf.int64),
'img_raw' : tf.FixedLenFeature([], tf.string),
})
image=tf.decode_raw(features['img_raw'],tf.uint8)
label=tf.cast(features['label'],tf.int32)
image=tf.reshape(image,[4096,1])
return image,label
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'):
self.w1=tf.get_variable('w1', [4096,1024],initializer=tf.contrib.layers.xavier_initializer_conv2d())
| tensorflow.reshape | 3,205 |
import tensorflow as tf
:, -1],
method=0)
gtboxes_in_img_r = self.drawer.draw_boxes_with_categories(img_batch=img,
boxes=gtboxes_and_label_r[
:, :-1],
labels=gtboxes_and_label_r[
:, -1],
method=1,
is_csl=True)
tf.summary.image('Compare/gtboxes_h_gpu:%d' % i, gtboxes_in_img_h)
tf.summary.image('Compare/gtboxes_r_gpu:%d' % i, gtboxes_in_img_r)
if cfgs.ADD_BOX_IN_TENSORBOARD:
detections_in_img = self.drawer.draw_boxes_with_categories_and_scores(
img_batch=img,
boxes=outputs[0],
scores=outputs[1],
labels=outputs[2],
method=1,
is_csl=True)
| tensorflow.summary.image | 3,206 |
from tensorflow.python.ops import variable_scope as vs
bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype)
with vs.variable_scope("gates"): # Reset gate and update gate.
| tensorflow.python.ops.variable_scope.variable_scope | 3,207 |
import tensorflow as tf
masks,
scores,
classes,
min_score_thresh=0.65,
min_iou_thresh=0.5,
is_class_agnostic=True)
nms_masks_expected1 = tf.stack([mask0, mask4])
nms_scores_expected1 = tf.constant([1.0, 0.85], dtype=tf.float32)
nms_classes_expected1 = tf.constant([1, 2], dtype=tf.int32)
(nms_masks2,
nms_scores2,
nms_classes2,
| tensorflow.stack | 3,208 |
import tensorflow as tf
# execute mean pooling based on pooling_mask[bs, sluh, sld] and pooling_data[bs,sluh,sld,hn]
pooling_data = mask_for_high_rank(pooling_data, pooling_mask) # [bs,sluh,sld,hn]
pooling_data_sum = tf.reduce_sum(pooling_data, -2) # [bs,sluh,hn]
pooling_den = tf.reduce_sum(tf.cast(pooling_mask, tf.int32), -1, keep_dims=True) # [bs,sluh]
pooling_den = tf.where(tf.equal(pooling_den, 0), tf.ones_like(pooling_den), pooling_den)
pooling_result = pooling_data_sum / tf.cast(pooling_den, tf.float32)
return pooling_result
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1./scale * x) | tensorflow.cast | 3,209 |
import tensorflow as tf
'''
Print all trainable and non-trainable variables
'''
if train_only:
t_vars = tf.trainable_variables()
print('[*] printing trainable variables')
else:
try:
| tensorflow.trainable_variables | 3,210 |
import tensorflow as tf
perturb_norm_length: a `float`, Norm length of adversarial perturbation
to be optimized with validatio
Returns:
adversial loss
"""
grad, = tf.gradients(
loss, embedded, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
grad = tf.stop_gradient(grad)
perturb = _scale_l2(grad, perturb_norm_length)
return loss_fn(embedded + perturb)
| tensorflow.gradients | 3,211 |
from tensorflow.python.ops import partitioned_variables
dropout = params.get("dropout")
gradient_clip_norm = params.get("gradient_clip_norm")
num_ps_replicas = config.num_ps_replicas if config else 0
embedding_lr_multipliers = params.get("embedding_lr_multipliers", {})
features = _get_feature_dict(features)
parent_scope = "dnn"
input_layer_partitioner = (partitioned_variables.min_max_variable_partitioner(
max_partitions=num_ps_replicas, min_slice_size=64 << 20))
input_layer_scope = parent_scope + "/input_from_feature_columns"
with variable_scope.variable_scope(
input_layer_scope,
values=list(six.itervalues(features)),
partitioner=input_layer_partitioner) as scope:
net = layers.input_from_feature_columns(
| tensorflow.python.ops.partitioned_variables.min_max_variable_partitioner | 3,212 |
import tensorflow as tf
self.queue_input_tensors = [tf.placeholder(dtype, shape) for dtype, shape in input_props]
dtypes, shapes = zip(*input_props)
queue = tf.PaddingFIFOQueue(capacity=10, dtypes=dtypes, shapes=shapes)
self.enqueue_op = queue.enqueue(self.queue_input_tensors)
self.input_tensors = queue.dequeue()
self.predictions, self.loss = self.get_predictions_and_loss(*self.input_tensors)
self.global_step = tf.Variable(0, name="global_step", trainable=False)
self.reset_global_step = tf.assign(self.global_step, 0)
learning_rate = tf.train.exponential_decay(self.config["learning_rate"], self.global_step,
self.config["decay_frequency"], self.config["decay_rate"], staircase=True)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
gradients, _ = tf.clip_by_global_norm(gradients, self.config["max_gradient_norm"])
optimizers = {
"adam" : tf.train.AdamOptimizer,
| tensorflow.assign | 3,213 |
import tensorflow as tf
# Count number of non-padding words in each sentence
sentence_lengths = tf.count_nonzero(
masks,
| tensorflow.count_nonzero | 3,214 |
import tensorflow as tf
w_ = tf.reshape(w, [ksize[0] * ksize[1] * ksize[2], -1])
q = tf.matmul(p_, w_)
| tensorflow.matmul | 3,215 |
from tensorflow.python.framework import ops
# avoid division by zero
epsilon = 1e-7
def compute_precision(name):
precision = math_ops.div(true_positives,
epsilon + true_positives + false_positives,
name='precision_' + name)
return precision
precision = compute_precision('value')
with ops.control_dependencies([true_positives_compute_op,
false_positives_compute_op]):
update_op = compute_precision('update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, precision)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
| tensorflow.python.framework.ops.control_dependencies | 3,216 |
import tensorflow as tf
h = norm(n+m, 'ln_2')
return h
def embed(X, we):
#X [-1,,2]
we = convert_gradient_to_tensor(we)
e = tf.gather(we, X)
h = tf.reduce_sum(e, 2)
return h
def clf(x, ny, w_init=tf.random_normal_initializer(stddev=0.02), b_init=tf.constant_initializer(0), train=False):
with tf.variable_scope('clf'):
| tensorflow.gather | 3,217 |
import tensorflow as tf
help='the directory of MNIST dataset')
parser.add_argument('--lr', type=float, default=0.01, help='learning rate')
parser.add_argument('--batch_size', type=int, default=32, help='batch size')
parser.add_argument('--max_train_step', type=int, default=50000, help='the maximum training step')
parser.add_argument('--model_path', type=str, default='', help='the path of checkpoint file')
args = parser.parse_args()
def model():
x = tf.placeholder(tf.float32, [None, 784], name='x')
gt = tf.placeholder(tf.float32, [None, 10], name='groundtruth')
with tf.variable_scope('layer1'):
w1 = tf.get_variable('weight1', [784, 1024], initializer=tf.random_normal_initializer())
b1 = tf.get_variable('bias1', [1024], initializer=tf.constant_initializer(0.0))
h1 = tf.nn.relu(tf.matmul(x, w1) + b1)
with tf.variable_scope('layer2'):
w2 = tf.get_variable('weight2', [1024, 1024], initializer=tf.random_normal_initializer())
b2 = tf.get_variable('bias2', [1024], initializer=tf.constant_initializer(0.0))
h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
with tf.variable_scope('layer3'):
w3 = tf.get_variable('weight3', [1024, 10], initializer=tf.random_normal_initializer())
b3 = tf.get_variable('bias3', [10], initializer=tf.constant_initializer(0.0))
y = tf.matmul(h2, w3) + b3
| tensorflow.random_normal_initializer | 3,218 |
import tensorflow as tf
elif params['optimizer'] == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
elif params['optimizer'] == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(learning_rate)
elif params['optimizer'] == 'adagrad':
optimizer = tf.train.AdagradOptimizer(learning_rate)
elif params['optimizer'] == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate, momentum=params['momentum'])
elif params['optimizer'] == 'lars':
| tensorflow.train.AdagradOptimizer | 3,219 |
import tensorflow as tf
pred_y = pred_y * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (height / 2.)
if config.PRED_DEBUG:
pred_indices_ = tf.squeeze(pred_indices)
image_ = tf.squeeze(image) * 255.
pred_heatmap = tf.one_hot(pred_indices_, heatmap_size*heatmap_size, on_value=1., off_value=0., axis=-1, dtype=tf.float32)
pred_heatmap = tf.reshape(pred_heatmap, [-1, heatmap_size, heatmap_size])
| tensorflow.squeeze | 3,220 |
import tensorflow as tf
should be used for the indicator function.
scope: Optional scope for `name_scope`.
Returns:
loss: A `Tensor` of the same shape as `logits` with the component-wise loss.
other_outputs: An empty dictionary, for consistency.
Raises:
ValueError: If `surrogate_type` is not `xent` or `hinge`.
"""
with tf.name_scope(scope, 'roc_auc', [labels, logits, weights]):
# Convert inputs to tensors and standardize dtypes.
labels, logits, weights, original_shape = _prepare_labels_logits_weights(labels, logits, weights)
# Create tensors of pairwise differences for logits and labels, and
# pairwise products of weights. These have shape
# [batch_size, batch_size, num_labels].
logits_difference = tf.expand_dims(logits, 0) - tf.expand_dims(logits, 1)
| tensorflow.name_scope | 3,221 |
import tensorflow as tf
num_shards, shuffled))
self.assertAllEqual(
problem.test_filepaths(data_dir, num_shards, shuffled),
problem.data_filepaths(problem_module.DatasetSplit.TEST, data_dir,
num_shards, shuffled))
if __name__ == "__main__":
tf.test.main()
| tensorflow.test.main | 3,222 |
import tensorflow as tf
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
elif not do_serve:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
| tensorflow.train.init_from_checkpoint | 3,223 |
import tensorflow as tf
dec_inp_dict2 = {}
dec_inp_dict2["0"] = [
tf.constant(0, tf.int32, shape=[2]) for _ in range(3)]
dec_inp_dict2["1"] = [
tf.constant(0, tf.int32, shape=[2]) for _ in range(4)]
with tf.variable_scope("other"):
outputs_dict3, _ = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict2, cell, 2, dec_symbols_dict,
embedding_size=2, feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
tf.get_variable_scope().reuse_variables()
outputs_dict1, _ = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict, cell, 2, dec_symbols_dict,
embedding_size=2, feed_previous=True)
outputs_dict2, _ = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict2, cell, 2, dec_symbols_dict,
embedding_size=2, feed_previous=True)
res1 = sess.run(outputs_dict1["0"])
res2 = sess.run(outputs_dict2["0"])
res3 = sess.run(outputs_dict3["0"])
| tensorflow.get_variable_scope | 3,224 |
import tensorflow as tf
return new_h, new_h
def prelu(_x, scope=''):
"""parametric ReLU activation"""
with tf.variable_scope(name_or_scope=scope, default_name="prelu"):
_alpha = tf.get_variable("prelu_"+scope, shape=_x.get_shape()[-1],
dtype=_x.dtype, initializer=tf.constant_initializer(0.1))
return tf.maximum(0.0, _x) + _alpha * tf.minimum(0.0, _x)
| tensorflow.variable_scope | 3,225 |
import tensorflow as tf
vf_old, vf_old_params, _, _ = self.build_cnet(batch['state'], 'oldvf')
self.vf, vf_params, self.vf_state_init, self.vf_state_final = self.build_cnet(batch['state'], 'vf')
self.vf_eval, _, self.vf_eval_state_init, self.vf_eval_state_final = self.build_cnet(self.state, 'vf', reuse=True, batch_size=1)
self.sample_action = tf.squeeze(pi_eval.sample(1), axis=0)
self.eval_action = pi_eval.mode()
self.global_step = tf.train.get_or_create_global_step()
self.saver = tf.train.Saver()
# Loss functions and training
epsilon_decay = tf.train.polynomial_decay(self.EPSILON, self.global_step, self.EPS_LEN, 0.1, power=1)
ratio = tf.maximum(pi.prob(batch['actions']), 1e-6) / tf.maximum(pi_old.prob(batch['actions']), 1e-6)
ratio = tf.clip_by_value(ratio, 0, 10)
surr1 = batch['advantage'] * ratio
surr2 = batch['advantage'] * tf.clip_by_value(ratio, 1 - epsilon_decay, 1 + epsilon_decay)
loss_pg = - 2.0 * tf.reduce_mean(tf.minimum(surr1, surr2))
loss_vf = 0.5 * tf.reduce_mean(tf.square(batch['rewards'] - self.vf))
loss_entropy = - 0.01 * tf.reduce_mean(pi.entropy())
loss = loss_pg + loss_vf + loss_entropy
opt = tf.train.AdamOptimizer(self.LR)
| tensorflow.train.polynomial_decay | 3,226 |
import tensorflow as tf
"""The actual input function."""
batch_size = params["batch_size"]
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
| tensorflow.data.TFRecordDataset | 3,227 |
import tensorflow as tf
# optimizer & gradients
optimizer_base = tf.train.MomentumOptimizer(lrn_rate, FLAGS.momentum)
if not FLAGS.enbl_multi_gpu:
optimizer = optimizer_base
else:
optimizer = mgw.DistributedOptimizer(optimizer_base)
grads_origin = optimizer.compute_gradients(loss, self.trainable_vars)
grads_pruned = self.__calc_grads_pruned(grads_origin)
# TF operations & model saver
self.sess_train = sess
with tf.control_dependencies(self.update_ops):
self.train_op = optimizer.apply_gradients(grads_pruned, global_step=self.global_step)
self.summary_op = tf.summary.merge_all()
self.log_op = [lrn_rate, loss, pr_trainable, pr_maskable] + list(metrics.values())
self.log_op_names = ['lr', 'loss', 'pr_trn', 'pr_msk'] + list(metrics.keys())
self.init_op = tf.variables_initializer(self.vars)
self.init_opt_op = tf.variables_initializer(optimizer_base.variables())
if FLAGS.enbl_multi_gpu:
self.bcast_op = mgw.broadcast_global_variables(0)
self.saver_train = tf.train.Saver(self.vars)
def __build_eval(self):
| tensorflow.control_dependencies | 3,228 |
import tensorflow as tf
]
if direction == 'forward':
i_direction = 0
else:
i_direction = 1
variable_scope_name = 'RNN_{0}/RNN/MultiRNNCell/Cell{1}'.format(
i_direction, i)
with tf.variable_scope(variable_scope_name):
layer_output, final_state = tf.nn.dynamic_rnn(
lstm_cell,
layer_input,
sequence_length=sequence_lengths,
initial_state=tf.nn.rnn_cell.LSTMStateTuple(
*batch_init_states),
)
| tensorflow.variable_scope | 3,229 |
from tensorflow.python.framework import constant_op
return features, label
def _infer_ranking_train_input_fn():
features = {
"f1": constant_op.constant([[3.], [2], [1.]]),
"f2": constant_op.constant([[0.1], [3.], [1.]])
}
return features, None
class BoostedTreeEstimatorTest(test_util.TensorFlowTestCase):
| tensorflow.python.framework.constant_op.constant | 3,230 |
import tensorflow as tf
An operation that will update var_matrix when run in a Session
'''
selected_rows = tf.nn.embedding_lookup(var_matrix, indices)
row_norms = tf.sqrt(tf.reduce_sum(tf.square(selected_rows), 1))
| tensorflow.nn.embedding_lookup | 3,231 |
import tensorflow as tf
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)
| tensorflow.nn.softmax | 3,232 |
import tensorflow as tf
'bar': tf.convert_to_tensor([0, 2, 0, 2], dtype=tf.int64),
}
# Annotate an arbitrary proto at the schema level (not sure what global
# schema boundaries would mean, but hey I'm just a test).
boundaries = tf.constant([[1.0]])
message_type = annotations_pb2.BucketBoundaries.DESCRIPTOR.full_name
sizes = tf.expand_dims([tf.size(boundaries)], axis=0)
message_proto = tf.raw_ops.EncodeProto(
sizes=sizes, values=[tf.cast(boundaries, tf.float32)],
| tensorflow.constant | 3,233 |
import tensorflow as tf
y0_f = tf.to_float(y0)
y1_f = tf.to_float(y1)
z0_f = tf.to_float(z0)
z1_f = tf.to_float(z1)
# Check the out-of-boundary case.
x0_valid = tf.to_float(
tf.less_equal(x0, max_x) & tf.greater_equal(x0, 0))
x1_valid = tf.to_float(
tf.less_equal(x1, max_x) & tf.greater_equal(x1, 0))
y0_valid = tf.to_float(
tf.less_equal(y0, max_y) & tf.greater_equal(y0, 0))
y1_valid = tf.to_float(
tf.less_equal(y1, max_y) & tf.greater_equal(y1, 0))
z0_valid = tf.to_float(
tf.less_equal(z0, max_z) & tf.greater_equal(z0, 0))
z1_valid = tf.to_float(
tf.less_equal(z1, max_z) & tf.greater_equal(z1, 0))
w_z0_y0_x0 = tf.expand_dims(((x1_f - x) * (y1_f - y) *
(z1_f - z) * x1_valid * y1_valid * z1_valid),
1)
w_z0_y0_x1 = tf.expand_dims(((x - x0_f) * (y1_f - y) *
(z1_f - z) * x0_valid * y1_valid * z1_valid),
1)
w_z0_y1_x0 = tf.expand_dims(((x1_f - x) * (y - y0_f) *
(z1_f - z) * x1_valid * y0_valid * z1_valid),
1)
w_z0_y1_x1 = tf.expand_dims(((x - x0_f) * (y - y0_f) *
| tensorflow.greater_equal | 3,234 |
import tensorflow as tf
# normalize
res = (input_ - used_mean) / tf.sqrt(used_var + epsilon)
# de-normalize
if scale:
res *= gamma
res += beta
# update variables
if train:
with tf.name_scope(name, "AssignMovingAvg", [mean, cur_mean, decay]):
with ops.colocate_with(mean):
new_mean = tf.assign_sub(
mean,
tf.check_numerics(decay * (mean - cur_mean), "NaN in moving mean."))
with tf.name_scope(name, "AssignMovingAvg", [var, cur_var, decay]):
with ops.colocate_with(var):
new_var = tf.assign_sub(
var,
tf.check_numerics(decay * (var - cur_var),
"NaN in moving variance."))
with tf.name_scope(name, "IncrementTime", [step]):
with ops.colocate_with(step):
new_step = tf.assign_add(step, 1.)
res += 0. * new_mean * new_var * new_step
return res
# batch normalization taking into account the volume transformation
| tensorflow.name_scope | 3,235 |
import tensorflow as tf
all_trainable_weights_vf = tf_util.get_trainable_vars('model/values_fn')
regularization_penalty_vf = tf.contrib.layers.apply_regularization(regularizervf, all_trainable_weights_vf)
if self.n_step:
values_losses = qf1_loss + qf2_loss + value_loss + regularization_penalty_vf + qf1_loss_n + qf2_loss_n
else:
values_losses = qf1_loss + qf2_loss + value_loss + regularization_penalty_vf
# Policy train op
# (has to be separate from value train op, because min_qf_pi appears in policy_loss)
policy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate_ph)
policy_train_op = policy_optimizer.minimize(policy_loss, var_list=tf_util.get_trainable_vars('model/pi'))
# Value train op
value_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate_ph)
values_params = tf_util.get_trainable_vars('model/values_fn')
source_params = tf_util.get_trainable_vars("model/values_fn/vf")
target_params = tf_util.get_trainable_vars("target/values_fn/vf")
# Polyak averaging for target variables
self.target_update_op = [
tf.assign(target, (1 - self.tau) * target + self.tau * source)
for target, source in zip(target_params, source_params)
]
# Initializing target to match source variables
target_init_op = [
tf.assign(target, source)
| tensorflow.train.AdamOptimizer | 3,236 |
import tensorflow as tf
optimizer = tf.train.MomentumOptimizer(learning_rate=truncated_learning_rate,
momentum=params['momentum'])
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step)
else:
train_op = None
cls_accuracy = tf.metrics.accuracy(glabels, predictions['classes'])
metrics = {'cls_accuracy': cls_accuracy}
# Create a tensor named train_accuracy for logging purposes.
tf.identity(cls_accuracy[1], name='cls_accuracy')
tf.summary.scalar('cls_accuracy', cls_accuracy[1])
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=metrics,
scaffold = tf.train.Scaffold(init_fn=train_helper.get_init_fn_for_scaffold(FLAGS)))
def parse_comma_list(args):
return [float(s.strip()) for s in args.split(',')]
def main(_):
# Using the Winograd non-fused algorithms provides a small performance boost.
| tensorflow.summary.scalar | 3,237 |
import tensorflow as tf
tf.app.flags.DEFINE_float(
'gpu_memory_fraction', 1., 'GPU memory fraction to use.')
# scaffold related configuration
tf.app.flags.DEFINE_string(
'data_dir', '../Datasets/tfrecords',#'/media/rs/0E06CD1706CD0127/Kapok/Chi/Datasets/tfrecords',
'The directory where the dataset input data is stored.')
tf.app.flags.DEFINE_string(
'dataset_name', '{}_????', 'The pattern of the dataset name to load.')
tf.app.flags.DEFINE_string(
'model_dir', './logs_sext_cpn/',
'The parent directory where the model will be stored.')
tf.app.flags.DEFINE_integer(
'log_every_n_steps', 10,
'The frequency with which logs are print.')
tf.app.flags.DEFINE_integer(
'save_summary_steps', 100,
| tensorflow.app.flags.DEFINE_string | 3,238 |
import tensorflow as tf
if double_q:
target_index = tf.math.argmax(q_func(obs_tp1_float, self.num_actions, 'q_func', reuse = tf.AUTO_REUSE), axis = 1, output_type = tf.int32)
target_v_ph = tf.gather_nd(target_q_ph, tf.stack([tf.range(tf.shape(target_q_ph)[0]), target_index], axis=1))
else:
target_v_ph = tf.math.reduce_max(target_q_ph, axis = 1)
| tensorflow.shape | 3,239 |
from tensorflow.contrib.eager.python.examples.linear_regression import linear_regression
true_w = [[1.0], [-0.5], [2.0]]
true_b = [1.0]
model = linear_regression.LinearModel()
dataset = linear_regression.synthetic_dataset(
true_w, true_b, noise_level=0., batch_size=64, num_batches=40)
| tensorflow.contrib.eager.python.examples.linear_regression.linear_regression.LinearModel | 3,240 |
import tensorflow as tf
def pool(inp, name, kind, size, stride, padding='SAME'):
assert kind in ['max', 'avg']
strides = [1, stride, stride, 1]
sizes = [1, size, size, 1]
with tf.variable_scope(name):
if kind == 'max':
out = tf.nn.max_pool(inp, sizes, strides=strides, padding=padding, name=kind)
else:
out = tf.nn.avg_pool(inp, sizes, strides=strides, padding=padding, name=kind)
return out
| tensorflow.variable_scope | 3,241 |
import tensorflow as tf
# The qdist will be defined with no cutoffs,
qdist = distributions.QuantizedDistribution(
base_dist_cls=distributions.Uniform,
lower_cutoff=None,
upper_cutoff=None,
a=tf.zeros(
batch_shape, dtype=tf.float32),
b=10 * tf.ones(
batch_shape, dtype=tf.float32))
# x is random integers in {-3,...,12}.
x = self._rng.randint(-3, 13, size=batch_shape).astype(np.float32)
# pmf
| tensorflow.ones | 3,242 |
import tensorflow as tf
If `min_fake`, minimizing the generator loss is to minimize the
probability of fake data being classified as fake.
Returns:
(scalar Tensor, scalar Tensor): (generator_loss, discriminator_loss).
"""
real_logits = discriminator_fn(real_data)
if isinstance(real_logits, (list, tuple)):
real_logits = real_logits[0]
real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=real_logits, labels=tf.ones_like(real_logits)))
fake_logits = discriminator_fn(fake_data)
if isinstance(fake_logits, (list, tuple)):
fake_logits = fake_logits[0]
fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=fake_logits, labels=tf.zeros_like(fake_logits)))
d_loss = real_loss + fake_loss
| tensorflow.ones_like | 3,243 |
import tensorflow as tf
double_obs_ph = double_policy.obs_ph
with tf.variable_scope("loss", reuse=reuse):
# set up placeholders
| tensorflow.variable_scope | 3,244 |
import tensorflow as tf
def gaussian_pdf(mean, loc_std, sample):
Z = 1.0 / (loc_std * tf.sqrt(2.0 * np.pi))
a = - tf.square(sample - mean) / (2.0 * tf.square(loc_std))
return Z * tf.exp(a)
class ACNet:
def __init__(self, scope, GRID_SIZE, a_size, trainer,TRAINING, GLOBAL_NET_SCOPE):
with tf.variable_scope(str(scope)+'/qvalues'):
#The input size may require more work to fit the interface.
self.inputs = tf.placeholder(shape=[None,GRID_SIZE,GRID_SIZE, num_channels], dtype=tf.float32) # input state
# self.goal_pos = tf.placeholder(shape=[None,2],dtype=tf.float32)
self.prev_loc = tf.placeholder(shape=[None,2], dtype=tf.float32)
# self.policy, self.next_loc, self.value, self.state_out, self.state_in, self.state_init, self.valids, self.blocking, self.mypos, self.goalpos, self.next_loc_mean = self._build_net(self.inputs, self.inputs_primal, self.prev_loc, RNN_SIZE, TRAINING,a_size)
'''
CHANGES
- removed target_blocking, blocking layers, blocking_loss
- removed imitation gradients and losss
- removed valid_loss
- removed train_valid
- commented out policy loss (since, discrete)
| tensorflow.placeholder | 3,245 |
import tensorflow as tf
batch_size: batch_size of the training or validation ops
eps: a float, prevents divide by zero
name: Optional scope/name for op_scope.
Returns:
A tensor with the kappa loss.
"""
with tf.name_scope(name):
labels = tf.to_float(labels)
repeat_op = tf.to_float(
tf.tile(tf.reshape(tf.range(0, num_ratings), [num_ratings, 1]), [1, num_ratings]))
repeat_op_sq = tf.square((repeat_op - tf.transpose(repeat_op)))
weights = repeat_op_sq / tf.to_float((num_ratings - 1)**2)
pred_ = predictions**y_pow
try:
| tensorflow.to_float | 3,246 |
import tensorflow as tf
is_reduction=is_reduction, is_train=is_train)
X2 = self._add_drop_path(X2, drop_path_keep_prob)
X = tf.add_n([X1, X2])
blocks.append(X)
| tensorflow.add_n | 3,247 |
import tensorflow as tf
if var.dtype.base_dtype == tf.float16:
eps = 1e-7 # Can't use 1e-8 due to underflow -- not sure if it makes a big difference.
else:
eps = 1e-8
v = self.get_slot(var, "v")
v_t = v.assign(beta2_t * v + (1. - beta2_t) * tf.square(grad))
m = self.get_slot(var, "m")
m_t = m.assign(beta1_t * m + (1. - beta1_t) * grad)
v_t_hat = tf.div(v_t, 1. - beta2_t)
m_t_hat = tf.div(m_t, 1. - beta1_t)
g_t = tf.div(m_t_hat, tf.sqrt(v_t_hat) + eps)
g_t_1 = self.get_slot(var, "g")
g_t = g_t_1.assign(g_t)
var_update = state_ops.assign_sub(var,
2. * lr_t * g_t - lr_t * g_t_1) # Adam would be lr_t * g_t
return control_flow_ops.group(*[var_update, m_t, v_t, g_t])
def _apply_sparse(self, grad, var):
raise NotImplementedError("Sparse gradient updates are not supported.")
class RegularizeGradientDescentOptimizer(optimizer.Optimizer):
| tensorflow.sqrt | 3,248 |
import tensorflow as tf
"""
if out_dim is not None:
with tf.variable_scope(name) :
self.gamma= tf.get_variable('gamma',[1,1,1,out_dim], initializer=tf.constant_initializer(1.0))
self.beta = tf.get_variable('beta',[out_dim], initializer=tf.constant_initializer(0.0))
else:
self.gamma = None
self.beta = None
| tensorflow.constant_initializer | 3,249 |
import tensorflow as tf
"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)
if scale:
gamma = variable_on_cpu("gamma", [dim], tf.constant_initializer(1.))
beta = variable_on_cpu("beta", [dim], tf.constant_initializer(0.))
# 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.:
| tensorflow.constant_initializer | 3,250 |
from tensorflow.python.framework import ops
"xw_plus_b_v1" is used.
Returns:
A 2-D Tensor computing matmul(x, weights) + biases.
Dimensions typically: batch, out_units.
"""
with ops.op_scope([x, weights, biases], name, "xw_plus_b_v1") 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")
mm = math_ops.matmul(x, weights)
return bias_add_v1(mm, biases, name=name)
# pylint: disable=invalid-name
def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):
"""Computes dropout.
| tensorflow.python.framework.ops.convert_to_tensor | 3,251 |
from tensorflow.python.framework import ops
self._BenchmarkOp(training_op, "cudnn_lstm %s %s" %
(config_name, self._GetConfigDesc(config)))
def benchmarkTfRNNLSTMTraining(self):
test_configs = self._GetTestConfig()
for config_name, config in test_configs.items():
num_layers = config["num_layers"]
num_units = config["num_units"]
batch_size = config["batch_size"]
seq_length = config["seq_length"]
with ops.Graph().as_default(), ops.device("/device:GPU:0"):
inputs = seq_length * [
array_ops.zeros([batch_size, num_units], dtypes.float32)
]
initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127)
cell = rnn_cell.LSTMCell(
num_units=num_units, initializer=initializer, state_is_tuple=True)
multi_cell = rnn_cell.MultiRNNCell(
[cell() for _ in range(num_layers)])
outputs, final_state = core_rnn.static_rnn(
| tensorflow.python.framework.ops.device | 3,252 |
import tensorflow as tf
from sklearn.metrics import classification_report
slim = tf.contrib.slim
global first
first = True
classnum=12
testnum = tf.placeholder(tf.int32)
trainnum = tf.placeholder(tf.int32)
validnum = tf.placeholder(tf.int32)
learnrate = tf.placeholder(tf.float32)
def getinputs(path):
filename_queue=tf.train.string_input_producer([path])
reader=tf.TFRecordReader()
| tensorflow.placeholder | 3,253 |
import tensorflow as tf
# Embedding variables
entity_var_shape = [entity_cnt, self.embedding_size]
rel_var_shape = [rel_cnt, self.embedding_size]
entity_init = tf.truncated_normal(entity_var_shape, stddev=init_sd)
rel_init = tf.truncated_normal(rel_var_shape, stddev=init_sd)
# Ensure maxnorm constraints are initially satisfied
entity_init = dense_maxnorm(entity_init, self.maxnorm)
self.entity_embedding_vars = tf.Variable(entity_init)
self.rel_embedding_vars = tf.Variable(rel_init)
# Embedding layer for each (head, rel, tail) triple being fed in as input
head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input)
tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input)
rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input)
# Relationship vector acts as a translation in entity embedding space
diff_vec = tail_embed - (head_embed + rel_embed)
# negative dist so higher scores are better (important for pairwise loss)
if self.dist == 'manhattan':
raw_output = -tf.reduce_sum(tf.abs(diff_vec), 1)
elif self.dist == 'euclidean':
# +eps because gradients can misbehave for small values in sqrt
raw_output = -tf.sqrt(tf.reduce_sum(tf.square(diff_vec), 1) + self.EPS)
elif self.dist == 'sqeuclidean':
| tensorflow.nn.embedding_lookup | 3,254 |
import tensorflow as tf
# shape is statically known.
self.assertEqual(2, a.shape[0].value)
assertions_triggered[0] += 1
return a
f0(tf.constant([1]))
f1(tf.constant([1]))
f2(tf.constant([1]))
self.assertEqual(3, assertions_triggered[0])
def test_out_of_order_execution1(self):
with self.test_session() as session:
batcher = dynamic_batching._Batcher(minimum_batch_size=1,
maximum_batch_size=1,
| tensorflow.constant | 3,255 |
import tensorflow as tf
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)
with tf.variable_scope('branch3_5x5'):
if o3s1>0:
conv5a = conv(inp, inSize, o3s1, 1, 1, 1, 1, 'SAME', 'conv1x1', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
conv5 = conv(conv5a, o3s1, o3s2, 5, 5, ks, ks, 'SAME', 'conv5x5', phase_train=phase_train, use_batch_norm=use_batch_norm, weight_decay=weight_decay)
net.append(conv5)
with tf.variable_scope('branch4_pool'):
if poolType=='MAX':
pool = mpool(inp, o4s1, o4s1, o4s3, o4s3, 'SAME', 'pool')
| tensorflow.variable_scope | 3,256 |
import tensorflow as tf
processed_l1_h2 = tf.nn.bias_add(
processed_l1_h2,
getattr(self, 'ff_bias_%s' % idx))
processed_l1_h2 = self.ff_nl(processed_l1_h2)
if self.batch_norm:
with tf.variable_scope(
'l1_h2_bn_ff_%s' % idx,
reuse=self.scope_reuse) as scope:
processed_l1_h2 = tf.contrib.layers.batch_norm(
inputs=processed_l1_h2,
scale=True,
center=True,
fused=True,
renorm=False,
param_initializers=self.param_initializer,
updates_collections=None,
| tensorflow.contrib.layers.batch_norm | 3,257 |
import tensorflow as tf
variables that exist in given checkpoint.
Returns:
List of all variables that need to be saved/restored.
"""
model_vars = tf.trainable_variables()
# Add batchnorm variables.
bn_vars = [v for v in tf.global_variables()
if 'moving_mean' in v.op.name or 'moving_variance' in v.op.name or
'mu' in v.op.name or 'sigma' in v.op.name or
'global_scale_var' in v.op.name]
model_vars.extend(bn_vars)
model_vars = sorted(model_vars, key=lambda x: x.op.name)
mapping = {}
if ckpt is not None:
ckpt_var = tf.contrib.framework.list_variables(ckpt)
ckpt_var_names = [name for (name, unused_shape) in ckpt_var]
ckpt_var_shapes = [shape for (unused_name, shape) in ckpt_var]
not_loaded = list(ckpt_var_names)
for v in model_vars:
if v.op.name not in ckpt_var_names:
# For backward compatibility, try additional matching.
v_additional_name = v.op.name.replace('egomotion_prediction/', '')
if v_additional_name in ckpt_var_names:
# Check if shapes match.
ind = ckpt_var_names.index(v_additional_name)
if ckpt_var_shapes[ind] == v.get_shape():
mapping[v_additional_name] = v
not_loaded.remove(v_additional_name)
continue
| tensorflow.contrib.framework.list_variables | 3,258 |
import tensorflow as tf
else:
self.c_maxlen, self.q_maxlen = config.para_limit, config.ques_limit
self.ch_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.ch, tf.bool), tf.int32), axis=2), [-1])
self.qh_len = tf.reshape(tf.reduce_sum(
tf.cast(tf.cast(self.qh, tf.bool), tf.int32), axis=2), [-1])
self.forward()
total_params()
if trainable:
self.lr = tf.minimum(config.learning_rate, 0.001 / tf.log(999.) * tf.log(tf.cast(self.global_step, tf.float32) + 1))
self.opt = tf.train.AdamOptimizer(learning_rate = self.lr, beta1 = 0.8, beta2 = 0.999, epsilon = 1e-7)
grads = self.opt.compute_gradients(self.loss)
gradients, variables = zip(*grads)
capped_grads, _ = tf.clip_by_global_norm(
gradients, config.grad_clip)
self.train_op = self.opt.apply_gradients(
zip(capped_grads, variables), global_step=self.global_step)
def forward(self):
config = self.config
N, PL, QL, CL, d, dc, nh = config.batch_size if not self.demo else config.batch_size, self.c_maxlen, self.q_maxlen, config.char_limit, config.hidden, config.char_dim, config.num_heads
| tensorflow.cast | 3,259 |
import tensorflow as tf
tf.logging.info("**** Trainable Variables ****")
| tensorflow.logging.info | 3,260 |
import tensorflow as tf
"""Adds the difference loss between the private and shared representations.
Args:
private_samples: a tensor of shape [num_samples, num_features].
shared_samples: a tensor of shape [num_samples, num_features].
weight: the weight of the incoherence loss.
name: the name of the tf summary.
"""
with tf.name_scope(name):
private_samples -= tf.reduce_mean(private_samples, 0)
shared_samples -= tf.reduce_mean(shared_samples, 0)
private_samples = tf.nn.l2_normalize(private_samples, 1)
shared_samples = tf.nn.l2_normalize(shared_samples, 1)
correlation_matrix = tf.matmul(private_samples, shared_samples, transpose_a=True)
| tensorflow.name_scope | 3,261 |
import tensorflow as tf
moving_averages.assign_moving_average(
self.ema_count,
tf.reduce_sum(
tf.reshape(
x_means_hot,
shape=[-1, self.hparams.num_blocks,
self.hparams.block_v_size]),
axis=0),
self.hparams.decay,
zero_debias=False)
dw = tf.matmul(
tf.transpose(x_means_hot, perm=[1, 2, 0]),
tf.transpose(x_reshaped, perm=[1, 0, 2]))
updated_ema_means = \
moving_averages.assign_moving_average(
self.ema_means, dw, self.hparams.decay,
zero_debias=False)
n = tf.reduce_sum(updated_ema_count, axis=-1, keep_dims=True)
updated_ema_count = ((updated_ema_count + self.hparams.epsilon) / (
n + 2**self.hparams.z_size * self.hparams.epsilon) * n)
updated_ema_means = updated_ema_means / tf.expand_dims(
updated_ema_count, axis=-1)
| tensorflow.transpose | 3,262 |
import tensorflow as tf
return weighted_average, weights
def local_attention(state, hidden_states, encoder, encoder_input_length, pos=None, scope=None, context=None, **kwargs):
batch_size = tf.shape(state)[0]
attn_length = tf.shape(hidden_states)[1]
if context is not None and encoder.use_context:
state = tf.concat([state, context], axis=1)
| tensorflow.shape | 3,263 |
import tensorflow as tf
return tf.train.Example(
features=tf.train.Features(feature=feature_dict)).SerializeToString()
def _deserialize(self, serialized_data, batch_size):
"""Convert serialized TFRecords into tensors.
Args:
serialized_data: A tensor containing serialized records.
batch_size: The data arrives pre-batched, so batch size is needed to
deserialize the data.
"""
feature_map = _TRAIN_FEATURE_MAP if self._is_training else _EVAL_FEATURE_MAP
features = tf.parse_single_example(serialized_data, feature_map)
users = tf.reshape(tf.decode_raw(
features[movielens.USER_COLUMN], rconst.USER_DTYPE), (batch_size,))
items = tf.reshape(tf.decode_raw(
features[movielens.ITEM_COLUMN], rconst.ITEM_DTYPE), (batch_size,))
def decode_binary(data_bytes):
# tf.decode_raw does not support bool as a decode type. As a result it is
# necessary to decode to int8 (7 of the bits will be ignored) and then
# cast to bool.
return tf.reshape(tf.cast(tf.decode_raw(data_bytes, tf.int8), tf.bool),
(batch_size,))
if self._is_training:
mask_start_index = tf.decode_raw(
| tensorflow.decode_raw | 3,264 |
import tensorflow as tf
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
"""U-Net Generator"""
def lrelu(x, alpha,name='lrelu'):
with tf.variable_scope(name):
return tf.nn.relu(x) - alpha * tf.nn.relu(-x)
def instance_norm(x,name='instance_norm'):
with tf.variable_scope(name):
if reuse:
tf.get_variable_scope().reuse_variables()
else:
assert tf.get_variable_scope().reuse is False
| tensorflow.nn.relu | 3,265 |
import tensorflow as tf
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)
| tensorflow.concat | 3,266 |
import tensorflow as tf
projection = batch_norm_lin(projection, dialogue_state_size, self.phase_train,
name='linear_projection_2_bn')
activation = tf.nn.relu(projection)
activation = dropout(activation, self.dropout_keep_prob)
projection = linear(
input=activation,
input_size=dialogue_state_size,
output_size=action_templates_vocabulary_length,
name='linear_projection_3_predictions_action'
)
self.predictions_action = tf.nn.softmax(projection, name="softmax_output_prediction_action")
# argument prediction
# first encode decoded action template and teh true action template
choice = tf.floor(tf.random_uniform([1], self.use_inputs_prob, 1 + self.use_inputs_prob, tf.float32))
prediction_action_argmax = tf.stop_gradient(tf.argmax(self.predictions_action, 1))
predicted_action_templates_embedding = embedding(
input=prediction_action_argmax,
length=action_templates_vocabulary_length,
| tensorflow.nn.softmax | 3,267 |
import tensorflow as tf
for source, target in clean_pairs:
if source and target:
lang1_resfile.write(source)
lang1_resfile.write("\n")
lang2_resfile.write(target)
lang2_resfile.write("\n")
else:
lang1_filename, lang2_filename = dataset[1]
lang1_filepath = os.path.join(tmp_dir, lang1_filename)
lang2_filepath = os.path.join(tmp_dir, lang2_filename)
is_sgm = (
lang1_filename.endswith("sgm") and lang2_filename.endswith("sgm"))
if not (tf.gfile.Exists(lang1_filepath) and
tf.gfile.Exists(lang2_filepath)):
# For .tar.gz and .tgz files, we read compressed.
mode = "r:gz" if compressed_filepath.endswith("gz") else "r"
with tarfile.open(compressed_filepath, mode) as corpus_tar:
corpus_tar.extractall(tmp_dir)
if lang1_filepath.endswith(".gz"):
new_filepath = lang1_filepath.strip(".gz")
generator_utils.gunzip_file(lang1_filepath, new_filepath)
lang1_filepath = new_filepath
if lang2_filepath.endswith(".gz"):
new_filepath = lang2_filepath.strip(".gz")
generator_utils.gunzip_file(lang2_filepath, new_filepath)
lang2_filepath = new_filepath
for example in text_problems.text2text_txt_iterator(
| tensorflow.gfile.Exists | 3,268 |
import tensorflow as tf
)
training_decoder_output, _, _ = tf.contrib.seq2seq.dynamic_decode(
decoder=training_decoder,
impute_finished=True,
maximum_iterations=tf.reduce_max(seq_lens),
)
self.Y_hat = training_decoder_output.rnn_output
out_decoder2 = tf.reshape(self.Y_hat, [tf.shape(self.Y_hat)[0], -1, n_mels])
dec = conv1d_banks(out_decoder2, K=decoder_num_banks, is_training=self.training)
dec = tf.layers.max_pooling1d(dec, pool_size=2, strides=1, padding="same")
dec = tf.layers.conv1d(dec, embed_size // 2, 3, name="decoder-conv1-1", padding="SAME")
dec = tf.nn.relu(tf.layers.batch_normalization(dec, training=self.training))
dec = tf.layers.conv1d(dec, embed_size // 2, 3, name="decoder-conv1-2", padding="SAME")
dec = tf.layers.batch_normalization(dec, training=self.training)
dec = tf.layers.dense(dec, embed_size // 2)
for i in range(4):
dec = highwaynet(
dec, num_units=embed_size // 2, scope="decoder-highwaynet-{}".format(i)
)
with tf.variable_scope("decoder-gru", reuse=False):
cell = tf.contrib.rnn.GRUCell(embed_size // 2)
cell_bw = tf.contrib.rnn.GRUCell(embed_size // 2)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(cell, cell_bw, dec, dtype=tf.float32)
outputs = tf.concat(outputs, 2)
self.Z_hat = tf.layers.dense(outputs, 1 + fourier_window_size // 2)
| tensorflow.layers.conv1d | 3,269 |
import tensorflow as tf
normalizer_fn=slim.batch_norm):
mobilenet_v1.mobilenet_v1_base(inputs)
total_params, _ = slim.model_analyzer.analyze_vars(
slim.get_model_variables())
self.assertAlmostEqual(3217920, total_params)
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]
_, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
inputs, num_classes, scope='depth_multiplied_net',
depth_multiplier=0.5)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
| tensorflow.random_uniform | 3,270 |
import tensorflow as tf
self.saver = tf.train.Saver(tf.global_variables())
def separate_gradient_update(self):
denoise_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "denoising_model")
ranking_model_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "ranking_model")
self.weighs_propen=denoise_params
| tensorflow.get_collection | 3,271 |
import tensorflow as tf
q_sqrt_r = tf.matrix_band_part(q_sqrt, -1, 0) # D x M x M
eKuf = tf.transpose(expectation(pXnew, (kern, feat))) # M x N (psi1)
if Luu is None:
Kuu = feat.Kuu(kern, jitter=settings.numerics.jitter_level) # M x M
Luu = tf.cholesky(Kuu) # M x M
if not white:
q_mu = tf.matrix_triangular_solve(Luu, q_mu, lower=True)
Luu_tiled = tf.tile(Luu[None, :, :], [num_func, 1, 1]) # remove line once issue 216 is fixed
q_sqrt_r = tf.matrix_triangular_solve(Luu_tiled, q_sqrt_r, lower=True)
Li_eKuf = tf.matrix_triangular_solve(Luu, eKuf, lower=True) # M x N
fmean = tf.matmul(Li_eKuf, q_mu, transpose_a=True)
eKff = expectation(pXnew, kern) # N (psi0)
eKuffu = expectation(pXnew, (kern, feat), (kern, feat)) # N x M x M (psi2)
Luu_tiled = tf.tile(Luu[None, :, :], [num_data, 1, 1]) # remove this line, once issue 216 is fixed
Li_eKuffu = tf.matrix_triangular_solve(Luu_tiled, eKuffu, lower=True)
| tensorflow.tile | 3,272 |
import tensorflow as tf
def test_images_and_additional_channels_errors(self):
input_tensor_dict = {
fields.InputDataFields.image:
tf.placeholder(tf.float32, [None, None, 3]),
fields.InputDataFields.image_additional_channels:
tf.placeholder(tf.float32, [None, None, 2]),
fields.InputDataFields.original_image:
tf.placeholder(tf.float32, [None, None, 3]),
}
with self.assertRaises(ValueError):
| tensorflow.placeholder | 3,273 |
from tensorflow.contrib import layers as contrib_layers
Returns:
A version of `input_tensor` with dropout applied.
"""
if dropout_prob is None or dropout_prob == 0.0:
return input_tensor
output = tf.nn.dropout(input_tensor, rate=dropout_prob)
return output
def layer_norm(input_tensor, name=None):
"""Run layer normalization on the last dimension of the tensor."""
return contrib_layers.layer_norm(
inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)
def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
"""Runs layer normalization followed by dropout."""
output_tensor = layer_norm(input_tensor, name)
output_tensor = dropout(output_tensor, dropout_prob)
return output_tensor
def create_initializer(initializer_range=0.02):
| tensorflow.contrib.layers.layer_norm | 3,274 |
import tensorflow as tf
if __name__ == '__main__':
tf.test.main()
| tensorflow.test.main | 3,275 |
import tensorflow as tf
noise of shape [cur_batch_size, image_size, image_size, 3].
real_images: tensor, real images from input of shape
[cur_batch_size, image_size, image_size, 3].
alpha_var: variable, alpha for weighted sum of fade-in of layers.
params: dict, user passed parameters.
Returns:
Discriminator's gradient penalty loss of shape [].
"""
func_name = "get_gradient_penalty_loss"
with tf.name_scope(name="{}/gradient_penalty".format(self.name)):
# Get a random uniform number rank 4 tensor.
random_uniform_num = tf.random.uniform(
shape=[cur_batch_size, 1, 1, 1],
minval=0., maxval=1.,
dtype=tf.float32,
name="random_uniform_num"
)
print_obj(
"\n" + func_name, "random_uniform_num", random_uniform_num
)
# Find the element-wise difference between images.
image_difference = fake_images - real_images
print_obj(func_name, "image_difference", image_difference)
| tensorflow.random.uniform | 3,276 |
import tensorflow as tf
self.processed_next_obs_ph = self.target_policy.processed_obs
self.action_target = self.target_policy.action_ph
self.terminals_ph = tf.placeholder(tf.float32, shape=(None, 1), name='terminals')
self.rewards_ph = tf.placeholder(tf.float32, shape=(None, 1), name='rewards')
self.is_demo_ph = tf.placeholder(tf.float32, shape=(None, 1), name='is_demonstrations')
self.weight_ph = tf.placeholder(tf.float32, shape=(None, 1), name='importance_weight')
self.actions_ph = tf.placeholder(tf.float32, shape=(None,) + self.action_space.shape,
name='actions')
self.learning_rate_ph = tf.placeholder(tf.float32, [], name="learning_rate_ph")
if self.n_step:
self.next_observations_ph_n = self.target_policy.obs_ph
self.processed_next_obs_ph_n = self.target_policy.processed_obs
self.rewards_ph_n = tf.placeholder(tf.float32, shape=(None, 1), name='n_step_rewards')
self.terminals_ph_n = tf.placeholder(tf.float32, shape=(None, 1), name='n_step_terminals')
| tensorflow.placeholder | 3,277 |
import tensorflow as tf
in0 = tf.strings.to_number(in0, tf.int32)
in1 = tf.strings.to_number(in1, tf.int32)
add = tf.add(in0, in1, "ADD")
sub = tf.subtract(in0, in1, "SUB")
# Cast or convert result to the output dtype.
if tf_output0_dtype == tf.string:
cast0 = tf.dtypes.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.dtypes.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
out0 = tf.identity(cast0, "TENSOR_OUTPUT0")
out1 = tf.identity(cast1, "TENSOR_OUTPUT1")
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel", input_dtype,
output0_dtype, output1_dtype)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
| tensorflow.cast | 3,278 |
import tensorflow as tf
x_data = tf.placeholder(tf.float32)
m = tf.constant(3.)
# Multiplication
prod = tf.mul(x_data, m)
for x_val in x_vals:
print(sess.run(prod, feed_dict={x_data: x_val}))
| tensorflow.mul | 3,279 |
import tensorflow as tf
vz = tf.contrib.lookup.MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=-1)
vx_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
vz_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
x_t = tf.gather(x, l)
x_t_len = tf.strings.length(x_t)
x_t = tf.string_split([x_t], delimiter='').values
z_t = tf.gather(y, m)
z_t_len = tf.strings.length(z_t)
z_t = tf.string_split([z_t], delimiter='').values
for i in tf.range(start=0, limit=x_t_len - self._p + 1, delta=1, dtype=None, name='range'):
u = tf.string_join(x_t[i:i + self._p], '')
vx_keys, r = tf.cond(
tf.greater(vx.lookup(u), -1),
true_fn=lambda: (vx_keys, tf.add(vx.lookup(u), 1)),
false_fn=lambda: (tf.concat([vx_keys, tf.reshape(u, (-1, 1))], axis=0),
tf.constant(1, dtype=tf.int64, name='constant'))
| tensorflow.gather | 3,280 |
import tensorflow as tf
def gradsafe_sqrt(x, clip_low=1e-18, name=None):
with tf.name_scope(name, "gradsafe_sqrt"):
return tf.sqrt(tf.clip_by_value(x, clip_low, x))
| tensorflow.name_scope | 3,281 |
import tensorflow as tf
weights = tf.to_float(mask) / lengths
weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1)
return weighted_average, weights
def last_state_attention(hidden_states, encoder_input_length, *args, **kwargs):
weights = tf.one_hot(encoder_input_length - 1, tf.shape(hidden_states)[1])
weights = tf.to_float(weights)
weighted_average = tf.reduce_sum(hidden_states * tf.expand_dims(weights, axis=2), axis=1)
return weighted_average, weights
| tensorflow.shape | 3,282 |
import tensorflow as tf
Returns:
loss: Loss tensor of type float.
"""
with tf.name_scope('segment_loss'):
# logits = tf.reshape(logits, (-1, num_classes))
epsilon = tf.constant(value=1e-7)
| tensorflow.name_scope | 3,283 |
import tensorflow as tf
# rotates around z, while we rotate around y so need to swap
center_1 = tf.reshape(box1[0:3][[0, 2, 1]], [1, 3])
center_2 = tf.reshape(box2[0:3][[0, 2, 1]], [1, 3])
rotation_z_1 = tf.reshape(box1[-1], [1])
rotation_z_2 = tf.reshape(box2[-1], [1])
length_1 = tf.reshape(box1[3 + 0], [1])
| tensorflow.reshape | 3,284 |
import tensorflow as tf
uniques, index = tf.unique(t_flatten)
return uniques, tf.reshape(index, shape=tf.shape(t))
| tensorflow.shape | 3,285 |
import tensorflow as tf
# Extract 8 most features as mentioned in paper
self.k_pooled = tf.nn.top_k(tf.transpose(self.layers[-1], [0,2,1]), k=8, name='k_pool', sorted=False)[0]
print("8-maxpooling:", self.k_pooled.get_shape())
self.flatten = tf.reshape(self.k_pooled, (-1, 512*8))
# fc1
with tf.variable_scope('fc1'):
w = tf.get_variable('w', [self.flatten.get_shape()[1], 2048], initializer=he_normal,
regularizer=regularizer)
b = tf.get_variable('b', [2048], initializer=tf.constant_initializer(1.0))
out = tf.matmul(self.flatten, w) + b
self.fc1 = tf.nn.relu(out)
# fc2
with tf.variable_scope('fc2'):
w = tf.get_variable('w', [self.fc1.get_shape()[1], 2048], initializer=he_normal,
regularizer=regularizer)
b = tf.get_variable('b', [2048], initializer=tf.constant_initializer(1.0))
out = tf.matmul(self.fc1, w) + b
| tensorflow.constant_initializer | 3,286 |
import tensorflow.contrib.eager as tfe
def tearDown(self):
shutil.rmtree(self._tmp_logdir)
super(LinearRegressionTest, self).tearDown()
def testSyntheticDataset(self):
true_w = tf.random_uniform([3, 1])
true_b = [1.0]
batch_size = 10
num_batches = 2
noise_level = 0.
dataset = linear_regression.synthetic_dataset(true_w, true_b, noise_level,
batch_size, num_batches)
it = tfe.Iterator(dataset)
for _ in range(2):
(xs, ys) = it.next()
self.assertEqual((batch_size, 3), xs.shape)
self.assertEqual((batch_size, 1), ys.shape)
self.assertEqual(tf.float32, xs.dtype)
self.assertEqual(tf.float32, ys.dtype)
with self.assertRaises(StopIteration):
it.next()
def testLinearRegression(self):
true_w = [[1.0], [-0.5], [2.0]]
true_b = [1.0]
| tensorflow.contrib.eager.Iterator | 3,287 |
import tensorflow as tf
task.sample(adv=True) #sample adversarially
logger.info('Task Sampled: %s', task.goal_velocity)
generated_adversarial_task.append(task.goal_velocity)
logger.info('Tasks dump!')
assert (task_generator == 'fixed')
test_summary['task'].append(task.goal_velocity)
if FLAGS.task.reset_policy:
# NOTE: reset policy and valuefunc
logger.info("Resetting Policy")
pol_params = tf.get_default_session().run([nn.utils.parameters_to_vector(policy.parameters())])
tf.get_default_session().run(tf.variables_initializer(policy.parameters()))
pol_params_after = tf.get_default_session().run([nn.utils.parameters_to_vector(policy.parameters())])
print ("pol_params:", np.linalg.norm(pol_params), "pol_params_after_reset:", np.linalg.norm(pol_params_after))
logger.info("Resetting Valuefunc")
tf.get_default_session().run(tf.variables_initializer(vfn.parameters()))
tf.get_default_session().run(tf.variables_initializer(warmup_policy.parameters()))
tf.get_default_session().run(tf.variables_initializer(warmup_vfn.parameters()))
for p in warmup_policy.parameters(): p.invalidate()
| tensorflow.get_default_session | 3,288 |
import tensorflow as tf
# Placeholders for input data and the targets
x_input = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Input')
x_input_l = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Labeled_Input')
y_input = tf.placeholder(dtype=tf.float32, shape=[batch_size, n_labels], name='Labels')
x_target = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Target')
real_distribution = tf.placeholder(dtype=tf.float32, shape=[batch_size, z_dim], name='Real_distribution')
categorial_distribution = tf.placeholder(dtype=tf.float32, shape=[batch_size, n_labels],
name='Categorical_distribution')
manual_decoder_input = tf.placeholder(dtype=tf.float32, shape=[1, z_dim + n_labels], name='Decoder_input')
| tensorflow.placeholder | 3,289 |
from tensorflow.python.ops import array_ops
if proba:
raise ValueError(
"logits to probabilities is not supported for _BinarySvmTargetColumn")
logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)
return math_ops.argmax(logits, 1)
# TODO(zakaria): use contrib losses.
def _mean_squared_loss(logits, target):
# To prevent broadcasting inside "-".
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
logits.get_shape().assert_is_compatible_with(target.get_shape())
return math_ops.square(logits - math_ops.to_float(target))
def _log_loss_with_two_classes(logits, target):
# sigmoid_cross_entropy_with_logits requires [batch_size, 1] target.
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
loss_vec = nn.sigmoid_cross_entropy_with_logits(
labels=math_ops.to_float(target), logits=logits)
| tensorflow.python.ops.array_ops.expand_dims | 3,290 |
import tensorflow as tf
self.nnweights.append(weights)
biases = tf.get_variable('biases', [hidden_layers_node[i]],
initializer=tf.constant_initializer(0.0))
layer_out = tf.nn.dropout(tf.matmul(prev_x, weights) + biases, dropout_keep_prob)
| tensorflow.constant_initializer | 3,291 |
import tensorflow as tf
self.obs = tf.placeholder(tf.float32, [None,
self.obs_space]) # ! self.obs = tf.placeholder(tf.float32, [None] + list(self.obs_space))
# ! self.obs_space = env.observation_space.shape
self.r = tf.placeholder(tf.float32, (None,1))
self.ac = tf.placeholder(tf.float32, (None, self.act_space))
self.adv = tf.placeholder(tf.float32, [None]) # unused
# specific to FeUdal
self.prev_g = tf.placeholder(tf.float32, (None, None, self.g_dim))
self.ri = tf.placeholder(tf.float32, (None,))
self.s_diff = tf.placeholder(tf.float32, (None, self.g_dim))
def build_perception(self):
self._obs = tf.expand_dims(self.obs, -1) # !
self._obs = tf.expand_dims(self._obs, -1) # !
conv1 = tf.layers.conv2d(inputs=self._obs,
filters=16,
kernel_size=[2, 1], # ! kernel_size = [8,8]
| tensorflow.placeholder | 3,292 |
import tensorflow as tf
if output_length == 1:
pool = tf.nn.avg_pool(input_data, [1, height, width, 1], strides=[1, 1, 1, 1], padding=padding)
pool = tf.reduce_mean(pool, axis=[1, 2])
pool = tf.squeeze(pool, axis=[1, 2])
return pool
else:
if num_channels_in != output_length:
conv_weight = tf.Variable(tf.truncated_normal([1, 1, num_channels_in, output_length], stddev=0.1, dtype=tf.float32))
conv = tf.nn.conv2d(input_data, conv_weight, strides=[1, 1, 1, 1], padding='SAME')
pool = tf.nn.avg_pool(conv, ksize=[1, height, width, 1], strides=[1, 1, 1, 1], padding=padding)
else:
pool = tf.nn.avg_pool(input_data, ksize=[1, height, width, 1], strides=[1, 1, 1, 1], padding=padding)
pool = tf.squeeze(pool, axis=[1, 2])
return pool
def avg_pool(input, scope, filter_dims, stride_dims, padding='SAME'):
assert (len(filter_dims) == 2) # filter height and width
assert (len(stride_dims) == 2) # stride height and width
filter_h, filter_w = filter_dims
stride_h, stride_w = stride_dims
with tf.variable_scope(scope):
| tensorflow.squeeze | 3,293 |
import tensorflow as tf
# build sprite image
ut.images_to_sprite(self.test_set, path=os.path.join(FLAGS.logdir, 'sprite.png'))
ut.generate_tsv(len(self.test_set), tsv_path)
def _add_loss_summary(self, name, var, collection='train'):
if var is not None:
tf.summary.scalar(name, var, [collection])
tf.summary.scalar('log_' + name, tf.log(var), [collection])
def _restore_model(self, session):
latest_checkpoint = self.get_latest_checkpoint()
print(latest_checkpoint)
if latest_checkpoint is not None:
latest_checkpoint = latest_checkpoint.replace(EMB_SUFFIX, '')
ut.print_info("latest checkpoint: %s" % latest_checkpoint)
| tensorflow.log | 3,294 |
from tensorflow.python.framework import ops
transpose_a=transpose_a,
transpose_b=transpose_b,
a_is_sparse=a_is_sparse,
b_is_sparse=b_is_sparse,
name=name)
else:
return gen_math_ops._mat_mul(a, b,
transpose_a=transpose_a,
transpose_b=transpose_b,
name=name)
sparse_matmul = gen_math_ops._sparse_mat_mul
batch_matmul = gen_math_ops._batch_mat_mul
ops.RegisterShape("MatMul")(common_shapes.matmul_shape)
ops.RegisterShape("SparseMatMul")(common_shapes.matmul_shape)
def _as_indexed_slices(x):
"""Convert 'x' to IndexedSlices.
Convert a dense Tensor to a block-sparse IndexedSlices.
Args:
x: Either a Tensor object, or an IndexedSlices object.
Returns:
An IndexedSlices object.
Raises:
| tensorflow.python.framework.ops.RegisterShape | 3,295 |
import tensorflow as tf
# or evaluation mode, depending on FLAGS.eval:
# Under the evaluation mode, this script will read a saved model,
# and compute the accuracy of the model against a validation dataset.
# Additional ops for accuracy and top_k predictors are only used under this
# mode.
# Under the benchmarking mode, user can specify whether nor not to use
# the forward-only option, which will only compute the loss function.
# forward-only cannot be enabled with eval at the same time.
tf.flags.DEFINE_boolean('eval', False, 'whether use eval or benchmarking')
tf.flags.DEFINE_boolean('forward_only', False, """whether use forward-only or
training for benchmarking""")
tf.flags.DEFINE_integer('batch_size', 0, 'batch size per compute device')
tf.flags.DEFINE_integer('num_batches', 100,
'number of batches to run, excluding warmup')
tf.flags.DEFINE_integer('num_warmup_batches', None,
'number of batches to run before timing')
tf.flags.DEFINE_integer('autotune_threshold', None,
'The autotune threshold for the models')
tf.flags.DEFINE_integer('num_gpus', 1, 'the number of GPUs to run on')
tf.flags.DEFINE_integer('display_every', 10,
"""Number of local steps after which progress is printed
out""")
tf.flags.DEFINE_string('data_dir', None, """Path to dataset in TFRecord format
(aka Example protobufs). If not specified,
synthetic data will be used.""")
tf.flags.DEFINE_string('data_name', None,
"""Name of dataset: imagenet or flowers.
If not specified, it is automatically guessed
| tensorflow.flags.DEFINE_integer | 3,296 |
import tensorflow as tf
Args:
chunk: A Tensor of text data.
Returns:
A namedtuple of input and target data.
"""
input_text = tf.map_fn(lambda x: x[:-1], chunk)
target_text = tf.map_fn(lambda x: x[1:], chunk)
return (input_text, target_text)
def build_to_ids_fn(vocab, max_seq_len):
"""Constructs function mapping examples to sequences of token indices."""
| tensorflow.map_fn | 3,297 |
import tensorflow as tf
image = tf.image.random_crop(image, [32, 32, 3])
image = tf.image.random_flip_left_right(image)
| tensorflow.image.random_flip_left_right | 3,298 |
import tensorflow as tf
h = h + b
return h
def conv2d(x, shape, name, bias=False, stride=2, padding='SAME'):
with tf.variable_scope(name):
W = weight_variable(shape)
h = tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding=padding)
if bias:
| tensorflow.variable_scope | 3,299 |
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