seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
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
def learn(self, s, a, r, s_, ISW):
_, abs_td = self.sess.run([self.train_op, self.abs_td], feed_dict={S: s, self.a: a, R: r, S_: s_, self.ISWeights: ISW})
if self.t_replace_counter % self.t_replace_iter == 0:
self.sess.run([tf.assign(t, e) for t, e in zip(self.t_params, self.e_params)])
self.t_replace_counter += 1
| tensorflow.layers.dense | 4,700 |
import tensorflow as tf
all_previous_modalities = []
# Transform the input features
for key, input_modality in six.iteritems(
self._problem_hparams.input_modality):
if key not in features:
tf.logging.warning("Missing feature %s - ignoring." % key)
continue
do_reuse = input_modality.name in all_previous_modalities
with tf.variable_scope(input_modality.name, reuse=do_reuse):
log_info("Transforming feature '%s' with %s.bottom", key,
input_modality.name)
| tensorflow.logging.warning | 4,701 |
import tensorflow as tf
with tarfile.open(compressed_filepath, mode) as corpus_tar:
corpus_tar.extractall(tmp_dir)
filenames = tf.gfile.Glob(os.path.join(tmp_dir, glob_pattern))
for tsv_filename in filenames:
if tsv_filename.endswith(".gz"):
new_filename = tsv_filename.strip(".gz")
generator_utils.gunzip_file(tsv_filename, new_filename)
tsv_filename = new_filename
with tf.gfile.Open(tsv_filename) as tsv_file:
for line in tsv_file:
if line and "\t" in line:
parts = line.split("\t")
source, target = parts[src_column], parts[trg_column]
source, target = source.strip(), target.strip()
if source and target:
lang1_resfile.write(source)
| tensorflow.gfile.Open | 4,702 |
import tensorflow as tf
q_tp1_best = tf.reduce_max(target_policy.q_values, axis=1)
q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked
# compute the error (potentially clipped)
td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
errors = tf_util.huber_loss(td_error)
weighted_error = tf.reduce_mean(importance_weights_ph * errors)
tf.summary.scalar("td_error", tf.reduce_mean(td_error))
tf.summary.scalar("loss", weighted_error)
| tensorflow.stop_gradient | 4,703 |
from tensorflow.python.framework import ops
Returns:
A Tensor with the same type as `x` if `x.dtype != qint32` otherwise
the return type is `quint8`.
"""
with ops.op_scope([x], name, "Tanh") as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops._tanh(x, name=name)
ops.RegisterShape("Abs")(common_shapes.unchanged_shape)
ops.RegisterShape("Ceil")(common_shapes.unchanged_shape)
ops.RegisterShape("Conj")(common_shapes.unchanged_shape)
ops.RegisterShape("Cos")(common_shapes.unchanged_shape)
ops.RegisterShape("Exp")(common_shapes.unchanged_shape)
ops.RegisterShape("Floor")(common_shapes.unchanged_shape)
ops.RegisterShape("Imag")(common_shapes.unchanged_shape)
ops.RegisterShape("Inv")(common_shapes.unchanged_shape)
ops.RegisterShape("IsFinite")(common_shapes.unchanged_shape)
ops.RegisterShape("IsInf")(common_shapes.unchanged_shape)
ops.RegisterShape("IsNan")(common_shapes.unchanged_shape)
ops.RegisterShape("Log")(common_shapes.unchanged_shape)
ops.RegisterShape("LogicalNot")(common_shapes.unchanged_shape)
ops.RegisterShape("Neg")(common_shapes.unchanged_shape)
ops.RegisterShape("Real")(common_shapes.unchanged_shape)
ops.RegisterShape("Rsqrt")(common_shapes.unchanged_shape)
ops.RegisterShape("Sign")(common_shapes.unchanged_shape)
ops.RegisterShape("Sin")(common_shapes.unchanged_shape)
ops.RegisterShape("Sqrt")(common_shapes.unchanged_shape)
| tensorflow.python.framework.ops.RegisterShape | 4,704 |
import tensorflow as tf
a = 2
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, mtype * 2 + 0].matshow(inter.numpy())
plt.colorbar(im, ax=axs[fig_obj_count, mtype * 2 + 0])
print(mtype, 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, mtype * 2 + 1].matshow(inter.numpy())
plt.colorbar(im, ax=axs[fig_obj_count, mtype * 2 + 1])
print(mtype, fig_obj_count, 1)
if mtype == 1:
| tensorflow.nn.relu | 4,705 |
import tensorflow as tf
from six.moves import urllib
import tensorflow as tf
from datasets import dataset_utils
tf.app.flags.DEFINE_integer('train_shards', 1000,
'Number of shards in training TFRecord files.')
FLAGS = tf.app.flags.FLAGS
# The URL where the CIFAR data can be downloaded.
| tensorflow.app.flags.DEFINE_integer | 4,706 |
import tensorflow as tf
idx_z1_y1_x0 = base_z1_y1 + x0_clip
idx_z1_y1_x1 = base_z1_y1 + x1_clip
# Use indices to lookup pixels in the flat image and restore
# channels dim
im_flat = tf.reshape(im, tf.stack([-1, channels]))
im_flat = tf.to_float(im_flat)
i_z0_y0_x0 = tf.gather(im_flat, idx_z0_y0_x0)
i_z0_y0_x1 = tf.gather(im_flat, idx_z0_y0_x1)
i_z0_y1_x0 = tf.gather(im_flat, idx_z0_y1_x0)
i_z0_y1_x1 = tf.gather(im_flat, idx_z0_y1_x1)
i_z1_y0_x0 = tf.gather(im_flat, idx_z1_y0_x0)
i_z1_y0_x1 = tf.gather(im_flat, idx_z1_y0_x1)
i_z1_y1_x0 = tf.gather(im_flat, idx_z1_y1_x0)
i_z1_y1_x1 = tf.gather(im_flat, idx_z1_y1_x1)
| tensorflow.gather | 4,707 |
import tensorflow as tf
with tf.device(tf.train.replica_device_setter(1, worker_device=worker_device)):
with tf.variable_scope("global"):
self.network = LSTMPolicy(env.observation_space.shape, env.action_space.n)
self.global_step = tf.get_variable("global_step", [], tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
with tf.device(worker_device):
with tf.variable_scope("local"):
self.local_network = pi = LSTMPolicy(env.observation_space.shape, env.action_space.n)
pi.global_step = self.global_step
self.ac = tf.placeholder(tf.float32, [None, env.action_space.n], name="ac")
self.adv = tf.placeholder(tf.float32, [None], name="adv")
| tensorflow.device | 4,708 |
import tensorflow as tf
all_logits = []
all_top_1_ops = []
all_top_5_ops = []
enqueue_ops = []
gpu_copy_stage_ops = []
gpu_compute_stage_ops = []
gpu_grad_stage_ops = []
use_synthetic_gpu_images = (self.dataset is None)
with tf.device(self.global_step_device):
global_step = tf.contrib.framework.get_or_create_global_step()
# Build the processing and model for the worker.
with tf.device(self.cpu_device):
nclass, images_splits, labels_splits = add_image_preprocessing(
self.dataset, input_nchan, image_size, self.batch_size,
len(self.devices), input_data_type, self.resize_method,
not FLAGS.eval)
update_ops = None
staging_delta_ops = []
| tensorflow.contrib.framework.get_or_create_global_step | 4,709 |
from tensorflow.python.framework import ops
merged_shape = merged_shape.merge_with(input_.get_shape())
return [merged_shape]
@ops.RegisterShape("Select")
def _SelectShape(op):
# All three inputs must have the same shape.
return [op.inputs[0].get_shape()
.merge_with(op.inputs[1].get_shape())
.merge_with(op.inputs[2].get_shape())]
@ops.RegisterShape("ArgMax")
@ops.RegisterShape("ArgMin")
def _ArgOpShape(op):
"""Common shape function for arg-reduction ops."""
dimension_shape = op.inputs[1].get_shape()
dimension_shape.assert_is_compatible_with(tensor_shape.scalar())
input_shape = op.inputs[0].get_shape()
if input_shape.ndims is None:
return [tensor_shape.unknown_shape()]
elif input_shape.ndims <= 1:
return [tensor_shape.scalar()]
| tensorflow.python.framework.ops.RegisterShape | 4,710 |
import tensorflow as tf
accuracy = tf.metrics.accuracy(
labels=label_ids, predictions=predictions, weights=is_real_example)
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
eval_metrics = (metric_fn,
[per_example_loss, label_ids, logits, is_real_example])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions={"probabilities": probabilities},
scaffold_fn=scaffold_fn)
| tensorflow.contrib.tpu.TPUEstimatorSpec | 4,711 |
import tensorflow as tf
def _crop_pool_layer(self, bottom, rois, name):
with tf.variable_scope(name):
# tf.squeeze()返回一个张量，这个张量是将原始input中所有维度中为1的那些维都删掉的结果
batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1])
# Get the normalized coordinates of bboxes
bottom_shape = tf.shape(bottom)
height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0])
width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0])
# rois除以h,w就得到了rois在特征图上的位置
x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width
y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height
x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width
y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height
# Won't be backpropagated to rois anyway, but to save time
bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1))
# 'roi_pooling_size', 7
pre_pool_size = cfg.FLAGS.roi_pooling_size * 2
# 把rois对于的特征图上的部分crop出来，然后resize打破14*14的大小
| tensorflow.slice | 4,712 |
import tensorflow as tf
H_pool = tf.concat(pooled_outputs, 3)
#flatten it for fully connected layer
H_pool_flat = tf.reshape(H_pool, [-1, total_filters])
with tf.name_scope("dropout"):
H_drop = tf.nn.dropout(H_pool_flat, keep_prob = keep_prob)
# Final (unnormalized) layer
with tf.name_scope("output"):
W = tf.get_variable("W",
shape=[total_filters, nb_classes],
initializer=tf.contrib.layers.xavier_initializer())
# add final layer bias
b = tf.Variable(tf.constant(0.1, shape=[nb_classes]), name="b")
# calc l2 losses
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
# do logit = W*X+b
logit = tf.nn.xw_plus_b(H_drop, W, b, name="scores")
predictions = tf.nn.softmax(logit, name="predictions")
#claulate loss and optimizer
with tf.variable_scope("FCoptimize", reuse=None):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits= logit, labels=Y)
| tensorflow.constant | 4,713 |
import tensorflow as tf
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))
| tensorflow.greater_equal | 4,714 |
import tensorflow as tf
# Add leading zero element to each element in the L_flat. We use this zero
# element when gathering L_flat into a lower triangular matrix L.
nb_rows = tf.shape(L_flat)[0]
zeros = tf.expand_dims(tf.tile(K.zeros((1,)), [nb_rows]), 1)
try:
# Old TF behavior.
L_flat = tf.concat(1, [zeros, L_flat])
except TypeError:
# New TF behavior
L_flat = tf.concat([zeros, L_flat], 1)
# Finally, process each element of the batch.
def fn(a, x):
x_ = tf.gather(x, diag_mask)
return x_
P = tf.scan(fn, L_flat, initializer=K.zeros((self.nb_actions, self.nb_actions)))
else:
raise RuntimeError('Unknown Keras backend "{}".'.format(K.backend()))
assert P is not None
assert K.ndim(P) == 3
# Combine a, mu and P into a scalar (over the batches). What we compute here is
# -.5 * (a - mu)^T * P * (a - mu), where * denotes the dot-product. Unfortunately
# TensorFlow handles vector * P slightly suboptimal, hence we convert the vectors to
# 1xd/dx1 matrices and finally flatten the resulting 1x1 matrix into a scalar. All
| tensorflow.gather | 4,715 |
import tensorflow as tf
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)
| tensorflow.contrib.rnn.GRUCell | 4,716 |
import tensorflow as tf
except tf.errors.OutOfRangeError:
break
print('\rTrained in %.3fs. Global step %i' % (time() - start, step+1))
return summary
class PPO_HC(PPO):
def build_anet(self, state_in, name, reuse=False):
reg = tf.contrib.layers.l2_regularizer(1e-3)
with tf.variable_scope(name, reuse=reuse):
layer_a1 = tf.layers.dense(state_in, 512, tf.nn.relu, kernel_regularizer=reg)
layer_a2 = tf.layers.dense(layer_a1, 256, tf.nn.relu, kernel_regularizer=reg)
mu = tf.layers.dense(layer_a2, self.a_dim, tf.nn.tanh, kernel_regularizer=reg)
sigma = tf.layers.dense(layer_a2, self.a_dim, tf.nn.softplus, kernel_regularizer=reg)
# sigma = tf.get_variable(name='pi_sigma', shape=self.a_dim, initializer=tf.constant_initializer(0.5))
sigma = tf.clip_by_value(sigma, 0.0, 1.0)
norm_dist = tf.distributions.Normal(loc=mu * self.a_bound, scale=sigma)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
return norm_dist, params
class PPO_LSTM(Base):
def __init__(self, env, summary_dir='./', gpu=False):
| tensorflow.layers.dense | 4,717 |
import tensorflow as tf
image = tf.image.draw_bounding_boxes(image, boxes) # 在image上画gt_truth
return tf.summary.image('ground_truth', image)
def _add_act_summary(self, tensor):
tf.summary.histogram('ACT/' + tensor.op.name + '/activations', tensor)
tf.summary.scalar('ACT/' + tensor.op.name + '/zero_fraction',
tf.nn.zero_fraction(tensor))
def _add_score_summary(self, key, tensor):
tf.summary.histogram('SCORE/' + tensor.op.name + '/' + key + '/scores', tensor)
def _add_train_summary(self, var):
tf.summary.histogram('TRAIN/' + var.op.name, var)
# Custom Layers #
def _reshape_layer(self, bottom, num_dim, name):
input_shape = tf.shape(bottom)
with tf.variable_scope(name):
# change the channel to the caffe format
| tensorflow.summary.histogram | 4,718 |
import tensorflow as tf
super(DeepLIFTRescale, self).__init__(T, X, session, keras_learning_phase, Y_shape)
def get_symbolic_attribution(self):
return [g * (x - b) for g, x, b in zip(
tf.gradients(ys=self.T, xs=self.X),
self.X if self.has_multiple_inputs else [self.X],
self.baseline if self.has_multiple_inputs else [self.baseline])]
| tensorflow.gradients | 4,719 |
import tensorflow as tf
result[k] = preds
losses.append(loss)
return (result,losses)
"""
Test RNN graph multi layer
"""
def test_rnn_kstep_layer(test_data_x,test_data_y, preds, rnn_outputs, g, checkpoint, input_prob, output_prob, state_prob, num_test, kstep = 3):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
result= {}
"read the trained graph"
g['saver'].restore(sess, checkpoint)
losses = []
for step_num in range(kstep):
k=step_num+1
for index,(X, Y, YP, S) in enumerate(gen_batch_kstep_layer(test_data_x[k:], test_data_y[k:], preds[:-1],rnn_outputs)):
feed_dict={g['x']: np.dstack((X,YP)), g['y']:Y, g['init_state']: S, g['batch_size']:num_test, g['input_prob']: input_prob,g['output_prob']:output_prob,g['state_prob']:state_prob}
preds, rnn_outputs= sess.run([g['preds'], g['final_state']], feed_dict)
| tensorflow.global_variables_initializer | 4,720 |
import tensorflow as tf
def __init__(self):
self.mse = tf.losses.MeanSquaredError()
def __call__(self, y_pred, target, target_weight):
batch_size = y_pred.shape[0]
num_of_joints = y_pred.shape[-1]
pred = tf.reshape(tensor=y_pred, shape=(batch_size, -1, num_of_joints))
heatmap_pred_list = tf.split(value=pred, num_or_size_splits=num_of_joints, axis=-1)
gt = tf.reshape(tensor=target, shape=(batch_size, -1, num_of_joints))
heatmap_gt_list = tf.split(value=gt, num_or_size_splits=num_of_joints, axis=-1)
loss = 0.0
for i in range(num_of_joints):
heatmap_pred = tf.squeeze(heatmap_pred_list[i])
heatmap_gt = tf.squeeze(heatmap_gt_list[i])
| tensorflow.split | 4,721 |
import tensorflow as tf
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)
labels_difference = tf.expand_dims(labels, 0) - tf.expand_dims(labels, 1)
weights_product = tf.expand_dims(weights, 0) * tf.expand_dims(weights, 1)
signed_logits_difference = labels_difference * logits_difference
raw_loss = losses_utils.weighted_surrogate_loss(
labels=tf.ones_like(signed_logits_difference),
logits=signed_logits_difference,
surrogate_type=surrogate_type)
weighted_loss = weights_product * raw_loss
| tensorflow.expand_dims | 4,722 |
import tensorflow as tf
output_layer = model.get_sequence_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias",[], 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.reduce_sum(tf.multiply(output_layer,output_weights),-1)
logits = tf.add(logits, output_bias)
| tensorflow.zeros_initializer | 4,723 |
import tensorflow as tf
Returns:
A preprocessed image.
"""
image = _aspect_preserving_resize(image, resize_side)
image = _central_crop([image], output_height, output_width)[0]
image.set_shape([output_height, output_width, 3])
image = tf.to_float(image)
return _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
def preprocess_image(image, output_height, output_width, is_training=False,
resize_side_min=_RESIZE_SIDE_MIN,
| tensorflow.to_float | 4,724 |
import tensorflow as tf
actions = experience.action[:, 0]
num_tasks = tasks.shape[0]
batch_size = states.shape[0]
task_dim = tasks.shape[1]
obs_dim = states.shape[1]
action_dim = actions.shape[1]
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
next_states_tiled = tf.tile(next_states[:, None], [1, num_tasks, 1])
next_states_tiled = tf.reshape(next_states_tiled,
[batch_size * num_tasks, obs_dim]) # B*B x D
next_relabelled_obs = self._task_distribution.combine(
next_states_tiled, tasks_tiled)
sampled_actions_tiled = self._actor(
next_relabelled_obs, step_type=(), network_state=())[0].sample()
critic_input = (next_relabelled_obs, sampled_actions_tiled)
q_vals, _ = self._critic(critic_input, training=False)
q_vals_vec = tf.reshape(q_vals, (batch_size, num_tasks))
| tensorflow.reshape | 4,725 |
import tensorflow as tf
pairwise_iou = isu.points_mask_pairwise_iou(masks1=masks1, masks2=masks2)
expected_iou = tf.constant([0, 1, 0.4, 0.2], dtype=tf.float32)
self.assertAllClose(pairwise_iou.numpy(), expected_iou.numpy())
if __name__ == '__main__':
tf.test.main()
| tensorflow.test.main | 4,726 |
from tensorflow.python.ops import math_ops
Args:
required_size: number or tf.Tensor specifying required array capacity.
growth_factor: optional number or tf.Tensor specifying the growth factor
between subsequent allocations.
Returns:
tf.Tensor with dtype=int32 giving the next array size.
"""
exponent = math_ops.ceil(
math_ops.log(math_ops.cast(required_size, dtypes.float32))
/ math_ops.log(math_ops.cast(growth_factor, dtypes.float32)))
return math_ops.cast(math_ops.ceil(growth_factor ** exponent), dtypes.int32)
def streaming_concat(values,
axis=0,
max_size=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Concatenate values along an axis across batches.
The function `streaming_concat` creates two local variables, `array` and
| tensorflow.python.ops.math_ops.ceil | 4,727 |
import tensorflow as tf
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
# losses
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=gt, logits=y))
# optimizer
optimizer = tf.train.GradientDescentOptimizer(args.lr)
# define one-step train ops
train_op = optimizer.minimize(cross_entropy)
return x, y, gt, train_op
if __name__ == "__main__":
max_train_step = args.max_train_step
batch_size = args.batch_size
mnist = input_data.read_data_sets(args.data_dir, one_hot=True)
x, y, gt, train_op = model()
| tensorflow.nn.softmax_cross_entropy_with_logits | 4,728 |
import tensorflow as tf
Args:
predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
labels: 2D or array tensor, [batch_size, num_classes] ground truth labels or target labels.
eps: a constant to set upper or lower limit for labels, smoothening factor
name: Optional scope/name for op_scope.
Returns:
A tensor with the log loss.
"""
with tf.name_scope(name):
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
predictions = tf.to_float(predictions)
labels = tf.to_float(labels)
losses = -tf.multiply(labels, tf.log(predictions + eps)) - tf.multiply(
(1 - labels), tf.log(1 - predictions + eps))
return tf.losses.compute_weighted_loss(losses, weights)
def kappa_loss(predictions, labels, y_pow=1, eps=1e-15, num_ratings=5, batch_size=32, name='kappa'):
"""Define a kappa loss, Its a continuous differentiable approximation of
discrete kappa loss.
| tensorflow.to_float | 4,729 |
import tensorflow as tf
def build_graph(self, image, label):
image = self.image_preprocess(image)
assert self.data_format in ['NCHW', 'NHWC']
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
logits = self.get_logits(image, label)
loss = ImageNetModel.compute_loss_and_error(
logits, label, label_smoothing=self.label_smoothing)
| tensorflow.transpose | 4,730 |
import tensorflow as tf
ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
pooled_outputs.append(pooled)
with tf.name_scope("preFc"):
# combine all pooled outputs
total_filters = num_filter * len(filter_list)
# concat all the pooled weights
H_pool = tf.concat(pooled_outputs, 3)
#flatten it for fully connected layer
H_pool_flat = tf.reshape(H_pool, [-1, total_filters])
| tensorflow.name_scope | 4,731 |
import tensorflow as tf
with tf.device('/cpu:0'), tf.name_scope("embedding_deps"):
| tensorflow.name_scope | 4,732 |
import tensorflow as tf
def merge_prob_dist_for_one_step(self, vocab_dist, attn_dist, p_gen, passage_word_idx, passage_mask=None):
'''
max_phrase_size: an input placehoder indications the maximum phrase size inside this batch
vocab_dist: [batch_size, vsize]
attn_dist: [batch_size, passage_length]
p_gen: [batch_size, 1]
passage_word_idx: [batch_size, passage_length]
passage_mask: [batch_size, passage_length]
'''
input_shape = tf.shape(vocab_dist)
batch_size = input_shape[0]
vsize = input_shape[1]
passage_length = tf.shape(passage_word_idx)[1]
with tf.variable_scope('final_distribution'):
vocab_dist = p_gen * vocab_dist
attn_dist = (1.0-p_gen) * attn_dist
# Concatenate some zeros to each vocabulary dist, to hold the probabilities for phrases
extended_vsize = vsize
if self.max_phrase_size is not None:
extended_vsize += self.max_phrase_size
extra_zeros = tf.zeros((batch_size, self.max_phrase_size))
vocab_dist = tf.concat(values=[vocab_dist, extra_zeros], axis=1) # [batch_size, extended_vsize]
if self.options.add_first_word_prob_for_phrase: # add prob of the first word to each phrase
attn_dist = add_first_word_prob_to_atten_dists(self.in_passage_words, self.phrase_starts,
vocab_dist, attn_dist)
| tensorflow.shape | 4,733 |
from tensorflow.python.ops import array_ops
logging_ops.scalar_summary(
["centered_bias_%d" % cb for cb in range(
self._target_column.num_label_columns)],
array_ops.reshape(centered_bias, [-1]))
return centered_bias
def _centered_bias_step(self, targets, features):
centered_bias = ops.get_collection(self._centered_bias_weight_collection)
batch_size = array_ops.shape(targets)[0]
logits = array_ops.reshape(
array_ops.tile(centered_bias[0], [batch_size]),
[batch_size, self._target_column.num_label_columns])
loss = self._target_column.loss(logits, targets, features)
# Learn central bias by an optimizer. 0.1 is a convervative lr for a single
# variable.
return training.AdagradOptimizer(0.1).minimize(loss, var_list=centered_bias)
| tensorflow.python.ops.array_ops.shape | 4,734 |
import tensorflow as tf
expected_cdf[x < 1] = 0.
expected_cdf[x > 10] = 1.
self.assertAllClose(expected_cdf, qdist.cdf(x).eval())
def test_sampling_from_batch_of_normals(self):
batch_shape = (2,)
with self.test_session():
qdist = distributions.QuantizedDistribution(
base_dist_cls=distributions.Normal,
lower_cutoff=0.,
upper_cutoff=None,
mu=tf.zeros(
batch_shape, dtype=tf.float32),
sigma=tf.ones(
batch_shape, dtype=tf.float32))
samps = qdist.sample_n(n=5000, seed=42)
samps_v = samps.eval()
# With lower_cutoff = 0, the interval j=0 is (-infty, 0], which holds 1/2
# of the mass of the normals.
# rtol chosen to be 2x as large as necessary to pass.
self.assertAllClose([0.5, 0.5], (samps_v == 0).mean(axis=0), rtol=0.03)
# The interval j=1 is (0, 1], which is from the mean to one standard
# deviation out. This should contain 0.6827 / 2 of the mass.
| tensorflow.ones | 4,735 |
import tensorflow as tf
token_type_ids=token_type_ids,
token_type_vocab_size=config.type_vocab_size,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=config.initializer_range,
max_position_embeddings=config.max_position_embeddings,
dropout_prob=config.hidden_dropout_prob)
with tf.variable_scope("encoder"):
# Run the stacked transformer.
# `sequence_output` shape = [batch_size, seq_length, hidden_size].
self.all_encoder_layers = transformer_model(
input_tensor=self.embedding_output,
attention_mask=input_mask,
hidden_size=config.hidden_size,
num_hidden_layers=config.num_hidden_layers,
| tensorflow.variable_scope | 4,736 |
import tensorflow as tf
with tf.variable_scope("FCoptimize", reuse=None):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits= logit, labels=Y)
+ l2_reg_lambda * l2_loss)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
| tensorflow.train.AdamOptimizer | 4,737 |
import tensorflow as tf
def import_ops(self):
"""Imports ops from collections."""
if self._is_training:
self._train_op = tf.get_collection_ref("train_op")[0]
self._lr = tf.get_collection_ref("lr")[0]
self._new_lr = tf.get_collection_ref("new_lr")[0]
self._lr_update = tf.get_collection_ref("lr_update")[0]
rnn_params = tf.get_collection_ref("rnn_params")
if self._cell and rnn_params:
params_saveable = tf.contrib.cudnn_rnn.RNNParamsSaveable(
| tensorflow.get_collection_ref | 4,738 |
import tensorflow as tf
same_start = tf.equal(tf.expand_dims(labeled_starts, 1), tf.expand_dims(candidate_starts, 0)) # [num_labeled, num_candidates]
same_end = tf.equal(tf.expand_dims(labeled_ends, 1), tf.expand_dims(candidate_ends, 0)) # [num_labeled, num_candidates]
| tensorflow.expand_dims | 4,739 |
import tensorflow as tf
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,logits=logits))
self.images_real = tf.placeholder(tf.float32,[None,self.image_size,self.image_size,self.input_dim + self.output_dim])
| tensorflow.placeholder | 4,740 |
import tensorflow as tf
with tf.variable_scope('self_attention'):
# @2.self-attention in block
# mask generation
sl_indices = tf.range(block_len, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col) # bl,bl
else:
direct_mask = tf.greater(sl_col, sl_row) # bl,bl
direct_mask_tile = tf.tile(
tf.expand_dims(tf.expand_dims(direct_mask, 0), 0), [bs, bn, 1, 1]) # bs,bn,bl,bl
rep_mask_tile_1 = tf.tile(tf.expand_dims(rep_mask_split, 2), [1, 1, bl, 1]) # bs,bn,bl,bl
rep_mask_tile_2 = tf.tile(tf.expand_dims(rep_mask_split, 3), [1, 1, 1, bl]) # bs,bn,bl,bl
rep_mask_tile = tf.logical_and(rep_mask_tile_1, rep_mask_tile_2)
attn_mask = tf.logical_and(direct_mask_tile, rep_mask_tile, name='attn_mask') # bs,bn,bl,bl
# attention
f_bias = tf.get_variable('f_bias', [ivec], tf.float32, tf.constant_initializer(0.))
dependent_head = linear(
rep_map, 2 * ivec, False, 0., 'linear_dependent_head', False, wd, keep_prob, is_train) # bs,bn,bl,2vec
dependent, head = tf.split(dependent_head, 2, 3)
dependent_etd = tf.expand_dims(dependent, 2) # bs,bn,1,bl,vec
head_etd = tf.expand_dims(head, 3) # bs,bn,bl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias, 5.0) # bs,bn,bl,bl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 3) # bs,bn,bl,bl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask) # bs,bn,bl,bl,vec
self_attn_result = tf.reduce_sum(attn_score * rep_map_tile, 3) # bs,bn,bl,vec
| tensorflow.logical_and | 4,741 |
import tensorflow as tf
tf.app.flags.DEFINE_string('mode', 'train', 'train or eval.')
tf.app.flags.DEFINE_string('train_data_path', '',
'Filepattern for training data.')
tf.app.flags.DEFINE_string('eval_data_path', '',
'Filepattern for eval data')
tf.app.flags.DEFINE_string('train_dir', '',
| tensorflow.app.flags.DEFINE_string | 4,742 |
import tensorflow as tf
if FLAGS.job_name == 'ps':
log_fn('Running parameter server %s' % self.task_index)
self.server.join()
return
with tf.Graph().as_default():
if FLAGS.eval:
self._eval_cnn()
else:
self._benchmark_cnn()
| tensorflow.Graph | 4,743 |
import tensorflow as tf
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def input_fn(params):
"""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)
d = d.apply(
tf.data.experimental.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
| tensorflow.data.TFRecordDataset | 4,744 |
import tensorflow as tf
def get_regression_loss(
FLAGS, features, is_training):
"""Loss for downstream regression tasks."""
bsz_per_core = tf.shape(features["input_ids"])[0]
inp = tf.transpose(features["input_ids"], [1, 0])
seg_id = tf.transpose(features["segment_ids"], [1, 0])
inp_mask = tf.transpose(features["input_mask"], [1, 0])
label = tf.reshape(features["label_ids"], [bsz_per_core])
| tensorflow.shape | 4,745 |
import tensorflow as tf
name="conv2d"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
# print("c", w.get_shape())
conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
return conv
class batch_norm(object):
def __init__(self, epsilon=1e-5, momentum = 0.9, name="batch_norm"):
with tf.variable_scope(name):
self.epsilon = epsilon
self.momentum = momentum
self.name = name
def __call__(self, x):
return tf.contrib.layers.batch_norm(x,
decay=self.momentum,
updates_collections=None,
epsilon=self.epsilon,
scale=True,
is_training=tftrain,
scope=self.name)
| tensorflow.variable_scope | 4,746 |
import tensorflow as tf
return safe_get(name, list(shape), initializer=conv_initializer, dtype=tf.float32)
def init_fc_weights_snn(shape, name=None):
weights = np.random.normal(scale=np.sqrt(1.0/shape[0]), size=shape).astype('f')
return safe_get(name, list(shape), initializer=tf.constant_initializer(weights), dtype=tf.float32)
def init_conv_weights_snn(shape, name=None):
weights = np.random.normal(scale=np.sqrt(1.0/(shape[0]*shape[1]*shape[2])), size=shape).astype('f')
| tensorflow.constant_initializer | 4,747 |
import tensorflow as tf
def __init__(self, hparams=None):
ClassifierBase.__init__(self, hparams)
with tf.variable_scope(self.variable_scope):
encoder_hparams = utils.dict_fetch(
hparams, Conv1DEncoder.default_hparams())
self._encoder = Conv1DEncoder(hparams=encoder_hparams)
| tensorflow.variable_scope | 4,748 |
import tensorflow as tf
start_top_log_probs, start_top_index = tf.nn.top_k(
start_log_probs, k=FLAGS.start_n_top)
start_index = tf.one_hot(start_top_index,
depth=seq_len, axis=-1, dtype=tf.float32)
start_features = tf.einsum("lbh,bkl->bkh", output, start_index)
end_input = tf.tile(output[:, :, None],
[1, 1, FLAGS.start_n_top, 1])
start_features = tf.tile(start_features[None],
| tensorflow.einsum | 4,749 |
import tensorflow as tf
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'):
| tensorflow.variable_scope | 4,750 |
import tensorflow as tf
# Scale
# scores = scores / (facts.get_shape().as_list()[-1] ** 0.5)
# Activation
if softmax_stag:
scores = tf.nn.softmax(scores) # [B, 1, T]
# Weighted sum
if mode == 'SUM':
output = tf.matmul(scores, facts) # [B, 1, H]
# output = tf.reshape(output, [-1, tf.shape(facts)[-1]])
else:
scores = tf.reshape(scores, [-1, tf.shape(facts)[1]])
output = facts * tf.expand_dims(scores, -1)
output = tf.reshape(output, tf.shape(facts))
if return_alphas:
return output, scores
return output
| tensorflow.matmul | 4,751 |
import tensorflow as tf
kernel_h, kernel_w = kernel_size
stride_h, stride_w = stride
outputs = tf.nn.max_pool(inputs,
ksize=[1, kernel_h, kernel_w, 1],
| tensorflow.nn.max_pool | 4,752 |
import tensorflow as tf
- **`pred`** is the prediction, a Tensor of shape `[batch_size]` \
and type `tf.int64`. For binary classification, the standard \
sigmoid function is used for prediction, and the class labels are \
`{0, 1}`.
"""
logits = self._encoder(inputs, sequence_length, dtype, mode)
num_classes = self._hparams.num_classes
is_binary = num_classes == 1
is_binary = is_binary or (num_classes <= 0 and logits.shape[1] == 1)
if is_binary:
pred = tf.greater(logits, 0)
logits = tf.reshape(logits, [-1])
else:
pred = tf.argmax(logits, 1)
pred = tf.cast(tf.reshape(pred, [-1]), tf.int64)
self._built = True
return logits, pred
@property
def trainable_variables(self):
"""The list of trainable variables of the module.
"""
| tensorflow.reshape | 4,753 |
import tensorflow as tf
batch_size = tf.placeholder(tf.int32, [], name='batch_size')
x = tf.placeholder(tf.float32, [None, num_steps, input_size_x], name='x')
y = tf.placeholder(tf.float32, [None, num_steps, input_size_y], name='y')
input_prob = tf.placeholder(tf.float32, name='input_prob')
state_prob = tf.placeholder(tf.float32,name='state_prob')
output_prob = tf.placeholder(tf.float32,name='output_prob')
rnn_inputs = x
"""Define a single cell with variational dropout"""
def get_a_cell(state_size,input_prob,state_prob,num_input):
| tensorflow.placeholder | 4,754 |
import tensorflow as tf
p.decoder.target_seq_len = 5
p.encoder.input_shape = input_shape
p.input = tig.TestInputGenerator.Params()
p.input.target_max_length = 5
p.input.source_shape = input_shape
p.input.target_shape = [2, 5]
p.name = 'test_mdl'
return p
def testMakeDecoderTheta(self):
# Test that decoder theta returns a copy of theta.decoder without changes.
with self.session(use_gpu=False, graph=tf.Graph()):
tf.set_random_seed(93820985)
p = self._testParams()
mdl = p.Instantiate()
mdl.FPropDefaultTheta()
decoder_theta = mdl._MakeDecoderTheta(theta=mdl.theta, input_batch=None)
mdl.BProp()
self.assertEqual(decoder_theta, mdl.theta.decoder)
def testFProp(self):
with self.session(use_gpu=False):
tf.set_random_seed(93820985)
p = self._testParams()
| tensorflow.set_random_seed | 4,755 |
import tensorflow as tf
"""
x_min1, y_min1, x_max1, y_max1 = tf.split(boxlist1, 4, axis=1)
x_min2, y_min2, x_max2, y_max2 = tf.split(boxlist2, 4, axis=1)
all_pairs_min_ymax = tf.minimum(y_max1, tf.transpose(y_max2))
all_pairs_max_ymin = tf.maximum(y_min1, tf.transpose(y_min2))
intersect_heights = tf.maximum(0.0, all_pairs_min_ymax - all_pairs_max_ymin)
all_pairs_min_xmax = tf.minimum(x_max1, tf.transpose(x_max2))
all_pairs_max_xmin = tf.maximum(x_min1, tf.transpose(x_min2))
intersect_widths = tf.maximum(0.0, all_pairs_min_xmax - all_pairs_max_xmin)
return intersect_heights * intersect_widths
| tensorflow.transpose | 4,756 |
import tensorflow as tf
def combined_static_and_dynamic_shape(tensor):
"""Returns a list containing static and dynamic values for the dimensions.
Returns a list of static and dynamic values for shape dimensions. This is
useful to preserve static shapes when available in reshape operation.
Args:
tensor: A tensor of any type.
Returns:
A list of size tensor.shape.ndims containing integers or a scalar tensor.
"""
static_tensor_shape = tensor.shape.as_list()
dynamic_tensor_shape = tf.shape(tensor)
combined_shape = []
for index, dim in enumerate(static_tensor_shape):
if dim is not None:
combined_shape.append(dim)
else:
combined_shape.append(dynamic_tensor_shape[index])
return combined_shape
def static_or_dynamic_map_fn(fn, elems, dtype=None,
parallel_iterations=32, back_prop=True):
"""Runs map_fn as a (static) for loop when possible.
| tensorflow.shape | 4,757 |
import tensorflow as tf
Returns:
A logits Tensor with shape [<batch_size>, self.num_classes].
"""
with self._model_variable_scope():
if self.data_format == 'channels_first':
# Convert the inputs from channels_last (NHWC) to channels_first (NCHW).
# This provides a large performance boost on GPU. See
# https://www.tensorflow.org/performance/performance_guide#data_formats
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = conv2d_fixed_padding(
inputs=inputs, filters=self.num_filters, kernel_size=self.kernel_size,
strides=self.conv_stride, data_format=self.data_format)
inputs = tf.identity(inputs, 'initial_conv')
# We do not include batch normalization or activation functions in V2
# for the initial conv1 because the first ResNet unit will perform these
# for both the shortcut and non-shortcut paths as part of the first
| tensorflow.transpose | 4,758 |
import tensorflow as tf
"char_mat", initializer=tf.constant(char_mat, dtype=tf.float32))
self.c_mask = tf.cast(self.c, tf.bool)
self.q_mask = tf.cast(self.q, tf.bool)
self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1)
self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1)
if opt:
# we have to hardcode the max batch size here! use the batch size from the generator as this will be used for PG
N, CL = config.batch_size if not self.demo else config.batch_size, config.char_limit
| tensorflow.cast | 4,759 |
import tensorflow as tf
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as writer:
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, prediction) in enumerate(result):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
| tensorflow.logging.info | 4,760 |
import tensorflow as tf
self.w3=tf.get_variable('w3', [3072,512],initializer=tf.contrib.layers.xavier_initializer_conv2d())
self.w4=tf.get_variable('w4', [512,classnum],initializer=tf.contrib.layers.xavier_initializer_conv2d())
self.b1 = tf.get_variable('b1', [2048],initializer=tf.constant_initializer(0.0))
self.b2 = tf.get_variable('b2', [3072],initializer=tf.constant_initializer(0.0))
self.b3 = tf.get_variable('b3', [512],initializer=tf.constant_initializer(0.0))
self.b4 = tf.get_variable('b4', [classnum],initializer=tf.constant_initializer(0.0))
def inference(self,images):
images=tf.cast(images,tf.float32)/255.0
l1 = tf.matmul(images, self.w1)+self.b1
l1=tf.nn.relu(l1)
l2 = tf.matmul(l1, self.w2)+self.b2
l2=tf.nn.relu(l2)
l3=tf.matmul(l2, self.w3)+self.b3
l3=tf.nn.relu(l3)
out=tf.matmul(l3, self.w4)+self.b4
return out
def test_inference(self,images):
images=tf.cast(images,tf.float32)/255.0
l1 = tf.matmul(images, self.w1)+self.b1
l1=tf.nn.relu(l1)
l2 = tf.matmul(l1, self.w2)+self.b2
l2=tf.nn.relu(l2)
l3=tf.matmul(l2, self.w3)+self.b3
l3=tf.nn.relu(l3)
| tensorflow.matmul | 4,761 |
import tensorflow as tf
"""Run cnn in benchmark mode. When forward_only on, it forwards CNN."""
(enqueue_ops, fetches) = self._build_model()
main_fetch_group = tf.group(*fetches)
execution_barrier = None
if self.job_name and not FLAGS.cross_replica_sync:
execution_barrier = self.add_sync_queues_and_barrier(
'execution_barrier_', [])
global_step = tf.contrib.framework.get_global_step()
with tf.device(self.global_step_device):
with tf.control_dependencies([main_fetch_group]):
inc_global_step = global_step.assign_add(1)
fetches.append(inc_global_step)
if self.job_name and FLAGS.cross_replica_sync:
# Block all replicas until all replicas are ready for next step.
fetches.append(self.add_sync_queues_and_barrier(
'sync_queues_step_end_', [main_fetch_group]))
variable_mgr_post_init_ops = self.variable_mgr.get_post_init_ops()
| tensorflow.control_dependencies | 4,762 |
import tensorflow as tf
state = tf.nn.dropout(state, keep_prob=encoder.attn_keep_prob, noise_shape=state_noise_shape)
hidden_noise_shape = [1, 1, tf.shape(hidden)[2]] if encoder.pervasive_dropout else None
hidden = tf.nn.dropout(hidden, keep_prob=encoder.attn_keep_prob, noise_shape=hidden_noise_shape)
| tensorflow.nn.dropout | 4,763 |
import tensorflow as tf
self.data_format = data_format
if( data_format =='NCHW' ):
self.strides = [1, 1, d_h, d_w]
else:
self.strides = [1, d_h, d_w, 1]
def __call__(self,input_var,name=None,**xargs):
shapes = tf.shape(input_var)
if( self.data_format == 'NCHW' ):
shapes = tf.stack([shapes[0],tf.shape(self.b)[0],shapes[2]*self.strides[2],shapes[3]*self.strides[3]])
else:
shapes = tf.stack([shapes[0],shapes[1]*self.strides[1],shapes[2]*self.strides[2],tf.shape(self.b)[0]])
return tf.nn.bias_add(
tf.nn.conv2d_transpose(input_var,self.w,output_shape=shapes,
| tensorflow.shape | 4,764 |
import tensorflow as tf
.. code-block:: python
is_train = session.run(global_mode_train())
# is_train == True
is_train = session.run(
global_mode_train()
feed_dict={tf.global_mode(): tf.estimator.ModeKeys.PREDICT})
# is_train == False
"""
mode = global_mode()
return tf.equal(mode, tf.estimator.ModeKeys.TRAIN)
def global_mode_eval():
"""Returns a bool Tensor indicating whether the global mode is EVAL.
"""
mode = global_mode()
return tf.equal(mode, tf.estimator.ModeKeys.EVAL)
def global_mode_predict():
| tensorflow.equal | 4,765 |
import tensorflow as tf
'c3',
nf=64,
rf=3,
stride=1,
init_scale=np.sqrt(2)))
nh = np.prod([v.value for v in c3.get_shape()[1:]])
h3 = tf.reshape(c3, [-1, nh])
pre_s = tf.nn.relu(self.fc(h3,
'fc1',
nh=512,
init_scale=np.sqrt(2)))
l1 = tf.layers.dense(inputs=pre_s,
| tensorflow.reshape | 4,766 |
import tensorflow as tf
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'labsheets', 'CIFAR10'))
import cifar10 as cf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('data-dir', os.getcwd() + '/dataset/',
'Directory where the dataset will be stored and checkpoint. (default: %(default)s)')
tf.app.flags.DEFINE_integer('max-steps', 10000,
'Number of mini-batches to train on. (default: %(default)d)')
tf.app.flags.DEFINE_integer('log-frequency', 10,
'Number of steps between logging results to the console and saving summaries (default: %(default)d)')
tf.app.flags.DEFINE_integer('save-model', 1000,
'Number of steps between model saves (default: %(default)d)')
# Optimisation hyperparameters
| tensorflow.app.flags.DEFINE_integer | 4,767 |
import tensorflow as tf
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
| tensorflow.distribute.cluster_resolver.TPUClusterResolver | 4,768 |
import tensorflow as tf
saver1 = tf.train.Saver({"v1": v1}, name="saver1")
tf.add_to_collection("savers", saver0)
tf.add_to_collection("savers", saver1)
tf.initialize_all_variables().run()
| tensorflow.add_to_collection | 4,769 |
import tensorflow as tf
grad, = tf.gradients(loss, x)
| tensorflow.gradients | 4,770 |
import tensorflow as tf
with tf.variable_scope('loss_' + self._tag):
# RPN, class loss
rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2])
rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1])
# 得到前景和背景anchor的index
rpn_select = tf.where(tf.not_equal(rpn_label, -1))
rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2])
rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1])
rpn_cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label))
# RPN, bbox loss
| tensorflow.gather | 4,771 |
import tensorflow as tf
def contra_step_lossV2(pred, tgt):
# Step-wise contrastive loss
pred1, pred2 = tf.split(pred, 2, axis=0)
tgt1, tgt2 = tf.split(tgt, 2, axis=0)
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
| tensorflow.split | 4,772 |
import tensorflow as tf
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
train_file = os.path.join(cached_dir, task_name + "_train.tf_record")
if not tf.gfile.Exists(train_file):
classifier_utils.file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer,
train_file, task_name)
| tensorflow.gfile.Exists | 4,773 |
import tensorflow as tf
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
if mask is not None:
mask = tf.equal(mask, tf.ones_like(mask))
key_masks = tf.expand_dims(mask, 1) # [B, 1, T]
paddings = tf.ones_like(scores) * (-2 ** 32 + 1)
scores = tf.where(key_masks, scores, paddings) # [B, 1, T]
# Activation
if softmax_stag:
scores = tf.nn.softmax(scores) # [B, 1, T]
| tensorflow.ones_like | 4,774 |
import tensorflow as tf
# (gamma (Dale) or normal (non-Dale) initialization)
self.W_rec = \
tf.get_variable(
'W_rec',
[N_rec, N_rec],
initializer=W_rec_initializer,
trainable=self.W_rec_train)
# Output weight matrix:
# (uniform initialization as in pycog)
self.W_out = tf.get_variable('W_out', [N_out, N_rec],
initializer=W_out_initializer,
trainable=self.W_out_train)
# Recurrent bias:
self.b_rec = tf.get_variable('b_rec', [N_rec], initializer=b_rec_initializer,
trainable=self.b_rec_train)
# Output bias:
self.b_out = tf.get_variable('b_out', [N_out], initializer=b_out_initializer,
trainable=self.b_out_train)
# ------------------------------------------------
# Non-trainable variables:
# Overall connectivity and Dale's law matrices
# ------------------------------------------------
# Recurrent Dale's law weight matrix:
self.Dale_rec = tf.get_variable('Dale_rec', [N_rec, N_rec],
initializer=tf.constant_initializer(self.dale_rec),
| tensorflow.get_variable | 4,775 |
import tensorflow as tf
if '_' in self.ent_coef:
init_value = float(self.ent_coef.split('_')[1])
assert init_value > 0., "The initial value of ent_coef must be greater than 0"
self.log_ent_coef = tf.get_variable('log_ent_coef', dtype=tf.float32,
initializer=np.log(init_value).astype(np.float32))
self.ent_coef = tf.exp(self.log_ent_coef)
else:
# Force conversion to float
# this will throw an error if a malformed string (different from 'auto')
# is passed
self.ent_coef = float(self.ent_coef)
with tf.variable_scope("target", reuse=False):
# Create the value network
_, _, value_target = self.target_policy.make_critics(self.processed_next_obs_ph,
create_qf=False, create_vf=True)
self.value_target = value_target
if self.n_step:
_,_,value_target_n = self.policy_tf.make_critics(self.processed_next_obs_ph_n,
create_qf=False, create_vf=True,reuse=True)
self.value_target_n = value_target_n
with tf.variable_scope("loss", reuse=False):
# Take the min of the two Q-Values (Double-Q Learning)
min_qf_pi = tf.minimum(qf1_pi, qf2_pi)
| tensorflow.variable_scope | 4,776 |
import tensorflow as tf
# Compare against the true gradient computed by the tape
with tf.GradientTape() as tape:
| tensorflow.GradientTape | 4,777 |
import tensorflow as tf
}
p.input_space_id = problem.SpaceID.IMAGE
p.target_space_id = problem.SpaceID.IMAGE
NetworkOutput = collections.namedtuple(
"NetworkOutput", "policy, value, action_postprocessing")
# TODO(koz4k): Translate it to T2TModel or remove.
def feed_forward_gaussian_fun(action_space, config, observations):
"""Feed-forward Gaussian."""
if not isinstance(action_space, gym.spaces.box.Box):
raise ValueError("Expecting continuous action space.")
mean_weights_initializer = tf.initializers.variance_scaling(
scale=config.init_mean_factor)
logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
flat_observations = tf.reshape(observations, [
tf.shape(observations)[0], tf.shape(observations)[1],
functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
with tf.variable_scope("network_parameters"):
with tf.variable_scope("policy"):
x = flat_observations
for size in config.policy_layers:
x = tf.layers.dense(x, size, activation=tf.nn.relu)
mean = tf.layers.dense(
x, action_space.shape[0], activation=tf.tanh,
| tensorflow.initializers.variance_scaling | 4,778 |
from tensorflow.contrib import layers
with variable_scope.variable_scope(
parent_scope + "/logits",
values=[net],
partitioner=hidden_layer_partitioner) as scope:
logits = layers.fully_connected(
net,
head.logits_dimension,
activation_fn=None,
| tensorflow.contrib.layers.fully_connected | 4,779 |
import tensorflow as tf
with tf.variable_scope('soft_replacement'):
self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = 'logs/' + current_time
| tensorflow.global_variables_initializer | 4,780 |
import tensorflow as tf
with tf.variable_scope("stats") as scope:
for i in range(hparams.tacotron_num_gpus):
tf.summary.histogram("mel_outputs %d" % i, model.tower_mel_outputs[i])
tf.summary.histogram("mel_targets %d" % i, model.tower_mel_targets[i])
tf.summary.scalar("before_loss", model.before_loss)
tf.summary.scalar("after_loss", model.after_loss)
if hparams.predict_linear:
tf.summary.scalar("linear_loss", model.linear_loss)
for i in range(hparams.tacotron_num_gpus):
tf.summary.histogram("mel_outputs %d" % i, model.tower_linear_outputs[i])
tf.summary.histogram("mel_targets %d" % i, model.tower_linear_targets[i])
tf.summary.scalar("regularization_loss", model.regularization_loss)
tf.summary.scalar("stop_token_loss", model.stop_token_loss)
tf.summary.scalar("loss", model.loss)
tf.summary.scalar("learning_rate", model.learning_rate) # Control learning rate decay speed
if hparams.tacotron_teacher_forcing_mode == "scheduled":
tf.summary.scalar("teacher_forcing_ratio", model.ratio) # Control teacher forcing
# ratio decay when mode = "scheduled"
gradient_norms = [tf.norm(grad) for grad in model.gradients]
tf.summary.histogram("gradient_norm", gradient_norms)
| tensorflow.summary.histogram | 4,781 |
import tensorflow as tf
"""Imports ops from collections."""
if self._is_training:
self._train_op = tf.get_collection_ref("train_op")[0]
self._lr = tf.get_collection_ref("lr")[0]
self._new_lr = tf.get_collection_ref("new_lr")[0]
self._lr_update = tf.get_collection_ref("lr_update")[0]
rnn_params = tf.get_collection_ref("rnn_params")
if self._cell and rnn_params:
params_saveable = tf.contrib.cudnn_rnn.RNNParamsSaveable(
self._cell,
self._cell.params_to_canonical,
self._cell.canonical_to_params,
rnn_params,
base_variable_scope="Model/RNN")
tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, params_saveable)
self._cost = tf.get_collection_ref(util.with_prefix(self._name, "cost"))[0]
| tensorflow.contrib.cudnn_rnn.RNNParamsSaveable | 4,782 |
from tensorflow.python.layers import pooling as pooling_layers
input_layer = self.top_layer
else:
self.top_size = num_channels_in
name = 'apool' + str(self.counts['apool'])
self.counts['apool'] += 1
pool = pooling_layers.average_pooling2d(
input_layer, [k_height, k_width], [d_height, d_width],
padding=mode,
data_format=self.channel_pos,
name=name)
| tensorflow.python.layers.pooling.average_pooling2d | 4,783 |
import tensorflow as tf
def pointer(inputs, state, hidden, mask, scope="pointer"):
with tf.variable_scope(scope):
u = tf.concat([tf.tile(tf.expand_dims(state, axis=1), [1, tf.shape(inputs)[1], 1]), inputs], axis=2) #[N,PL,2d]
s0 = tf.nn.tanh(dense(u, hidden, use_bias=False, scope="s0"))
s = dense(s0, 1, use_bias=False, scope="s")
s1 = softmax_mask(tf.squeeze(s, [2]), mask)#[N,PL]
a = tf.expand_dims(tf.nn.softmax(s1), axis=2)#[N,PL,1]
res = tf.reduce_sum(a * inputs, axis=1)
return res, s1 # attention_sum probability
def summ(memory, hidden, mask, keep_prob=1.0, is_train=None, scope="summ"):
with tf.variable_scope(scope):
d_memory = dropout(memory, keep_prob=keep_prob, is_train=is_train)
s0 = tf.nn.tanh(dense(d_memory, hidden, scope="s0"))
s = dense(s0, 1, use_bias=False, scope="s")
s1 = softmax_mask(tf.squeeze(s, [2]), mask)
a = tf.expand_dims(tf.nn.softmax(s1), axis=2)
res = tf.reduce_sum(a * memory, axis=1)
return res
def dot_attention(inputs, memory, mask, hidden, keep_prob=1.0, is_train=None, scope="dot_attention"):
with tf.variable_scope(scope):
| tensorflow.variable_scope | 4,784 |
import tensorflow as tf
'Wether we will train on cloud.')
tf.app.flags.DEFINE_boolean(
| tensorflow.app.flags.DEFINE_boolean | 4,785 |
import tensorflow as tf
label_ids = features["label_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
(total_loss, per_example_loss, logits) = create_model(
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
if use_tpu:
def tpu_scaffold():
| tensorflow.trainable_variables | 4,786 |
import tensorflow as tf
output_weight = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02)
)
output_bias = tf.get_variable(
"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)
def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings):
| tensorflow.reshape | 4,787 |
import tensorflow as tf
to the length of each input
Returns:
A tensor of shape [T, B] that contains the loss per example, per time step.
"""
with tf.name_scope("cross_entropy_sequence_loss"):
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets)
loss_mask = tf.sequence_mask(tf.to_int32(sequence_length), tf.to_int32(tf.shape(targets)[0]))
losses = losses * tf.transpose(tf.to_float(loss_mask), [1, 0])
return losses
def dice_loss(predictions, targets, weights=1., name='dice_loss'):
| tensorflow.to_int32 | 4,788 |
import tensorflow as tf
model_lam=3e-4,
activation=output_activation,
name="Out")
x = out_layer(x)
regularization += out_layer.regularization
return x, regularization
def mlp_dropout(x, hidden_sizes=(32,), activation=tf.tanh, output_activation=None, dropout_rate=0):
for h in hidden_sizes[:-1]:
x = tf.layers.dense(x, units=h, activation=activation)
x = tf.layers.dropout(x, rate=dropout_rate, training=True)
x = tf.layers.dropout(x, rate=dropout_rate, training=True)
return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)
def mlp(x, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):
for h in hidden_sizes[:-1]:
x = tf.layers.dense(x, units=h, activation=activation)
return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)
def get_vars(scope):
| tensorflow.layers.dropout | 4,789 |
from tensorflow.python.ops import math_ops
target = target[self.name] if isinstance(target, dict) else target
loss_unweighted = self._loss_fn(logits, target)
weight_tensor = self.get_weight_tensor(features)
if weight_tensor is None:
return math_ops.reduce_mean(loss_unweighted, name="loss")
else:
loss_unweighted = array_ops.reshape(loss_unweighted, shape=(-1,))
loss_weighted = math_ops.mul(
loss_unweighted,
array_ops.reshape(weight_tensor, shape=(-1,)))
return math_ops.div(
math_ops.reduce_sum(loss_weighted),
math_ops.to_float(math_ops.reduce_sum(weight_tensor)),
name="loss")
class _RegressionTargetColumn(_TargetColumn):
"""_TargetColumn for regression."""
def __init__(self, loss_fn, label_name, weight_column_name, target_dimension):
super(_RegressionTargetColumn, self).__init__(
loss_fn=loss_fn,
num_label_columns=target_dimension,
label_name=label_name,
weight_column_name=weight_column_name)
| tensorflow.python.ops.math_ops.reduce_sum | 4,790 |
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
X = encoder_conf('e2', X1, 256, 3, 1, norm, reuse_encoder, is_train, self.args.dropout) # 30 > 28
X2 = encoder_conf('d2', X, 256, 2, 2, norm, reuse_encoder, is_train, self.args.dropout) # 28 > 14 @8
X = encoder_conf('e3', X2, 512, 3, 1, norm, reuse_encoder, is_train, self.args.dropout) # 14 > 12
X_MIDDLE = X
# ===============================================================================DECODER
| tensorflow.variable_scope | 4,791 |
from tensorflow.python.ops import variable_scope
update_op: An operation that updates the underlying variables appropriately.
Raises:
ValueError: If `labels` and `predictions` are of different sizes, or if
`weights` is the wrong size, or if either `metrics_collections` or
`updates_collections` are not a `list` or `tuple`.
"""
with variable_scope.variable_scope(name, 'pearson_r', [predictions, labels]):
predictions, labels = tensor_util.remove_squeezable_dimensions(
predictions, labels)
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
cov, update_cov = streaming_covariance(
predictions, labels, weights=weights, name='covariance')
var_predictions, update_var_predictions = streaming_covariance(
| tensorflow.python.ops.variable_scope.variable_scope | 4,792 |
import tensorflow as tf
# update_target_fn will be called periodically to copy Q network to target Q network
update_target_expr = []
for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_expr.append(var_target.assign(var))
update_target_expr = tf.group(*update_target_expr)
# Create callable functions
train = U.function(
inputs=[
obs_t_input,
| tensorflow.group | 4,793 |
import tensorflow as tf
for bi in range(b):
with tf.variable_scope('block_{}'.format(bi)):
idx1 = cell_arch[bi][0]
op1 = cell_arch[bi][1]
idx2 = cell_arch[bi][2]
op2 = cell_arch[bi][3]
with tf.variable_scope('X1'):
X1 = self._add_op_dynamic(cell_inputs, blocks, idx1, op1, w, h, block_ch, is_train=is_train)
X1 = self._add_drop_path(X1, drop_path_keep_prob)
with tf.variable_scope('X2'):
X2 = self._add_op_dynamic(cell_inputs, blocks, idx2, op2, w, h, block_ch, is_train=is_train)
X2 = self._add_drop_path(X2, drop_path_keep_prob)
X = tf.add_n([X1, X2])
blocks.append(X)
(X, comb_ch) = self._combine_cell_blocks_dynamic(cell_inputs, blocks, cell_arch, w, h, block_ch, is_train)
X = tf.reshape(X, (-1, w, h, comb_ch)) # Sanity shape check
layers.append((X, w, h, comb_ch))
def _add_static_cell(self, cell_arch, layers, w, h, block_ch, drop_path_keep_prob, is_train=False, is_reduction=False):
b = CELL_NUM_BLOCKS
# Calibrate inputs as necessary to last input layer's dimensions and add them to hidden states
cell_inputs = [layers[-2] if len(layers) > 1 else layers[-1], layers[-1]]
| tensorflow.add_n | 4,794 |
import tensorflow as tf
shape=[num_examples, seq_length],
dtype=tf.int32),
"segment_ids":
tf.constant(
all_segment_ids,
shape=[num_examples, seq_length],
dtype=tf.int32),
"label_ids":
tf.constant(all_label_ids, shape=[num_examples,seq_length], dtype=tf.int32),
})
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
| tensorflow.constant | 4,795 |
import tensorflow as tf
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range),
)
output_bias = tf.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer()
)
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs)
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]
)
| tensorflow.reduce_mean | 4,796 |
import tensorflow as tf
opt=None):
self.embedding_size = embedding_size
self.maxnorm = maxnorm
self.batch_pos_cnt = batch_pos_cnt
self.max_iter = max_iter
self.model_type = model_type
self.add_bias = add_bias
if opt is None:
opt = tf.train.AdagradOptimizer(1.0)
self.opt = opt
self.sess = None
self.train_step = None
self.post_step = None
self.graph = tf.Graph()
with self.graph.as_default():
self.head_input = tf.placeholder(tf.int32, shape=[None])
| tensorflow.train.AdagradOptimizer | 4,797 |
import tensorflow as tf
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]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
return output_tensor
| tensorflow.range | 4,798 |
import tensorflow as tf
Returns:
A tuple containing:
- Input tensor of the restored model.
- Prediction tensor of the restored model.
- Output tensor, which is the softwmax result of the prediction tensor.
- new session of the restored model.
"""
model_dir = tempfile.mkdtemp()
saver.save(sess, model_dir)
# Reset the graph.
tf.reset_default_graph()
x, prediction, output_class = self.buildModel(lstm_layer, is_dynamic_rnn)
new_sess = tf.compat.v1.Session(config=CONFIG)
saver = tf.train.Saver()
saver.restore(new_sess, model_dir)
return x, prediction, output_class, new_sess
def getInferenceResult(self, x, output_class, sess):
"""Get inference result given input tensor and output tensor.
Args:
x: The input tensor.
output_class: The output tensor.
| tensorflow.reset_default_graph | 4,799 |
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