seed
stringlengths 25
2.89k
| seed_api
stringlengths 14
102
| index
int64 0
14.8k
|
|---|---|---|
import tensorflow as tf
def fn_not_eos():
return tf.not_equal( # Check if the last predicted element is a EOS
tf.squeeze(result[:, -1, :, :]), text_encoder.EOS_ID)
not_eos = tf.cond(
# We only check for early stoping if there is at least 1 element (
# otherwise not_eos will crash)
tf.not_equal(length, 0),
fn_not_eos,
lambda: True,
)
return tf.cond(
tf.equal(batch_size, 1),
# If batch_size == 1, we check EOS for early stoping
lambda: tf.logical_and(not_overflow, not_eos),
# Else, just wait for max length
lambda: not_overflow)
return not_overflow
result, logits, loss = tf.while_loop(
while_exit_cond,
infer_step, [result, logits, loss],
shape_invariants=[
tf.TensorShape([None, None, None, None]),
tf.TensorShape([None, None, None, None, None]),
tf.TensorShape([]),
| tensorflow.equal | 5,200 |
import tensorflow as tf
encoder_input_length=None, feed_argmax=True, rewards=None, use_baseline=True,
training=True, global_step=None,
monotonicity_weight=None, monotonicity_dist=None, monotonicity_decay=None, **kwargs):
decoder = decoders[0]
targets = targets[0] # single decoder
if encoder_input_length is None:
encoder_input_length = []
for encoder_inputs_ in encoder_inputs:
mask = get_weights(encoder_inputs_, utils.EOS_ID, include_first_eos=True)
encoder_input_length.append(tf.to_int32(tf.reduce_sum(mask, axis=1)))
parameters = dict(encoders=encoders, decoder=decoder, encoder_inputs=encoder_inputs,
feed_argmax=feed_argmax, training=training)
attention_states, encoder_state, encoder_input_length = multi_encoder(
encoder_input_length=encoder_input_length, **parameters)
outputs, attention_weights, _, _, samples, beam_fun, initial_data = attention_decoder(
attention_states=attention_states, initial_state=encoder_state, feed_previous=feed_previous,
| tensorflow.reduce_sum | 5,201 |
import tensorflow as tf
n_actions = ac_space.nvec if isinstance(ac_space, MultiDiscrete) else ac_space.n
random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=n_actions, dtype=tf.int64)
chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
perturbed_stochastic_actions = tf.where(chose_random, random_actions, perturbed_deterministic_actions)
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
perturbed_output_actions = tf.cond(stochastic_ph, lambda: perturbed_stochastic_actions,
lambda: deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
updates = [
update_eps_expr,
tf.cond(reset_ph, lambda: perturb_vars(original_scope="model", perturbed_scope="perturbed_model/model"),
lambda: tf.group(*[])),
tf.cond(update_param_noise_scale_ph, lambda: update_scale(), lambda: tf.Variable(0., trainable=False)),
update_param_noise_thres_expr,
]
_act = tf_util.function(inputs=[policy.obs_ph, stochastic_ph, update_eps_ph],
outputs=output_actions,
givens={update_eps_ph: -1.0, stochastic_ph: True},
updates=[update_eps_expr])
_perturbed_act = tf_util.function(
inputs=[policy.obs_ph, stochastic_ph, update_eps_ph, reset_ph, update_param_noise_threshold_ph,
update_param_noise_scale_ph],
| tensorflow.group | 5,202 |
import tensorflow as tf
logging.info("DropconnectDenseLayer %s: %d %s" % (name, n_units, act.__name__))
if W_init_args is None:
W_init_args = {}
if b_init_args is None:
b_init_args = {}
self.inputs = prev_layer.outputs
if self.inputs.get_shape().ndims != 2:
raise Exception("The input dimension must be rank 2")
n_in = int(self.inputs.get_shape()[-1])
self.n_units = n_units
with tf.variable_scope(name):
W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **W_init_args)
b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=LayersConfig.tf_dtype, **b_init_args)
# self.outputs = act(tf.matmul(self.inputs, W) + b)
LayersConfig.set_keep[name] = tf.placeholder(tf.float32)
W_dropcon = tf.nn.dropout(W, LayersConfig.set_keep[name])
self.outputs = act(tf.matmul(self.inputs, W_dropcon) + b)
# self.all_layers = list(layer.all_layers)
# self.all_params = list(layer.all_params)
# self.all_drop = dict(layer.all_drop)
self.all_drop.update({LayersConfig.set_keep[name]: keep})
self.all_layers.append(self.outputs)
self.all_params.extend([W, b])
| tensorflow.get_variable | 5,203 |
import tensorflow as tf
horizon_pred = horizon_sumV1(pred, horizon)
horizon_tgt = horizon_sumV1(tgt, horizon)
pred_flat = tf.reshape(horizon_pred, [-1])
tgt_flat = tf.reshape(horizon_tgt, [-1])
batch = tf.stack([pred_flat, tgt_flat], 1)
sample_func = sample_pair(batch)
| tensorflow.reshape | 5,204 |
from tensorflow.contrib.learn.python.learn.datasets import base
def fake():
return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
train = fake()
validation = fake()
test = fake()
return base.Datasets(train=train, validation=validation, test=test)
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
VALIDATION_SIZE = 5000
local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
SOURCE_URL + TRAIN_IMAGES)
train_images = extract_images(local_file)
local_file = base.maybe_download(TRAIN_LABELS, train_dir,
SOURCE_URL + TRAIN_LABELS)
train_labels = extract_labels(local_file, one_hot=one_hot)
local_file = base.maybe_download(TEST_IMAGES, train_dir,
SOURCE_URL + TEST_IMAGES)
test_images = extract_images(local_file)
local_file = base.maybe_download(TEST_LABELS, train_dir,
SOURCE_URL + TEST_LABELS)
test_labels = extract_labels(local_file, one_hot=one_hot)
| tensorflow.contrib.learn.python.learn.datasets.base.maybe_download | 5,205 |
import tensorflow as tf
):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
masked_lm_positions = features["masked_lm_positions"]
masked_lm_ids = features["masked_lm_ids"]
masked_lm_weights = features["masked_lm_weights"]
| tensorflow.logging.info | 5,206 |
import tensorflow as tf
train_op = ema.apply(tf.trainable_variables())
return tf.estimator.EstimatorSpec(mode, loss=total_loss, train_op=train_op)
def add_weight_decay(weight_decay):
"""Add L2 regularization to all (or some) trainable kernel weights."""
weight_decay = tf.constant(
weight_decay, tf.float32,
[], 'weight_decay'
)
trainable_vars = tf.trainable_variables()
kernels = [
v for v in trainable_vars
if ('weights' in v.name or 'kernel' in v.name) and 'depthwise_weights' not in v.name
]
for K in kernels:
x = tf.multiply(weight_decay, tf.nn.l2_loss(K))
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, x)
class RestoreMovingAverageHook(tf.train.SessionRunHook):
| tensorflow.trainable_variables | 5,207 |
import tensorflow as tf
worker_device=worker, ps_device='/cpu:0', ps_tasks=1)
with tf.name_scope('{}_{}'.format(mode, i)) as scope:
with tf.device(device_setter):
net_outputs = self._model(shards[i], mode, **self.config)
if mode == Mode.TRAIN:
loss = self._loss(net_outputs, shards[i], **self.config)
loss += tf.reduce_sum(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
scope))
model_params = tf.trainable_variables()
grad = tf.gradients(loss, model_params)
tower_losses.append(loss)
tower_gradvars.append(zip(grad, model_params))
if i == 0:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
scope)
elif mode == Mode.EVAL:
tower_metrics.append(self._metrics(
net_outputs, shards[i], **self.config))
else:
tower_preds.append(net_outputs)
if mode == Mode.TRAIN:
return tower_losses, tower_gradvars, update_ops
elif mode == Mode.EVAL:
return tower_metrics
else:
return tower_preds
| tensorflow.get_collection | 5,208 |
import tensorflow as tf
writer = tf.train.SummaryWriter('logs/', sess.graph)
else: # tensorflow version >= 0.12
writer = tf.summary.FileWriter("logs/", sess.graph)
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer()
| tensorflow.__version__.split | 5,209 |
import tensorflow as tf
output_1 = contrib.layers.fully_connected(dropout3_1, n_output_1, activation_fn=None, scope="output_1")
output_2 = contrib.layers.fully_connected(dropout3_2, n_output_2, activation_fn=None, scope="output_2")
with tf.variable_scope("loss"):
loss_base_1 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_1, logits=output_1))
loss_base_2 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_2, logits=output_2))
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
loss_total = loss_base_1 + loss_base_2 + tf.reduce_sum(reg_losses)
with tf.variable_scope("evaluation"):
accuracy_1 = tf.reduce_mean(tf.cast(tf.equal(
tf.argmax(output_1, axis=-1),
tf.argmax(y_1, axis=-1)), tf.float32), name="accuracy_1")
accuracy_2 = tf.reduce_mean(tf.cast(tf.equal(
tf.argmax(output_2, axis=-1),
tf.argmax(y_2, axis=-1)), tf.float32), name="accuracy_2")
accuracy = tf.divide(accuracy_1 + accuracy_2, 2.0, name="accuracy")
with tf.variable_scope("train"):
global_step = tf.get_variable("global_step", shape=(), dtype=tf.int32, trainable=False)
train_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss_total, global_step=global_step)
with tf.variable_scope("summary"):
summary_loss_total = tf.summary.scalar("loss_total", loss_total)
summary_accuracy_test = tf.summary.scalar("accuracy_test", accuracy)
| tensorflow.argmax | 5,210 |
import tensorflow as tf
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
output_z_ = lrelu(linear(trans_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(tf.concat([output_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(tf.concat([output_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(tf.concat([output_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(tf.concat([output_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
| tensorflow.concat | 5,211 |
import tensorflow as tf
w_ctx2out = tf.get_variable('w_ctx2out', [self.D, self.M], initializer=self.weight_initializer)
h_logits += tf.matmul(context, w_ctx2out)
if self.prev2out:
h_logits += x
h_logits = tf.nn.tanh(h_logits)
if dropout:
h_logits = tf.nn.dropout(h_logits, 0.5)
out_logits = tf.matmul(h_logits, w_out) + b_out
| tensorflow.nn.tanh | 5,212 |
from tensorflow.python.framework import ops
start: A 0-D (scalar) of type `int32`. First entry in sequence.
limit: A 0-D (scalar) of type `int32`. Upper limit of sequence,
exclusive.
delta: A 0-D `Tensor` (scalar) of type `int32`. Optional. Default is 1.
Number that increments `start`.
name: A name for the operation (optional).
Returns:
An 1-D `int32` `Tensor`.
"""
return gen_math_ops._range(start, limit, delta, name=name)
@ops.RegisterShape("Range")
def _RangeShape(op):
start_value = tensor_util.ConstantValue(op.inputs[0])
limit_value = tensor_util.ConstantValue(op.inputs[1])
delta_value = tensor_util.ConstantValue(op.inputs[2])
if start_value is None or limit_value is None or delta_value is None:
return [tensor_shape.vector(None)]
else:
return [tensor_shape.vector((limit_value - start_value + delta_value - 1) //
delta_value)]
# Reduction operations
| tensorflow.python.framework.ops.RegisterShape | 5,213 |
import tensorflow as tf
global_step,
decay_steps=lr_decay_params["lr_decay_steps"],
decay_rate=lr_decay_params["lr_decay_rate"],
staircase=lr_decay_params.get("staircase", True),
)
optimizer_params["learning_rate"] = learning_rate
var_list = None
if self.params["warm_start_dir"]:
output_vars1 = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="task_dependent")
output_vars2 = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="task_independent/Variable_1")
var_list = [output_vars1, output_vars2]
train_op = tf.train.AdamOptimizer(
# learning_rate=self.params["learning_rate"]
# **self.params.get("optimizer_params", {})
# learning_rate=learning_rate
**optimizer_params
).minimize(loss, global_step=global_step, var_list=var_list)
| tensorflow.get_collection | 5,214 |
import tensorflow as tf
def compute_error_loss(pred1, pred2, tgt1, tgt2, hard_ratio=1.0):
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
loss = tf.maximum(0., (tgt_larg - tgt_small) - (pred_larg - pred_small))
if hard_ratio < 1.0:
hard_num = tf.cast(tools.shape(pred1)[0] * hard_ratio, tf.int32)
loss = tf.reshape(loss, [-1])
hard_loss, _ = tf.math.top_k(loss, k=hard_num)
return hard_loss
return loss
def sample_pair(batch):
num_sam = tools.shape(batch)[0]
index = tf.range(num_sam)
tgt1 = tf.slice(batch, [0, 1], [num_sam, 1])
pred1 = tf.slice(batch, [0, 0], [num_sam, 1])
| tensorflow.math.top_k | 5,215 |
import tensorflow as tf
padded1 = tf.pad(sampled1, [[0, 0], [HALF_DIFF, HALF_DIFF], [HALF_DIFF, HALF_DIFF], [0, 0]])
padded2 = tf.pad(sampled2, [[0, 0], [HALF_DIFF, HALF_DIFF], [HALF_DIFF, HALF_DIFF], [0, 0]])
img_orig = tf.concat([image[:, :, :, 0], image[:, :, :, 1]], 1) # b x 2h x w
transform1 = tf.concat([padded1[:, :, :, 0], padded1[:, :, :, 1]], 1)
transform2 = tf.concat([padded2[:, :, :, 0], padded2[:, :, :, 1]], 1)
stacked = tf.concat([img_orig, transform1, transform2], 2, 'viz')
tf.summary.image('visualize',
tf.expand_dims(stacked, -1), max_outputs=30)
sampled = tf.concat([sampled1, sampled2], 3, 'sampled_concat')
logits = (LinearWrap(sampled)
.FullyConnected('fc1', out_dim=256, nl=tf.nn.relu)
.FullyConnected('fc2', out_dim=128, nl=tf.nn.relu)
.FullyConnected('fct', out_dim=19, nl=tf.identity)())
tf.nn.softmax(logits, name='prob')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
| tensorflow.concat | 5,216 |
import tensorflow as tf
"tpu_name", None,
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
"url.")
tf.flags.DEFINE_string(
"tpu_zone", None,
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
| tensorflow.flags.DEFINE_string | 5,217 |
import tensorflow as tf
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(1):
| tensorflow.global_variables_initializer | 5,218 |
from tensorflow.python.ops import array_ops
mask_logits(end_logits, mask=tf.reshape(self.c_mask, [N, -1]))]
self.logits1, self.logits2 = [l for l in self.logits]
outer = tf.matmul(tf.expand_dims(tf.nn.softmax(self.logits1), axis=2),
tf.expand_dims(tf.nn.softmax(self.logits2), axis=1))
outer = tf.matrix_band_part(outer, 0, self.max_a_len)
self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
def _compute_loss(self):
def focal_loss(logits, labels, weights=None, alpha=0.25, gamma=2):
logits = tf.nn.sigmoid(logits)
zeros = array_ops.zeros_like(logits, dtype=logits.dtype)
pos_p_sub = array_ops.where(labels > zeros, labels - logits, zeros)
neg_p_sub = array_ops.where(labels > zeros, zeros, logits)
cross_ent = - alpha * (pos_p_sub ** gamma) * tf.log(tf.clip_by_value(logits, 1e-8, 1.0)) \
- (1 - alpha) * (neg_p_sub ** gamma) * tf.log(tf.clip_by_value(1.0 - logits, 1e-8, 1.0))
return tf.reduce_sum(cross_ent, 1)
start_label = tf.one_hot(self.start_label, tf.shape(self.logits1)[1], axis=1)
end_label = tf.one_hot(self.end_label, tf.shape(self.logits2)[1], axis=1)
if self.config.loss_type == 'cross_entropy':
start_loss = tf.nn.softmax_cross_entropy_with_logits(
logits=self.logits1, labels=start_label)
end_loss = tf.nn.softmax_cross_entropy_with_logits(
| tensorflow.python.ops.array_ops.zeros_like | 5,219 |
import tensorflow as tf
biases = tf.get_variable("biases", [nOut], initializer=tf.constant_initializer(), dtype=inpOp.dtype)
bias = tf.nn.bias_add(conv_bn, biases)
conv1 = tf.nn.relu(bias)
return conv1
| tensorflow.nn.relu | 5,220 |
from tensorflow.python.ops import variable_scope as vs
return self._num_units
def __call__(self, inputs, state, att_score):
return self.call(inputs, state, att_score)
def call(self, inputs, state, att_score=None):
"""Gated recurrent unit (GRU) with nunits cells."""
if self._gate_linear is None:
bias_ones = self._bias_initializer
if self._bias_initializer is None:
bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype)
with vs.variable_scope("gates"): # Reset gate and update gate.
self._gate_linear = _Linear(
[inputs, state],
2 * self._num_units,
True,
bias_initializer=bias_ones,
kernel_initializer=self._kernel_initializer)
value = math_ops.sigmoid(self._gate_linear([inputs, state]))
| tensorflow.python.ops.variable_scope.variable_scope | 5,221 |
import tensorflow as tf
if pos is not None:
pos = tf.reshape(pos, [-1, 1])
pos = tf.minimum(pos, encoder_input_length - 1)
if pos is not None and encoder.attn_window_size > 0:
# `pred_edits` scenario, where we know the aligned pos
# when the windows size is non-zero, we concatenate consecutive encoder states
# and map it to the right attention vector size.
weights = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos, axis=1)), depth=attn_length))
weighted_average = []
for offset in range(-encoder.attn_window_size, encoder.attn_window_size + 1):
pos_ = pos + offset
pos_ = tf.minimum(pos_, encoder_input_length - 1)
pos_ = tf.maximum(pos_, 0) # TODO: when pos is < 0, use <S> or </S>
weights_ = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos_, axis=1)), depth=attn_length))
weighted_average_ = tf.reduce_sum(tf.expand_dims(weights_, axis=2) * hidden_states, axis=1)
weighted_average.append(weighted_average_)
weighted_average = tf.concat(weighted_average, axis=1)
weighted_average = dense(weighted_average, encoder.attn_size)
elif pos is not None:
weights = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos, axis=1)), depth=attn_length))
weighted_average = tf.reduce_sum(tf.expand_dims(weights, axis=2) * hidden_states, axis=1)
else:
# Local attention of Luong et al. (http://arxiv.org/abs/1508.04025)
wp = get_variable('Wp', [state_size, state_size])
vp = get_variable('vp', [state_size, 1])
| tensorflow.maximum | 5,222 |
import tensorflow as tf
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)
state_size = state.get_shape()[1].value
with tf.variable_scope(scope or 'attention_{}'.format(encoder.name)):
encoder_input_length = tf.to_float(tf.expand_dims(encoder_input_length, axis=1))
if pos is not None:
pos = tf.reshape(pos, [-1, 1])
pos = tf.minimum(pos, encoder_input_length - 1)
if pos is not None and encoder.attn_window_size > 0:
# `pred_edits` scenario, where we know the aligned pos
# when the windows size is non-zero, we concatenate consecutive encoder states
# and map it to the right attention vector size.
weights = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos, axis=1)), depth=attn_length))
weighted_average = []
for offset in range(-encoder.attn_window_size, encoder.attn_window_size + 1):
pos_ = pos + offset
pos_ = tf.minimum(pos_, encoder_input_length - 1)
pos_ = tf.maximum(pos_, 0) # TODO: when pos is < 0, use <S> or </S>
weights_ = tf.to_float(tf.one_hot(tf.to_int32(tf.squeeze(pos_, axis=1)), depth=attn_length))
| tensorflow.reshape | 5,223 |
import tensorflow as tf
blk_shape = tf.shape(blk_indices)
ksize = tf.shape(w)
| tensorflow.shape | 5,224 |
import tensorflow as tf
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction)
config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False, intra_op_parallelism_threads = FLAGS.num_cpu_threads, inter_op_parallelism_threads = FLAGS.num_cpu_threads, gpu_options = gpu_options)
| tensorflow.ConfigProto | 5,225 |
import tensorflow as tf
reg += self.L1_out * tf.reduce_mean(tf.abs(self.W_out) * self.output_Connectivity)
# L2 weight regularization
reg += self.L2_in * tf.reduce_mean(tf.square(tf.abs(self.W_in) * self.input_Connectivity))
reg += self.L2_rec * tf.reduce_mean(tf.square(tf.abs(self.W_rec) * self.rec_Connectivity))
if self.dale_ratio:
reg += self.L2_out * tf.reduce_mean(tf.square(
tf.matmul(tf.abs(self.W_out) * self.output_Connectivity, self.Dale_out)))
| tensorflow.abs | 5,226 |
import tensorflow as tf
with self.session(use_gpu=False) as sess:
tf.set_random_seed(93820985)
| tensorflow.set_random_seed | 5,227 |
import tensorflow as tf
strides = [1, 1, stride, stride]
bshape = [1, nf, 1, 1]
else:
raise NotImplementedError
bias_var_shape = [nf] if one_dim_bias else [1, nf, 1, 1]
nin = x.get_shape()[channel_ax].value
wshape = [rf, rf, nin, nf]
with tf.variable_scope(scope):
w = tf.get_variable("w", wshape, initializer=ortho_init(init_scale))
b = tf.get_variable("b", bias_var_shape, initializer=tf.constant_initializer(0.0))
if not one_dim_bias and data_format == 'NHWC':
b = tf.reshape(b, bshape)
return tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format) + b
def fc(x, scope, nh, *, init_scale=1.0, init_bias=0.0):
with tf.variable_scope(scope):
nin = x.get_shape()[1].value
w = tf.get_variable("w", [nin, nh], initializer=ortho_init(init_scale))
print("w is "+str(w))
b = tf.get_variable("b", [nh], initializer=tf.constant_initializer(init_bias))
return tf.matmul(x, w)+b
def batch_to_seq(h, nbatch, nsteps, flat=False):
if flat:
| tensorflow.nn.conv2d | 5,228 |
import tensorflow as tf
CHANGES
- Added one more block consisting of 3 conv layers and 1 max pool layer
- kernel size was changed (3,3) -> (8,8), strides from 1 to 4, to get 1 x 1 in last layer
- removed policy layers
'''
w_init = tf.contrib.layers.variance_scaling_initializer()
# glimpse1 = tf.image.extract_glimpse(inputs, [glimpse_size1,glimpse_size1], self.prev_loc, centered=True, normalized=True)
# glimpse2 = tf.image.extract_glimpse(inputs, [glimpse_size2,glimpse_size2], self.prev_loc, centered=True, normalized=True)
| tensorflow.contrib.layers.variance_scaling_initializer | 5,229 |
import tensorflow as tf
dtype=input_data_type,
stddev=1e-1,
name='synthetic_images')
images = tf.contrib.framework.local_variable(
images, name='gpu_cached_images')
labels = host_labels
with tf.device(self.devices[device_num]):
# Rescale to [0, 1)
images *= 1. / 256
# Rescale to [-1,1] instead of [0, 1)
images = tf.subtract(images, 0.5)
images = tf.multiply(images, 2.0)
if self.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
if input_data_type != data_type:
images = tf.cast(images, data_type)
network = ConvNetBuilder(
images, input_nchan, phase_train, self.data_format, data_type)
self.model_conf.add_inference(network)
# Add the final fully-connected class layer
logits = network.affine(nclass, activation='linear')
if not phase_train:
top_1_op = tf.reduce_sum(
tf.cast(tf.nn.in_top_k(logits, labels, 1), data_type))
top_5_op = tf.reduce_sum(
tf.cast(tf.nn.in_top_k(logits, labels, 5), data_type))
return (logits, top_1_op, top_5_op)
loss = loss_function(logits, labels)
| tensorflow.transpose | 5,230 |
import tensorflow as tf
if "GuidedBackProp" not in ops._gradient_registry._registry:
@ops.RegisterGradient("GuidedBackProp")
def _GuidedBackProp(op, grad):
dtype = op.inputs[0].dtype
return grad * tf.cast(grad > 0., dtype) * \
tf.cast(op.inputs[0] > 0., dtype)
def compile_saliency_function(model, activation_layer='block5_conv3'): #mixed10 'activation_49' add_16 add_32 activation_98
| tensorflow.cast | 5,231 |
from tensorflow.python.ops import math_ops
if curve == 'ROC':
fp_rate = math_ops.div(fp, fp + tn + epsilon)
x = fp_rate
y = recall
else: # curve == 'PR'.
precision = math_ops.div(tp + epsilon, tp + fp + epsilon)
x = recall
y = precision
return math_ops.reduce_sum(math_ops.mul(
x[:num_thresholds - 1] - x[1:],
(y[:num_thresholds - 1] + y[1:]) / 2.), name=name)
# sum up the areas of all the trapeziums
auc = compute_auc(tp, fn, tn, fp, 'value')
update_op = compute_auc(
tp_update_op, fn_update_op, tn_update_op, fp_update_op, 'update_op')
| tensorflow.python.ops.math_ops.mul | 5,232 |
import tensorflow as tf
self.optimizer_Adam = tf.compat.v1.train.AdamOptimizer()
| tensorflow.compat.v1.train.AdamOptimizer | 5,233 |
import tensorflow as tf
logits = cifar10.inference(images)
# Build the portion of the Graph calculating the losses. Note that we will
# assemble the total_loss using a custom function below.
_ = cifar10.loss(logits, labels)
# Assemble all of the losses for the current tower only.
losses = tf.get_collection('losses', scope)
# Calculate the total loss for the current tower.
total_loss = tf.add_n(losses, name='total_loss')
# Attach a scalar summary to all individual losses and the total loss; do the
# same for the averaged version of the losses.
| tensorflow.get_collection | 5,234 |
import tensorflow as tf
y += dense(pos_feats, encoder.attn_size, use_bias=False, name='P_a')
if encoder.attn_filters:
filter_shape = [encoder.attn_filter_length * 2 + 1, 1, 1, encoder.attn_filters]
filter_ = get_variable('filter', filter_shape)
prev_weights = tf.reshape(prev_weights, tf.stack([batch_size, time_steps, 1, 1]))
conv = tf.nn.conv2d(prev_weights, filter_, [1, 1, 1, 1], 'SAME')
conv = tf.squeeze(conv, axis=2)
y += dense(conv, encoder.attn_size, use_bias=False, name='C_a')
v = get_variable('v_a', [encoder.attn_size])
return tf.reduce_sum(v * tf.tanh(y), axis=2)
| tensorflow.squeeze | 5,235 |
import tensorflow as tf
mu,var = tf.nn.moments(t,axes=[0,1,2])
std = tf.sqrt(var+self.epsilon)
return [tf.assign(self.g,1/std),tf.assign(self.b,-1.*mu/std)]
require_init = tf.reduce_any(tf.is_nan(self.g))
init_ops = tf.cond(require_init,_init,lambda : [self.g,self.b])
with tf.control_dependencies(init_ops):
w = tf.reshape(self.g,[1,1,1,tf.shape(self.v)[-1]]) * tf.nn.l2_normalize(self.v,axis=[0,1,2])
return tf.nn.bias_add(
tf.nn.conv2d(input_var, w,data_format='NHWC',
strides=self.strides, padding=self.padding),
self.b,data_format='NHWC',name=name)
| tensorflow.control_dependencies | 5,236 |
import tensorflow as tf
feature_emb_list.append(antecedent_distance_emb)
feature_emb = tf.concat(feature_emb_list, 2) # [k, c, emb]
feature_emb = tf.nn.dropout(feature_emb, self.dropout) # [k, c, emb]
target_emb = tf.expand_dims(top_span_emb, 1) # [k, 1, emb]
| tensorflow.nn.dropout | 5,237 |
import tensorflow as tf
batch_size = tf.shape(targets)[0]
time_steps = tf.shape(targets)[1]
| tensorflow.shape | 5,238 |
from tensorflow.python.training import moving_averages
second_moment: The second_moment value to update with.
is_training: Boolean Tensor to indicate if we're currently in
training mode.
"""
def build_update_ops():
"""Builds the exponential moving average update ops."""
update_mean_op = moving_averages.assign_moving_average(
variable=self._moving_mean,
value=mean,
decay=self._decay_rate,
name="update_moving_mean").op
update_second_moment_op = moving_averages.assign_moving_average(
variable=self._moving_second_moment,
| tensorflow.python.training.moving_averages.assign_moving_average | 5,239 |
import tensorflow as tf
class Model:
def __init__(self, num_layers, size_layers, learning_rate=1e-3, dropout=1.0):
self.X = tf.placeholder(tf.int32, (None, None))
self.training = tf.placeholder(tf.bool, None)
lookup_table = tf.get_variable(
| tensorflow.placeholder | 5,240 |
import tensorflow as tf
# Every new connection creates a new op which adds its contribution
# to the running average when ran.
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_mean_op)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_second_moment_op)
def _build(self, input_batch, is_training=True, test_local_stats=True):
| tensorflow.add_to_collection | 5,241 |
import tensorflow as tf
# Step-wise contrastive loss
even = [2 * i for i in range(25)]
odd = [2 * i + 1 for i in range(25)]
pred1 = tf.gather(pred, even)
pred2 = tf.gather(pred, odd)
tgt1 = tf.gather(tgt, even)
tgt2 = tf.gather(tgt, odd)
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
loss = tf.maximum(0.0, (tgt_larg - tgt_small) - (pred_larg - pred_small))
# loss = tf.maximum(0.0, tf.math.abs(tgt_larg - pred_larg) - tf.math.abs(tgt_small - pred_small))
loss = tf.reduce_mean(loss)
return loss
| tensorflow.where | 5,242 |
import tensorflow as tf
if self.data_shape is None:
self.data_shape = output_shapes
# Handle for the feedable iterator
self.handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
self.handle, output_types, output_shapes)
data = iterator.get_next()
# Build the actual training and evaluation models
self._train_graph(data)
self._eval_graph(data)
self.summaries = tf.summary.merge_all()
# Prediction network with feed_dict
self.pred_in = {i: tf.placeholder(self.input_spec[i]['type'], shape=s, name=i)
for i, s in self.data_shape.items()}
self._pred_graph(self.pred_in)
# Start session
sess_config = tf.ConfigProto(device_count={'GPU': self.n_gpus})
sess_config.gpu_options.allow_growth = True
self.sess = tf.Session(config=sess_config)
# Register tf dataset handles
if self.datasets:
self.dataset_handles = {}
for n, i in self.dataset_iterators.items():
self.dataset_handles[n] = self.sess.run(i.string_handle())
| tensorflow.placeholder | 5,243 |
from tensorflow.python.framework import ops
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):
| tensorflow.python.framework.ops.colocate_with | 5,244 |
import tensorflow as tf
if not vars_to_load:
raise ValueError('Variables to load is empty.')
return tf.train.Scaffold()
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
grads_and_vars = optimizer.compute_gradients(total_loss, var_list)
gradients, variables = zip(*grads_and_vars)
grads_and_vars = []
# Special treatment for biases (beta is named as bias in reference model)
| tensorflow.get_collection | 5,245 |
import tensorflow as tf
Aw = (self.r + self.alpha * self.ri) - self.worker_vf
worker_vf_loss = .5 * tf.reduce_sum(tf.square(Aw))
| tensorflow.square | 5,246 |
import tensorflow as tf
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
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 {
| tensorflow.argmax | 5,247 |
import tensorflow as tf
def testParallelAssignWithoutLocking(self):
with self.test_session() as sess:
ones_t = tf.fill([1024, 1024], float(1))
p = tf.Variable(tf.zeros([1024, 1024]))
assigns = [tf.assign(p, tf.mul(ones_t, float(i)), False)
for i in range(1, 21)]
tf.initialize_all_variables().run()
def run_assign(assign_op):
sess.run(assign_op)
threads = [self.checkedThread(target=run_assign, args=(assign_op,))
for assign_op in assigns]
| tensorflow.initialize_all_variables | 5,248 |
import tensorflow as tf
for hop_edge_types, count in zip(edge_types, counts):
neighbors, weights, types = sample_neighbor(
neighbors_list[-1], hop_edge_types, count, default_node=default_node)
neighbors_list.append(tf.reshape(neighbors, [-1]))
weights_list.append(tf.reshape(weights, [-1]))
type_list.append(tf.reshape(types, [-1]))
return neighbors_list, weights_list, type_list
| tensorflow.reshape | 5,249 |
import tensorflow as tf
import dnnlib.tflib as tflib
from functools import partial
def create_stub(name, batch_size):
return tf.constant(0, dtype='float32', shape=(batch_size, 0))
def create_variable_for_generator(name, batch_size):
return tf.get_variable('learnable_dlatents',
| tensorflow.constant | 5,250 |
import tensorflow as tf
class ParameterizedTruncatedNormalGpuTest(ParameterizedTruncatedNormalTest):
_use_gpu = True
# Benchmarking code
def parameterized_vs_naive(shape, num_iters, use_gpu=False):
np.random.seed(1618) # Make it reproducible.
# No CSE/CF.
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)
config = tf.ConfigProto(
graph_options=tf.GraphOptions(optimizer_options=optimizer_options))
with tf.Session(config=config) as sess:
with tf.device("/cpu:0" if not use_gpu else None):
param_op = tf.group(random_ops.parameterized_truncated_normal(shape))
naive_op = tf.group(random_ops.truncated_normal(shape))
# Burn-in to avoid session setup costs in the timing.
sess.run(param_op)
sess.run(param_op)
param_dt = timeit.timeit(lambda: sess.run(param_op), number=num_iters)
sess.run(naive_op)
sess.run(naive_op)
naive_dt = timeit.timeit(lambda: sess.run(naive_op), number=num_iters)
return param_dt, naive_dt
| tensorflow.Session | 5,251 |
import tensorflow as tf
"""
def mlp_actor_critic(x, a, hidden_sizes=(400,300), activation=tf.nn.relu,
output_activation=tf.tanh, action_space=None,
dropout_rate=0, nn_type='mlp_variational'):
act_dim = a.shape.as_list()[-1]
act_limit = action_space.high[0]
if nn_type == 'mlp':
with tf.variable_scope('pi'):
pi = act_limit * mlp(x, list(hidden_sizes) + [act_dim], activation, output_activation)
with tf.variable_scope('q1'):
q1 = tf.squeeze(mlp(tf.concat([x, a], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
with tf.variable_scope('q2'):
q2 = tf.squeeze(mlp(tf.concat([x, a], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
with tf.variable_scope('q1', reuse=True):
q1_pi = tf.squeeze(mlp(tf.concat([x, pi], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
elif nn_type == 'mlp_dropout':
with tf.variable_scope('pi'):
pi = act_limit * mlp_dropout(x, list(hidden_sizes)+[act_dim], activation, output_activation)
with tf.variable_scope('q'):
q = tf.squeeze(mlp_dropout(tf.concat([x,a], axis=-1), list(hidden_sizes)+[1], activation, None, dropout_rate), axis=1)
| tensorflow.concat | 5,252 |
import tensorflow as tf
if n_bits < 8:
rgb = tf.floor(rgb/(2**(8-n_bits)))
| tensorflow.floor | 5,253 |
import tensorflow as tf
initializer=initialization.xavier_initializer(
shape=bias_shape,
dtype=self.dtype,
uniform=self.normal_initializer,
mask=None)))
elif self.lesion_mu:
setattr(
self,
'mu_%s' % layer,
tf.constant(0.))
else:
setattr(
self,
'mu_%s' % layer,
tf.constant(1.))
if self.gamma:
setattr(
self,
| tensorflow.constant | 5,254 |
import tensorflow as tf
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):
| tensorflow.reduce_sum | 5,255 |
import tensorflow as tf
tf.nn.nce_loss(
weights=nce_weights,
biases=nce_biases,
inputs=embed,
labels=train_labels,
num_sampled=num_sampled,
num_classes=vocabulary_size,
**nce_loss_args))
self.outputs = embed
self.normalized_embeddings = tf.nn.l2_normalize(embeddings, 1)
self.all_layers = [self.outputs]
self.all_params = [embeddings, nce_weights, nce_biases]
self.all_drop = {}
class EmbeddingInputlayer(Layer):
"""
The :class:`EmbeddingInputlayer` class is a look-up table for word embedding.
| tensorflow.nn.l2_normalize | 5,256 |
import tensorflow as tf
loss_summary = tf.summary.scalar('Loss', cross_entropy)
acc_summary = tf.summary.scalar('Accuracy', accuracy)
# summaries for TensorBoard visualisation
validation_summary = tf.summary.merge([img_summary, acc_summary])
training_summary = tf.summary.merge([img_summary, loss_summary])
test_summary = tf.summary.merge([img_summary, acc_summary])
# saver for checkpoints
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(run_log_dir + '_train', sess.graph, flush_secs=5)
summary_writer_validation = tf.summary.FileWriter(run_log_dir + '_validate', sess.graph, flush_secs=5)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
| tensorflow.global_variables | 5,257 |
import tensorflow as tf
tf.add_n(sharded_num) / tf.maximum(1.0, tf.add_n(sharded_den)))
else:
mean_loss = tf.reduce_mean(all_shards)
| tensorflow.reduce_mean | 5,258 |
import tensorflow as tf
)
print_obj(func_name, "squared_difference", squared_difference)
# Get gradient penalty scalar.
gradient_penalty = tf.reduce_mean(
input_tensor=squared_difference, name="gradient_penalty"
)
print_obj(func_name, "gradient_penalty", gradient_penalty)
| tensorflow.reduce_mean | 5,259 |
import tensorflow as tf
init = tf.initialize_all_variables()
config=tf.ConfigProto()
| tensorflow.ConfigProto | 5,260 |
from tensorflow.python.ops import nn
"""
in_top_k = math_ops.to_float(nn.in_top_k(predictions, labels, k))
| tensorflow.python.ops.nn.in_top_k | 5,261 |
import tensorflow as tf
... (?, 8)
"""
def __init__(self, inputs=None, depth=None, on_value=None, off_value=None, axis=None, dtype=None, name='input'):
super(OneHotInputLayer, self).__init__(prev_layer=None, name=name)
logging.info("OneHotInputLayer %s: %s" % (self.name, inputs.get_shape()))
# assert depth != None, "depth is not given"
if depth is None:
logging.info(" [*] depth == None the number of output units is undefined")
self.outputs = tf.one_hot(inputs, depth, on_value=on_value, off_value=off_value, axis=axis, dtype=dtype)
self.all_layers = []
self.all_params = []
self.all_drop = {}
class Word2vecEmbeddingInputlayer(Layer):
"""
The :class:`Word2vecEmbeddingInputlayer` class is a fully connected layer.
For Word Embedding, words are input as integer index.
The output is the embedded word vector.
| tensorflow.one_hot | 5,262 |
from tensorflow.python.eager import context
features["inputs"] = tf.expand_dims(features["inputs"], 2)
if not self.has_input:
features["partial_targets"] = tf.to_int64(features["inputs"])
# Save the targets in a var and reassign it after the tf.while loop to avoid
# having targets being in a 'while' frame. This ensures targets when used
# in metric functions stays in the same frame as other vars.
targets_old = features.get("targets", None)
target_modality = self._problem_hparams.target_modality
def infer_step(recent_output, recent_logits, unused_loss):
"""Inference step."""
if not context.in_eager_mode():
recent_output.set_shape([None, None, None, 1])
padded = tf.pad(recent_output, [[0, 0], [0, 1], [0, 0], [0, 0]])
features["targets"] = padded
# This is inefficient in that it generates samples at all timesteps,
# not just the last one, except if target_modality is pointwise.
samples, logits, losses = self.sample(features)
# Concatenate the already-generated recent_output with last timestep
# of the newly-generated samples.
if target_modality.top_is_pointwise:
cur_sample = samples[:, -1, :, :]
else:
| tensorflow.python.eager.context.in_eager_mode | 5,263 |
import tensorflow as tf
# self.simloss /= tf.reduce_mean(var)
print(tgtimg_z.get_shape())
self.out = output_h4# + contextimg#tf.nn.tanh(h4)
self.out2 = truthoutput_h4
self.recon1 = tf.nn.l2_loss(tgtimg - self.out)
self.recon2 = tf.nn.l2_loss(tgtimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss
if ablation_type == "None":
self.loss = self.recon1 + self.recon2 + self.simloss
elif ablation_type == "L2":
| tensorflow.nn.l2_loss | 5,264 |
import tensorflow as tf
from tensorflow.python.framework import errors
from tensorflow.python.ops import script_ops
class PyOpTest(tf.test.TestCase):
def testBasic(self):
def my_func(x, y):
return np.sinh(x) + np.cosh(y)
# scalar
with self.test_session():
x = tf.constant(1.0, tf.float32)
y = tf.constant(2.0, tf.float32)
z = tf.py_func(my_func, [x, y], [tf.float32])
self.assertEqual(z[0].eval(), my_func(1.0, 2.0).astype(np.float32))
# array
with self.test_session():
x = tf.constant([1.0, 2.0], tf.float64)
y = tf.constant([2.0, 3.0], tf.float64)
z = tf.py_func(my_func, [x, y], [tf.float64])
self.assertAllEqual(
z[0].eval(),
my_func([1.0, 2.0], [2.0, 3.0]).astype(np.float64))
# a bit exotic type (complex64)
with self.test_session():
x = tf.constant(1+2j, tf.complex64)
| tensorflow.py_func | 5,265 |
import tensorflow as tf
self.eval_loss = interpreter.l2_loss(target, eval_decode, name='predictive_reconstruction')
self.eval_decode = self._tensor_to_image(eval_decode)
self.loss_ae = interpreter.l2_loss(target, model.decode, name='reconstruction')
self.decode = self._tensor_to_image(model.decode)
self.losses = [self.loss_ae]
def build_predictive_model(self):
self.build_ae_model() # builds on top of AE model. Due to auxilary operations init
self.inputs = tf.placeholder(tf.uint8, [3] + self.batch_shape, name='inputs')
self.targets = tf.placeholder(tf.uint8, [3] + self.batch_shape, name='targets')
# transform inputs
self.raw_inputs = [self._image_to_tensor(self.inputs[i]) for i in range(3)]
self.raw_targets = [self._image_to_tensor(self.targets[i]) for i in range(3)]
# build AE objective for triplet
config = self.model.config
models = [interpreter.build_autoencoder(x, config) for x in self.raw_inputs]
reco_losses = [1./3 * interpreter.l2_loss(models[i].decode, self.raw_targets[i]) for i in range(3)] # business as usual
| tensorflow.placeholder | 5,266 |
import tensorflow as tf
block_conv_3 = self.conv_layer(block_relu_2, 1, channel_list[1], channel_list[2], 1, name + "_branch2c")
block_res = tf.add(block_conv_input, block_conv_3)
relu = tf.nn.relu(block_res)
return relu
def avg_pool(self, bottom, kernal_size = 2, stride = 2, name = "avg"):
return tf.nn.avg_pool(bottom, ksize=[1, kernal_size, kernal_size, 1], strides=[1, stride, stride, 1], padding='VALID', name=name)
def max_pool(self, bottom, kernal_size = 2, stride = 2, name = "max"):
return tf.nn.max_pool(bottom, ksize=[1, kernal_size, kernal_size, 1], strides=[1, stride, stride, 1], padding='SAME', name=name)
def conv_layer(self, bottom, kernal_size, in_channels, out_channels, stride, name):
with tf.variable_scope(name):
filt, conv_biases = self.get_conv_var(kernal_size, in_channels, out_channels, name)
conv = tf.nn.conv2d(bottom, filt, [1,stride,stride,1], padding='SAME')
bias = tf.nn.bias_add(conv, conv_biases)
tf.summary.histogram('weight', filt)
tf.summary.histogram('bias', conv_biases)
| tensorflow.nn.max_pool | 5,267 |
import tensorflow as tf
loss_summary = tf.summary.scalar('Loss', cross_entropy)
acc_summary = tf.summary.scalar('Accuracy', accuracy)
# summaries for TensorBoard visualisation
validation_summary = tf.summary.merge([img_summary, acc_summary])
training_summary = tf.summary.merge([img_summary, loss_summary])
test_summary = tf.summary.merge([img_summary, acc_summary])
# saver for checkpoints
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(run_log_dir + '_train', sess.graph, flush_secs=5)
summary_writer_validation = tf.summary.FileWriter(run_log_dir + '_validate', sess.graph, flush_secs=5)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# Training and validation
for step in range(FLAGS.max_steps):
# Training: Backpropagation using train set
(trainImages, trainLabels) = cifar.getTrainBatch()
(testImages, testLabels) = cifar.getTestBatch()
_, summary_str = sess.run([optimiser, training_summary], feed_dict={x: trainImages, y_: trainLabels, train: True})
| tensorflow.summary.FileWriter | 5,268 |
import tensorflow as tf
import tensorflow as tf
import os
#from PIL import Image
import random
import numpy as np
from datetime import datetime
from sklearn.metrics import confusion_matrix
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()
_,serialized_example=reader.read(filename_queue)
features=tf.parse_single_example(serialized_example,
features={
'label':tf.FixedLenFeature([], tf.int64),
'img_raw' : tf.FixedLenFeature([], tf.string),
})
image=tf.decode_raw(features['img_raw'],tf.uint8)
| tensorflow.placeholder | 5,269 |
import tensorflow as tf
# logloss not being an upper bound on the indicator function.
weighted_loss = weights * losses_utils.weighted_surrogate_loss(
labels,
logits,
surrogate_type=surrogate_type,
positive_weights=1.0,
negative_weights=lambdas)
maybe_log2 = tf.log(2.0) if surrogate_type == 'xent' else 1.0
maybe_log2 = tf.cast(maybe_log2, logits.dtype.base_dtype)
lambda_term = lambdas * target_rate * (1.0 - label_priors) * maybe_log2
loss = tf.reshape(weighted_loss - lambda_term, original_shape)
other_outputs = {
'lambdas':
lambdas_variable,
| tensorflow.log | 5,270 |
from tensorflow.python.framework import ops
noise_shape: A 1-D `Tensor` of type `int32`, representing the
shape for randomly generated keep/drop flags.
seed: A Python integer. Used to create random seeds. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for this operation (optional).
Returns:
A Tensor of the same shape of `x`.
Raises:
ValueError: If `keep_prob` is not in `(0, 1]`.
"""
with ops.op_scope([x], name, "dropout") as name:
x = ops.convert_to_tensor(x, name="x")
if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
raise ValueError("keep_prob must be a scalar tensor or a float in the "
"range (0, 1], got %g" % keep_prob)
keep_prob = ops.convert_to_tensor(
keep_prob, dtype=x.dtype, name="keep_prob")
keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar())
noise_shape = noise_shape or array_ops.shape(x)
# uniform [keep_prob, 1.0 + keep_prob)
random_tensor = keep_prob
random_tensor += random_ops.random_uniform(
| tensorflow.python.framework.ops.op_scope | 5,271 |
import tensorflow as tf
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])
| tensorflow.reshape | 5,272 |
from tensorflow.contrib.framework.python.ops import variables as contrib_variables
self._monitors = monitors
def begin(self):
self._last_step = None
self._global_step_tensor = contrib_variables.get_global_step()
for m in self._monitors:
m.begin(max_steps=None)
| tensorflow.contrib.framework.python.ops.variables.get_global_step | 5,273 |
import tensorflow as tf
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(run_log_dir + '_train', sess.graph, flush_secs=5)
summary_writer_validation = tf.summary.FileWriter(run_log_dir + '_validate', sess.graph, flush_secs=5)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# Training and validation
for step in range(FLAGS.max_steps):
# Training: Backpropagation using train set
| tensorflow.local_variables_initializer | 5,274 |
import tensorflow as tf
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)
lstm_a = tf.nn.rnn_cell.LSTMCell(num_units=256)
lstm_a = tf.nn.rnn_cell.DropoutWrapper(lstm_a, output_keep_prob=self.keep_prob)
state_init_a = lstm_a.zero_state(batch_size=batch_size, dtype=tf.float32)
lstm_ain = tf.expand_dims(layer_a2, axis=1)
out_a, state_final_a = tf.nn.dynamic_rnn(cell=lstm_a, inputs=lstm_ain, initial_state=state_init_a)
cell_out_a = tf.reshape(out_a, [-1, 256])
mu = tf.layers.dense(cell_out_a, self.a_dim, tf.nn.tanh, kernel_regularizer=reg)
sigma = tf.layers.dense(cell_out_a, self.a_dim, tf.nn.softplus, kernel_regularizer=reg)
| tensorflow.expand_dims | 5,275 |
import tensorflow as tf
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
| tensorflow.where | 5,276 |
import tensorflow as tf
lm_emb = tf.stack([tf.concat([word_emb, word_emb], -1),
lm_embeddings["lstm_outputs1"],
lm_embeddings["lstm_outputs2"]], -1) # [num_sentences, max_sentence_length, 1024, 3]
lm_emb_size = util.shape(lm_emb, 2)
lm_num_layers = util.shape(lm_emb, 3)
with tf.variable_scope("lm_aggregation"):
self.lm_weights = tf.nn.softmax(tf.get_variable("lm_scores", [lm_num_layers], initializer=tf.constant_initializer(0.0)))
self.lm_scaling = tf.get_variable("lm_scaling", [], initializer=tf.constant_initializer(1.0))
flattened_lm_emb = tf.reshape(lm_emb, [num_sentences * max_sentence_length * lm_emb_size, lm_num_layers])
flattened_aggregated_lm_emb = tf.matmul(flattened_lm_emb, tf.expand_dims(self.lm_weights, 1)) # [num_sentences * max_sentence_length * emb, 1]
aggregated_lm_emb = tf.reshape(flattened_aggregated_lm_emb, [num_sentences, max_sentence_length, lm_emb_size])
aggregated_lm_emb *= self.lm_scaling
context_emb_list.append(aggregated_lm_emb)
context_emb = tf.concat(context_emb_list, 2) # [num_sentences, max_sentence_length, emb]
head_emb = tf.concat(head_emb_list, 2) # [num_sentences, max_sentence_length, emb]
context_emb = tf.nn.dropout(context_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
head_emb = tf.nn.dropout(head_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length) # [num_sentence, max_sentence_length]
| tensorflow.reshape | 5,277 |
import tensorflow as tf
# https://stackoverflow.com/questions/37063952/confused-by-the-behavior-of-tf-cond for
# a more detailed discussion.
def perturb_vars(original_scope, perturbed_scope):
all_vars = U.scope_vars(U.absolute_scope_name("q_func"))
all_perturbed_vars = U.scope_vars(U.absolute_scope_name("perturbed_q_func"))
assert len(all_vars) == len(all_perturbed_vars)
perturb_ops = []
for var, perturbed_var in zip(all_vars, all_perturbed_vars):
if param_noise_filter_func(perturbed_var):
# Perturb this variable.
op = tf.assign(perturbed_var, var + tf.random_normal(shape=tf.shape(var), mean=0., stddev=param_noise_scale))
else:
# Do not perturb, just assign.
op = tf.assign(perturbed_var, var)
perturb_ops.append(op)
assert len(perturb_ops) == len(all_vars)
return tf.group(*perturb_ops)
# Set up functionality to re-compute `param_noise_scale`. This perturbs yet another copy
# of the network and measures the effect of that perturbation in action space. If the perturbation
# is too big, reduce scale of perturbation, otherwise increase.
q_values_adaptive = q_func(observations_ph.get(), num_actions, scope="adaptive_q_func")
perturb_for_adaption = perturb_vars(original_scope="q_func", perturbed_scope="adaptive_q_func")
kl = tf.reduce_sum(tf.nn.softmax(q_values) * (tf.log(tf.nn.softmax(q_values)) - tf.log(tf.nn.softmax(q_values_adaptive))), axis=-1)
mean_kl = tf.reduce_mean(kl)
def update_scale():
| tensorflow.assign | 5,278 |
import tensorflow as tf
bert_config, model.get_pooled_output(), next_sentence_labels)
total_loss = masked_lm_loss + next_sentence_loss
tvars = tf.trainable_variables()
initialized_variable_names = {}
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
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
| tensorflow.train.Scaffold | 5,279 |
import tensorflow as tf
Args:
var: tensor to regularize.
weight_l1: an optional weight to modulate the l1 loss.
weight_l2: an optional weight to modulate the l2 loss.
name: Optional scope/name for op_scope.
Returns:
the l1+L2 loss op.
"""
with tf.name_scope(name):
weight_l1_t = tf.convert_to_tensor(weight_l1, dtype=var.dtype.base_dtype, name='weight_l1')
weight_l2_t = tf.convert_to_tensor(weight_l2, dtype=var.dtype.base_dtype, name='weight_l2')
reg_l1 = tf.multiply(weight_l1_t, tf.reduce_sum(tf.abs(var)), name='value_l1')
reg_l2 = tf.multiply(weight_l2_t, tf.nn.l2_loss(var), name='value_l2')
return tf.add(reg_l1, reg_l2, name='value')
def l1_regularizer(scale, name='l1_regularizer'):
"""Returns a function that can be used to apply L1 regularization to weights.
L1 regularization encourages sparsity.
Args:
scale: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
name: An optional name/scope name.
Returns:
| tensorflow.nn.l2_loss | 5,280 |
import tensorflow as tf
self.assertEqual((2, 2), res[0][0].h.shape)
self.assertEqual((2, 2), res[0][1].c.shape)
self.assertEqual((2, 2), res[0][1].h.shape)
# pylint: disable=unused-variable,invalid-name
def testDynamicAttentionDecoderStateIsTuple(self):
with self.test_session() as sess:
with tf.variable_scope(
"root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell] * 2,
state_is_tuple=True)
inp = tf.constant(0.5, shape=[2, 2, 2])
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual(2, len(res[0]))
self.assertEqual((2, 2), res[0][0].c.shape)
| tensorflow.reshape | 5,281 |
import tensorflow as tf
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
| tensorflow.gfile.MakeDirs | 5,282 |
import tensorflow as tf
if not full_cov:
fvar = tf.transpose(fvar) # N x R
| tensorflow.transpose | 5,283 |
import tensorflow as tf
# ddi
ddi_dataset = dataset.batch(n_ddi_batch)
ddi_batch = ddi_dataset.make_one_shot_iterator().get_next()
# post processing
im = self.post_process(training_batch)
ddi_im = self.post_process(ddi_batch)
self.im = im
self.ddi_im = ddi_im
def data_map(self, img_path):
n_bits = config.model.data.n_bits
n_bins = 2**n_bits
rgb = tf.image.decode_png(tf.read_file(img_path), channels=3, dtype=tf.uint8)
h = config.model.data.dimensions.h
w = config.model.data.dimensions.w
c = config.model.data.dimensions.c
# rgb.set_shape([h,w,c]) # don't set because going to crop anyway
# crop for lsun 96, see realnvp and glow for specifics
rgb = tf.image.random_crop(rgb,size=[h,w,c])
# crop for patch training
crop_h = h//self.crop_factor
crop_w = w//self.crop_factor
rgb = tf.image.random_crop(rgb,size=[crop_h,crop_w,c])
| tensorflow.read_file | 5,284 |
import tensorflow as tf
# Run the decoder RNN cell. cell_output = decoder state
cell_output, state_t = self.cell(x, state_t_1)
context_t, attn_dist, coverage_t = self.attention(state_t, options.attention_vec_size, encoder_states,
encoder_features, passage_mask, v, w_c=w_c,
use_coverage=options.use_coverage, coverage=coverage_t_1)
# Calculate p_gen, Equation (8)
if options.pointer_gen:
with tf.variable_scope('calculate_pgen'):
p_gen = linear([context_t, state_t.c, state_t.h, x], 1, True) # [batch_size, 1]
p_gen = tf.sigmoid(p_gen)
# Concatenate the cell_output (= decoder state) and the context vector, and pass them through a linear layer
# This is V[s_t, h*_t] + b in the paper
with variable_scope.variable_scope("AttnOutputProjection"):
output_t = linear([cell_output] + [context_t], options.gen_hidden_size, True)
| tensorflow.variable_scope | 5,285 |
import tensorflow as tf
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4,
num_heads=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
| tensorflow.global_variables_initializer | 5,286 |
import tensorflow as tf
c = c*(1-m)
h = h*(1-m)
z = _ln(tf.matmul(x, wx), gx, bx) + _ln(tf.matmul(h, wh), gh, bh) + b
i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
i = tf.nn.sigmoid(i)
f = tf.nn.sigmoid(f)
o = tf.nn.sigmoid(o)
u = tf.tanh(u)
| tensorflow.nn.sigmoid | 5,287 |
import tensorflow as tf
ATTENTION_SIZE, mask, softmax_stag=1, stag=stag,
mode='LIST')
self_attention_tmp = tf.reduce_sum(self_attention_tmp, 1)
output = output.write(i, self_attention_tmp)
| tensorflow.reduce_sum | 5,288 |
import tensorflow as tf
mask_c = tf.expand_dims(self.c_mask, 2)
S_T = tf.transpose(tf.nn.softmax(mask_logits(S, mask=mask_c), dim=1), (0, 2, 1))
self.c2q = tf.matmul(S_, self.q_embed_encoding)
self.q2c = tf.matmul(tf.matmul(S_, S_T), self.c_embed_encoding)
self.attention_outputs = [self.c_embed_encoding, self.c2q, self.c_embed_encoding * self.c2q,
self.c_embed_encoding * self.q2c]
N, PL, QL, CL, d, dc, nh = self._params()
if self.config.fix_pretrained_vector:
dc = self.char_mat.get_shape()[-1]
with tf.variable_scope("Model_Encoder_Layer"):
inputs = tf.concat(self.attention_outputs, axis=-1)
self.enc = [conv(inputs, d, name="input_projection")]
for i in range(3):
if i % 2 == 0:
self.enc[i] = tf.nn.dropout(self.enc[i], 1.0 - self.dropout)
self.enc.append(
residual_block(self.enc[i],
num_blocks=1,
num_conv_layers=2,
kernel_size=5,
mask=self.c_mask,
num_filters=d,
num_heads=nh,
seq_len=self.c_len,
scope="Model_Encoder",
bias=False,
reuse=True if i > 0 else None,
dropout=self.dropout)
)
| tensorflow.nn.dropout | 5,289 |
import tensorflow as tf
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
onehot_labels,
name="xentropy")
loss = tf.reduce_mean(cross_entropy, name="xentropy_mean")
tf.scalar_summary(loss.op.name, loss)
| tensorflow.reduce_mean | 5,290 |
import tensorflow as tf
# ------------------------------------------------
# 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),
trainable=False)
# Output Dale's law weight matrix:
self.Dale_out = tf.get_variable('Dale_out', [N_rec, N_rec],
initializer=tf.constant_initializer(self.dale_out),
trainable=False)
# Connectivity weight matrices:
self.input_Connectivity = tf.get_variable('input_Connectivity', [N_rec, N_in],
initializer=tf.constant_initializer(
self.input_connectivity_mask),
trainable=False)
| tensorflow.constant_initializer | 5,291 |
import tensorflow as tf
if len(input_dims) == 4:
_, input_h, input_w, num_channels = input_dims
in_dim = input_h * input_w * num_channels
flat_input = tf.reshape(input_data, [-1, in_dim])
else:
in_dim = input_dims[-1]
flat_input = input_data
if initial_value is None:
fc_weight = tf.get_variable("weights", shape=[in_dim, out_dim], initializer=tf.random_normal_initializer(mean=0., stddev=0.01))
fc_bias = tf.get_variable("bias", shape=[out_dim], initializer=tf.constant_initializer(0.0))
else:
fc_weight = tf.get_variable("weights", initializer=initial_value[0])
fc_bias = tf.get_variable("bias", shape=[out_dim], initializer=initial_value[1])
if use_bias:
output = tf.add(tf.matmul(flat_input, fc_weight), fc_bias)
else:
output = tf.matmul(flat_input, fc_weight)
if non_linear_fn is None:
return output
else:
activation = non_linear_fn(output)
| tensorflow.get_variable | 5,292 |
import tensorflow as tf
def contra_traj_lossV6(pred, tgt, horizon=12):
horizon_pred, horizon_tgt = horizon_sumV1(pred, horizon), horizon_sumV1(tgt, horizon)
# horizon_pred, horizon_tgt = horizon_sumV2(pred, tgt, horizon)
pred_flat1, pred_flat2 = tf.reshape(horizon_pred, [-1, 1]), tf.reshape(horizon_pred, [1, -1])
tgt_flat1, tgt_flat2 = tf.reshape(horizon_tgt, [-1, 1]), tf.reshape(horizon_tgt, [1, -1])
tgt_dif = tgt_flat1 - tgt_flat2
pred_dif = pred_flat1 - pred_flat2
geq = tf.cast(tgt_dif > 0, tf.bool)
| tensorflow.reshape | 5,293 |
import tensorflow as tf
print(x_prob.get_shape(), y.get_shape(), tf.reduce_sum(x_prob * y, axis=-1).get_shape())
return -tf.reduce_sum(x_prob * y, axis=-1) # higher the better
def mse(x, y):
x = x - tf.reduce_mean(x, axis=-1, keepdims=True)
y = y - tf.reduce_mean(y, axis=-1, keepdims=True)
return tf.reduce_sum((x-y)**2, axis=-1) # lower the better
| tensorflow.reduce_mean | 5,294 |
import tensorflow as tf
# p = tf.print('cur_loss', [final_loss, avg_loss])
# with tf.control_dependencies([p]):
# avg_loss = tf.identity(avg_loss)
# print(final_loss, avg_loss)
# p = tf.print('debug loss ', [final_loss, avg_loss])
# with tf.control_dependencies([p]):
# avg_loss = 1. * avg_loss
# print(avg_loss)
# exit()
return avg_loss
def compute_contra_loss(pred1, pred2, tgt1, tgt2, hard_ratio=1.0):
geq = tf.cast((tgt1 - tgt2) > 0, tf.bool)
tgt_larg = tf.where(geq, tgt1, tgt2)
tgt_small = tf.where(geq, tgt2, tgt1)
pred_larg = tf.where(geq, pred1, pred2)
pred_small = tf.where(geq, pred2, pred1)
loss = tf.maximum(0., (tgt_larg - tgt_small) - (pred_larg - pred_small))
if hard_ratio < 1.0:
hard_num = tf.cast(tools.shape(pred1)[0] * hard_ratio, tf.int32)
loss = tf.reshape(loss, [-1])
hard_loss, _ = tf.math.top_k(loss, k=hard_num)
return hard_loss
return loss
def compute_error_loss(pred1, pred2, tgt1, tgt2, hard_ratio=1.0):
| tensorflow.where | 5,295 |
import tensorflow as tf
checkpoints""")
tf.flags.DEFINE_string('train_dir', None,
"""Path to session checkpoints.""")
tf.flags.DEFINE_string('eval_dir', '/tmp/tf_cnn_benchmarks/eval',
"""Directory where to write eval event logs.""")
tf.flags.DEFINE_string('pretrain_dir', None,
| tensorflow.flags.DEFINE_string | 5,296 |
import tensorflow as tf
if self.config["coarse_to_fine"]:
top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets = self.coarse_to_fine_pruning(top_span_emb, top_span_mention_scores, c)
else:
top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets = self.distance_pruning(top_span_emb, top_span_mention_scores, c)
dummy_scores = tf.zeros([k, 1]) # [k, 1]
for i in range(self.config["coref_depth"]):
with tf.variable_scope("coref_layer", reuse=(i > 0)):
top_antecedent_emb = tf.gather(top_span_emb, top_antecedents) # [k, c, emb]
top_antecedent_scores = top_fast_antecedent_scores + self.get_slow_antecedent_scores(top_span_emb, top_antecedents, top_antecedent_emb, top_antecedent_offsets, top_span_speaker_ids, genre_emb) # [k, c]
top_antecedent_weights = tf.nn.softmax(tf.concat([dummy_scores, top_antecedent_scores], 1)) # [k, c + 1]
| tensorflow.zeros | 5,297 |
import tensorflow as tf
step = tf.to_float(global_step)
warmup_steps = tf.to_float(params.warmup_steps)
multiplier = params.hidden_size ** -0.5
decay = params.r0 * multiplier * tf.minimum((step + 1) * (warmup_steps ** -1.0) * (warmup_steps ** -0.5),
(step + 1) ** -0.5)
return learning_rate * decay
elif params.learning_rate_decay == "rnnplus_warmup_decay":
step = tf.to_float(global_step)
n = float(len(params.device_list))
warmup_steps = tf.to_float(params.warmup_steps)
decay = tf.minimum(1 + step * (n - 1) / (n * warmup_steps), tf.minimum(n, n * ((2*n) ** ((params.s - n * step) / (params.e - params.s)))))
return tf.maximum(learning_rate * decay, 5e-6)
elif params.learning_rate_decay == "piecewise_constant":
return tf.train.piecewise_constant(tf.to_int32(global_step),
params.learning_rate_boundaries,
params.learning_rate_values)
elif params.learning_rate_decay == "none":
return learning_rate
| tensorflow.to_float | 5,298 |
import tensorflow as tf
def gradient_difference_loss(x, y):
x_h_diff = x[:, 1:] - x[:, :-1]
x_w_diff = x[:, :, 1:] - x[:, :, :-1]
y_h_diff = y[:, 1:] - y[:, :-1]
y_w_diff = y[:, :, 1:] - y[:, :, :-1]
h_diff = tf.abs(tf.abs(x_h_diff) - tf.abs(y_h_diff))
w_diff = tf.abs(tf.abs(x_w_diff) - tf.abs(y_w_diff))
return h_diff + tf.transpose(w_diff)
def leaky_relu(x, leak=0.2, name='leaky_relu'):
with tf.variable_scope(name):
| tensorflow.abs | 5,299 |
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