seed
stringlengths 25
1.88k
| seed_api
stringlengths 14
102
| index
int64 0
1.05k
|
|---|---|---|
import tensorflow as tf
transpose=transpose)
if w_project is not None:
x = tf.conv2d(x, w_project, strides, padding='SAME')
# Set shape for BN in the residual function.
| tensorflow.conv2d | 700 |
from tensorflow.python.ops import array_ops
if proba:
raise ValueError(
"logits to probabilities is not supported for _BinarySvmTargetColumn")
logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)
return math_ops.argmax(logits, 1)
# TODO(zakaria): use contrib losses.
def _mean_squared_loss(logits, target):
# To prevent broadcasting inside "-".
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
logits.get_shape().assert_is_compatible_with(target.get_shape())
return math_ops.square(logits - math_ops.to_float(target))
def _log_loss_with_two_classes(logits, target):
# sigmoid_cross_entropy_with_logits requires [batch_size, 1] target.
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
loss_vec = nn.sigmoid_cross_entropy_with_logits(
labels=math_ops.to_float(target), logits=logits)
| tensorflow.python.ops.array_ops.expand_dims | 701 |
from tensorflow.contrib.learn.python.learn.estimators import composable_model
"""
super(_DNNLinearCombinedBaseEstimator, self).__init__(
model_dir=model_dir, config=config)
num_ps_replicas = config.num_ps_replicas if config else 0
self._linear_model = composable_model.LinearComposableModel(
num_label_columns=target_column.num_label_columns,
optimizer=linear_optimizer,
gradient_clip_norm=gradient_clip_norm,
num_ps_replicas=num_ps_replicas)
| tensorflow.contrib.learn.python.learn.estimators.composable_model.LinearComposableModel | 702 |
from tensorflow.python.ops import rnn_cell
seq_length = config["seq_length"]
with ops.Graph().as_default(), ops.device("/device:GPU:0"):
inputs = seq_length * [
array_ops.zeros([batch_size, num_units], dtypes.float32)
]
initializer = init_ops.random_uniform_initializer(-0.01, 0.01, seed=127)
cell = rnn_cell.LSTMCell(
num_units=num_units, initializer=initializer, state_is_tuple=True)
multi_cell = rnn_cell.MultiRNNCell(
[cell() for _ in range(num_layers)])
outputs, final_state = core_rnn.static_rnn(
multi_cell, inputs, dtype=dtypes.float32)
trainable_variables = ops.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES)
| tensorflow.python.ops.rnn_cell.LSTMCell | 703 |
import tensorflow as tf
cast0 = tf.dtypes.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.dtypes.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
out0 = tf.identity(cast0, "OUTPUT0")
| tensorflow.dtypes.as_string | 704 |
from tensorflow.contrib.eager.python.examples.linear_regression import linear_regression
# Perform burn-in.
linear_regression.fit(model, burn_in_dataset, optimizer)
| tensorflow.contrib.eager.python.examples.linear_regression.linear_regression.fit | 705 |
import tensorflow as tf
if encoder.attn_keep_prob is not None:
state_noise_shape = [1, tf.shape(state)[1]] if encoder.pervasive_dropout else None
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)
if encoder.mult_attn:
state = dense(state, encoder.attn_size, use_bias=False, name='state')
hidden = dense(hidden, encoder.attn_size, use_bias=False, name='hidden')
return tf.einsum('ijk,ik->ij', hidden, state)
y = dense(state, encoder.attn_size, use_bias=not encoder.layer_norm, name='W_a')
y = tf.expand_dims(y, axis=1)
if encoder.layer_norm:
y = tf.contrib.layers.layer_norm(y, scope='layer_norm_state')
hidden = tf.contrib.layers.layer_norm(hidden, center=False, scope='layer_norm_hidden')
| tensorflow.einsum | 706 |
import tensorflow as tf
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
| tensorflow.sigmoid | 707 |
from tensorflow.core.framework import function_pb2
Args:
graph: Graph.
inputs: List of tensors. Inputs to the function.
outputs: List of tensors. Outputs of the function.
out_names: Optional list of string names for the outputs.
Returns:
A FunctionDef protocol buffer.
Raises:
ValueError: if out_names is specified and the wrong length.
"""
func = function_pb2.FunctionDef()
func.signature.name = "_"
used_names = set()
func.signature.input_arg.extend([_tensor_to_argdef(i, used_names=used_names)
for i in inputs])
if out_names is None:
used_names = set()
func.signature.output_arg.extend([
_tensor_to_argdef(o, used_names=used_names) for o in outputs])
elif len(outputs) != len(out_names):
raise ValueError(
"Length of out_names (%d) does not match number of outputs (%d): %s" %
(len(out_names), len(outputs), ", ".join(out_names)))
| tensorflow.core.framework.function_pb2.FunctionDef | 708 |
import tensorflow as tf
run_config = tf.estimator.RunConfig(
save_summary_steps=1,
train_distribute=strategy,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_ckpt_steps,
log_step_count_steps=1,
)
else:
distribution = tf.contrib.distribute.MirroredStrategy(
num_gpus=FLAGS.num_gpus
)
run_config = tf.estimator.RunConfig(train_distribute=distribution)
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
| tensorflow.contrib.distribute.MirroredStrategy | 709 |
import tensorflow as tf
eval_input_fn = None
'''
# Add hooks
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss), # Monitors the loss tensor and stops training if loss is NaN
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
| tensorflow.train.NanTensorHook | 710 |
from tensorflow.contrib.rnn import BasicLSTMCell, RNNCell, DropoutWrapper, MultiRNNCell
if encoder.cell_type.lower() == 'lstm':
cell = CellWrapper(BasicLSTMCell(encoder.cell_size, reuse=reuse))
| tensorflow.contrib.rnn.BasicLSTMCell | 711 |
import tensorflow as tf
argmax = lambda: tf.argmax(output_, 1)
target = lambda: inputs.read(time + 1)
softmax = lambda: tf.squeeze(tf.multinomial(tf.log(tf.nn.softmax(output_)), num_samples=1),
axis=1)
use_target = tf.logical_and(time < time_steps - 1, tf.random_uniform([]) >= feed_previous)
predicted_symbol = tf.case([
(use_target, target),
(tf.logical_not(feed_argmax), softmax)],
default=argmax) # default case is useful for beam-search
predicted_symbol.set_shape([None])
predicted_symbol = tf.stop_gradient(predicted_symbol)
input_ = embed(predicted_symbol)
pos = update_pos(pos, predicted_symbol, encoder_input_length[align_encoder_id])
| tensorflow.logical_not | 712 |
import tensorflow as tf
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
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)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
| tensorflow.nn.bias_add | 713 |
from tensorflow.python import tf2
def _check_tensorflow_version():
"""Check that we're using a compatible TF version.
Raises a warning if either Tensorflow version is less that 2.0 or TF 2.x is
not enabled.
If TF 2.x is enabled, but version is < TF 2.3, raises a warning to indicate
that resources may not be initialized.
"""
major, minor, _ = tf.version.VERSION.split('.')
if not (int(major) >= 2 and tf2.enabled()):
tf.compat.v1.logging.warning(
'Tensorflow version (%s) found. TransformFeaturesLayer is supported '
'only for TF 2.x with TF 2.x behaviors enabled and may not work as '
'intended.', tf.version.VERSION)
elif int(major) == 2 and int(minor) < 3:
# TODO(varshaan): Log a more specific warning.
tf.compat.v1.logging.warning(
'Tensorflow version (%s) found. TransformFeaturesLayer may not work '
'as intended if the SavedModel contains an initialization op.',
tf.version.VERSION)
| tensorflow.python.tf2.enabled | 714 |
import tensorflow as tf
Li_eKuf = tf.matrix_triangular_solve(Luu, eKuf, lower=True) # M x N
fmean = tf.matmul(Li_eKuf, q_mu, transpose_a=True)
eKff = expectation(pXnew, kern) # N (psi0)
eKuffu = expectation(pXnew, (kern, feat), (kern, feat)) # N x M x M (psi2)
Luu_tiled = tf.tile(Luu[None, :, :], [num_data, 1, 1]) # remove this line, once issue 216 is fixed
Li_eKuffu = tf.matrix_triangular_solve(Luu_tiled, eKuffu, lower=True)
Li_eKuffu_Lit = tf.matrix_triangular_solve(Luu_tiled, tf.matrix_transpose(Li_eKuffu), lower=True) # N x M x M
cov = tf.matmul(q_sqrt_r, q_sqrt_r, transpose_b=True) # D x M x M
if mean_function is None or isinstance(mean_function, mean_functions.Zero):
e_related_to_mean = tf.zeros((num_data, num_func, num_func), dtype=settings.float_type)
else:
# Update mean: \mu(x) + m(x)
| tensorflow.matrix_transpose | 715 |
from tensorflow.python.ops import math_ops
if self._n_classes < 2:
loss_vec = math_ops.square(logits - math_ops.to_float(target))
elif self._n_classes == 2:
loss_vec = nn.sigmoid_cross_entropy_with_logits(logits,
math_ops.to_float(target))
else:
loss_vec = nn.sparse_softmax_cross_entropy_with_logits(
logits, array_ops.reshape(target, [-1]))
| tensorflow.python.ops.math_ops.to_float | 716 |
from tensorflow.python.ops.rnn_cell_impl import _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]))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
r_state = r * state
if self._candidate_linear is None:
with vs.variable_scope("candidate"):
self._candidate_linear = _Linear(
[inputs, r_state],
self._num_units,
True,
bias_initializer=self._bias_initializer,
kernel_initializer=self._kernel_initializer)
c = self._activation(self._candidate_linear([inputs, r_state]))
u = (1.0 - att_score) * u
new_h = u * state + (1 - u) * c
return new_h, new_h
def prelu(_x, scope=''):
| tensorflow.python.ops.rnn_cell_impl._Linear | 717 |
import tensorflow as tf
var_shape = var.get_shape().as_list()
if var_shape == saved_shapes[name]:
restored.append(var)
else:
shape_conflicts.add(name)
found_names -= shape_conflicts
return (restored, sorted(found_names),
sorted(missing_names), sorted(shape_conflicts))
def load(self, checkpoint_path, flexible_restore=True):
if tf.gfile.IsDirectory(checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
if checkpoint_path is None:
raise ValueError('Checkpoint directory is empty.')
if flexible_restore:
var_list, found, missing, conflicts = self._checkpoint_var_search(
checkpoint_path)
tf.logging.info('Restoring variables: \n\t{}'.format(
'\n\t'.join(found)))
if len(missing) > 0:
| tensorflow.gfile.IsDirectory | 718 |
import tensorflow as tf
# regularizer = tf.contrib.layers.l2_regularizer(0.001)
# reg = regularizer(embedding_variable)
# loss += reg
return loss
def crf_decode_layer(self, logits, crf_params, nwords):
with tf.name_scope("CRF_decode"):
pred_ids, _ = tf.contrib.crf.crf_decode(logits, crf_params, nwords)
return pred_ids
def compute_metrics(self, tags, pred_ids, num_tags, indices, nwords):
weights = tf.sequence_mask(nwords)
# metrics_correct_rate, golden, predict = correct_rate(tags, pred_ids)
| tensorflow.contrib.crf.crf_decode | 719 |
import tensorflow as tf
if self.has_inputs:
data_fields["inputs"] = tf.VarLenFeature(tf.int64)
# hack: ignoring true targets and putting dist_targets in targets
data_items_to_decoders = {
"inputs": tf.contrib.slim.tfexample_decoder.Tensor("inputs"),
"targets": tf.contrib.slim.tfexample_decoder.Tensor("dist_targets"),
}
return (data_fields, data_items_to_decoders)
def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
"""Get vocab for distill problems."""
| tensorflow.contrib.slim.tfexample_decoder.Tensor | 720 |
from tensorflow.python.ops import math_ops
count = _create_local('count_tensor', shape=values.get_shape())
num_values = array_ops.ones_like(values)
if weights is not None:
weights = math_ops.to_float(weights)
values = math_ops.mul(values, weights)
num_values = math_ops.mul(num_values, weights)
total_compute_op = state_ops.assign_add(total, values)
count_compute_op = state_ops.assign_add(count, num_values)
def compute_mean(total, count, name):
non_zero_count = math_ops.maximum(count,
array_ops.ones_like(count),
name=name)
return math_ops.truediv(total, non_zero_count, name=name)
mean = compute_mean(total, count, 'value')
with ops.control_dependencies([total_compute_op, count_compute_op]):
update_op = compute_mean(total, count, 'update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, mean)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return mean, update_op
| tensorflow.python.ops.math_ops.truediv | 721 |
from tensorflow.python.ops import gen_resource_variable_ops
return None
@property
def op(self):
return self.get().op
def _read_variable_op(self):
if _enclosing_tpu_context() is None:
return self._primary_var.read_value()
v = gen_resource_variable_ops.read_variable_op(self.handle, self._dtype)
return v
def read_value(self):
return self._read_variable_op()
def assign(self, value, use_locking=None, name=None, read_value=False):
del use_locking
with _handle_graph(self.handle), self._assign_dependencies():
| tensorflow.python.ops.gen_resource_variable_ops.read_variable_op | 722 |
import tensorflow as tf
# queue.
image, label = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
images, sparse_labels = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=8,
capacity=1000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
min_after_dequeue=1000)
| tensorflow.train.shuffle_batch | 723 |
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('ws_save_path', './models_ws/model.ckpt', 'WS: model\'s save path')
tf.app.flags.DEFINE_float('ws_prune_ratio', 0.75, 'WS: target pruning ratio')
tf.app.flags.DEFINE_string('ws_prune_ratio_prtl', 'optimal',
'WS: pruning ratio protocol (\'uniform\' | \'heurist\' | \'optimal\')')
tf.app.flags.DEFINE_integer('ws_nb_rlouts', 200, 'WS: # of roll-outs for the RL agent')
tf.app.flags.DEFINE_integer('ws_nb_rlouts_min', 50,
'WS: minimal # of roll-outs for the RL agent to start training')
tf.app.flags.DEFINE_string('ws_reward_type', 'single-obj',
'WS: reward type (\'single-obj\' OR \'multi-obj\')')
tf.app.flags.DEFINE_float('ws_lrn_rate_rg', 3e-2, 'WS: learning rate for layerwise regression')
| tensorflow.app.flags.DEFINE_integer | 724 |
import tensorflow as tf
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
# Optimizer
with tf.name_scope('optimizer'):
if model_str == 'gcn_ae':
opt = OptimizerAE(preds=model.reconstructions,
labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'],
validate_indices=False), [-1]),
pos_weight=pos_weight,
norm=norm)
elif model_str == 'gcn_vae':
opt = OptimizerVAE(preds=model.reconstructions,
labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'],
validate_indices=False), [-1]),
| tensorflow.sparse_tensor_to_dense | 725 |
import tensorflow as tf
normalized_grad = old_div(grad, avoid_nan_norm)
elif ord == 2:
red_ind = list(range(1, len(x.get_shape())))
avoid_zero_div = 1e-8
square = tf.maximum(avoid_zero_div,
reduce_sum(tf.square(grad),
reduction_indices=red_ind,
keepdims=True))
normalized_grad = old_div(grad, tf.sqrt(square))
else:
normalized_grad = tf.sign(grad)
normalized_grad = tf.stop_gradient(normalized_grad)
scaled_grad = eps * normalized_grad
#目标是让loss下降
adv_x = x - scaled_grad
if (clip_min is not None) and (clip_max is not None):
adv_x = tf.clip_by_value(adv_x, clip_min, clip_max)
| tensorflow.sign | 726 |
import tensorflow as tf
cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(24)] * 2,
state_is_tuple=True)
return tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=classes,
num_decoder_symbols=classes, embedding_size=24)
targets = [dec_inp[i+1] for i in range(len(dec_inp) - 1)] + [0]
return tf.nn.seq2seq.model_with_buckets(
enc_inp, dec_inp, targets, weights, buckets, GRUSeq2Seq,
per_example_loss=per_example_loss)
# Now we construct the copy model.
inp = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
| tensorflow.nn.seq2seq.model_with_buckets | 727 |
import tensorflow as tf
else:
q = tf.nn.softmax(q_logits)
p = tf.nn.softmax(p_logits)
kl = tf.reduce_sum(q * (tf.log(q) - tf.log(p)), 1)
num_labels = tf.reduce_sum(weights)
num_labels = tf.where(tf.equal(num_labels, 0.), 1., num_labels)
kl.get_shape().assert_has_rank(2)
weights.get_shape().assert_has_rank(1)
loss = tf.identity(tf.reduce_sum(tf.expand_dims(weights, -1) * kl) / num_labels, name='kl')
return loss
| tensorflow.equal | 728 |
from tensorflow.python.platform import gfile
IOError, lambda e: "Cannot parse file"):
tf.train.import_meta_graph(filename)
# Deletes the file
gfile.Remove(filename)
with self.assertRaisesWithPredicateMatch(
IOError, lambda e: "does not exist"):
| tensorflow.python.platform.gfile.Remove | 729 |
import tensorflow as tf
# compute norms in case they need to be logged
self.gradient_norms = [tf.norm(g) + NUMTOL for (g, v) in clipped_grads_and_vars]
self.weight_norms = [tf.norm(v) + NUMTOL for (g, v) in clipped_grads_and_vars]
# check that gradients are finite
grads = [tf.check_numerics(g, "grads is not finite") for (g, v) in clipped_grads_and_vars]
variables = [tf.check_numerics(v, "grads is not finite") for (g, v) in clipped_grads_and_vars]
self.gradient_weight_global_norms = [tf.global_norm(grads), tf.global_norm(variables)]
# 2nd part of minimize: apply_gradient
optimizer_step = self._optimizer.apply_gradients(clipped_grads_and_vars, global_step=self.global_step)
| tensorflow.check_numerics | 730 |
import tensorflow as tf
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)
+ l2_reg_lambda * l2_loss)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
# calculate accuracy
correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
print ("done...")
print ("************")
path='save/'
ckpt_name = 'save/model.ckpt'
fname = 'model.tf'
dst_nodes = ['output/predictions']
saver = tf.train.Saver()
| tensorflow.argmax | 731 |
import tensorflow as tf
hist_rater_b = tf.reduce_sum(labels, 0)
conf_mat = tf.matmul(tf.transpose(pred_norm), labels)
nom = tf.reduce_sum(weights * conf_mat)
denom = tf.reduce_sum(weights * tf.matmul(
tf.reshape(hist_rater_a, [num_ratings, 1]), tf.reshape(hist_rater_b, [1, num_ratings])) /
tf.to_float(batch_size))
try:
return -(1 - nom / denom)
except Exception:
return -(1 - nom / (denom + eps))
| tensorflow.to_float | 732 |
from tensorflow.python.eager import context
initial_output = tf.slice(initial_output, [0, 0, 0, 0],
common_layers.shape_list(initial_output))
target_modality = self._problem_hparams.target_modality
if target_modality.is_class_modality:
decode_length = 1
else:
decode_length = common_layers.shape_list(
features["inputs"])[1] + decode_length
# Initial values of result, logits and loss.
result = initial_output
# tensor of shape [batch_size, time, 1, 1, vocab_size]
logits = tf.zeros((batch_size, 0, 1, 1, target_modality.top_dimensionality))
if not context.in_eager_mode():
logits.set_shape([None, None, None, None, None])
loss = 0.0
def while_exit_cond(result, logits, loss): # pylint: disable=unused-argument
"""Exit the loop either if reach decode_length or EOS."""
length = common_layers.shape_list(result)[1]
not_overflow = length < decode_length
if self._problem_hparams.stop_at_eos:
| tensorflow.python.eager.context.in_eager_mode | 733 |
from tensorflow.python.framework import ops
total = _create_local('total', shape=[])
count = _create_local('count', shape=[])
if weights is not None:
weights = math_ops.to_float(weights)
values = math_ops.mul(values, weights)
num_values = math_ops.reduce_sum(_broadcast_weights(weights, values))
else:
num_values = math_ops.to_float(array_ops.size(values))
total_compute_op = state_ops.assign_add(total, math_ops.reduce_sum(values))
count_compute_op = state_ops.assign_add(count, num_values)
mean = _safe_div(total, count, 'value')
with ops.control_dependencies([total_compute_op, count_compute_op]):
update_op = _safe_div(total, count, 'update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, mean)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return mean, update_op
def streaming_mean_tensor(values, weights=None, metrics_collections=None,
updates_collections=None, name=None):
| tensorflow.python.framework.ops.control_dependencies | 734 |
from tensorflow.contrib import metrics as metrics_lib
result = {"loss": metrics_lib.streaming_mean(self._loss(
logits, targets,
weight_tensor=self._get_weight_tensor(features)))}
# Adding default metrics
if metrics is None and self._n_classes > 1:
metrics = {"accuracy": metrics_lib.streaming_accuracy}
if self._n_classes == 2:
predictions = math_ops.sigmoid(logits)
result["eval_auc"] = metrics_lib.streaming_auc(predictions, targets)
if metrics:
predictions = self._logits_to_predictions(logits, proba=False)
result.update(self._run_metrics(predictions, targets, metrics,
self._get_weight_tensor(features)))
return result
def _get_predict_ops(self, features):
| tensorflow.contrib.metrics.streaming_auc | 735 |
from tensorflow.python.training import server_lib
cs = server_lib.ClusterSpec({
"worker": ["localhost:%s" % port1],
"ps": ["localhost:%s" % port2]
})
worker = server_lib.Server(cs, job_name="worker", start=True)
ps = server_lib.Server(cs, job_name="ps", start=True)
return worker, ps
@contextlib.contextmanager
def _maybeWithDevice(self, device):
| tensorflow.python.training.server_lib.Server | 736 |
import tensorflow as tf
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# In this example, we limit mnist data
Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)
Xte, Yte = mnist.test.next_batch(200) #200 for testing
# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))),
reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.arg_min(distance, 0)
accuracy = 0.
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.compat.v1.Session() as sess:
# Run the initializer
sess.run(init)
# Add the fault injection code here to instrument the graph
# We start injecting the fault right away here unlike earlier
| tensorflow.arg_min | 737 |
import tensorflow as tf
epsilon=hparams.vq_epsilon,
ema=hparams.ema,
means=means)
inputs = None
batch_size = hparams.batch_size
targets = tf.random_uniform([batch_size,
hparams.img_len,
hparams.img_len,
hparams.hidden_size],
minval=-1., maxval=1.)
target_space_id = None
tf.train.create_global_step()
decoder_output, losses, cache = latent_layers.transformer_autoencoder(
inputs, targets, target_space_id, hparams)
self.assertEqual(set(six.iterkeys(losses)),
{"extra", "extra_loss", "latent_pred"})
self.evaluate(tf.global_variables_initializer())
decoder_output_, extra_loss_, latent_pred_ = self.evaluate(
[decoder_output, losses["extra_loss"], losses["latent_pred"]])
self.assertEqual(decoder_output_.shape, (batch_size,
hparams.img_len,
hparams.img_len,
| tensorflow.train.create_global_step | 738 |
from tensorflow.python.ops import gen_nn_ops
"""
with ops.op_scope([value, bias], name, "BiasAdd") as name:
value = ops.convert_to_tensor(value, name="input")
bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
return gen_nn_ops._bias_add(value, bias, data_format=data_format, name=name)
ops.RegisterShape("BiasAdd")(common_shapes.bias_add_shape)
| tensorflow.python.ops.gen_nn_ops._bias_add | 739 |
from tensorflow.python.training import server_lib
# distributed jobs, such as "/job:ps" which are not present.
config._cluster_spec = server_lib.ClusterSpec({})
| tensorflow.python.training.server_lib.ClusterSpec | 740 |
import tensorflow as tf
from google.protobuf import text_format
import tensorflow as tf
import preprocessing
import datasets
NUM_TEST_IMAGES = 50000
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.compat.v1.GraphDef()
import os
file_ext = os.path.splitext(model_file)[1]
with open(model_file, "rb") as f:
if file_ext == '.pbtxt':
text_format.Merge(f.read(), graph_def)
else:
graph_def.ParseFromString(f.read())
| tensorflow.compat.v1.GraphDef | 741 |
from tensorflow.python.ops import math_ops
def _prob(self, x):
return math_ops.exp(self._log_prob(x))
def _log_cdf(self, x):
return math_ops.log(self._cdf(x))
def _cdf(self, x):
x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
self.validate_args else [], x)
# Note that igammac returns the upper regularized incomplete gamma
# function Q(a, x), which is what we want for the CDF.
return math_ops.igammac(self.alpha, self.beta / x)
@distribution_util.AppendDocstring(
"""This is defined to be
```
entropy = alpha - log(beta) + log(Gamma(alpha))
+ (1-alpha)digamma(alpha)
```
where digamma(alpha) is the digamma function.""")
| tensorflow.python.ops.math_ops.igammac | 742 |
import tensorflow as tf
# wrapper to make the optimizer work with TPUs
if params['use_tpu']:
gen_optimizer = tf.contrib.tpu.CrossShardOptimizer(gen_optimizer)
dis_optimizer = tf.contrib.tpu.CrossShardOptimizer(dis_optimizer)
gan_train_ops = tf.contrib.gan.gan_train_ops(gan_model, gan_loss, gen_optimizer, dis_optimizer)
| tensorflow.contrib.tpu.CrossShardOptimizer | 743 |
from tensorflow.python.ops import math_ops
Returns:
A [D1, ... DN] `Tensor` of false negative counts.
"""
with ops.name_scope(None, 'false_negatives', (predictions_idx, labels)):
labels, predictions_idx = _maybe_select_class_id(labels,
predictions_idx,
class_id)
fn = set_ops.set_size(set_ops.set_difference(predictions_idx,
labels,
aminusb=False))
fn = math_ops.to_double(fn)
if weights is not None:
weights = math_ops.to_double(weights)
fn = math_ops.mul(fn, weights)
return fn
def _streaming_sparse_false_negative_at_k(predictions_idx,
labels,
k,
class_id=None,
weights=None,
name=None):
"""Calculates weighted per step false negatives for recall@k.
| tensorflow.python.ops.math_ops.to_double | 744 |
import tensorflow as tf
# Create connected layers: fc1, fc2
with tf.contrib.framework.arg_scope([tf.contrib.layers.fully_connected],
normalizer_fn=tf.contrib.layers.batch_norm,
| tensorflow.contrib.framework.arg_scope | 745 |
from tensorflow.python.framework import ops
mean,
tf.check_numerics(decay * (mean - cur_mean), "NaN in moving mean."))
with tf.name_scope(name, "AssignMovingAvg", [var, cur_var, decay]):
with ops.colocate_with(var):
new_var = tf.assign_sub(
var,
tf.check_numerics(decay * (var - cur_var),
"NaN in moving variance."))
with tf.name_scope(name, "IncrementTime", [step]):
with ops.colocate_with(step):
new_step = tf.assign_add(step, 1.)
res += 0. * new_mean * new_var * new_step
return res
# batch normalization taking into account the volume transformation
def batch_norm_log_diff(input_,
| tensorflow.python.framework.ops.colocate_with | 746 |
from tensorflow.python.ops import math_ops
@distribution_util.AppendDocstring(
"""Variance for inverse gamma is defined only for `alpha > 2`. If
`self.allow_nan_stats` is `False`, an exception will be raised rather
than returning `NaN`.""")
def _variance(self):
var = (math_ops.square(self.beta) /
(math_ops.square(self.alpha - 1.) * (self.alpha - 2.)))
if self.allow_nan_stats:
nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
return array_ops.where(
self.alpha > 2., var,
array_ops.fill(self.batch_shape(), nan, name="nan"))
else:
| tensorflow.python.ops.math_ops.square | 747 |
import tensorflow as tf
else: #was used to train LM
c = tf.nn.conv1d(x, w, stride=1, padding=pad)+b
| tensorflow.nn.conv1d | 748 |
import tensorflow as tf
logits = tf.reshape(logits, [-1, bil_lstm_win_size, 256*amp_factor])
forward_cell = tf.nn.rnn_cell.LSTMCell(128)
backward_cell = tf.nn.rnn_cell.LSTMCell(128)
encoder_outputs,_ = tf.nn.bidirectional_dynamic_rnn(
forward_cell,
backward_cell,
logits,
| tensorflow.nn.bidirectional_dynamic_rnn | 749 |
import tensorflow as tf
"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,
| tensorflow.nn.conv2d | 750 |
import tensorflow as tf
logits += self.b_soft_no_learn
op_id = tf.multinomial(logits, 1)
| tensorflow.multinomial | 751 |
from tensorflow.python.ops import gen_math_ops
def tanh(x, name=None):
"""Computes hyperbolic tangent of `x` element-wise.
Args:
x: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,
or `qint32`.
name: A name for the operation (optional).
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)
| tensorflow.python.ops.gen_math_ops._tanh | 752 |
import tensorflow as tf
def _forward(self, x1, x2, **kwargs):
log_sigmas = self.parameterizer(x1)
z2, fldj = half_gaussianize(x2, log_sigmas)
return z2, fldj
def _inverse(self, x1, z2, **kwargs):
log_sigmas = self.parameterizer(x1)
x2, ildj = half_gaussianize(z2, log_sigmas, inverse=tf.constant(True))
return x2, ildj
def exponentiate(x, log_lambdas, inverse=tf.constant(False)):
if not inverse:
z = tf.math.exp(log_lambdas)*x
ldj = tf.math.reduce_sum(log_lambdas, axis=[1,2,3])
else:
z = x*tf.math.exp(-log_lambdas)
ldj = -tf.math.reduce_sum(log_lambdas, axis=[1,2,3])
return z, ldj
class Exponentiate(Parameterize):
"""
Implementation of parameterize for an exponetial prior.
"""
def __init__(self, input_shape=None, name='gaussianize', *args, **kwargs):
| tensorflow.math.exp | 753 |
import tensorflow as tf
filter_class_true = tf.boolean_mask(tf.cast(mask_pos, tf.float32),
loss_class_mask)
filter_class_pred = tf.boolean_mask(class_pred, loss_class_mask_b)
filter_class_pred = tf.reshape(filter_class_pred, [-1, num_class])
loss_class = tf.keras.losses.sparse_categorical_crossentropy(
y_true=filter_class_true, y_pred=filter_class_pred)
loss_class = tf.reduce_mean(loss_class)
| tensorflow.keras.losses.sparse_categorical_crossentropy | 754 |
from tensorflow.python.ops import check_ops
value variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
Returns:
value_tensor: A tensor representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `weights` is not `None` and its shape doesn't match `values`,
or if either `metrics_collections` or `updates_collections` are not a list
or tuple.
"""
check_ops.assert_type(values, dtypes.bool)
count = _create_local('count', shape=[])
values = math_ops.to_float(values)
if weights is not None:
weights = math_ops.to_float(weights)
values = math_ops.mul(values, weights)
value_tensor = array_ops.identity(count)
update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))
if metrics_collections:
ops.add_to_collections(metrics_collections, value_tensor)
| tensorflow.python.ops.check_ops.assert_type | 755 |
import tensorflow as tf
if encoder.binary:
raise NotImplementedError
pad = tf.nn.embedding_lookup(embeddings, utils.BOS_ID)
pad = tf.expand_dims(tf.expand_dims(pad, axis=0), axis=1)
pad = tf.tile(pad, [batch_size, 1, 1])
| tensorflow.nn.embedding_lookup | 756 |
import tensorflow as tf
beam_width=beam_width, blank_index=blank_index, top_paths=1,
blank_label=0)
decoded = tf.sparse.SparseTensor(indices[0], values[0], shape[0])
decoded = tf.cast(tf.sparse.to_dense(decoded), tf.int32)
decoded_u = tf.sparse.SparseTensor(indices_u[0], values_u[0], shape_u[0])
decoded_u = tf.cast(tf.sparse.to_dense(decoded_u), tf.int32)
# Adjust event vals according to representation
decoded = tf.where(tf.not_equal(decoded, 0), decoded+shift, decoded)
| tensorflow.sparse.SparseTensor | 757 |
from tensorflow.python.ops import math_ops
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
predictions, normalizer = tensor_util.remove_squeezable_dimensions(
predictions, normalizer)
predictions.get_shape().assert_is_compatible_with(normalizer.get_shape())
relative_errors = math_ops.select(
math_ops.equal(normalizer, 0.0),
array_ops.zeros_like(labels),
math_ops.div(math_ops.abs(labels - predictions), normalizer))
return streaming_mean(relative_errors, weights, metrics_collections,
updates_collections, name or 'mean_relative_error')
def streaming_mean_squared_error(predictions, labels, weights=None,
metrics_collections=None,
updates_collections=None,
| tensorflow.python.ops.math_ops.abs | 758 |
from tensorflow.python.ops import gen_math_ops
A `Tensor` of the same type as `a`.
"""
with ops.op_scope([a, b], name, "MatMul") as name:
a = ops.convert_to_tensor(a, name="a")
b = ops.convert_to_tensor(b, name="b")
if a.dtype == types.float32 and (a_is_sparse or b_is_sparse):
return sparse_matmul(a, b,
transpose_a=transpose_a,
transpose_b=transpose_b,
a_is_sparse=a_is_sparse,
b_is_sparse=b_is_sparse,
name=name)
else:
return gen_math_ops._mat_mul(a, b,
transpose_a=transpose_a,
transpose_b=transpose_b,
name=name)
sparse_matmul = gen_math_ops._sparse_mat_mul
batch_matmul = gen_math_ops._batch_mat_mul
ops.RegisterShape("MatMul")(common_shapes.matmul_shape)
ops.RegisterShape("SparseMatMul")(common_shapes.matmul_shape)
def _as_indexed_slices(x):
| tensorflow.python.ops.gen_math_ops._mat_mul | 759 |
import tensorflow as tf
np.random.seed(self.seed)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
| tensorflow.compat.v1.logging.set_verbosity | 760 |
import tensorflow as tf
channels = tf.unstack(image, axis=-1)
image = tf.stack([channels[2], channels[1], channels[0]], axis=-1)
# dims for normalization
width = tf.to_float(tf.shape(image)[2])
height = tf.to_float(tf.shape(image)[1])
# from [x1, y1, x2, y2, cls] to normalized [y1, x1, y1, x1]
cols = tf.unstack(boxes, axis=1)
boxes = tf.stack([cols[1] / height,
cols[0] / width,
cols[3] / height,
cols[2] / width], axis=1)
# add batch dimension (assume batch_size==1)
#assert image.get_shape()[0] == 1
boxes = tf.expand_dims(boxes, dim=0)
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)
| tensorflow.image.draw_bounding_boxes | 761 |
import tensorflow as tf
correct = tf.cast(correct, tf.float32)
accuracy = tf.reduce_mean(correct)*100.0
tf.summary.scalar(scope+'accuracy',accuracy)
return accuracy
def num_correct_prediction(logits, labels):
'''
Evaluate the quality of the logits at predicting the label
'''
correct = tf.equal(tf.arg_max(logits,1), tf.arg_max(labels,1))
correct = tf.cast(correct, tf.int32)
n_correct = tf.reduce_sum(correct)
return n_correct
def optimize(loss, learning_rate, global_step):
'''
Optimization, use Gradient Descent as default
'''
| tensorflow.arg_max | 762 |
import tensorflow as tf
features = sp.identity(features.shape[0]) # featureless
# Some preprocessing
adj_norm = preprocess_graph(adj)
# Define placeholders
placeholders = {
'features': tf.sparse_placeholder(tf.float32),
'adj': tf.sparse_placeholder(tf.float32),
'adj_orig': tf.sparse_placeholder(tf.float32),
'dropout': tf.placeholder_with_default(0., shape=())
}
num_nodes = adj.shape[0]
features = sparse_to_tuple(features.tocoo())
| tensorflow.sparse_placeholder | 763 |
from tensorflow.python.framework import ops
def _define_vars(self, params):
pass
def inference_graph(self, data):
with ops.device(self.device_assigner):
# Compute activations for the neural network.
nn_activations = [layers.fully_connected(data, self.params.layer_size)]
for _ in range(1, self.params.num_layers):
| tensorflow.python.framework.ops.device | 764 |
import tensorflow as tf
vz = tf.contrib.lookup.MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=-1)
vx_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
vz_keys = tf.reshape(tf.Variable([], collections=[], dtype=tf.string), (-1, 1))
x_t = tf.gather(x, l)
x_t_len = tf.strings.length(x_t)
x_t = tf.string_split([x_t], delimiter='').values
z_t = tf.gather(y, m)
z_t_len = tf.strings.length(z_t)
z_t = tf.string_split([z_t], delimiter='').values
for i in tf.range(start=0, limit=x_t_len - self._p + 1, delta=1, dtype=None, name='range'):
u = tf.string_join(x_t[i:i + self._p], '')
vx_keys, r = tf.cond(
tf.greater(vx.lookup(u), -1),
| tensorflow.string_split | 765 |
import tensorflow as tf
next_indices = tf.stack([neighbor.indices[:, 0], next_idx], 1)
next_values = weight.values
next_shape = tf.stack([tf.size(nodes), tf.size(next_nodes)])
next_shape = tf.cast(next_shape, tf.int64)
next_adj = tf.SparseTensor(next_indices, next_values, next_shape)
next_adj = tf.sparse_reorder(next_adj)
nodes_list.append(next_nodes)
adj_list.append(next_adj)
nodes = next_nodes
return nodes_list, adj_list
| tensorflow.sparse_reorder | 766 |
from tensorflow.python.ops import sparse_ops
hashed_output=True,
num_buckets=1024,
hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)
cross_dense = sparse_ops.sparse_tensor_to_dense(cross)
with session.Session():
values = cross_dense.eval()
| tensorflow.python.ops.sparse_ops.sparse_tensor_to_dense | 767 |
import tensorflow as tf
batch_size=batch_size,
num_parallel_batches=num_cpu_threads,
drop_remainder=True))
return d
return input_fn
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
| tensorflow.parse_single_example | 768 |
from tensorflow.python.feature_column import feature_column_lib as core_feature_column
model = estimator.GradientBoostedDecisionTreeEstimator(
head=head_fn,
learner_config=learner_config,
num_trees=1,
examples_per_layer=3,
model_dir=model_dir,
config=config,
feature_columns=[core_feature_column.numeric_column("x")],
use_core_libs=True)
model.fit(input_fn=_train_input_fn, steps=15)
model.evaluate(input_fn=_eval_input_fn, steps=1)
model.export(self._export_dir_base)
| tensorflow.python.feature_column.feature_column_lib.numeric_column | 769 |
import tensorflow.contrib.graph_editor as ge
for ts in [ts_filtered, ts_all]:
# get all bottlenecks in the graph
bottleneck_ts = []
for t in ts:
b = set(ge.get_backward_walk_ops(t.op, inclusive=True, within_ops=fwd_ops))
f = set(ge.get_forward_walk_ops(t.op, inclusive=False, within_ops=fwd_ops))
# check that there are not shortcuts
b_inp = set([inp for op in b for inp in op.inputs]).intersection(ts_all)
f_inp = set([inp for op in f for inp in op.inputs]).intersection(ts_all)
| tensorflow.contrib.graph_editor.get_backward_walk_ops | 770 |
from tensorflow.contrib.eager.python.examples.revnet import revnet
with tfe.execution_mode(execution_mode):
device, data_format = device_and_format
model = revnet.RevNet(config=config)
if defun:
| tensorflow.contrib.eager.python.examples.revnet.revnet.RevNet | 771 |
import tensorflow as tf
eval_metric_ops=eval_metric_ops
)
assert mode == tf.estimator.ModeKeys.TRAIN
with tf.variable_scope('learning_rate'):
global_step = tf.train.get_global_step()
learning_rate = tf.train.cosine_decay(
params['initial_learning_rate'],
global_step, decay_steps=params['num_steps']
)
tf.summary.scalar('learning_rate', learning_rate)
| tensorflow.train.cosine_decay | 772 |
import tensorflow.contrib.graph_editor as ge
if hasattr(ops, '__iter__') and not isinstance(ops, str):
l = [(op.name if hasattr(op, "name") else str(op)) for op in ops]
if sort_outputs:
return sorted(l)
return l
else:
return ops.name if hasattr(ops, "name") else str(ops)
def my_add_control_inputs(wait_to_do_ops, inputs_to_do_before):
for op in wait_to_do_ops:
ci = [i for i in inputs_to_do_before if op.control_inputs is None or i not in op.control_inputs]
ge.add_control_inputs(op, ci)
| tensorflow.contrib.graph_editor.add_control_inputs | 773 |
import tensorflow as tf
train = dataset['train'].map(
load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)
test = dataset['test'].map(load_image_test)
train_dataset = train.cache().shuffle(1000).batch(batch_size).repeat()
train_dataset = train_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
test_dataset = test.batch(batch_size)
config = hparams_config.get_efficientdet_config('efficientdet-d0')
config.heads = ['segmentation']
model = efficientdet_keras.EfficientDetNet(config=config)
model.build((1, 512, 512, 3))
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
val_subsplits = 5
val_steps = info.splits['test'].num_examples // batch_size // val_subsplits
model.fit(
train_dataset,
epochs=20,
steps_per_epoch=steps_per_epoch,
validation_steps=val_steps,
validation_data=test_dataset,
callbacks=[])
model.save_weights('./test/segmentation')
| tensorflow.keras.losses.SparseCategoricalCrossentropy | 774 |
import tensorflow as tf
# load pretrained model
vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = []
for var in vars_list:
vname = var.name
from_name = vname
var_value = tf.contrib.framework.load_variable(MODEL_DIR, from_name)
assign_ops.append(tf.assign(var, var_value))
sess.run(assign_ops)
print('Model loaded.')
result = sess.run(output)
cv2.imwrite("output.png", result[0])
tf.reset_default_graph()
#return FileResponse("output.png", media_type="image/png")
with open("output.png", "rb") as image_file:
image_string = "data:image/png;base64,{}".format(base64.b64encode(image_file.read()).decode())
return {
"image": image_string
}
if __name__ == '__main__':
uvicorn.run(app, host="0.0.0.0", port=8080)
| tensorflow.reset_default_graph | 775 |
from tensorflow.contrib.tpu.python.tpu.datasets import StreamingFilesDataset
if epochs_between_evals > 1:
raise ValueError("epochs_between_evals > 1 not supported for file "
"based dataset.")
epoch_data_dir = self._result_queue.get(timeout=300)
if not self._is_training:
self._result_queue.put(epoch_data_dir) # Eval data is reused.
file_pattern = os.path.join(
epoch_data_dir, rconst.SHARD_TEMPLATE.format("*"))
dataset = StreamingFilesDataset(
files=file_pattern, worker_job="worker",
num_parallel_reads=rconst.NUM_FILE_SHARDS, num_epochs=1,
sloppy=not self._deterministic)
map_fn = functools.partial(self._deserialize, batch_size=batch_size)
dataset = dataset.map(map_fn, num_parallel_calls=16)
else:
types = {movielens.USER_COLUMN: rconst.USER_DTYPE,
| tensorflow.contrib.tpu.python.tpu.datasets.StreamingFilesDataset | 776 |
import tensorflow as tf
# sum over hidden units
num = tf.reduce_sum(tf.square(num), axis=2)
denom = tf.reduce_sum(tf.square(denom), axis=2)
bounded = tf.where(tf.greater(denom, 1e-20), tf.div(num, 1.0 * denom), tf.ones_like(num))
nelems = tf.reduce_mean(tf.where(tf.greater(denom, 1e-20), 1.0 * tf.ones_like(num), 1.0 * tf.zeros_like(num)), axis=1)
# sum mean over each batch by time steps
Omega = tf.square(bounded - 1.0)
Omega = tf.reduce_sum(tf.reduce_mean(Omega, axis=1)) / (1.0 * tf.reduce_sum(nelems))
out = tf.gradients(Omega, self.W_rec)
out[0] = tf.Print(out[0], [out[0], self.W_rec, Omega], "omega grads")
out[0] = tf.verify_tensor_all_finite(out[0], "dead omega grad")
return out, test
def sussillo_reg(self):
states = self.states
reg = 0
for state in states:
dJr = tf.matmul(tf.nn.relu(state),
tf.matmul(tf.abs(self.W_rec) * self.rec_Connectivity, self.Dale_rec))
reg += tf.reduce_sum(tf.square(dJr))
| tensorflow.verify_tensor_all_finite | 777 |
from tensorflow.python.ops import nn
logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)
if proba:
return nn.softmax(logits)
else:
return math_ops.argmax(logits, 1)
| tensorflow.python.ops.nn.softmax | 778 |
import tensorflow as tf
# -1 otherwise. For instance, candidate[3] = id(token[3] + token[4]).
# First, split into basic UTF-8 characters (letters).
chars = tf.strings.unicode_split(word, 'UTF-8')
tokens = self._StringToToken(chars)
| tensorflow.strings.unicode_split | 779 |
import tensorflow as tf
x: The input tensor.
prediction: The prediction class tensor.
output_class: The output tensor.
sess: The graph session.
"""
# input label placeholder
y = tf.placeholder("float", [None, self.n_classes])
# Loss function
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
# Optimization
opt = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).minimize(loss)
# Initialize variables
init = tf.global_variables_initializer()
sess.run(init)
for _ in range(TRAIN_STEPS):
batch_x, batch_y = self.mnist.train.next_batch(
batch_size=self.batch_size, shuffle=False)
batch_x = batch_x.reshape((self.batch_size, self.time_steps,
self.n_input))
sess.run(opt, feed_dict={x: batch_x, y: batch_y})
def saveAndRestoreModel(self, lstm_layer, sess, saver, is_dynamic_rnn):
"""Saves and restores the model to mimic the most common use case.
Args:
lstm_layer: The lstm layer either a single lstm cell or a multi lstm cell.
| tensorflow.global_variables_initializer | 780 |
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)
| tensorflow.one_hot | 781 |
from tensorflow.python.framework import sparse_tensor
linear_feature_columns=(bucketized_feature,),
dnn_feature_columns=(cont_feature,),
dnn_hidden_units=(3, 3))
input_fn = test_data.iris_input_multiclass_fn
metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
input_fn=input_fn, steps=100)
self._assertCommonMetrics(metrics)
def benchmarkPartitionedVariables(self):
def _input_fn():
features = {
'language':
sparse_tensor.SparseTensor(
values=('en', 'fr', 'zh'),
indices=((0, 0), (0, 1), (2, 0)),
dense_shape=(3, 2))
}
labels = constant_op.constant(((1,), (0,), (0,)))
return features, labels
# The given hash_bucket_size results in variables larger than the
# default min_slice_size attribute, so the variables are partitioned.
sparse_feature = feature_column.sparse_column_with_hash_bucket(
'language', hash_bucket_size=2e7)
embedding_feature = feature_column.embedding_column(
sparse_feature, dimension=1)
| tensorflow.python.framework.sparse_tensor.SparseTensor | 782 |
import tensorflow as tf
# Write images to disk.
image_write_ops = None
if FLAGS.write_to_disk:
image_write_ops = tf.write_file(
'%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
tf.image.encode_png(data_provider.float_image_to_uint8(
reshaped_img[0])))
# For unit testing, use `run_eval_loop=False`.
if not run_eval_loop: return
tf.contrib.training.evaluate_repeatedly(
FLAGS.checkpoint_dir,
hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
tf.contrib.training.StopAfterNEvalsHook(1)],
eval_ops=image_write_ops,
max_number_of_evaluations=FLAGS.max_number_of_evaluations)
def _get_generator_inputs(num_images_per_class, num_classes, noise_dims):
# Since we want a grid of numbers for the conditional generator, manually
# construct the desired class labels.
num_images_generated = num_images_per_class * num_classes
noise = tf.random_normal([num_images_generated, noise_dims])
labels = [lbl for lbl in range(num_classes) for _
in range(num_images_per_class)]
| tensorflow.contrib.training.SummaryAtEndHook | 783 |
import tensorflow as tf
self.w_attn_2 = tf.get_variable("w_2", [self.lstm_size, self.lstm_size])
self.v_attn = tf.get_variable("v", [self.lstm_size, 1])
def _build_sampler(self, prev_c=None, prev_h=None, use_bias=False):
"""Build the sampler ops and the log_prob ops."""
print ("-" * 80)
print ("Build controller sampler")
anchors = tf.TensorArray(
tf.float32, size=self.num_cells + 2, clear_after_read=False)
anchors_w_1 = tf.TensorArray(
tf.float32, size=self.num_cells + 2, clear_after_read=False)
arc_seq = tf.TensorArray(tf.int32, size=self.num_cells * 4)
if prev_c is None:
assert prev_h is None, "prev_c and prev_h must both be None"
prev_c = [tf.zeros([1, self.lstm_size], tf.float32)
for _ in range(self.lstm_num_layers)]
| tensorflow.TensorArray | 784 |
import tensorflow as tf
If `x` is integral, the output is cast to [u]int64. If `x` is sparse and
reduce_inst_dims is False will return 0 in place where column has no values
across batches.
Raises:
TypeError: If the type of `x` is not supported.
"""
with tf.compat.v1.name_scope(name, 'sum'):
if reduce_instance_dims:
x = tf.reduce_sum(input_tensor=tf_utils.get_values(x))
elif isinstance(x, tf.SparseTensor):
if x.dtype == tf.uint8 or x.dtype == tf.uint16:
x = tf.cast(x, tf.int64)
elif x.dtype == tf.uint32 or x.dtype == tf.uint64:
TypeError('Data type %r is not supported' % x.dtype)
x = tf.sparse.reduce_sum(x, axis=0)
elif isinstance(x, tf.RaggedTensor):
raise NotImplementedError(
'Elementwise sum does not support RaggedTensors.')
else:
x = tf.reduce_sum(input_tensor=x, axis=0)
output_dtype, sum_fn = _sum_combine_fn_and_dtype(x.dtype)
return _numeric_combine(
inputs=[x],
fn=sum_fn,
default_accumulator_value=0,
reduce_instance_dims=reduce_instance_dims,
output_dtypes=[output_dtype])[0]
| tensorflow.sparse.reduce_sum | 785 |
import tensorflow as tf
def conv1d_banks(inputs, K=16, is_training=True, scope="conv1d_banks"):
with tf.variable_scope(scope):
outputs = tf.layers.conv1d(inputs, embed_size // 2, 1, padding="SAME")
for k in range(2, K + 1):
with tf.variable_scope("num_{}".format(k)):
output = tf.layers.conv1d(inputs, embed_size // 2, k, padding="SAME")
outputs = tf.concat((outputs, output), -1)
outputs = tf.nn.relu(tf.layers.batch_normalization(outputs, training=is_training))
return outputs
| tensorflow.layers.conv1d | 786 |
import tensorflow as tf
mode,
data_parallelism=data_parallelism,
decode_hparams=decode_hparams)
global_step = tf.train.get_global_step()
mesh_shape = mtf.convert_to_shape(hparams.mesh_shape)
layout_rules = mtf.convert_to_layout_rules(hparams.layout)
| tensorflow.train.get_global_step | 787 |
import tensorflow as tf
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)
params = self.variable_mgr.trainable_variables_on_device(device_num)
l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in params])
weight_decay = FLAGS.weight_decay
if weight_decay is not None and weight_decay != 0.:
loss += weight_decay * l2_loss
aggmeth = tf.AggregationMethod.DEFAULT
grads = tf.gradients(loss, params, aggregation_method=aggmeth)
| tensorflow.nn.in_top_k | 788 |
import tensorflow as tf
For the scale (gamma), a ones initializer is used by default.
Args:
var_name: name of variable as a string.
"""
if var_name not in self._initializers:
if var_name == self.GAMMA:
self._initializers[self.GAMMA] = tf.ones_initializer()
elif var_name == self.BETA:
self._initializers[self.BETA] = tf.zeros_initializer()
def _build_statistics_variance(self, input_batch,
reduction_indices, use_batch_stats):
"""Builds the statistics part of the graph when using moving variance.
| tensorflow.ones_initializer | 789 |
import tensorflow as tf
layers = tf.keras.layers
def parse(line):
"""Parse a line from the colors dataset."""
# Each line of the dataset is comma-separated and formatted as
# color_name, r, g, b
# so `items` is a list [color_name, r, g, b].
items = tf.string_split([line], ",").values
rgb = tf.string_to_number(items[1:], out_type=tf.float32) / 255.
# Represent the color name as a one-hot encoded character sequence.
color_name = items[0]
chars = tf.one_hot(tf.decode_raw(color_name, tf.uint8), depth=256)
# The sequence length is needed by our RNN.
length = tf.cast(tf.shape(chars)[0], dtype=tf.int64)
return rgb, chars, length
def maybe_download(filename, work_directory, source_url):
"""Download the data from source url, unless it's already here.
| tensorflow.string_to_number | 790 |
import tensorflow as tf
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.
Args:
predictions: 2D tensor or array, [batch_size, num_classes] predictions of the network .
| tensorflow.losses.compute_weighted_loss | 791 |
import tensorflow as tf
return 196.0 * 21.0 / 4096.0
else:
rec = tf.cast(kw * kh, tf.float32)
n_max = 7 + tf.math.ceil(tf.math.log(rec) / tf.math.log(2.))
ns = tf.range(0., n_max)
ns_pow = tf.pow(2., ns)
ks = tf.round(ns_pow / rec)
diffs = tf.math.abs(ks / ns_pow - 1 / rec)
n = tf.argmin(diffs)
k = ks[n]
scale = k / tf.pow(2., tf.cast(n, tf.float32))
scale *= rec
return scale
| tensorflow.math.abs | 792 |
import tensorflow as tf
# List of dicts to dict of lists
metrics = dict(zip(metrics[0], zip(*[m.values() for m in metrics])))
metrics = {m: np.nanmean(metrics[m], axis=0) for m in metrics}
return metrics
def _checkpoint_var_search(self, checkpoint_path):
reader = tf.train.NewCheckpointReader(checkpoint_path)
saved_shapes = reader.get_variable_to_shape_map()
model_names = tf.model_variables() # Used by tf.slim layers
if not len(tf.model_variables()):
model_names = tf.global_variables() # Fallback when slim is not used
model_names = set([v.name.split(':')[0] for v in model_names])
checkpoint_names = set(saved_shapes.keys())
found_names = model_names & checkpoint_names
missing_names = model_names - checkpoint_names
shape_conflicts = set()
| tensorflow.model_variables | 793 |
from tensorflow.python.ops import array_ops
def reallocate():
next_size = _next_array_size(new_size)
next_shape = array_ops.pack([next_size] + fixed_shape)
new_value = array_ops.zeros(next_shape, dtype=values.dtype)
old_value = array.value()
| tensorflow.python.ops.array_ops.pack | 794 |
from tensorflow.python.ops import math_ops
weights=weights)
metric = math_ops.div(tp, math_ops.add(tp, fp), name=name)
update = math_ops.div(
| tensorflow.python.ops.math_ops.add | 795 |
import tensorflow as tf
original_tensor_shape = tf.shape(tensor)
in_dim = int(tensor.get_shape()[-1])
rank = _rank(tensor)
if rank > 2:
# -- time distributed dense
tensor = tf.reshape(tensor, shape=(-1, in_dim))
name = opts.get("name", "")
if weight is None:
initializer = tf.contrib.layers.xavier_initializer(uniform=True)
weight = tf.get_variable("{}_dense_W".format(name), initializer=initializer(shape=(in_dim, hidden_dims)))
if bias is None:
bias = tf.get_variable("{}_dense_b".format(name), initializer=tf.zeros(shape=hidden_dims))
out = tf.add(tf.matmul(tensor, weight), bias)
if rank > 2:
# reshape back to time dimension
out = tf.reshape(out, shape=original_tensor_shape)
| tensorflow.contrib.layers.xavier_initializer | 796 |
from tensorflow.python.ops import array_ops
logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)
| tensorflow.python.ops.array_ops.zeros_like | 797 |
import tensorflow as tf
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
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)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
| tensorflow.nn.log_softmax | 798 |
from tensorflow.python.platform import gfile
# Adding s2 again (old s2 is removed first, then new s2 appended)
s2 = save2.save(sess, os.path.join(save_dir, "s2"))
self.assertEqual([s3, s2], save2.last_checkpoints)
# Created by the first helper.
self.assertTrue(gfile.Exists(s1))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s1)))
# Deleted by the first helper.
self.assertFalse(gfile.Exists(s3))
self.assertFalse(gfile.Exists(save._MetaGraphFilename(s3)))
self.assertTrue(gfile.Exists(s2))
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s2)))
# Adding s1 (s3 should now be deleted as oldest in list)
s1 = save2.save(sess, os.path.join(save_dir, "s1"))
| tensorflow.python.platform.gfile.Exists | 799 |
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