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22,100 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | make_diet_var_getter | def make_diet_var_getter(params):
"""Create a custom variable getter for diet variables according to params."""
def diet_var_initializer(shape, dtype, partition_info=None):
"""Initializer for a diet variable."""
del dtype
del partition_info
with common_layers.fn_device_dependency("diet_init") as o... | python | def make_diet_var_getter(params):
"""Create a custom variable getter for diet variables according to params."""
def diet_var_initializer(shape, dtype, partition_info=None):
"""Initializer for a diet variable."""
del dtype
del partition_info
with common_layers.fn_device_dependency("diet_init") as o... | [
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22,101 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | _fn_with_diet_vars | def _fn_with_diet_vars(fn, args, params):
"""Call function with args; use diet variables according to params."""
vs_ctr = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Custom gradient function."""
del outputs # recomputing below
with common_layers.fn_device_dependency("diet_grad",
... | python | def _fn_with_diet_vars(fn, args, params):
"""Call function with args; use diet variables according to params."""
vs_ctr = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Custom gradient function."""
del outputs # recomputing below
with common_layers.fn_device_dependency("diet_grad",
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22,102 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | fn_with_diet_vars | def fn_with_diet_vars(params):
"""Decorator for graph-building function to use diet variables."""
params = copy.copy(params)
def dec(fn):
def wrapped(*args):
return _fn_with_diet_vars(fn, args, params)
return wrapped
return dec | python | def fn_with_diet_vars(params):
"""Decorator for graph-building function to use diet variables."""
params = copy.copy(params)
def dec(fn):
def wrapped(*args):
return _fn_with_diet_vars(fn, args, params)
return wrapped
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22,103 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | DietAdamOptimizer.create_slots | def create_slots(self, var):
"""Create the factorized Adam accumulators for diet variables."""
params = self.params
shape = var.get_shape().as_list()
if not hasattr(params, "slots"):
params.slots = defaultdict(dict)
name = var.op.name
slots = params.slots[name]
if params.factored_se... | python | def create_slots(self, var):
"""Create the factorized Adam accumulators for diet variables."""
params = self.params
shape = var.get_shape().as_list()
if not hasattr(params, "slots"):
params.slots = defaultdict(dict)
name = var.op.name
slots = params.slots[name]
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22,104 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | DietAdamOptimizer.update_variable | def update_variable(self, var, grad_var):
"""Update the variable and its slots."""
params = self.params
global_step = tf.to_float(self.global_step) + 1
# compute learning rate
lrate = params.learning_rate
if params.learning_rate_decay_scheme == "noam":
lrate *= tf.minimum(global_step * pa... | python | def update_variable(self, var, grad_var):
"""Update the variable and its slots."""
params = self.params
global_step = tf.to_float(self.global_step) + 1
# compute learning rate
lrate = params.learning_rate
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lrate *= tf.minimum(global_step * pa... | [
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22,105 | tensorflow/tensor2tensor | tensor2tensor/utils/mtf_model.py | MtfModel.estimator_spec_eval | def estimator_spec_eval(
self, features, logits, labels, loss, restore_hook, use_tpu):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
problem = hparams.problem
if logits.get_shape().ndims == 3:
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
# Support for mult... | python | def estimator_spec_eval(
self, features, logits, labels, loss, restore_hook, use_tpu):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
problem = hparams.problem
if logits.get_shape().ndims == 3:
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
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22,106 | tensorflow/tensor2tensor | tensor2tensor/data_generators/desc2code.py | generator_samples | def generator_samples(tmp_dir, pb_cst):
"""Generator for the dataset samples.
If not present, download and extract the dataset.
Args:
tmp_dir: path to the directory where to download the dataset.
pb_cst: CodingPbConstants object defining paths
Yields:
A CodingPbInfo object containing the next cha... | python | def generator_samples(tmp_dir, pb_cst):
"""Generator for the dataset samples.
If not present, download and extract the dataset.
Args:
tmp_dir: path to the directory where to download the dataset.
pb_cst: CodingPbConstants object defining paths
Yields:
A CodingPbInfo object containing the next cha... | [
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22,107 | tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm | def lstm(inputs, sequence_length, hparams, train, name, initial_state=None):
"""Adds a stack of LSTM layers on top of input.
Args:
inputs: The input `Tensor`, shaped `[batch_size, time_steps, hidden_size]`.
sequence_length: Lengths of the actual input sequence, excluding padding; a
`Tensor` shaped ... | python | def lstm(inputs, sequence_length, hparams, train, name, initial_state=None):
"""Adds a stack of LSTM layers on top of input.
Args:
inputs: The input `Tensor`, shaped `[batch_size, time_steps, hidden_size]`.
sequence_length: Lengths of the actual input sequence, excluding padding; a
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22,108 | tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq_internal | def lstm_seq2seq_internal(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model, main step used for training."""
with tf.variable_scope("lstm_seq2seq"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs = common_layers.flatt... | python | def lstm_seq2seq_internal(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model, main step used for training."""
with tf.variable_scope("lstm_seq2seq"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs = common_layers.flatt... | [
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22,109 | tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq_internal_bid_encoder | def lstm_seq2seq_internal_bid_encoder(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model with bidirectional encoder."""
with tf.variable_scope("lstm_seq2seq_bid_encoder"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs... | python | def lstm_seq2seq_internal_bid_encoder(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model with bidirectional encoder."""
with tf.variable_scope("lstm_seq2seq_bid_encoder"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
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22,110 | tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq | def lstm_seq2seq():
"""hparams for LSTM."""
hparams = common_hparams.basic_params1()
hparams.daisy_chain_variables = False
hparams.batch_size = 1024
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_decay ... | python | def lstm_seq2seq():
"""hparams for LSTM."""
hparams = common_hparams.basic_params1()
hparams.daisy_chain_variables = False
hparams.batch_size = 1024
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_decay ... | [
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22,111 | tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_attention_base | def lstm_attention_base():
"""Base attention params."""
hparams = lstm_seq2seq()
hparams.add_hparam("attention_layer_size", hparams.hidden_size)
hparams.add_hparam("output_attention", True)
hparams.add_hparam("num_heads", 1)
return hparams | python | def lstm_attention_base():
"""Base attention params."""
hparams = lstm_seq2seq()
hparams.add_hparam("attention_layer_size", hparams.hidden_size)
hparams.add_hparam("output_attention", True)
hparams.add_hparam("num_heads", 1)
return hparams | [
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22,112 | tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_asr_v1 | def lstm_asr_v1():
"""Basic LSTM Params."""
hparams = lstm_bahdanau_attention()
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.batch_size = 36
hparams.max_input_seq_length = 600000
hparams.max_target_seq_length = 350
hparams.max_length = hparams.max_input_seq_length
hparams.min_length... | python | def lstm_asr_v1():
"""Basic LSTM Params."""
hparams = lstm_bahdanau_attention()
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.batch_size = 36
hparams.max_input_seq_length = 600000
hparams.max_target_seq_length = 350
hparams.max_length = hparams.max_input_seq_length
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22,113 | tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_area_attention_base | def lstm_area_attention_base():
"""Hparams for LSTM with area attention."""
hparams = lstm_luong_attention()
hparams.batch_size = 16384
hparams.num_hidden_layers = 2
hparams.hidden_size = 1024
hparams.num_heads = 4
hparams.dropout = 0.2
hparams.learning_rate = 0.1
hparams.max_area_width = 2
hparams.... | python | def lstm_area_attention_base():
"""Hparams for LSTM with area attention."""
hparams = lstm_luong_attention()
hparams.batch_size = 16384
hparams.num_hidden_layers = 2
hparams.hidden_size = 1024
hparams.num_heads = 4
hparams.dropout = 0.2
hparams.learning_rate = 0.1
hparams.max_area_width = 2
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22,114 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_attack.py | prepare_data | def prepare_data(problem, hparams, params, config):
"""Construct input pipeline."""
input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams, force_repeat=True)
dataset = input_fn(params, config)
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["... | python | def prepare_data(problem, hparams, params, config):
"""Construct input pipeline."""
input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams, force_repeat=True)
dataset = input_fn(params, config)
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["... | [
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22,115 | tensorflow/tensor2tensor | tensor2tensor/data_generators/audio_encoder.py | AudioEncoder.encode | def encode(self, s):
"""Transform a string with a filename into a list of float32.
Args:
s: path to the file with a waveform.
Returns:
samples: list of int16s
"""
# Make sure that the data is a single channel, 16bit, 16kHz wave.
# TODO(chorowski): the directory may not be writable,... | python | def encode(self, s):
"""Transform a string with a filename into a list of float32.
Args:
s: path to the file with a waveform.
Returns:
samples: list of int16s
"""
# Make sure that the data is a single channel, 16bit, 16kHz wave.
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22,116 | tensorflow/tensor2tensor | tensor2tensor/data_generators/audio_encoder.py | AudioEncoder.decode | def decode(self, ids):
"""Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the waveform was saved.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
_, tmp_file_path = te... | python | def decode(self, ids):
"""Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
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22,117 | tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.new_vertex | def new_vertex(self):
"""Creates and returns a new vertex.
Returns:
A new Vertex instance with a unique index.
"""
vertex = Vertex(len(self.vertices))
self.vertices.append(vertex)
return vertex | python | def new_vertex(self):
"""Creates and returns a new vertex.
Returns:
A new Vertex instance with a unique index.
"""
vertex = Vertex(len(self.vertices))
self.vertices.append(vertex)
return vertex | [
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22,118 | tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.get_vertex | def get_vertex(self, key):
"""Returns or Creates a Vertex mapped by key.
Args:
key: A string reference for a vertex. May refer to a new Vertex in which
case it will be created.
Returns:
A the Vertex mapped to by key.
"""
if key in self.vertex_map:
return self.vertex_map[ke... | python | def get_vertex(self, key):
"""Returns or Creates a Vertex mapped by key.
Args:
key: A string reference for a vertex. May refer to a new Vertex in which
case it will be created.
Returns:
A the Vertex mapped to by key.
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22,119 | tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.add_edge | def add_edge(self, source, target):
"""Returns a new edge connecting source and target vertices.
Args:
source: The source Vertex.
target: The target Vertex.
Returns:
A new Edge linking source to target.
"""
edge = Edge(len(self.edges))
self.edges.append(edge)
source.out_e... | python | def add_edge(self, source, target):
"""Returns a new edge connecting source and target vertices.
Args:
source: The source Vertex.
target: The target Vertex.
Returns:
A new Edge linking source to target.
"""
edge = Edge(len(self.edges))
self.edges.append(edge)
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22,120 | tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.to_dict | def to_dict(self):
"""Returns a simplified dictionary representing the Graph.
Returns:
A dictionary that can easily be serialized to JSON.
"""
return {
"node": [v.to_dict() for v in self.vertices],
"edge": [e.to_dict() for e in self.edges]
} | python | def to_dict(self):
"""Returns a simplified dictionary representing the Graph.
Returns:
A dictionary that can easily be serialized to JSON.
"""
return {
"node": [v.to_dict() for v in self.vertices],
"edge": [e.to_dict() for e in self.edges]
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22,121 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | attend | def attend(x, source, hparams, name):
"""Self-attention layer with source as memory antecedent."""
with tf.variable_scope(name):
x = tf.squeeze(x, axis=2)
if len(source.get_shape()) > 3:
source = tf.squeeze(source, axis=2)
source = common_attention.add_timing_signal_1d(source)
y = common_atten... | python | def attend(x, source, hparams, name):
"""Self-attention layer with source as memory antecedent."""
with tf.variable_scope(name):
x = tf.squeeze(x, axis=2)
if len(source.get_shape()) > 3:
source = tf.squeeze(source, axis=2)
source = common_attention.add_timing_signal_1d(source)
y = common_atten... | [
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22,122 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | ae_latent_sample | def ae_latent_sample(latents_dense, inputs, ed, embed, iters, hparams):
"""Sample from the latent space in the autoencoder."""
if hparams.num_decode_blocks < 2 and hparams.sampling_temp == 0.0:
# TODO(lukaszkaiser): beam-search only works in non-blocked mode for now.
tf.logging.info("Running beam-search for... | python | def ae_latent_sample(latents_dense, inputs, ed, embed, iters, hparams):
"""Sample from the latent space in the autoencoder."""
if hparams.num_decode_blocks < 2 and hparams.sampling_temp == 0.0:
# TODO(lukaszkaiser): beam-search only works in non-blocked mode for now.
tf.logging.info("Running beam-search for... | [
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22,123 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | imagetransformer_ae_cifar | def imagetransformer_ae_cifar():
"""Hyperparameters for CIFAR-10 experiments."""
hparams = transformer_ae_small()
hparams.filter_size = 512
hparams.num_compress_steps = 3
hparams.startup_steps = 10000
hparams.is_2d = 0
hparams.learning_rate_warmup_steps = 8000
hparams.learning_rate = 0.2
hparams.hidde... | python | def imagetransformer_ae_cifar():
"""Hyperparameters for CIFAR-10 experiments."""
hparams = transformer_ae_small()
hparams.filter_size = 512
hparams.num_compress_steps = 3
hparams.startup_steps = 10000
hparams.is_2d = 0
hparams.learning_rate_warmup_steps = 8000
hparams.learning_rate = 0.2
hparams.hidde... | [
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22,124 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | imagetransformer_ae_imagenet | def imagetransformer_ae_imagenet():
"""For 64x64 ImageNet. ~56M trainable variables."""
hparams = imagetransformer_ae_cifar()
hparams.max_length = int(64 * 64 * 3)
hparams.img_len = 64
hparams.num_heads = 4 # Heads are expensive on TPUs.
# Reduce architecture from 32x32 CIFAR-10 in order to fit in memory.
... | python | def imagetransformer_ae_imagenet():
"""For 64x64 ImageNet. ~56M trainable variables."""
hparams = imagetransformer_ae_cifar()
hparams.max_length = int(64 * 64 * 3)
hparams.img_len = 64
hparams.num_heads = 4 # Heads are expensive on TPUs.
# Reduce architecture from 32x32 CIFAR-10 in order to fit in memory.
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22,125 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_sketch.py | transformer_sketch | def transformer_sketch():
"""Basic transformer_sketch hparams."""
hparams = transformer.transformer_small()
hparams.num_compress_steps = 4
hparams.batch_size = 32
hparams.clip_grad_norm = 2.
hparams.sampling_method = "random"
return hparams | python | def transformer_sketch():
"""Basic transformer_sketch hparams."""
hparams = transformer.transformer_small()
hparams.num_compress_steps = 4
hparams.batch_size = 32
hparams.clip_grad_norm = 2.
hparams.sampling_method = "random"
return hparams | [
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22,126 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layers | def layers():
"""Get the layers module good for TF 1 and TF 2 work for now."""
global _cached_layers
if _cached_layers is not None:
return _cached_layers
layers_module = tf.layers
try:
from tensorflow.python import tf2 # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
if tf2.enable... | python | def layers():
"""Get the layers module good for TF 1 and TF 2 work for now."""
global _cached_layers
if _cached_layers is not None:
return _cached_layers
layers_module = tf.layers
try:
from tensorflow.python import tf2 # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
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22,127 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | dropout_with_broadcast_dims | def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
"""Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
a list of dimension numbers in which noise_shape should be 1. The random
keep/drop tensor... | python | def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
"""Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
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22,128 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | inverse_exp_decay | def inverse_exp_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay exponentially from 0.01 to 1.0 reached at max_step."""
inv_base = tf.exp(tf.log(min_value) / float(max_step))
if step is None:
step = tf.train.get_global_step()
if step is None:
return 1.0
step = to_float(step)
return inv_b... | python | def inverse_exp_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay exponentially from 0.01 to 1.0 reached at max_step."""
inv_base = tf.exp(tf.log(min_value) / float(max_step))
if step is None:
step = tf.train.get_global_step()
if step is None:
return 1.0
step = to_float(step)
return inv_b... | [
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22,129 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | inverse_lin_decay | def inverse_lin_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay linearly from 0.01 to 1.0 reached at max_step."""
if step is None:
step = tf.train.get_global_step()
if step is None:
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step = to_float(step)
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return progress * (1.... | python | def inverse_lin_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay linearly from 0.01 to 1.0 reached at max_step."""
if step is None:
step = tf.train.get_global_step()
if step is None:
return 1.0
step = to_float(step)
progress = tf.minimum(step / float(max_step), 1.0)
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22,130 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | shakeshake2_py | def shakeshake2_py(x, y, equal=False, individual=False):
"""The shake-shake sum of 2 tensors, python version."""
if equal:
alpha = 0.5
elif individual:
alpha = tf.random_uniform(tf.get_shape(x)[:1])
else:
alpha = tf.random_uniform([])
return alpha * x + (1.0 - alpha) * y | python | def shakeshake2_py(x, y, equal=False, individual=False):
"""The shake-shake sum of 2 tensors, python version."""
if equal:
alpha = 0.5
elif individual:
alpha = tf.random_uniform(tf.get_shape(x)[:1])
else:
alpha = tf.random_uniform([])
return alpha * x + (1.0 - alpha) * y | [
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22,131 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | shakeshake | def shakeshake(xs, equal_grad=False):
"""Multi-argument shake-shake, currently approximated by sums of 2."""
if len(xs) == 1:
return xs[0]
div = (len(xs) + 1) // 2
arg1 = shakeshake(xs[:div], equal_grad=equal_grad)
arg2 = shakeshake(xs[div:], equal_grad=equal_grad)
if equal_grad:
return shakeshake2_... | python | def shakeshake(xs, equal_grad=False):
"""Multi-argument shake-shake, currently approximated by sums of 2."""
if len(xs) == 1:
return xs[0]
div = (len(xs) + 1) // 2
arg1 = shakeshake(xs[:div], equal_grad=equal_grad)
arg2 = shakeshake(xs[div:], equal_grad=equal_grad)
if equal_grad:
return shakeshake2_... | [
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22,132 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | expand_squeeze_to_nd | def expand_squeeze_to_nd(x, n, squeeze_dim=2, expand_dim=-1):
"""Make x n-d with squeeze and expand_dims."""
if len(x.shape) > n:
while len(x.shape) != n:
x = tf.squeeze(x, [squeeze_dim])
else:
while len(x.shape) != n:
x = tf.expand_dims(x, expand_dim)
return x | python | def expand_squeeze_to_nd(x, n, squeeze_dim=2, expand_dim=-1):
"""Make x n-d with squeeze and expand_dims."""
if len(x.shape) > n:
while len(x.shape) != n:
x = tf.squeeze(x, [squeeze_dim])
else:
while len(x.shape) != n:
x = tf.expand_dims(x, expand_dim)
return x | [
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22,133 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | standardize_images | def standardize_images(x):
"""Image standardization on batches and videos."""
with tf.name_scope("standardize_images", values=[x]):
x_shape = shape_list(x)
x = to_float(tf.reshape(x, [-1] + x_shape[-3:]))
x_mean = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
x_variance = tf.reduce_mean(
tf.... | python | def standardize_images(x):
"""Image standardization on batches and videos."""
with tf.name_scope("standardize_images", values=[x]):
x_shape = shape_list(x)
x = to_float(tf.reshape(x, [-1] + x_shape[-3:]))
x_mean = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
x_variance = tf.reduce_mean(
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22,134 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | flatten4d3d | def flatten4d3d(x):
"""Flatten a 4d-tensor into a 3d-tensor by joining width and height."""
xshape = shape_list(x)
result = tf.reshape(x, [xshape[0], xshape[1] * xshape[2], xshape[3]])
return result | python | def flatten4d3d(x):
"""Flatten a 4d-tensor into a 3d-tensor by joining width and height."""
xshape = shape_list(x)
result = tf.reshape(x, [xshape[0], xshape[1] * xshape[2], xshape[3]])
return result | [
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22,135 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | gather | def gather(params, indices, dtype=tf.float32):
"""Version of tf.gather that works faster on tpu."""
if not is_xla_compiled():
return tf.gather(params, indices)
vocab_size = params.get_shape().as_list()[0]
indices_flat = tf.reshape(indices, [-1])
out = tf.matmul(tf.one_hot(indices_flat, vocab_size, dtype=d... | python | def gather(params, indices, dtype=tf.float32):
"""Version of tf.gather that works faster on tpu."""
if not is_xla_compiled():
return tf.gather(params, indices)
vocab_size = params.get_shape().as_list()[0]
indices_flat = tf.reshape(indices, [-1])
out = tf.matmul(tf.one_hot(indices_flat, vocab_size, dtype=d... | [
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22,136 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | cumsum | def cumsum(x, axis=0, exclusive=False):
"""TPU hack for tf.cumsum.
This is equivalent to tf.cumsum and is faster on TPU as of 04/2018 unless
the axis dimension is very large.
Args:
x: a Tensor
axis: an integer
exclusive: a boolean
Returns:
Tensor of the same shape as x.
"""
if not is_xl... | python | def cumsum(x, axis=0, exclusive=False):
"""TPU hack for tf.cumsum.
This is equivalent to tf.cumsum and is faster on TPU as of 04/2018 unless
the axis dimension is very large.
Args:
x: a Tensor
axis: an integer
exclusive: a boolean
Returns:
Tensor of the same shape as x.
"""
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22,137 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | dropout_no_scaling | def dropout_no_scaling(x, keep_prob):
"""Like tf.nn.dropout, but does not scale up. Works on integers also.
Args:
x: a Tensor
keep_prob: a floating point number
Returns:
Tensor of the same shape as x.
"""
if keep_prob == 1.0:
return x
mask = tf.less(tf.random_uniform(tf.shape(x)), keep_pr... | python | def dropout_no_scaling(x, keep_prob):
"""Like tf.nn.dropout, but does not scale up. Works on integers also.
Args:
x: a Tensor
keep_prob: a floating point number
Returns:
Tensor of the same shape as x.
"""
if keep_prob == 1.0:
return x
mask = tf.less(tf.random_uniform(tf.shape(x)), keep_pr... | [
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22,138 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | embedding | def embedding(x,
vocab_size,
dense_size,
name=None,
reuse=None,
multiplier=1.0,
symbol_dropout_rate=0.0,
embedding_var=None,
dtype=tf.float32):
"""Embed x of type int64 into dense vectors, reducing to max 4... | python | def embedding(x,
vocab_size,
dense_size,
name=None,
reuse=None,
multiplier=1.0,
symbol_dropout_rate=0.0,
embedding_var=None,
dtype=tf.float32):
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22,139 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv_stride2_multistep | def conv_stride2_multistep(x, nbr_steps, output_filters, name=None, reuse=None):
"""Use a strided convolution to downsample x by 2, `nbr_steps` times.
We use stride and filter size 2 to avoid the checkerboard problem of deconvs.
As detailed in http://distill.pub/2016/deconv-checkerboard/.
Args:
x: a `Tens... | python | def conv_stride2_multistep(x, nbr_steps, output_filters, name=None, reuse=None):
"""Use a strided convolution to downsample x by 2, `nbr_steps` times.
We use stride and filter size 2 to avoid the checkerboard problem of deconvs.
As detailed in http://distill.pub/2016/deconv-checkerboard/.
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x: a `Tens... | [
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22,140 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv_internal | def conv_internal(conv_fn, inputs, filters, kernel_size, **kwargs):
"""Conditional conv_fn making kernel 1d or 2d depending on inputs shape."""
static_shape = inputs.get_shape()
if not static_shape or len(static_shape) != 4:
raise ValueError("Inputs to conv must have statically known rank 4. "
... | python | def conv_internal(conv_fn, inputs, filters, kernel_size, **kwargs):
"""Conditional conv_fn making kernel 1d or 2d depending on inputs shape."""
static_shape = inputs.get_shape()
if not static_shape or len(static_shape) != 4:
raise ValueError("Inputs to conv must have statically known rank 4. "
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22,141 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | subseparable_conv | def subseparable_conv(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution. If separability == 0 it's a separable_conv."""
def conv_fn(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution, splits into separability-many blocks."""
separability = None
if "separability" i... | python | def subseparable_conv(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution. If separability == 0 it's a separable_conv."""
def conv_fn(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution, splits into separability-many blocks."""
separability = None
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22,142 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layer_norm_vars | def layer_norm_vars(filters):
"""Create Variables for layer norm."""
scale = tf.get_variable(
"layer_norm_scale", [filters], initializer=tf.ones_initializer())
bias = tf.get_variable(
"layer_norm_bias", [filters], initializer=tf.zeros_initializer())
return scale, bias | python | def layer_norm_vars(filters):
"""Create Variables for layer norm."""
scale = tf.get_variable(
"layer_norm_scale", [filters], initializer=tf.ones_initializer())
bias = tf.get_variable(
"layer_norm_bias", [filters], initializer=tf.zeros_initializer())
return scale, bias | [
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22,143 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layer_norm_compute | def layer_norm_compute(x, epsilon, scale, bias, layer_collection=None):
"""Layer norm raw computation."""
# Save these before they get converted to tensors by the casting below
params = (scale, bias)
epsilon, scale, bias = [cast_like(t, x) for t in [epsilon, scale, bias]]
mean = tf.reduce_mean(x, axis=[-1],... | python | def layer_norm_compute(x, epsilon, scale, bias, layer_collection=None):
"""Layer norm raw computation."""
# Save these before they get converted to tensors by the casting below
params = (scale, bias)
epsilon, scale, bias = [cast_like(t, x) for t in [epsilon, scale, bias]]
mean = tf.reduce_mean(x, axis=[-1],... | [
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22,144 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layer_norm | def layer_norm(x,
filters=None,
epsilon=1e-6,
name=None,
reuse=None,
layer_collection=None):
"""Layer normalize the tensor x, averaging over the last dimension."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(... | python | def layer_norm(x,
filters=None,
epsilon=1e-6,
name=None,
reuse=None,
layer_collection=None):
"""Layer normalize the tensor x, averaging over the last dimension."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(... | [
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22,145 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | noam_norm | def noam_norm(x, epsilon=1.0, name=None):
"""One version of layer normalization."""
with tf.name_scope(name, default_name="noam_norm", values=[x]):
shape = x.get_shape()
ndims = len(shape)
return (tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(
to_float(shape[-1]))) | python | def noam_norm(x, epsilon=1.0, name=None):
"""One version of layer normalization."""
with tf.name_scope(name, default_name="noam_norm", values=[x]):
shape = x.get_shape()
ndims = len(shape)
return (tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(
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22,146 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | l2_norm | def l2_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None):
"""Layer normalization with l2 norm."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(name, default_name="l2_norm", values=[x], reuse=reuse):
scale = tf.get_variable(
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"""Layer normalization with l2 norm."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(name, default_name="l2_norm", values=[x], reuse=reuse):
scale = tf.get_variable(
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22,147 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | apply_spectral_norm | def apply_spectral_norm(x):
"""Normalizes x using the spectral norm.
The implementation follows Algorithm 1 of
https://arxiv.org/abs/1802.05957. If x is not a 2-D Tensor, then it is
reshaped such that the number of channels (last-dimension) is the same.
Args:
x: Tensor with the last dimension equal to t... | python | def apply_spectral_norm(x):
"""Normalizes x using the spectral norm.
The implementation follows Algorithm 1 of
https://arxiv.org/abs/1802.05957. If x is not a 2-D Tensor, then it is
reshaped such that the number of channels (last-dimension) is the same.
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x: Tensor with the last dimension equal to t... | [
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22,148 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | apply_norm | def apply_norm(x, norm_type, depth, epsilon, layer_collection=None):
"""Apply Normalization."""
if layer_collection is not None:
assert norm_type == "layer"
if norm_type == "layer":
return layer_norm(
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if norm_type == "group":
... | python | def apply_norm(x, norm_type, depth, epsilon, layer_collection=None):
"""Apply Normalization."""
if layer_collection is not None:
assert norm_type == "layer"
if norm_type == "layer":
return layer_norm(
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22,149 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | zero_add | def zero_add(previous_value, x, name=None, reuse=None):
"""Resnet connection with zero initialization.
Another type of resnet connection which returns previous_value + gamma * x.
gamma is a trainable scalar and initialized with zero. It is useful when a
module is plugged into a trained model and we want to mak... | python | def zero_add(previous_value, x, name=None, reuse=None):
"""Resnet connection with zero initialization.
Another type of resnet connection which returns previous_value + gamma * x.
gamma is a trainable scalar and initialized with zero. It is useful when a
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22,150 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layer_prepostprocess | def layer_prepostprocess(previous_value,
x,
sequence,
dropout_rate,
norm_type,
depth,
epsilon,
default_name,
name=None,
... | python | def layer_prepostprocess(previous_value,
x,
sequence,
dropout_rate,
norm_type,
depth,
epsilon,
default_name,
name=None,
... | [
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22,151 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layer_preprocess | def layer_preprocess(layer_input, hparams, layer_collection=None):
"""Apply layer preprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
that may be used are:
layer_preprocess_sequence
layer_prepostprocess_dropout
norm_type
... | python | def layer_preprocess(layer_input, hparams, layer_collection=None):
"""Apply layer preprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
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22,152 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layer_postprocess | def layer_postprocess(layer_input, layer_output, hparams):
"""Apply layer postprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
that may be used are:
layer_postprocess_sequence
layer_prepostprocess_dropout
norm_type
hi... | python | def layer_postprocess(layer_input, layer_output, hparams):
"""Apply layer postprocessing.
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layer_postprocess_sequence
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22,153 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv_block_internal | def conv_block_internal(conv_fn,
inputs,
filters,
dilation_rates_and_kernel_sizes,
first_relu=True,
use_elu=False,
separabilities=None,
**kwargs):
"""... | python | def conv_block_internal(conv_fn,
inputs,
filters,
dilation_rates_and_kernel_sizes,
first_relu=True,
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separabilities=None,
**kwargs):
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22,154 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv_block | def conv_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 2d convolutions."""
return conv_block_internal(conv, inputs, filters,
dilation_rates_and_kernel_sizes, **kwargs) | python | def conv_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 2d convolutions."""
return conv_block_internal(conv, inputs, filters,
dilation_rates_and_kernel_sizes, **kwargs) | [
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22,155 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv1d_block | def conv1d_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 1d convolutions."""
return conv_block_internal(conv1d, inputs, filters,
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"""A block of standard 1d convolutions."""
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22,156 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv_block_downsample | def conv_block_downsample(x,
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reuse=None):
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22,157 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | get_timing_signal | def get_timing_signal(length,
min_timescale=1,
max_timescale=1e4,
num_timescales=16):
"""Create Tensor of sinusoids of different frequencies.
Args:
length: Length of the Tensor to create, i.e. Number of steps.
min_timescale: a float
max_... | python | def get_timing_signal(length,
min_timescale=1,
max_timescale=1e4,
num_timescales=16):
"""Create Tensor of sinusoids of different frequencies.
Args:
length: Length of the Tensor to create, i.e. Number of steps.
min_timescale: a float
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22,158 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | mask_from_embedding | def mask_from_embedding(emb):
"""Input embeddings -> padding mask.
We have hacked symbol_modality to return all-zero embeddings for padding.
Returns a mask with 0.0 in the padding positions and 1.0 elsewhere.
Args:
emb: a Tensor with shape [batch, width, height, depth].
Returns:
a 0.0/1.0 Tensor wit... | python | def mask_from_embedding(emb):
"""Input embeddings -> padding mask.
We have hacked symbol_modality to return all-zero embeddings for padding.
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22,159 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | length_from_embedding | def length_from_embedding(emb):
"""Compute the length of each sequence in the batch.
Args:
emb: a sequence embedding Tensor with shape [batch, max_time, 1, depth].
Returns:
a Tensor with shape [batch].
"""
return tf.cast(tf.reduce_sum(mask_from_embedding(emb), [1, 2, 3]), tf.int32) | python | def length_from_embedding(emb):
"""Compute the length of each sequence in the batch.
Args:
emb: a sequence embedding Tensor with shape [batch, max_time, 1, depth].
Returns:
a Tensor with shape [batch].
"""
return tf.cast(tf.reduce_sum(mask_from_embedding(emb), [1, 2, 3]), tf.int32) | [
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22,160 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | maybe_zero_out_padding | def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask):
"""If necessary, zero out inputs to a conv for padding positions.
Args:
inputs: a Tensor with shape [batch, length, ...]
kernel_size: an integer or pair of integers
nonpadding_mask: a Tensor with shape [batch, length]
Returns:
Ten... | python | def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask):
"""If necessary, zero out inputs to a conv for padding positions.
Args:
inputs: a Tensor with shape [batch, length, ...]
kernel_size: an integer or pair of integers
nonpadding_mask: a Tensor with shape [batch, length]
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22,161 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | dense_dropconnect | def dense_dropconnect(inputs,
output_size,
dropconnect_dropout=0.0,
name="dense_dropconnect",
**kwargs):
"""Dense layer with dropconnect."""
if dropconnect_dropout != 0.0:
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output_size,
dropconnect_dropout=0.0,
name="dense_dropconnect",
**kwargs):
"""Dense layer with dropconnect."""
if dropconnect_dropout != 0.0:
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22,162 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv_gru | def conv_gru(x,
kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional GRU in 1 dimension."""
# Let's make a shorthand for conv call first.
def do_conv(args, name, bias_start, padding):
... | python | def conv_gru(x,
kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional GRU in 1 dimension."""
# Let's make a shorthand for conv call first.
def do_conv(args, name, bias_start, padding):
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22,163 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | gru_feedfwd | def gru_feedfwd(a_t, h_prev, filters, name=None):
"""position-wise Feed-fwd GRU gates following the MPNN.
Args:
a_t: Tensor of shape [batch, length, depth] of current input
h_prev: Tensor of shape [batch, length, depth] of prev input
filters: an integer specifying number of dimensions of the filters
... | python | def gru_feedfwd(a_t, h_prev, filters, name=None):
"""position-wise Feed-fwd GRU gates following the MPNN.
Args:
a_t: Tensor of shape [batch, length, depth] of current input
h_prev: Tensor of shape [batch, length, depth] of prev input
filters: an integer specifying number of dimensions of the filters
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22,164 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | conv_lstm | def conv_lstm(x,
kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional LSTM in 1 dimension."""
with tf.variable_scope(
name, default_name="conv_lstm", values=[x], reuse=reuse):
... | python | def conv_lstm(x,
kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional LSTM in 1 dimension."""
with tf.variable_scope(
name, default_name="conv_lstm", values=[x], reuse=reuse):
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22,165 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | pad_to_same_length | def pad_to_same_length(x, y, final_length_divisible_by=1, axis=1):
"""Pad tensors x and y on axis 1 so that they have the same length."""
if axis not in [1, 2]:
raise ValueError("Only axis=1 and axis=2 supported for now.")
with tf.name_scope("pad_to_same_length", values=[x, y]):
x_length = shape_list(x)[a... | python | def pad_to_same_length(x, y, final_length_divisible_by=1, axis=1):
"""Pad tensors x and y on axis 1 so that they have the same length."""
if axis not in [1, 2]:
raise ValueError("Only axis=1 and axis=2 supported for now.")
with tf.name_scope("pad_to_same_length", values=[x, y]):
x_length = shape_list(x)[a... | [
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22,166 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | pad_with_zeros | def pad_with_zeros(logits, labels):
"""Pad labels on the length dimension to match logits length."""
with tf.name_scope("pad_with_zeros", values=[logits, labels]):
logits, labels = pad_to_same_length(logits, labels)
if len(labels.shape) == 3: # 2-d labels.
logits, labels = pad_to_same_length(logits, ... | python | def pad_with_zeros(logits, labels):
"""Pad labels on the length dimension to match logits length."""
with tf.name_scope("pad_with_zeros", values=[logits, labels]):
logits, labels = pad_to_same_length(logits, labels)
if len(labels.shape) == 3: # 2-d labels.
logits, labels = pad_to_same_length(logits, ... | [
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22,167 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | check_nonnegative | def check_nonnegative(value):
"""Check that the value is nonnegative."""
if isinstance(value, tf.Tensor):
with tf.control_dependencies([tf.assert_greater_equal(value, 0)]):
value = tf.identity(value)
elif value < 0:
raise ValueError("Value must be non-negative.")
return value | python | def check_nonnegative(value):
"""Check that the value is nonnegative."""
if isinstance(value, tf.Tensor):
with tf.control_dependencies([tf.assert_greater_equal(value, 0)]):
value = tf.identity(value)
elif value < 0:
raise ValueError("Value must be non-negative.")
return value | [
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22,168 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | weights_multi_problem_all | def weights_multi_problem_all(labels, taskid=-1):
"""Assign weight 1.0 to only examples from the given task."""
taskid = check_nonnegative(taskid)
weights = to_float(tf.not_equal(labels, 0))
past_taskid = tf.cumsum(to_float(tf.equal(labels, taskid)), axis=1)
# Additionally zero out the task id location
past... | python | def weights_multi_problem_all(labels, taskid=-1):
"""Assign weight 1.0 to only examples from the given task."""
taskid = check_nonnegative(taskid)
weights = to_float(tf.not_equal(labels, 0))
past_taskid = tf.cumsum(to_float(tf.equal(labels, taskid)), axis=1)
# Additionally zero out the task id location
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22,169 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | weights_multi_problem_input | def weights_multi_problem_input(labels, taskid=-1):
"""Assign weight 1.0 to only the inputs for the given task."""
taskid = check_nonnegative(taskid)
weights_all_tokens = weights_multi_problem_all(labels, taskid)
weights_target = weights_multi_problem(labels, taskid)
return weights_all_tokens - weights_target | python | def weights_multi_problem_input(labels, taskid=-1):
"""Assign weight 1.0 to only the inputs for the given task."""
taskid = check_nonnegative(taskid)
weights_all_tokens = weights_multi_problem_all(labels, taskid)
weights_target = weights_multi_problem(labels, taskid)
return weights_all_tokens - weights_target | [
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22,170 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | weights_concatenated | def weights_concatenated(labels):
"""Assign weight 1.0 to the "target" part of the concatenated labels.
The labels look like:
source English I love you . ID1 target French Je t'aime . ID1 source
English the cat ID1 target French le chat ID1 source English ...
We want to assign weight 1.0 to all words ... | python | def weights_concatenated(labels):
"""Assign weight 1.0 to the "target" part of the concatenated labels.
The labels look like:
source English I love you . ID1 target French Je t'aime . ID1 source
English the cat ID1 target French le chat ID1 source English ...
We want to assign weight 1.0 to all words ... | [
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22,171 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | dml_loss | def dml_loss(pred, labels, weights_fn=_weights_one_third, reduce_sum=True):
"""Discretized mixture of logistics loss.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard deviations (on... | python | def dml_loss(pred, labels, weights_fn=_weights_one_third, reduce_sum=True):
"""Discretized mixture of logistics loss.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
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22,172 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | split_to_discretized_mix_logistic_params | def split_to_discretized_mix_logistic_params(inputs):
"""Splits input tensor into parameters of discretized mixture logistic.
Args:
inputs: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard devi... | python | def split_to_discretized_mix_logistic_params(inputs):
"""Splits input tensor into parameters of discretized mixture logistic.
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inputs: A [batch, height, width, num_mixtures*10] tensor of floats
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22,173 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | discretized_mix_logistic_loss | def discretized_mix_logistic_loss(pred, labels):
"""Computes negative log probability for the discretized mixture of logistics.
The distribution of a whole pixel is a mixture of 3-dimensional discretized
logistic distributions. The 3-D discretized logistic factorizes as 3 1-D
discretized logistic distributions... | python | def discretized_mix_logistic_loss(pred, labels):
"""Computes negative log probability for the discretized mixture of logistics.
The distribution of a whole pixel is a mixture of 3-dimensional discretized
logistic distributions. The 3-D discretized logistic factorizes as 3 1-D
discretized logistic distributions... | [
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The distribution of a whole pixel is a mixture of 3-dimensional discretized
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discretized logistic distributions, one for each channel. It defines
```none
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22,174 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | sample_from_discretized_mix_logistic | def sample_from_discretized_mix_logistic(pred, seed=None):
"""Sampling from a discretized mixture of logistics.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard deviations (one per ... | python | def sample_from_discretized_mix_logistic(pred, seed=None):
"""Sampling from a discretized mixture of logistics.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
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22,175 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | smoothing_cross_entropy | def smoothing_cross_entropy(logits,
labels,
vocab_size,
confidence,
gaussian=False):
"""Cross entropy with label smoothing to limit over-confidence.
Args:
logits: Tensor of shape [batch_size, ?, ?, ?... | python | def smoothing_cross_entropy(logits,
labels,
vocab_size,
confidence,
gaussian=False):
"""Cross entropy with label smoothing to limit over-confidence.
Args:
logits: Tensor of shape [batch_size, ?, ?, ?... | [
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22,176 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | global_pool_1d | def global_pool_1d(inputs, pooling_type="MAX", mask=None):
"""Pool elements across the last dimension.
Useful to convert a list of vectors into a single vector so as
to get a representation of a set.
Args:
inputs: A tensor of shape [batch_size, sequence_length, input_dims]
containing the sequences o... | python | def global_pool_1d(inputs, pooling_type="MAX", mask=None):
"""Pool elements across the last dimension.
Useful to convert a list of vectors into a single vector so as
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Args:
inputs: A tensor of shape [batch_size, sequence_length, input_dims]
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22,177 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | running_global_pool_1d | def running_global_pool_1d(inputs, pooling_type="MAX"):
"""Same global pool, but only for the elements up to the current element.
Useful for outputs where the state of future elements is not known.
Takes no mask as all elements up to the current element are assumed to exist.
Currently only supports maximum. Eq... | python | def running_global_pool_1d(inputs, pooling_type="MAX"):
"""Same global pool, but only for the elements up to the current element.
Useful for outputs where the state of future elements is not known.
Takes no mask as all elements up to the current element are assumed to exist.
Currently only supports maximum. Eq... | [
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22,178 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | gated_linear_unit_layer | def gated_linear_unit_layer(x, name=None):
"""Gated linear unit layer.
Paper: Language Modeling with Gated Convolutional Networks.
Link: https://arxiv.org/abs/1612.08083
x = Wx * sigmoid(W'x).
Args:
x: A tensor
name: A string
Returns:
A tensor of the same shape as x.
"""
with tf.variable_... | python | def gated_linear_unit_layer(x, name=None):
"""Gated linear unit layer.
Paper: Language Modeling with Gated Convolutional Networks.
Link: https://arxiv.org/abs/1612.08083
x = Wx * sigmoid(W'x).
Args:
x: A tensor
name: A string
Returns:
A tensor of the same shape as x.
"""
with tf.variable_... | [
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22,179 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | linear_set_layer | def linear_set_layer(layer_size,
inputs,
context=None,
activation_fn=tf.nn.relu,
dropout=0.0,
name=None):
"""Basic layer type for doing funky things with sets.
Applies a linear transformation to each element in... | python | def linear_set_layer(layer_size,
inputs,
context=None,
activation_fn=tf.nn.relu,
dropout=0.0,
name=None):
"""Basic layer type for doing funky things with sets.
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22,180 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | fn_device_dependency_dict | def fn_device_dependency_dict():
"""State container for fn_device_dependency."""
default_graph = tf.get_default_graph()
if not hasattr(default_graph, "dependency_dict"):
default_graph.dependency_dict = collections.defaultdict(list)
return default_graph.dependency_dict | python | def fn_device_dependency_dict():
"""State container for fn_device_dependency."""
default_graph = tf.get_default_graph()
if not hasattr(default_graph, "dependency_dict"):
default_graph.dependency_dict = collections.defaultdict(list)
return default_graph.dependency_dict | [
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22,181 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | fn_device_dependency | def fn_device_dependency(name, device=""):
"""Add control deps for name and device."""
key = name + "_" + device
outs = []
def body():
with tf.control_dependencies(fn_device_dependency_dict()[key]):
yield outs
assert outs
deps = outs
if isinstance(outs[0], (list, tuple)):
a... | python | def fn_device_dependency(name, device=""):
"""Add control deps for name and device."""
key = name + "_" + device
outs = []
def body():
with tf.control_dependencies(fn_device_dependency_dict()[key]):
yield outs
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22,182 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | underlying_variable_ref | def underlying_variable_ref(t):
"""Find the underlying variable ref.
Traverses through Identity, ReadVariableOp, and Enter ops.
Stops when op type has Variable or VarHandle in name.
Args:
t: a Tensor
Returns:
a Tensor that is a variable ref, or None on error.
"""
while t.op.type in ["Identity",... | python | def underlying_variable_ref(t):
"""Find the underlying variable ref.
Traverses through Identity, ReadVariableOp, and Enter ops.
Stops when op type has Variable or VarHandle in name.
Args:
t: a Tensor
Returns:
a Tensor that is a variable ref, or None on error.
"""
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Traverses through Identity, ReadVariableOp, and Enter ops.
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Returns:
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22,183 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | underlying_variable | def underlying_variable(t):
"""Find the underlying tf.Variable object.
Args:
t: a Tensor
Returns:
tf.Variable.
"""
t = underlying_variable_ref(t)
assert t is not None
# make sure that the graph has a variable index and that it is up-to-date
if not hasattr(tf.get_default_graph(), "var_index"):
... | python | def underlying_variable(t):
"""Find the underlying tf.Variable object.
Args:
t: a Tensor
Returns:
tf.Variable.
"""
t = underlying_variable_ref(t)
assert t is not None
# make sure that the graph has a variable index and that it is up-to-date
if not hasattr(tf.get_default_graph(), "var_index"):
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22,184 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | approximate_split | def approximate_split(x, num_splits, axis=0):
"""Split approximately equally into num_splits parts.
Args:
x: a Tensor
num_splits: an integer
axis: an integer.
Returns:
a list of num_splits Tensors.
"""
size = shape_list(x)[axis]
size_splits = [tf.div(size + i, num_splits) for i in range(nu... | python | def approximate_split(x, num_splits, axis=0):
"""Split approximately equally into num_splits parts.
Args:
x: a Tensor
num_splits: an integer
axis: an integer.
Returns:
a list of num_splits Tensors.
"""
size = shape_list(x)[axis]
size_splits = [tf.div(size + i, num_splits) for i in range(nu... | [
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22,185 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | smoothing_cross_entropy_factored_grad | def smoothing_cross_entropy_factored_grad(op, dy):
"""Gradient function for smoothing_cross_entropy_factored."""
a = op.inputs[0]
b = op.inputs[1]
labels = op.inputs[2]
confidence = op.inputs[3]
num_splits = 16
vocab_size = shape_list(b)[0]
labels = approximate_split(labels, num_splits)
a = approximat... | python | def smoothing_cross_entropy_factored_grad(op, dy):
"""Gradient function for smoothing_cross_entropy_factored."""
a = op.inputs[0]
b = op.inputs[1]
labels = op.inputs[2]
confidence = op.inputs[3]
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vocab_size = shape_list(b)[0]
labels = approximate_split(labels, num_splits)
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22,186 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | fn_with_custom_grad | def fn_with_custom_grad(grad_fn, use_global_vars=False):
"""Decorator to create a subgraph with a custom gradient function.
The subgraph created by the decorated function is NOT put in a Defun and so
does not suffer from the limitations of the Defun (all subgraph ops on the
same device, no summaries).
Args:... | python | def fn_with_custom_grad(grad_fn, use_global_vars=False):
"""Decorator to create a subgraph with a custom gradient function.
The subgraph created by the decorated function is NOT put in a Defun and so
does not suffer from the limitations of the Defun (all subgraph ops on the
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22,187 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | _fn_with_custom_grad | def _fn_with_custom_grad(fn, inputs, grad_fn, use_global_vars=False):
"""Create a subgraph with a custom gradient.
Args:
fn: function that takes inputs as arguments and produces 1 or more Tensors.
inputs: list<Tensor>, will be passed as fn(*inputs).
grad_fn: function with signature
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"""Create a subgraph with a custom gradient.
Args:
fn: function that takes inputs as arguments and produces 1 or more Tensors.
inputs: list<Tensor>, will be passed as fn(*inputs).
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22,188 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | shape_list | def shape_list(x):
"""Return list of dims, statically where possible."""
x = tf.convert_to_tensor(x)
# If unknown rank, return dynamic shape
if x.get_shape().dims is None:
return tf.shape(x)
static = x.get_shape().as_list()
shape = tf.shape(x)
ret = []
for i, dim in enumerate(static):
if dim ... | python | def shape_list(x):
"""Return list of dims, statically where possible."""
x = tf.convert_to_tensor(x)
# If unknown rank, return dynamic shape
if x.get_shape().dims is None:
return tf.shape(x)
static = x.get_shape().as_list()
shape = tf.shape(x)
ret = []
for i, dim in enumerate(static):
if dim ... | [
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22,189 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | ones_matrix_band_part | def ones_matrix_band_part(rows, cols, num_lower, num_upper, out_shape=None):
"""Matrix band part of ones.
Args:
rows: int determining number of rows in output
cols: int
num_lower: int, maximum distance backward. Negative values indicate
unlimited.
num_upper: int, maximum distance forward. Neg... | python | def ones_matrix_band_part(rows, cols, num_lower, num_upper, out_shape=None):
"""Matrix band part of ones.
Args:
rows: int determining number of rows in output
cols: int
num_lower: int, maximum distance backward. Negative values indicate
unlimited.
num_upper: int, maximum distance forward. Neg... | [
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num_upper: int, maximum distance forward. Negative values indicate
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22,190 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | reshape_like_all_dims | def reshape_like_all_dims(a, b):
"""Reshapes a to match the shape of b."""
ret = tf.reshape(a, tf.shape(b))
if not tf.executing_eagerly():
ret.set_shape(b.get_shape())
return ret | python | def reshape_like_all_dims(a, b):
"""Reshapes a to match the shape of b."""
ret = tf.reshape(a, tf.shape(b))
if not tf.executing_eagerly():
ret.set_shape(b.get_shape())
return ret | [
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22,191 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | recompute_grad | def recompute_grad(fn):
"""Decorator that recomputes the function on the backwards pass.
Args:
fn: a function that takes Tensors (all as positional arguments) and returns
a tuple of Tensors.
Returns:
A wrapped fn that is identical to fn when called, but its activations will
be discarded and re... | python | def recompute_grad(fn):
"""Decorator that recomputes the function on the backwards pass.
Args:
fn: a function that takes Tensors (all as positional arguments) and returns
a tuple of Tensors.
Returns:
A wrapped fn that is identical to fn when called, but its activations will
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Args:
fn: a function that takes Tensors (all as positional arguments) and returns
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Returns:
A wrapped fn that is identical to fn when called, but its activations will
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22,192 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | _recompute_grad | def _recompute_grad(fn, args):
"""See recompute_grad."""
cached_vs = []
cached_arg_scope = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Recompute outputs for gradient computation."""
del outputs
variables = [underlying_variable_ref(v) for v in variables]
# Recompute outputs
... | python | def _recompute_grad(fn, args):
"""See recompute_grad."""
cached_vs = []
cached_arg_scope = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Recompute outputs for gradient computation."""
del outputs
variables = [underlying_variable_ref(v) for v in variables]
# Recompute outputs
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22,193 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | dense | def dense(x, units, **kwargs):
"""Identical to layers.dense."""
layer_collection = kwargs.pop("layer_collection", None)
activations = layers().Dense(units, **kwargs)(x)
if layer_collection:
# We need to find the layer parameters using scope name for the layer, so
# check that the layer is named. Otherwi... | python | def dense(x, units, **kwargs):
"""Identical to layers.dense."""
layer_collection = kwargs.pop("layer_collection", None)
activations = layers().Dense(units, **kwargs)(x)
if layer_collection:
# We need to find the layer parameters using scope name for the layer, so
# check that the layer is named. Otherwi... | [
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22,194 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | batch_dense | def batch_dense(inputs,
units,
activation=None,
kernel_initializer=None,
reuse=None,
name=None):
"""Multiply a batch of input matrices by a batch of parameter matrices.
Each input matrix is multiplied by the corresponding parameter mat... | python | def batch_dense(inputs,
units,
activation=None,
kernel_initializer=None,
reuse=None,
name=None):
"""Multiply a batch of input matrices by a batch of parameter matrices.
Each input matrix is multiplied by the corresponding parameter mat... | [
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Each input matrix is multiplied by the corresponding parameter matrix.
This is useful in a mixture-of-experts where the batch represents different
experts with different inputs.
Args:
inputs: a Tensor with shape [batch, length, input_un... | [
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22,195 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | mix | def mix(x1,
x2,
steps,
is_training,
min_prob=0.0,
max_prob=1.0,
mode="lin",
simple=False,
broadcast_last=False):
"""Mix starting with x2, mixing mixing, going towards x1."""
with tf.name_scope("mix"):
if not is_training:
if max_prob >= 1.0:
... | python | def mix(x1,
x2,
steps,
is_training,
min_prob=0.0,
max_prob=1.0,
mode="lin",
simple=False,
broadcast_last=False):
"""Mix starting with x2, mixing mixing, going towards x1."""
with tf.name_scope("mix"):
if not is_training:
if max_prob >= 1.0:
... | [
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22,196 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | gelu | def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
x with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.04471... | python | def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
x with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.04471... | [
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Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
x with the GELU activation applied. | [
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22,197 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | argmax_with_score | def argmax_with_score(logits, axis=None):
"""Argmax along with the value."""
axis = axis or len(logits.get_shape()) - 1
predictions = tf.argmax(logits, axis=axis)
logits_shape = shape_list(logits)
prefix_shape, vocab_size = logits_shape[:-1], logits_shape[-1]
prefix_size = 1
for d in prefix_shape:
pr... | python | def argmax_with_score(logits, axis=None):
"""Argmax along with the value."""
axis = axis or len(logits.get_shape()) - 1
predictions = tf.argmax(logits, axis=axis)
logits_shape = shape_list(logits)
prefix_shape, vocab_size = logits_shape[:-1], logits_shape[-1]
prefix_size = 1
for d in prefix_shape:
pr... | [
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22,198 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | top_kth_iterative | def top_kth_iterative(x, k):
"""Compute the k-th top element of x on the last axis iteratively.
This assumes values in x are non-negative, rescale if needed.
It is often faster than tf.nn.top_k for small k, especially if k < 30.
Note: this does not support back-propagation, it stops gradients!
Args:
x: ... | python | def top_kth_iterative(x, k):
"""Compute the k-th top element of x on the last axis iteratively.
This assumes values in x are non-negative, rescale if needed.
It is often faster than tf.nn.top_k for small k, especially if k < 30.
Note: this does not support back-propagation, it stops gradients!
Args:
x: ... | [
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22,199 | tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | top_1_tpu | def top_1_tpu(inputs):
"""find max and argmax over the last dimension.
Works well on TPU
Args:
inputs: A tensor with shape [..., depth]
Returns:
values: a Tensor with shape [...]
indices: a Tensor with shape [...]
"""
inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
mask = tf.to_i... | python | def top_1_tpu(inputs):
"""find max and argmax over the last dimension.
Works well on TPU
Args:
inputs: A tensor with shape [..., depth]
Returns:
values: a Tensor with shape [...]
indices: a Tensor with shape [...]
"""
inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
mask = tf.to_i... | [
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indices: a Tensor with shape [...] | [
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