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tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | TransformerMemory.read | def read(self, x):
"""Read from the memory.
An external component can use the results via a simple MLP,
e.g., fn(x W_x + retrieved_mem W_m).
Args:
x: a tensor in the shape of [batch_size, length, depth].
Returns:
access_logits: the logits for accessing the memory in shape of
... | python | def read(self, x):
"""Read from the memory.
An external component can use the results via a simple MLP,
e.g., fn(x W_x + retrieved_mem W_m).
Args:
x: a tensor in the shape of [batch_size, length, depth].
Returns:
access_logits: the logits for accessing the memory in shape of
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tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | TransformerMemory.write | def write(self, x, access_logits):
"""Write to the memory based on a combination of similarity and least used.
Based on arXiv:1607.00036v2 [cs.LG].
Args:
x: a tensor in the shape of [batch_size, length, depth].
access_logits: the logits for accessing the memory.
Returns:
the update o... | python | def write(self, x, access_logits):
"""Write to the memory based on a combination of similarity and least used.
Based on arXiv:1607.00036v2 [cs.LG].
Args:
x: a tensor in the shape of [batch_size, length, depth].
access_logits: the logits for accessing the memory.
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tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | TransformerMemory.reset | def reset(self, entries_to_reset):
"""Reset the entries in the memory.
Args:
entries_to_reset: a 1D tensor.
Returns:
the reset op.
"""
num_updates = tf.size(entries_to_reset)
update_vals = tf.scatter_update(
self.mem_vals, entries_to_reset,
tf.tile(tf.expand_dims(
... | python | def reset(self, entries_to_reset):
"""Reset the entries in the memory.
Args:
entries_to_reset: a 1D tensor.
Returns:
the reset op.
"""
num_updates = tf.size(entries_to_reset)
update_vals = tf.scatter_update(
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tf.tile(tf.expand_dims(
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tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | TransformerMemory.pre_attention | def pre_attention(self, segment_number, query_antecedent,
memory_antecedent, bias):
"""Called prior to self-attention, to incorporate memory items.
Args:
segment_number: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
... | python | def pre_attention(self, segment_number, query_antecedent,
memory_antecedent, bias):
"""Called prior to self-attention, to incorporate memory items.
Args:
segment_number: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
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segment_number: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: must be None. Attention normally allows this to be a
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tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | TransformerMemory.post_attention | def post_attention(self, token, x):
"""Called after self-attention. The memory can be updated here.
Args:
token: Data returned by pre_attention, which can be used to carry over
state related to the current memory operation.
x: a Tensor of data after self-attention and feed-forward
Retur... | python | def post_attention(self, token, x):
"""Called after self-attention. The memory can be updated here.
Args:
token: Data returned by pre_attention, which can be used to carry over
state related to the current memory operation.
x: a Tensor of data after self-attention and feed-forward
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... | Called after self-attention. The memory can be updated here.
Args:
token: Data returned by pre_attention, which can be used to carry over
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x: a Tensor of data after self-attention and feed-forward
Returns:
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tensorflow/tensor2tensor | tensor2tensor/rl/ppo_learner.py | _define_train | def _define_train(
train_env,
ppo_hparams,
eval_env_fn=None,
sampling_temp=1.0,
**collect_kwargs
):
"""Define the training setup."""
memory, collect_summary, train_initialization = (
_define_collect(
train_env,
ppo_hparams,
"ppo_train",
eval_phase=Fa... | python | def _define_train(
train_env,
ppo_hparams,
eval_env_fn=None,
sampling_temp=1.0,
**collect_kwargs
):
"""Define the training setup."""
memory, collect_summary, train_initialization = (
_define_collect(
train_env,
ppo_hparams,
"ppo_train",
eval_phase=Fa... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/ppo_learner.py | _run_train | def _run_train(ppo_hparams,
event_dir,
model_dir,
restarter,
train_summary_op,
eval_summary_op,
initializers,
report_fn=None,
model_save_fn=None):
"""Train."""
summary_writer = tf.summary.FileWrit... | python | def _run_train(ppo_hparams,
event_dir,
model_dir,
restarter,
train_summary_op,
eval_summary_op,
initializers,
report_fn=None,
model_save_fn=None):
"""Train."""
summary_writer = tf.summary.FileWrit... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/ppo_learner.py | _rollout_metadata | def _rollout_metadata(batch_env):
"""Metadata for rollouts."""
batch_env_shape = batch_env.observ.get_shape().as_list()
batch_size = [batch_env_shape[0]]
shapes_types_names = [
# TODO(piotrmilos): possibly retrieve the observation type for batch_env
(batch_size + batch_env_shape[1:], batch_env.obser... | python | def _rollout_metadata(batch_env):
"""Metadata for rollouts."""
batch_env_shape = batch_env.observ.get_shape().as_list()
batch_size = [batch_env_shape[0]]
shapes_types_names = [
# TODO(piotrmilos): possibly retrieve the observation type for batch_env
(batch_size + batch_env_shape[1:], batch_env.obser... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/ppo_learner.py | _define_collect | def _define_collect(batch_env, ppo_hparams, scope, frame_stack_size, eval_phase,
sampling_temp, force_beginning_resets):
"""Collect trajectories.
Args:
batch_env: Batch environment.
ppo_hparams: PPO hparams, defined in tensor2tensor.models.research.rl.
scope: var scope.
frame_st... | python | def _define_collect(batch_env, ppo_hparams, scope, frame_stack_size, eval_phase,
sampling_temp, force_beginning_resets):
"""Collect trajectories.
Args:
batch_env: Batch environment.
ppo_hparams: PPO hparams, defined in tensor2tensor.models.research.rl.
scope: var scope.
frame_st... | [
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Args:
batch_env: Batch environment.
ppo_hparams: PPO hparams, defined in tensor2tensor.models.research.rl.
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tensorflow/tensor2tensor | tensor2tensor/models/vanilla_gan.py | deconv2d | def deconv2d(
input_, output_shape, k_h, k_w, d_h, d_w, stddev=0.02, name="deconv2d"):
"""Deconvolution layer."""
with tf.variable_scope(name):
w = tf.get_variable(
"w", [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
deconv ... | python | def deconv2d(
input_, output_shape, k_h, k_w, d_h, d_w, stddev=0.02, name="deconv2d"):
"""Deconvolution layer."""
with tf.variable_scope(name):
w = tf.get_variable(
"w", [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
deconv ... | [
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tensorflow/tensor2tensor | tensor2tensor/models/vanilla_gan.py | sliced_gan | def sliced_gan():
"""Basic parameters for a vanilla_gan."""
hparams = common_hparams.basic_params1()
hparams.optimizer = "adam"
hparams.learning_rate_constant = 0.0002
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup"
hparams.label_smoothing = 0.0
hpara... | python | def sliced_gan():
"""Basic parameters for a vanilla_gan."""
hparams = common_hparams.basic_params1()
hparams.optimizer = "adam"
hparams.learning_rate_constant = 0.0002
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup"
hparams.label_smoothing = 0.0
hpara... | [
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tensorflow/tensor2tensor | tensor2tensor/models/vanilla_gan.py | AbstractGAN.discriminator | def discriminator(self, x, is_training, reuse=False):
"""Discriminator architecture based on InfoGAN.
Args:
x: input images, shape [bs, h, w, channels]
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
out_logit: the output logi... | python | def discriminator(self, x, is_training, reuse=False):
"""Discriminator architecture based on InfoGAN.
Args:
x: input images, shape [bs, h, w, channels]
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
out_logit: the output logi... | [
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Args:
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is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
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tensorflow/tensor2tensor | tensor2tensor/models/vanilla_gan.py | AbstractGAN.generator | def generator(self, z, is_training, out_shape):
"""Generator outputting image in [0, 1]."""
hparams = self.hparams
height, width, c_dim = out_shape
batch_size = hparams.batch_size
with tf.variable_scope(
"generator",
initializer=tf.random_normal_initializer(stddev=0.02)):
net =... | python | def generator(self, z, is_training, out_shape):
"""Generator outputting image in [0, 1]."""
hparams = self.hparams
height, width, c_dim = out_shape
batch_size = hparams.batch_size
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tensorflow/tensor2tensor | tensor2tensor/models/vanilla_gan.py | AbstractGAN.body | def body(self, features):
"""Body of the model.
Args:
features: a dictionary with the tensors.
Returns:
A pair (predictions, losses) where predictions is the generated image
and losses is a dictionary of losses (that get added for the final loss).
"""
features["targets"] = featur... | python | def body(self, features):
"""Body of the model.
Args:
features: a dictionary with the tensors.
Returns:
A pair (predictions, losses) where predictions is the generated image
and losses is a dictionary of losses (that get added for the final loss).
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | inputs | def inputs(num_devices, dataset_name, data_dir=None, input_name=None,
num_chunks=0, append_targets=False):
"""Make Inputs for built-in datasets.
Args:
num_devices: how many devices to build the inputs for.
dataset_name: a TFDS or T2T dataset name. If it's a T2T dataset name, prefix
with "t... | python | def inputs(num_devices, dataset_name, data_dir=None, input_name=None,
num_chunks=0, append_targets=False):
"""Make Inputs for built-in datasets.
Args:
num_devices: how many devices to build the inputs for.
dataset_name: a TFDS or T2T dataset name. If it's a T2T dataset name, prefix
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dataset_name: a TFDS or T2T dataset name. If it's a T2T dataset name, prefix
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data_dir: data directory.
input_name: optional, name of the inputs from the dictionary.
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | random_inputs | def random_inputs(
num_devices,
input_shape=gin.REQUIRED, input_dtype=np.int32, input_range=(0, 255),
output_shape=gin.REQUIRED, output_dtype=np.int32, output_range=(0, 9)):
"""Make random Inputs for debugging.
Args:
num_devices: how many devices to build the inputs for.
input_shape: the shape ... | python | def random_inputs(
num_devices,
input_shape=gin.REQUIRED, input_dtype=np.int32, input_range=(0, 255),
output_shape=gin.REQUIRED, output_dtype=np.int32, output_range=(0, 9)):
"""Make random Inputs for debugging.
Args:
num_devices: how many devices to build the inputs for.
input_shape: the shape ... | [
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | dataset_to_stream | def dataset_to_stream(dataset, input_name, num_chunks=0, append_targets=False):
"""Takes a tf.Dataset and creates a numpy stream of ready batches."""
for example in tfds.as_numpy(dataset):
inp, out = example[0][input_name], example[1]
if len(out.shape) > 1 and out.shape[-1] == 1:
out = np.squeeze(out,... | python | def dataset_to_stream(dataset, input_name, num_chunks=0, append_targets=False):
"""Takes a tf.Dataset and creates a numpy stream of ready batches."""
for example in tfds.as_numpy(dataset):
inp, out = example[0][input_name], example[1]
if len(out.shape) > 1 and out.shape[-1] == 1:
out = np.squeeze(out,... | [
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | _train_and_eval_dataset_v1 | def _train_and_eval_dataset_v1(problem_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys."""
assert not tf.executing_eagerly(), "tf.eager mode must be turned off."
problem = t2t_problems.problem(problem_name)
train_dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, d... | python | def _train_and_eval_dataset_v1(problem_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys."""
assert not tf.executing_eagerly(), "tf.eager mode must be turned off."
problem = t2t_problems.problem(problem_name)
train_dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, d... | [
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | batch_fun | def batch_fun(dataset, training, shapes, target_names, num_devices,
batch_size_per_device=32, batch_size=None, eval_batch_size=32,
bucket_length=32, buckets=None,
batch_shuffle_size=128, max_eval_length=None):
"""Batching function."""
del target_names
# Batch size is batc... | python | def batch_fun(dataset, training, shapes, target_names, num_devices,
batch_size_per_device=32, batch_size=None, eval_batch_size=32,
bucket_length=32, buckets=None,
batch_shuffle_size=128, max_eval_length=None):
"""Batching function."""
del target_names
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | lm1b_preprocess | def lm1b_preprocess(dataset, training,
max_target_length=-1, max_eval_target_length=-1):
"""Preprocessing for LM1B: filter out targets exceeding maximum length."""
def target_right_length(_, target):
return tf.less(tf.shape(target)[0], max_target_length + 1)
def eval_target_right_length(... | python | def lm1b_preprocess(dataset, training,
max_target_length=-1, max_eval_target_length=-1):
"""Preprocessing for LM1B: filter out targets exceeding maximum length."""
def target_right_length(_, target):
return tf.less(tf.shape(target)[0], max_target_length + 1)
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | shuffle_and_batch_data | def shuffle_and_batch_data(dataset,
target_names,
features_info,
training,
num_devices,
shuffle_buffer_size=1024,
preprocess_fun=no_preprocess):
"""Shuffle ... | python | def shuffle_and_batch_data(dataset,
target_names,
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shuffle_buffer_size=1024,
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tensorflow/tensor2tensor | tensor2tensor/trax/inputs.py | _train_and_eval_batches | def _train_and_eval_batches(dataset, data_dir, input_name, num_devices):
"""Return train and eval batches with input name and shape."""
(train_data, eval_data, features_info, keys) = train_and_eval_dataset(
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"""Return train and eval batches with input name and shape."""
(train_data, eval_data, features_info, keys) = train_and_eval_dataset(
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | get_multi_dataset | def get_multi_dataset(datasets, pmf=None):
"""Returns a Dataset that samples records from one or more Datasets.
Args:
datasets: A list of one or more Dataset objects to sample from.
pmf: A tensor of shape [len(datasets)], the probabilities to sample each
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | get_schedule_distribution | def get_schedule_distribution(schedule, global_step=None):
"""Computes the pmf of a schedule given the global_step.
Args:
schedule: A schedule tuple, see encode_schedule for details.
global_step: A scalar tensor, the step to query the schedule.
Returns:
A 1-D tensor of probs, the sampling distributi... | python | def get_schedule_distribution(schedule, global_step=None):
"""Computes the pmf of a schedule given the global_step.
Args:
schedule: A schedule tuple, see encode_schedule for details.
global_step: A scalar tensor, the step to query the schedule.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | categorical_case | def categorical_case(pmf, fns, rand=None):
"""Returns the outputs of fns[i] with probability pmf[i].
Args:
pmf: A 1-D tensor of probabilities, the probability mass function.
fns: A list of callables that return tensors, same length as pmf.
rand: An optional scalar between 0.0 and 1.0, the output of an ... | python | def categorical_case(pmf, fns, rand=None):
"""Returns the outputs of fns[i] with probability pmf[i].
Args:
pmf: A 1-D tensor of probabilities, the probability mass function.
fns: A list of callables that return tensors, same length as pmf.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | linear_interpolation | def linear_interpolation(x, xp, fp, **kwargs):
"""Multi-dimensional linear interpolation.
Returns the multi-dimensional piecewise linear interpolant to a function with
given discrete data points (xp, fp), evaluated at x.
Note that *N and *M indicate zero or more dimensions.
Args:
x: An array of shape [... | python | def linear_interpolation(x, xp, fp, **kwargs):
"""Multi-dimensional linear interpolation.
Returns the multi-dimensional piecewise linear interpolant to a function with
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | step_interpolation | def step_interpolation(x, xp, fp, **kwargs):
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"""Multi-dimensional step interpolation.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | epoch_rates_to_pmf | def epoch_rates_to_pmf(problems, epoch_rates=None):
"""Create a probability-mass-function based on relative epoch rates.
if epoch_rates=None, then we use uniform epoch rates [1.0] * len(problems)
i.e. it takes each problem the same time to go through one epoch.
If epoch_rates is given, then these are the rela... | python | def epoch_rates_to_pmf(problems, epoch_rates=None):
"""Create a probability-mass-function based on relative epoch rates.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | encode_schedule | def encode_schedule(schedule):
"""Encodes a schedule tuple into a string.
Args:
schedule: A tuple containing (interpolation, steps, pmfs), where
interpolation is a string specifying the interpolation strategy, steps
is an int array_like of shape [N] specifying the global steps, and pmfs is
an... | python | def encode_schedule(schedule):
"""Encodes a schedule tuple into a string.
Args:
schedule: A tuple containing (interpolation, steps, pmfs), where
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | decode_schedule | def decode_schedule(string):
"""Decodes a string into a schedule tuple.
Args:
string: The string encoding of a schedule tuple.
Returns:
A schedule tuple, see encode_schedule for details.
"""
splits = string.split()
steps = [int(x[1:]) for x in splits[1:] if x[0] == '@']
pmfs = np.reshape(
... | python | def decode_schedule(string):
"""Decodes a string into a schedule tuple.
Args:
string: The string encoding of a schedule tuple.
Returns:
A schedule tuple, see encode_schedule for details.
"""
splits = string.split()
steps = [int(x[1:]) for x in splits[1:] if x[0] == '@']
pmfs = np.reshape(
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | tuplize | def tuplize(nested):
"""Recursively converts iterables into tuples.
Args:
nested: A nested structure of items and iterables.
Returns:
A nested structure of items and tuples.
"""
if isinstance(nested, str):
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return... | python | def tuplize(nested):
"""Recursively converts iterables into tuples.
Args:
nested: A nested structure of items and iterables.
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A nested structure of items and tuples.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | MultiProblemV2.filepattern | def filepattern(self, *args, **kwargs):
"""Returns a list of filepatterns, one for each problem."""
return [p.filepattern(*args, **kwargs) for p in self.problems] | python | def filepattern(self, *args, **kwargs):
"""Returns a list of filepatterns, one for each problem."""
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | MultiProblemV2.generate_data | def generate_data(self, *args, **kwargs):
"""Generates data for each problem."""
for p in self.problems:
p.generate_data(*args, **kwargs) | python | def generate_data(self, *args, **kwargs):
"""Generates data for each problem."""
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | MultiProblemV2.dataset | def dataset(self, mode, hparams=None, global_step=None, **kwargs):
"""Returns a dataset containing examples from multiple problems.
Args:
mode: A member of problem.DatasetSplit.
hparams: A tf.HParams object, the model hparams.
global_step: A scalar tensor used to compute the sampling distribu... | python | def dataset(self, mode, hparams=None, global_step=None, **kwargs):
"""Returns a dataset containing examples from multiple problems.
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mode: A member of problem.DatasetSplit.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | MultiText2TextProblem.normalize_example | def normalize_example(self, example, hparams):
"""Assumes that example contains both inputs and targets."""
length = self.max_length(hparams)
def _to_constant_shape(tensor):
tensor = tensor[:length]
tensor = tf.pad(tensor, [(0, length - tf.shape(tensor)[0])])
return tf.reshape(tensor, [le... | python | def normalize_example(self, example, hparams):
"""Assumes that example contains both inputs and targets."""
length = self.max_length(hparams)
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tensor = tensor[:length]
tensor = tf.pad(tensor, [(0, length - tf.shape(tensor)[0])])
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tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem_v2.py | MultiText2TextProblem.generate_data_with_shared_vocab | def generate_data_with_shared_vocab(self, data_dir, tmp_dir, task_id=-1):
"""Generates TF-Records for problems using a global vocabulary file."""
global_vocab_filename = os.path.join(data_dir, self.vocab_filename)
if not tf.gfile.Exists(global_vocab_filename):
raise ValueError(
'Global vocab... | python | def generate_data_with_shared_vocab(self, data_dir, tmp_dir, task_id=-1):
"""Generates TF-Records for problems using a global vocabulary file."""
global_vocab_filename = os.path.join(data_dir, self.vocab_filename)
if not tf.gfile.Exists(global_vocab_filename):
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tensorflow/tensor2tensor | tensor2tensor/layers/area_attention.py | lengths_to_area_mask | def lengths_to_area_mask(feature_length, length, max_area_size):
"""Generates a non-padding mask for areas based on lengths.
Args:
feature_length: a tensor of [batch_size]
length: the length of the batch
max_area_size: the maximum area size considered
Returns:
mask: a tensor in shape of [batch_si... | python | def lengths_to_area_mask(feature_length, length, max_area_size):
"""Generates a non-padding mask for areas based on lengths.
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feature_length: a tensor of [batch_size]
length: the length of the batch
max_area_size: the maximum area size considered
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mask: a tensor in shape of [batch_si... | [
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tensorflow/tensor2tensor | tensor2tensor/layers/area_attention.py | _pool_one_shape | def _pool_one_shape(features_2d, area_width, area_height, batch_size,
width, height, depth, fn=tf.reduce_max, name=None):
"""Pools for an area in features_2d.
Args:
features_2d: a Tensor in a shape of [batch_size, height, width, depth].
area_width: the max width allowed for an area.
... | python | def _pool_one_shape(features_2d, area_width, area_height, batch_size,
width, height, depth, fn=tf.reduce_max, name=None):
"""Pools for an area in features_2d.
Args:
features_2d: a Tensor in a shape of [batch_size, height, width, depth].
area_width: the max width allowed for an area.
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tensorflow/tensor2tensor | tensor2tensor/layers/area_attention.py | basic_pool | def basic_pool(features, max_area_width, max_area_height=1, height=1,
fn=tf.reduce_max, name=None):
"""Pools for each area based on a given pooling function (fn).
Args:
features: a Tensor in a shape of [batch_size, height * width, depth].
max_area_width: the max width allowed for an area.
... | python | def basic_pool(features, max_area_width, max_area_height=1, height=1,
fn=tf.reduce_max, name=None):
"""Pools for each area based on a given pooling function (fn).
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features: a Tensor in a shape of [batch_size, height * width, depth].
max_area_width: the max width allowed for an area.
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tensorflow/tensor2tensor | tensor2tensor/layers/area_attention.py | _compute_sum_image | def _compute_sum_image(features, max_area_width, max_area_height=1, height=1,
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"""Computes area sums for features.
Args:
features: a Tensor in a shape of [batch_size, height * width, depth].
max_area_width: the max width allowed for an area.
max_area_height: the max he... | python | def _compute_sum_image(features, max_area_width, max_area_height=1, height=1,
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tensorflow/tensor2tensor | tensor2tensor/layers/area_attention.py | compute_area_features | def compute_area_features(features, max_area_width, max_area_height=1, height=1,
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"""Computes features for each area.
Args:
features: a Tensor in a shape of [batch_size, height * width, depth].
max_area_width: the max width allowed for an area.
max_area_height: t... | python | def compute_area_features(features, max_area_width, max_area_height=1, height=1,
epsilon=1e-6):
"""Computes features for each area.
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features: a Tensor in a shape of [batch_size, height * width, depth].
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tensorflow/tensor2tensor | tensor2tensor/layers/area_attention.py | compute_area_key | def compute_area_key(features, max_area_width, max_area_height=1, height=1,
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"""Computes the key for each area.
Args:
features: a Tensor in a shape of [batch_size, height * width, depth].
max_area_width: the max width allowed for an area.
max_... | python | def compute_area_key(features, max_area_width, max_area_height=1, height=1,
mode="mean", training=True, name=None):
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features: a Tensor in a shape of [batch_size, height * width, depth].
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tensorflow/tensor2tensor | tensor2tensor/layers/area_attention.py | dot_product_area_attention | def dot_product_area_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
attention... | python | def dot_product_area_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
attention... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | setup_directories | def setup_directories(base_dir, subdirs):
"""Setup directories."""
base_dir = os.path.expanduser(base_dir)
tf.gfile.MakeDirs(base_dir)
all_dirs = {}
for subdir in subdirs:
if isinstance(subdir, six.string_types):
subdir_tuple = (subdir,)
else:
subdir_tuple = subdir
dir_name = os.path.... | python | def setup_directories(base_dir, subdirs):
"""Setup directories."""
base_dir = os.path.expanduser(base_dir)
tf.gfile.MakeDirs(base_dir)
all_dirs = {}
for subdir in subdirs:
if isinstance(subdir, six.string_types):
subdir_tuple = (subdir,)
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subdir_tuple = subdir
dir_name = os.path.... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | make_relative_timing_fn | def make_relative_timing_fn():
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start_time = time.time()
def format_relative_time():
time_delta = time.time() - start_time
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def log_relative_time():
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"""Make a function that logs the duration since it was made."""
start_time = time.time()
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | train_supervised | def train_supervised(problem, model_name, hparams, data_dir, output_dir,
train_steps, eval_steps, local_eval_frequency=None,
schedule="continuous_train_and_eval"):
"""Train supervised."""
if local_eval_frequency is None:
local_eval_frequency = FLAGS.local_eval_frequency... | python | def train_supervised(problem, model_name, hparams, data_dir, output_dir,
train_steps, eval_steps, local_eval_frequency=None,
schedule="continuous_train_and_eval"):
"""Train supervised."""
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | train_agent | def train_agent(real_env, learner, world_model_dir, hparams, epoch):
"""Train the PPO agent in the simulated environment."""
initial_frame_chooser = rl_utils.make_initial_frame_chooser(
real_env, hparams.frame_stack_size, hparams.simulation_random_starts,
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"""Train the PPO agent in the simulated environment."""
initial_frame_chooser = rl_utils.make_initial_frame_chooser(
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | train_agent_real_env | def train_agent_real_env(env, learner, hparams, epoch):
"""Train the PPO agent in the real environment."""
base_algo_str = hparams.base_algo
train_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
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"""Train the PPO agent in the real environment."""
base_algo_str = hparams.base_algo
train_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | train_world_model | def train_world_model(
env, data_dir, output_dir, hparams, world_model_steps_num, epoch
):
"""Train the world model on problem_name."""
world_model_steps_num += world_model_step_increment(
hparams, is_initial_epoch=(epoch == 0)
)
model_hparams = trainer_lib.create_hparams(hparams.generative_model_para... | python | def train_world_model(
env, data_dir, output_dir, hparams, world_model_steps_num, epoch
):
"""Train the world model on problem_name."""
world_model_steps_num += world_model_step_increment(
hparams, is_initial_epoch=(epoch == 0)
)
model_hparams = trainer_lib.create_hparams(hparams.generative_model_para... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | load_metrics | def load_metrics(event_dir, epoch):
"""Loads metrics for this epoch if they have already been written.
This reads the entire event file but it's small with just per-epoch metrics.
Args:
event_dir: TODO(koz4k): Document this.
epoch: TODO(koz4k): Document this.
Returns:
metrics.
"""
metrics = {... | python | def load_metrics(event_dir, epoch):
"""Loads metrics for this epoch if they have already been written.
This reads the entire event file but it's small with just per-epoch metrics.
Args:
event_dir: TODO(koz4k): Document this.
epoch: TODO(koz4k): Document this.
Returns:
metrics.
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based.py | training_loop | def training_loop(hparams, output_dir, report_fn=None, report_metric=None):
"""Run the main training loop."""
if report_fn:
assert report_metric is not None
# Directories
subdirectories = [
"data", "tmp", "world_model", ("world_model", "debug_videos"),
"policy", "eval_metrics"
]
directories... | python | def training_loop(hparams, output_dir, report_fn=None, report_metric=None):
"""Run the main training loop."""
if report_fn:
assert report_metric is not None
# Directories
subdirectories = [
"data", "tmp", "world_model", ("world_model", "debug_videos"),
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tensorflow/tensor2tensor | tensor2tensor/models/research/gene_expression.py | conv_layer | def conv_layer(x,
hidden_size,
kernel_size,
stride,
pooling_window,
dropout_rate,
dilation_rate,
name="conv"):
"""Single conv layer with relu, optional pooling, and dropout."""
with tf.variable_scope(name):
... | python | def conv_layer(x,
hidden_size,
kernel_size,
stride,
pooling_window,
dropout_rate,
dilation_rate,
name="conv"):
"""Single conv layer with relu, optional pooling, and dropout."""
with tf.variable_scope(name):
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tensorflow/tensor2tensor | tensor2tensor/models/research/gene_expression.py | gene_expression_conv_base | def gene_expression_conv_base():
"""Hparams for GeneExpressionConv model."""
hparams = common_hparams.basic_params1()
batch_size = 10
output_length = 2048
inputs_per_output = 128
chunk_size = 4
input_length = output_length * inputs_per_output // chunk_size
hparams.batch_size = input_length * batch_size... | python | def gene_expression_conv_base():
"""Hparams for GeneExpressionConv model."""
hparams = common_hparams.basic_params1()
batch_size = 10
output_length = 2048
inputs_per_output = 128
chunk_size = 4
input_length = output_length * inputs_per_output // chunk_size
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | compress_self_attention_layer | def compress_self_attention_layer(x, hparams, name=None):
"""Attend function."""
with tf.variable_scope(name, default_name="compress_self_attention"):
x, xshape, _ = cia.maybe_reshape_4d_to_3d(x)
y = common_attention.multihead_attention(
common_layers.layer_preprocess(x, hparams),
None,
... | python | def compress_self_attention_layer(x, hparams, name=None):
"""Attend function."""
with tf.variable_scope(name, default_name="compress_self_attention"):
x, xshape, _ = cia.maybe_reshape_4d_to_3d(x)
y = common_attention.multihead_attention(
common_layers.layer_preprocess(x, hparams),
None,
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | compute_nats_and_bits_per_dim | def compute_nats_and_bits_per_dim(data_dim,
latent_dim,
average_reconstruction,
average_prior):
"""Computes negative ELBO, which is an upper bound on the negative likelihood.
Args:
data_dim: int-like indicatin... | python | def compute_nats_and_bits_per_dim(data_dim,
latent_dim,
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | multinomial_sample | def multinomial_sample(x, vocab_size=None, sampling_method="random",
temperature=1.0):
"""Multinomial sampling from a n-dimensional tensor.
Args:
x: Tensor of shape [..., vocab_size]. Parameterizes logits of multinomial.
vocab_size: Number of classes in multinomial distribution.
... | python | def multinomial_sample(x, vocab_size=None, sampling_method="random",
temperature=1.0):
"""Multinomial sampling from a n-dimensional tensor.
Args:
x: Tensor of shape [..., vocab_size]. Parameterizes logits of multinomial.
vocab_size: Number of classes in multinomial distribution.
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | ae_latent_softmax | def ae_latent_softmax(latents_pred, latents_discrete_hot, vocab_size, hparams):
"""Latent prediction and loss.
Args:
latents_pred: Tensor of shape [..., depth].
latents_discrete_hot: Tensor of shape [..., vocab_size].
vocab_size: an int representing the vocab size.
hparams: HParams.
Returns:
... | python | def ae_latent_softmax(latents_pred, latents_discrete_hot, vocab_size, hparams):
"""Latent prediction and loss.
Args:
latents_pred: Tensor of shape [..., depth].
latents_discrete_hot: Tensor of shape [..., vocab_size].
vocab_size: an int representing the vocab size.
hparams: HParams.
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | ae_latent_sample_beam | def ae_latent_sample_beam(latents_dense_in, inputs, ed, embed, hparams):
"""Samples from the latent space in the autoencoder.
Args:
latents_dense_in: Tensor of shape [batch, length_q, ...]. Only the shape of
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"""Samples from the latent space in the autoencoder.
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latents_dense_in: Tensor of shape [batch, length_q, ...]. Only the shape of
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | residual_block_layer | def residual_block_layer(inputs, hparams):
"""Residual block over inputs.
Runs a residual block consisting of
conv: kernel_size x kernel_size
conv: 1x1
dropout, add and normalize according to hparams.layer_postprocess_sequence.
Args:
inputs: Tensor of shape [batch, height, width, hparams.hidden_... | python | def residual_block_layer(inputs, hparams):
"""Residual block over inputs.
Runs a residual block consisting of
conv: kernel_size x kernel_size
conv: 1x1
dropout, add and normalize according to hparams.layer_postprocess_sequence.
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | compress_encoder | def compress_encoder(inputs,
hparams,
strides=(2, 2),
kernel_size=(3, 3),
name=None):
"""Encoder that compresses 2-D inputs by 2**num_compress_steps.
Args:
inputs: Tensor of shape [batch, height, width, channels].
hparams: ... | python | def compress_encoder(inputs,
hparams,
strides=(2, 2),
kernel_size=(3, 3),
name=None):
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | compress_encoder_2d | def compress_encoder_2d(x, hparams, name=None):
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Args:
x: Tensor of shape [batch, height, width, channels].
hparams: HParams.
name: string, variable scope.
Returns:
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... | python | def compress_encoder_2d(x, hparams, name=None):
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Args:
x: Tensor of shape [batch, height, width, channels].
hparams: HParams.
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | compress_encoder_1d | def compress_encoder_1d(x, hparams, name=None):
"""Encoder that compresses 1-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, length, channels].
hparams: HParams.
name: string, variable scope.
Returns:
Tensor of shape [batch, latent_length, hparams.hidden_size], where
la... | python | def compress_encoder_1d(x, hparams, name=None):
"""Encoder that compresses 1-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, length, channels].
hparams: HParams.
name: string, variable scope.
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | decompress_decoder | def decompress_decoder(inputs,
hparams,
strides=(2, 2),
kernel=(3, 3),
name=None):
"""Decoder that decompresses 2-D inputs by 2**num_compress_steps.
Args:
inputs: Tensor of shape [batch, compress_height, compress_width,... | python | def decompress_decoder(inputs,
hparams,
strides=(2, 2),
kernel=(3, 3),
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | decompress_decoder_2d | def decompress_decoder_2d(x, hparams, name=None):
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Args:
x: Tensor of shape [batch, compress_height, compress_width, channels].
hparams: HParams.
name: string, variable scope.
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | decompress_decoder_1d | def decompress_decoder_1d(x, hparams, name=None):
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Args:
x: Tensor of shape [batch, compress_length, channels].
hparams: HParams.
name: string, variable scope.
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | transformer_text_encoder | def transformer_text_encoder(inputs,
target_space,
hparams,
name=None):
"""Transformer text encoder over inputs with unmasked full attention.
Args:
inputs: Tensor of shape [batch, length, 1, hparams.hidden_size].
target_... | python | def transformer_text_encoder(inputs,
target_space,
hparams,
name=None):
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Args:
inputs: Tensor of shape [batch, length, 1, hparams.hidden_size].
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | transformer_image_decoder | def transformer_image_decoder(targets,
encoder_output,
ed_attention_bias,
hparams,
name=None):
"""Transformer image decoder over targets with local attention.
Args:
targets: Tensor of shape [... | python | def transformer_image_decoder(targets,
encoder_output,
ed_attention_bias,
hparams,
name=None):
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Args:
targets: Tensor of shape [... | [
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | transformer_latent_decoder | def transformer_latent_decoder(x,
encoder_output,
ed_attention_bias,
hparams,
name=None):
"""Transformer decoder over latents using latent_attention_type.
Args:
x: Tensor of shape [batch,... | python | def transformer_latent_decoder(x,
encoder_output,
ed_attention_bias,
hparams,
name=None):
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Args:
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | bottleneck_layer | def bottleneck_layer(inputs,
hparams,
name="discrete_bottleneck"):
"""Computes latents given inputs (typically, compressed targets)."""
[
latents_dense,
latents_discrete,
extra_loss,
embed_fn,
_,
] = hparams.bottleneck(inputs=inputs,
... | python | def bottleneck_layer(inputs,
hparams,
name="discrete_bottleneck"):
"""Computes latents given inputs (typically, compressed targets)."""
[
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extra_loss,
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | latent_prediction_model | def latent_prediction_model(inputs,
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latents_discrete,
latents_dense,
hparams,
vocab_size=None,
name=None):
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latents_dense,
hparams,
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name=None):
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | transformer_autoencoder | def transformer_autoencoder(inputs,
targets,
target_space,
hparams,
cache=None,
predict_mask=1.0):
"""Auto-encoder using a Transformer decoder and a prior over latent sequences.
... | python | def transformer_autoencoder(inputs,
targets,
target_space,
hparams,
cache=None,
predict_mask=1.0):
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tensorflow/tensor2tensor | tensor2tensor/layers/latent_layers.py | iaf_flow | def iaf_flow(one_hot_assignments,
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num_codes,
summary=True,
name=None):
"""Performs a single IAF flow using scale and normalization transformations.
Args:
one_hot_assignments: Assignments Tensor with shape [num_samples, ... | python | def iaf_flow(one_hot_assignments,
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tensorflow/tensor2tensor | tensor2tensor/data_generators/image_lsun.py | _get_lsun | def _get_lsun(directory, category, split_name):
"""Downloads all lsun files to directory unless they are there."""
generator_utils.maybe_download(directory,
_LSUN_DATA_FILENAME % (category, split_name),
_LSUN_URL % (category, split_name)) | python | def _get_lsun(directory, category, split_name):
"""Downloads all lsun files to directory unless they are there."""
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tensorflow/tensor2tensor | tensor2tensor/utils/optimize.py | _mixed_precision_is_enabled | def _mixed_precision_is_enabled(hparams):
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tensorflow/tensor2tensor | tensor2tensor/utils/optimize.py | optimize | def optimize(loss, learning_rate, hparams, use_tpu=False, variables=None):
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# Print trainable variables.
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"""Minimize loss."""
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# Weight decay.
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tensorflow/tensor2tensor | tensor2tensor/utils/optimize.py | log_variable_sizes | def log_variable_sizes(var_list=None, tag=None, verbose=False):
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var_list: a list of variables; defaults to trainable_variables
tag: a string; defaults to "Trainable Variables"
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tensorflow/tensor2tensor | tensor2tensor/utils/optimize.py | summarize_variables | def summarize_variables(var_list=None, tag=None):
"""Summarize the variables.
Args:
var_list: a list of variables; defaults to trainable_variables.
tag: name scope of the summary; defaults to training_variables/.
"""
if var_list is None:
var_list = tf.trainable_variables()
if tag is None:
tag... | python | def summarize_variables(var_list=None, tag=None):
"""Summarize the variables.
Args:
var_list: a list of variables; defaults to trainable_variables.
tag: name scope of the summary; defaults to training_variables/.
"""
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tensorflow/tensor2tensor | tensor2tensor/utils/optimize.py | get_variable_initializer | def get_variable_initializer(hparams):
"""Get variable initializer from hparams."""
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return None
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value=hparams.initializer_gain,
hparams=hparams)
... | python | def get_variable_initializer(hparams):
"""Get variable initializer from hparams."""
if not hparams.initializer:
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tensorflow/tensor2tensor | tensor2tensor/layers/vqa_layers.py | summarize_tensors | def summarize_tensors(tensor_dict, tag=None):
"""Summarize the tensors.
Args:
tensor_dict: a dictionary of tensors.
tag: name scope of the summary; defaults to tensors/.
"""
if tag is None:
tag = "tensors/"
for t_name in list(tensor_dict):
t = tensor_dict[t_name]
tf.summary.histogram(tag... | python | def summarize_tensors(tensor_dict, tag=None):
"""Summarize the tensors.
Args:
tensor_dict: a dictionary of tensors.
tag: name scope of the summary; defaults to tensors/.
"""
if tag is None:
tag = "tensors/"
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tensorflow/tensor2tensor | tensor2tensor/layers/vqa_layers.py | image_embedding | def image_embedding(images,
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is_training=True,
weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
... | python | def image_embedding(images,
model_fn=resnet_v1_152,
trainable=True,
is_training=True,
weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
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tensorflow/tensor2tensor | tensor2tensor/layers/vqa_layers.py | multihead_attention | def multihead_attention(query_antecedent,
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total_key_depth,
total_value_depth,
output_depth,
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dropout_rate,
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total_key_depth,
total_value_depth,
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tensorflow/tensor2tensor | tensor2tensor/data_generators/audio.py | _get_timit | def _get_timit(directory):
"""Extract TIMIT datasets to directory unless directory/timit exists."""
if os.path.exists(os.path.join(directory, "timit")):
return
assert FLAGS.timit_paths
for path in FLAGS.timit_paths.split(","):
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with tarfile.open(fileobj=f, mode="r:g... | python | def _get_timit(directory):
"""Extract TIMIT datasets to directory unless directory/timit exists."""
if os.path.exists(os.path.join(directory, "timit")):
return
assert FLAGS.timit_paths
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tensorflow/tensor2tensor | tensor2tensor/data_generators/audio.py | _collect_data | def _collect_data(directory, input_ext, target_ext):
"""Traverses directory collecting input and target files."""
# Directory from string to tuple pair of strings
# key: the filepath to a datafile including the datafile's basename. Example,
# if the datafile was "/path/to/datafile.wav" then the key would be
... | python | def _collect_data(directory, input_ext, target_ext):
"""Traverses directory collecting input and target files."""
# Directory from string to tuple pair of strings
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tensorflow/tensor2tensor | tensor2tensor/data_generators/audio.py | timit_generator | def timit_generator(data_dir,
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start_from=0,
eos_list=None,
vocab_filename=None,
vocab_size=0):
"""Data generator for TIMIT transcription problem.
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tmp_dir,
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vocab_filename=None,
vocab_size=0):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wikitext103.py | _build_vocab | def _build_vocab(filename, vocab_dir, vocab_name):
"""Reads a file to build a vocabulary.
Args:
filename: file to read list of words from.
vocab_dir: directory where to save the vocabulary.
vocab_name: vocab file name.
Returns:
text encoder.
"""
vocab_path = os.path.join(vocab_dir, vocab_nam... | python | def _build_vocab(filename, vocab_dir, vocab_name):
"""Reads a file to build a vocabulary.
Args:
filename: file to read list of words from.
vocab_dir: directory where to save the vocabulary.
vocab_name: vocab file name.
Returns:
text encoder.
"""
vocab_path = os.path.join(vocab_dir, vocab_nam... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wikitext103.py | _maybe_download_corpus | def _maybe_download_corpus(tmp_dir, vocab_type):
"""Download and unpack the corpus.
Args:
tmp_dir: directory containing dataset.
vocab_type: which vocabulary are we using.
Returns:
The list of names of files.
"""
if vocab_type == text_problems.VocabType.CHARACTER:
dataset_url = ("https://s3... | python | def _maybe_download_corpus(tmp_dir, vocab_type):
"""Download and unpack the corpus.
Args:
tmp_dir: directory containing dataset.
vocab_type: which vocabulary are we using.
Returns:
The list of names of files.
"""
if vocab_type == text_problems.VocabType.CHARACTER:
dataset_url = ("https://s3... | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/aligned.py | get_batch_coordinate | def get_batch_coordinate(x):
"""Return a flat int32 tensor of shape [1, batch_size*length, 1]."""
# Compute the batch coordinate before flattening all batches
batch_coordinate = tf.expand_dims(
common_attention.coordinate_tensor(
common_layers.shape_list(x)[:-1], axis=0),
axis=-1)
return b... | python | def get_batch_coordinate(x):
"""Return a flat int32 tensor of shape [1, batch_size*length, 1]."""
# Compute the batch coordinate before flattening all batches
batch_coordinate = tf.expand_dims(
common_attention.coordinate_tensor(
common_layers.shape_list(x)[:-1], axis=0),
axis=-1)
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tensorflow/tensor2tensor | tensor2tensor/models/research/aligned.py | aligned_base | def aligned_base():
"""Set of hyperparameters.
languagemodel_wiki_scramble1k50, 1gpu, 7k steps (10min): log(ppl)_eval = 2.60
12.0 steps/sec on P100
8gpu (8x batch), 7k steps: log(ppl)_eval = 2.00
Returns:
a hparams object
"""
hparams = common_hparams.basic_params1()
hparams.hidden_size = 512
hpa... | python | def aligned_base():
"""Set of hyperparameters.
languagemodel_wiki_scramble1k50, 1gpu, 7k steps (10min): log(ppl)_eval = 2.60
12.0 steps/sec on P100
8gpu (8x batch), 7k steps: log(ppl)_eval = 2.00
Returns:
a hparams object
"""
hparams = common_hparams.basic_params1()
hparams.hidden_size = 512
hpa... | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/aligned.py | aligned_8k_grouped | def aligned_8k_grouped():
"""version for languagemodel_wiki_scramble8k50.
languagemodel_wiki_scramble1k50, 1gpu, 7k steps: log(ppl)_eval = 2.92
3.3 steps/sec on P100
8gpu (8x batch), 7k steps: log(ppl)_eval = 2.15
Returns:
a hparams object
"""
hparams = aligned_grouped()
hparams.batch_size = 8192
... | python | def aligned_8k_grouped():
"""version for languagemodel_wiki_scramble8k50.
languagemodel_wiki_scramble1k50, 1gpu, 7k steps: log(ppl)_eval = 2.92
3.3 steps/sec on P100
8gpu (8x batch), 7k steps: log(ppl)_eval = 2.15
Returns:
a hparams object
"""
hparams = aligned_grouped()
hparams.batch_size = 8192
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | _merge_beam_dim | def _merge_beam_dim(tensor):
"""Reshapes first two dimensions in to single dimension.
Args:
tensor: Tensor to reshape of shape [A, B, ...]
Returns:
Reshaped tensor of shape [A*B, ...]
"""
shape = common_layers.shape_list(tensor)
shape[0] *= shape[1] # batch -> batch * beam_size
shape.pop(1) # ... | python | def _merge_beam_dim(tensor):
"""Reshapes first two dimensions in to single dimension.
Args:
tensor: Tensor to reshape of shape [A, B, ...]
Returns:
Reshaped tensor of shape [A*B, ...]
"""
shape = common_layers.shape_list(tensor)
shape[0] *= shape[1] # batch -> batch * beam_size
shape.pop(1) # ... | [
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | _unmerge_beam_dim | def _unmerge_beam_dim(tensor, batch_size, beam_size):
"""Reshapes first dimension back to [batch_size, beam_size].
Args:
tensor: Tensor to reshape of shape [batch_size*beam_size, ...]
batch_size: Tensor, original batch size.
beam_size: int, original beam size.
Returns:
Reshaped tensor of shape [... | python | def _unmerge_beam_dim(tensor, batch_size, beam_size):
"""Reshapes first dimension back to [batch_size, beam_size].
Args:
tensor: Tensor to reshape of shape [batch_size*beam_size, ...]
batch_size: Tensor, original batch size.
beam_size: int, original beam size.
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Reshaped tensor of shape [... | [
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | _expand_to_beam_size | def _expand_to_beam_size(tensor, beam_size):
"""Tiles a given tensor by beam_size.
Args:
tensor: tensor to tile [batch_size, ...]
beam_size: How much to tile the tensor by.
Returns:
Tiled tensor [batch_size, beam_size, ...]
"""
tensor = tf.expand_dims(tensor, axis=1)
tile_dims = [1] * tensor.s... | python | def _expand_to_beam_size(tensor, beam_size):
"""Tiles a given tensor by beam_size.
Args:
tensor: tensor to tile [batch_size, ...]
beam_size: How much to tile the tensor by.
Returns:
Tiled tensor [batch_size, beam_size, ...]
"""
tensor = tf.expand_dims(tensor, axis=1)
tile_dims = [1] * tensor.s... | [
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | get_state_shape_invariants | def get_state_shape_invariants(tensor):
"""Returns the shape of the tensor but sets middle dims to None."""
shape = tensor.shape.as_list()
for i in range(1, len(shape) - 1):
shape[i] = None
return tf.TensorShape(shape) | python | def get_state_shape_invariants(tensor):
"""Returns the shape of the tensor but sets middle dims to None."""
shape = tensor.shape.as_list()
for i in range(1, len(shape) - 1):
shape[i] = None
return tf.TensorShape(shape) | [
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | compute_batch_indices | def compute_batch_indices(batch_size, beam_size):
"""Computes the i'th coordinate that contains the batch index for gathers.
Batch pos is a tensor like [[0,0,0,0,],[1,1,1,1],..]. It says which
batch the beam item is in. This will create the i of the i,j coordinate
needed for the gather.
Args:
batch_size... | python | def compute_batch_indices(batch_size, beam_size):
"""Computes the i'th coordinate that contains the batch index for gathers.
Batch pos is a tensor like [[0,0,0,0,],[1,1,1,1],..]. It says which
batch the beam item is in. This will create the i of the i,j coordinate
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | fast_tpu_gather | def fast_tpu_gather(params, indices, name=None):
"""Fast gather implementation for models running on TPU.
This function use one_hot and batch matmul to do gather, which is faster
than gather_nd on TPU. For params that have dtype of int32 (sequences to
gather from), batch_gather is used to keep accuracy.
Arg... | python | def fast_tpu_gather(params, indices, name=None):
"""Fast gather implementation for models running on TPU.
This function use one_hot and batch matmul to do gather, which is faster
than gather_nd on TPU. For params that have dtype of int32 (sequences to
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | _create_make_unique | def _create_make_unique(inputs):
"""Replaces the lower bits of each element with iota.
The iota is used to derive the index, and also serves the purpose to
make each element unique to break ties.
Args:
inputs: A tensor with rank of 2 and dtype of tf.float32.
[batch_size, original_size].
Returns:
... | python | def _create_make_unique(inputs):
"""Replaces the lower bits of each element with iota.
The iota is used to derive the index, and also serves the purpose to
make each element unique to break ties.
Args:
inputs: A tensor with rank of 2 and dtype of tf.float32.
[batch_size, original_size].
Returns:
... | [
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | _create_topk_unique | def _create_topk_unique(inputs, k):
"""Creates the top k values in sorted order with indices.
Args:
inputs: A tensor with rank of 2. [batch_size, original_size].
k: An integer, number of top elements to select.
Returns:
topk_r2: A tensor, the k largest elements. [batch_size, k].
topk_indices_r2:... | python | def _create_topk_unique(inputs, k):
"""Creates the top k values in sorted order with indices.
Args:
inputs: A tensor with rank of 2. [batch_size, original_size].
k: An integer, number of top elements to select.
Returns:
topk_r2: A tensor, the k largest elements. [batch_size, k].
topk_indices_r2:... | [
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