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train | categorical_case | 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 RNG.
Returns:
A tensor, the output of fns... | tensor2tensor/data_generators/multi_problem_v2.py | 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 ... | 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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train | linear_interpolation | Multi-dimensional linear interpolation.
Returns the multi-dimensional piecewise linear interpolant to a function with
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Note that *N and *M indicate zero or more dimensions.
Args:
x: An array of shape [*N], the x-coordinates of the interpolated values.
... | tensor2tensor/data_generators/multi_problem_v2.py | 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 [... | 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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Note that *N and *M indicate zero or more dimensions.
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x: An array of shape [... | [
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train | step_interpolation | Multi-dimensional step interpolation.
Returns the multi-dimensional step interpolant to a function with
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Note that *N and *M indicate zero or more dimensions.
Args:
x: An array of shape [*N], the x-coordinates of the interpolated values.
xp: An np.a... | tensor2tensor/data_generators/multi_problem_v2.py | def step_interpolation(x, xp, fp, **kwargs):
"""Multi-dimensional step interpolation.
Returns the multi-dimensional step interpolant to a function with
given discrete data points (xp, fp), evaluated at x.
Note that *N and *M indicate zero or more dimensions.
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x: An array of shape [*N], the x-coord... | def step_interpolation(x, xp, fp, **kwargs):
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train | epoch_rates_to_pmf | 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 relative numbers of epochs
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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... | def epoch_rates_to_pmf(problems, epoch_rates=None):
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train | encode_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
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"""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
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"""Encodes a schedule tuple into a string.
Args:
schedule: A tuple containing (interpolation, steps, pmfs), where
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train | decode_schedule | 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. | tensor2tensor/data_generators/multi_problem_v2.py | def decode_schedule(string):
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Args:
string: The string encoding of a schedule tuple.
Returns:
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"""
splits = string.split()
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"""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.
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splits = string.split()
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train | tuplize | Recursively converts iterables into tuples.
Args:
nested: A nested structure of items and iterables.
Returns:
A nested structure of items and tuples. | tensor2tensor/data_generators/multi_problem_v2.py | 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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Args:
nested: A nested structure of items and iterables.
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A nested structure of items and tuples.
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train | MultiProblemV2.filepattern | Returns a list of filepatterns, one for each problem. | tensor2tensor/data_generators/multi_problem_v2.py | def filepattern(self, *args, **kwargs):
"""Returns a list of filepatterns, one for each problem."""
return [p.filepattern(*args, **kwargs) for p in self.problems] | def filepattern(self, *args, **kwargs):
"""Returns a list of filepatterns, one for each problem."""
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train | MultiProblemV2.generate_data | Generates data for each problem. | tensor2tensor/data_generators/multi_problem_v2.py | def generate_data(self, *args, **kwargs):
"""Generates data for each problem."""
for p in self.problems:
p.generate_data(*args, **kwargs) | def generate_data(self, *args, **kwargs):
"""Generates data for each problem."""
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train | MultiProblemV2.dataset | Returns a dataset containing examples from multiple problems.
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mode: A member of problem.DatasetSplit.
hparams: A tf.HParams object, the model hparams.
global_step: A scalar tensor used to compute the sampling distribution.
If global_step is None, we call tf.train.get_or_create_globa... | tensor2tensor/data_generators/multi_problem_v2.py | 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... | def dataset(self, mode, hparams=None, global_step=None, **kwargs):
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train | MultiText2TextProblem.normalize_example | Assumes that example contains both inputs and targets. | tensor2tensor/data_generators/multi_problem_v2.py | 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]
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train | MultiText2TextProblem.generate_data_with_shared_vocab | Generates TF-Records for problems using a global vocabulary file. | tensor2tensor/data_generators/multi_problem_v2.py | 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... | 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(
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train | lengths_to_area_mask | 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_size, num_areas] | tensor2tensor/layers/area_attention.py | 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... | 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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train | _pool_one_shape | 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.
area_height: the max height allowed for an area.
batch_size: the batch size.
width: the width of the memory.
height: the height of the... | tensor2tensor/layers/area_attention.py | 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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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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train | basic_pool | 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.
max_area_height: the max height allowed for an area.
height: the height of the image.
fn: the TF function for ... | tensor2tensor/layers/area_attention.py | 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.
... | 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.
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train | _compute_sum_image | Computes area sums for features.
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max_area_width: the max width allowed for an area.
max_area_height: the max height allowed for an area.
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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.
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features: a Tensor in a shape of [batch_size, height * width, depth].
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train | compute_area_features | Computes features for each area.
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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.
max_area_height: the max height allowed for an area.
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epsilon=1e-6):
"""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... | 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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train | compute_area_key | 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_area_height: the max height allowed for an area.
height: the height of the image.
mode: whether to combine different area features or ... | tensor2tensor/layers/area_attention.py | def compute_area_key(features, max_area_width, max_area_height=1, height=1,
mode="mean", training=True, name=None):
"""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_... | def compute_area_key(features, max_area_width, max_area_height=1, height=1,
mode="mean", training=True, name=None):
"""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.
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train | dot_product_area_attention | Dot-product area attention.
Args:
q: Tensor with shape [..., length_q, depth_k].
k: Tensor with shape [..., length_kv, depth_k]. Leading dimensions must
match with q.
v: Tensor with shape [..., length_kv, depth_v] Leading dimensions must
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bias: bias Tensor (see attention_bias... | tensor2tensor/layers/area_attention.py | def dot_product_area_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
attention... | def dot_product_area_attention(q,
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bias,
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image_shapes=None,
name=None,
attention... | [
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train | setup_directories | Setup directories. | tensor2tensor/rl/trainer_model_based.py | 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.... | 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.... | [
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train | make_relative_timing_fn | Make a function that logs the duration since it was made. | tensor2tensor/rl/trainer_model_based.py | def make_relative_timing_fn():
"""Make a function that logs the duration since it was made."""
start_time = time.time()
def format_relative_time():
time_delta = time.time() - start_time
return str(datetime.timedelta(seconds=time_delta))
def log_relative_time():
tf.logging.info("Timing: %s", format... | def make_relative_timing_fn():
"""Make a function that logs the duration since it was made."""
start_time = time.time()
def format_relative_time():
time_delta = time.time() - start_time
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train | train_supervised | Train supervised. | tensor2tensor/rl/trainer_model_based.py | 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... | 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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train | train_agent | Train the PPO agent in the simulated environment. | tensor2tensor/rl/trainer_model_based.py | 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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train | train_agent_real_env | Train the PPO agent in the real environment. | tensor2tensor/rl/trainer_model_based.py | 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)
rl_utils.update_hparams_from_hparams(
train_hparams, hparams, "real_" + base_algo_str + "_"
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"""Train the PPO agent in the real environment."""
base_algo_str = hparams.base_algo
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train | train_world_model | Train the world model on problem_name. | tensor2tensor/rl/trainer_model_based.py | 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(
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)
model_hparams = trainer_lib.create_hparams(hparams.generative_model_para... | def train_world_model(
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):
"""Train the world model on problem_name."""
world_model_steps_num += world_model_step_increment(
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)
model_hparams = trainer_lib.create_hparams(hparams.generative_model_para... | [
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train | load_metrics | 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. | tensor2tensor/rl/trainer_model_based.py | 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 = {... | 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 = {... | [
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train | training_loop | Run the main training loop. | tensor2tensor/rl/trainer_model_based.py | 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... | 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 = [
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"policy", "eval_metrics"
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train | conv_layer | Single conv layer with relu, optional pooling, and dropout. | tensor2tensor/models/research/gene_expression.py | 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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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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train | gene_expression_conv_base | Hparams for GeneExpressionConv model. | tensor2tensor/models/research/gene_expression.py | 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... | 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
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train | compress_self_attention_layer | Attend function. | tensor2tensor/layers/latent_layers.py | 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,
... | 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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train | compute_nats_and_bits_per_dim | Computes negative ELBO, which is an upper bound on the negative likelihood.
Args:
data_dim: int-like indicating data dimensionality.
latent_dim: int-like indicating latent dimensionality.
average_reconstruction: Scalar Tensor indicating the reconstruction cost
averaged over all data dimensions and ... | tensor2tensor/layers/latent_layers.py | def compute_nats_and_bits_per_dim(data_dim,
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average_reconstruction,
average_prior):
"""Computes negative ELBO, which is an upper bound on the negative likelihood.
Args:
data_dim: int-like indicatin... | def compute_nats_and_bits_per_dim(data_dim,
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average_reconstruction,
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"""Computes negative ELBO, which is an upper bound on the negative likelihood.
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data_dim: int-like indicatin... | [
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train | multinomial_sample | 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.
sampling_method: String, "random" or otherwise deterministic.
temperature: Positive float.
Returns:
Tens... | tensor2tensor/layers/latent_layers.py | 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.
... | 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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train | ae_latent_softmax | 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:
sample: Tensor of shape [...], a sample from a multinomial distribution.
loss: T... | tensor2tensor/layers/latent_layers.py | 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:
... | 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:
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train | ae_latent_sample_beam | Samples from the latent space in the autoencoder.
Args:
latents_dense_in: Tensor of shape [batch, length_q, ...]. Only the shape of
its first two dimensions are used. length_q is the latent length, which is
height * width * hparams.num_latents / (2**hparams.num_compress_steps).
inputs: Tensor of ... | tensor2tensor/layers/latent_layers.py | 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
its first two dimensions are used. length_q is the latent length, which is
height * width *... | 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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train | residual_block_layer | 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_size].
hparams: HParams.
Returns:
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"""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_... | 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_... | [
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train | compress_encoder | Encoder that compresses 2-D inputs by 2**num_compress_steps.
Args:
inputs: Tensor of shape [batch, height, width, channels].
hparams: HParams.
strides: Tuple, strides for conv block.
kernel_size: Tuple, kernel window size for conv block.
name: string, variable scope.
Returns:
Tensor of sha... | tensor2tensor/layers/latent_layers.py | 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: ... | 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: ... | [
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train | compress_encoder_2d | Encoder that compresses 2-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, height, width, channels].
hparams: HParams.
name: string, variable scope.
Returns:
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hparams.num_latents * (he... | tensor2tensor/layers/latent_layers.py | def compress_encoder_2d(x, hparams, name=None):
"""Encoder that compresses 2-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, height, width, channels].
hparams: HParams.
name: string, variable scope.
Returns:
Tensor of shape [batch, latent_length, hparams.hidden_size], where
... | def compress_encoder_2d(x, hparams, name=None):
"""Encoder that compresses 2-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, height, width, channels].
hparams: HParams.
name: string, variable scope.
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train | compress_encoder_1d | 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
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hparams.num_latents * length / 2... | tensor2tensor/layers/latent_layers.py | 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... | def compress_encoder_1d(x, hparams, name=None):
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Args:
x: Tensor of shape [batch, length, channels].
hparams: HParams.
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Returns:
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train | decompress_decoder | Decoder that decompresses 2-D inputs by 2**num_compress_steps.
Args:
inputs: Tensor of shape [batch, compress_height, compress_width, channels].
hparams: HParams.
strides: Tuple, strides for conv block.
kernel: Tuple, kernel window size for conv block.
name: string, variable scope.
Returns:
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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,... | 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,... | [
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train | decompress_decoder_2d | Decoder that decompresses 2-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, compress_height, compress_width, channels].
hparams: HParams.
name: string, variable scope.
Returns:
Tensor of shape [batch, height, width, hparams.hidden_size]. | tensor2tensor/layers/latent_layers.py | def decompress_decoder_2d(x, hparams, name=None):
"""Decoder that decompresses 2-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, compress_height, compress_width, channels].
hparams: HParams.
name: string, variable scope.
Returns:
Tensor of shape [batch, height, width, hparams... | 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.
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train | decompress_decoder_1d | Decoder that decompresses 1-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, compress_length, channels].
hparams: HParams.
name: string, variable scope.
Returns:
Tensor of shape [batch, length, hparams.hidden_size]. | tensor2tensor/layers/latent_layers.py | 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.
Returns:
Tensor of shape [batch, length, hparams.hidden_size].
"""
... | def decompress_decoder_1d(x, hparams, name=None):
"""Decoder that decompresses 1-D inputs by 2**num_compress_steps.
Args:
x: Tensor of shape [batch, compress_length, channels].
hparams: HParams.
name: string, variable scope.
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train | transformer_text_encoder | Transformer text encoder over inputs with unmasked full attention.
Args:
inputs: Tensor of shape [batch, length, 1, hparams.hidden_size].
target_space: int. Used for encoding inputs under a target space id.
hparams: HParams.
name: string, variable scope.
Returns:
encoder_output: Tensor of shap... | tensor2tensor/layers/latent_layers.py | 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_... | 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].
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train | transformer_image_decoder | Transformer image decoder over targets with local attention.
Args:
targets: Tensor of shape [batch, ...], and whose size is batch * height *
width * hparams.num_channels * hparams.hidden_size.
encoder_output: Tensor of shape [batch, length_kv, hparams.hidden_size].
ed_attention_bias: Tensor which b... | tensor2tensor/layers/latent_layers.py | 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 [... | def transformer_image_decoder(targets,
encoder_output,
ed_attention_bias,
hparams,
name=None):
"""Transformer image decoder over targets with local attention.
Args:
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train | transformer_latent_decoder | Transformer decoder over latents using latent_attention_type.
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height * width * hparams.num_latents / (2**hparams.num_compress_steps).
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encoder_output,
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hparams,
name=None):
"""Transformer decoder over latents using latent_attention_type.
Args:
x: Tensor of shape [batch,... | def transformer_latent_decoder(x,
encoder_output,
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hparams,
name=None):
"""Transformer decoder over latents using latent_attention_type.
Args:
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train | bottleneck_layer | Computes latents given inputs (typically, compressed targets). | tensor2tensor/layers/latent_layers.py | def bottleneck_layer(inputs,
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train | latent_prediction_model | Transformer-based latent prediction model.
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Args:
inputs: Tensor of shape [batch, length_kv, hparams.hidden_size]. Inputs to
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name=None):
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train | transformer_autoencoder | Auto-encoder using a Transformer decoder and a prior over latent sequences.
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inputs: Tensor of shape [batch, length, 1, hparams.hidden_size] or None.
targets: Tensor of shape [batch, ..., channels]. Ellipses may be 1 or 2
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target_space: int. Used for encodin... | tensor2tensor/layers/latent_layers.py | def transformer_autoencoder(inputs,
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"""Auto-encoder using a Transformer decoder and a prior over latent sequences.
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train | iaf_flow | Performs a single IAF flow using scale and normalization transformations.
Args:
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scale_weights: Tensor corresponding to lower triangular matrix used to
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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, ... | def iaf_flow(one_hot_assignments,
scale_weights,
scale_bias,
num_codes,
summary=True,
name=None):
"""Performs a single IAF flow using scale and normalization transformations.
Args:
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train | _get_lsun | Downloads all lsun files to directory unless they are there. | tensor2tensor/data_generators/image_lsun.py | def _get_lsun(directory, category, split_name):
"""Downloads all lsun files to directory unless they are there."""
generator_utils.maybe_download(directory,
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train | _mixed_precision_is_enabled | Should be the same as in common_attention, avoiding import. | tensor2tensor/utils/optimize.py | def _mixed_precision_is_enabled(hparams):
"""Should be the same as in common_attention, avoiding import."""
activation_dtype = hparams.activation_dtype
weight_dtype = hparams.weight_dtype
return activation_dtype == tf.float16 and weight_dtype == tf.float32 | def _mixed_precision_is_enabled(hparams):
"""Should be the same as in common_attention, avoiding import."""
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weight_dtype = hparams.weight_dtype
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train | optimize | Minimize loss. | tensor2tensor/utils/optimize.py | def optimize(loss, learning_rate, hparams, use_tpu=False, variables=None):
"""Minimize loss."""
loss = weight_decay_and_noise(loss, hparams, learning_rate)
loss = tf.identity(loss, name="total_loss")
if variables is None:
variables = tf.trainable_variables()
# Print trainable variables.
log_variable_siz... | def optimize(loss, learning_rate, hparams, use_tpu=False, variables=None):
"""Minimize loss."""
loss = weight_decay_and_noise(loss, hparams, learning_rate)
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# Print trainable variables.
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train | weight_decay_and_noise | Apply weight decay and weight noise. | tensor2tensor/utils/optimize.py | def weight_decay_and_noise(loss, hparams, learning_rate, var_list=None):
"""Apply weight decay and weight noise."""
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var_list = tf.trainable_variables()
decay_vars = [v for v in var_list]
noise_vars = [v for v in var_list if "/body/" in v.name]
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"""Apply weight decay and weight noise."""
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train | weight_noise | Apply weight noise to vars in var_list. | tensor2tensor/utils/optimize.py | def weight_noise(noise_rate, learning_rate, var_list):
"""Apply weight noise to vars in var_list."""
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tf.logging.info("Applying weight noise scaled by learning rate, "
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train | weight_decay | Apply weight decay to vars in var_list. | tensor2tensor/utils/optimize.py | def weight_decay(decay_rate, var_list, skip_biases=True):
"""Apply weight decay to vars in var_list."""
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train | log_variable_sizes | Log the sizes and shapes of variables, and the total size.
Args:
var_list: a list of variables; defaults to trainable_variables
tag: a string; defaults to "Trainable Variables"
verbose: bool, if True, log every weight; otherwise, log total size only. | tensor2tensor/utils/optimize.py | def log_variable_sizes(var_list=None, tag=None, verbose=False):
"""Log the sizes and shapes of variables, and the total size.
Args:
var_list: a list of variables; defaults to trainable_variables
tag: a string; defaults to "Trainable Variables"
verbose: bool, if True, log every weight; otherwise, log to... | def log_variable_sizes(var_list=None, tag=None, verbose=False):
"""Log the sizes and shapes of variables, and the total size.
Args:
var_list: a list of variables; defaults to trainable_variables
tag: a string; defaults to "Trainable Variables"
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train | summarize_variables | Summarize the variables.
Args:
var_list: a list of variables; defaults to trainable_variables.
tag: name scope of the summary; defaults to training_variables/. | tensor2tensor/utils/optimize.py | 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()
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tag... | 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()
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train | get_variable_initializer | Get variable initializer from hparams. | tensor2tensor/utils/optimize.py | def get_variable_initializer(hparams):
"""Get variable initializer from hparams."""
if not hparams.initializer:
return None
mlperf_log.transformer_print(key=mlperf_log.MODEL_HP_INITIALIZER_GAIN,
value=hparams.initializer_gain,
hparams=hparams)
... | def get_variable_initializer(hparams):
"""Get variable initializer from hparams."""
if not hparams.initializer:
return None
mlperf_log.transformer_print(key=mlperf_log.MODEL_HP_INITIALIZER_GAIN,
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train | summarize_tensors | Summarize the tensors.
Args:
tensor_dict: a dictionary of tensors.
tag: name scope of the summary; defaults to tensors/. | tensor2tensor/layers/vqa_layers.py | 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... | 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... | [
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train | image_embedding | Extract image features from pretrained resnet model. | tensor2tensor/layers/vqa_layers.py | 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,
batch_norm_scale=True,
... | 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,
batch_norm_scale=True,
... | [
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train | multihead_attention | Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_v... | tensor2tensor/layers/vqa_layers.py | def multihead_attention(query_antecedent,
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bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
dropout_rate,
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dropout_rate,
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train | _get_timit | Extract TIMIT datasets to directory unless directory/timit exists. | tensor2tensor/data_generators/audio.py | 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(","):
with tf.gfile.GFile(path) as f:
with tarfile.open(fileobj=f, mode="r:g... | 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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train | _collect_data | Traverses directory collecting input and target files. | tensor2tensor/data_generators/audio.py | 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
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"""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,
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train | timit_generator | Data generator for TIMIT transcription problem.
Args:
data_dir: path to the data directory.
tmp_dir: path to temporary storage directory.
training: a Boolean; if true, we use the train set, otherwise the test set.
how_many: how many inputs and labels to generate.
start_from: from which input to s... | tensor2tensor/data_generators/audio.py | def timit_generator(data_dir,
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how_many,
start_from=0,
eos_list=None,
vocab_filename=None,
vocab_size=0):
"""Data generator for TIMIT transcription problem.
... | def timit_generator(data_dir,
tmp_dir,
training,
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start_from=0,
eos_list=None,
vocab_filename=None,
vocab_size=0):
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train | _build_vocab | 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. | tensor2tensor/data_generators/wikitext103.py | 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... | def _build_vocab(filename, vocab_dir, vocab_name):
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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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train | _maybe_download_corpus | 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. | tensor2tensor/data_generators/wikitext103.py | def _maybe_download_corpus(tmp_dir, vocab_type):
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Args:
tmp_dir: directory containing dataset.
vocab_type: which vocabulary are we using.
Returns:
The list of names of files.
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Args:
tmp_dir: directory containing dataset.
vocab_type: which vocabulary are we using.
Returns:
The list of names of files.
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train | get_batch_coordinate | Return a flat int32 tensor of shape [1, batch_size*length, 1]. | tensor2tensor/models/research/aligned.py | 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(
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return b... | 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... | [
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train | 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 | tensor2tensor/models/research/aligned.py | 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... | 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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train | 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 | tensor2tensor/models/research/aligned.py | 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
... | 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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train | _merge_beam_dim | Reshapes first two dimensions in to single dimension.
Args:
tensor: Tensor to reshape of shape [A, B, ...]
Returns:
Reshaped tensor of shape [A*B, ...] | tensor2tensor/utils/beam_search.py | 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) # ... | 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
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train | _unmerge_beam_dim | 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 [batch_size, beam_size, ...] | tensor2tensor/utils/beam_search.py | 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 [... | 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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train | _expand_to_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, ...] | tensor2tensor/utils/beam_search.py | 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... | def _expand_to_beam_size(tensor, beam_size):
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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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train | get_state_shape_invariants | Returns the shape of the tensor but sets middle dims to None. | tensor2tensor/utils/beam_search.py | 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) | def get_state_shape_invariants(tensor):
"""Returns the shape of the tensor but sets middle dims to None."""
shape = tensor.shape.as_list()
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shape[i] = None
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train | compute_batch_indices | 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: Batch size
beam_size: Size of the beam.
Returns... | tensor2tensor/utils/beam_search.py | 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... | 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.
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train | fast_tpu_gather | Fast gather implementation for models running on TPU.
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Args:
params: A tensor from which to gather values.
... | tensor2tensor/utils/beam_search.py | 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.
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This function use one_hot and batch matmul to do gather, which is faster
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train | _create_make_unique | Replaces the lower bits of each element with iota.
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Args:
inputs: A tensor with rank of 2 and dtype of tf.float32.
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Returns:
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"""Replaces the lower bits of each element with iota.
The iota is used to derive the index, and also serves the purpose to
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Args:
inputs: A tensor with rank of 2 and dtype of tf.float32.
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Returns:
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"""Replaces the lower bits of each element with iota.
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inputs: A tensor with rank of 2 and dtype of tf.float32.
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train | _create_topk_unique | 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: A tensor, indices of the top k values. [... | tensor2tensor/utils/beam_search.py | 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:... | 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].
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train | top_k_with_unique | Finds the values and indices of the k largests entries.
Instead of doing sort like tf.nn.top_k, this function finds the max value
k times. The running time is proportional to k, which is be faster when k
is small. The current implementation supports only inputs of rank 2.
In addition, iota is used to replace t... | tensor2tensor/utils/beam_search.py | def top_k_with_unique(inputs, k):
"""Finds the values and indices of the k largests entries.
Instead of doing sort like tf.nn.top_k, this function finds the max value
k times. The running time is proportional to k, which is be faster when k
is small. The current implementation supports only inputs of rank 2.
... | def top_k_with_unique(inputs, k):
"""Finds the values and indices of the k largests entries.
Instead of doing sort like tf.nn.top_k, this function finds the max value
k times. The running time is proportional to k, which is be faster when k
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train | compute_topk_scores_and_seq | Given sequences and scores, will gather the top k=beam size sequences.
This function is used to grow alive, and finished. It takes sequences,
scores, and flags, and returns the top k from sequences, scores_to_gather,
and flags based on the values in scores.
This method permits easy introspection using tfdbg. ... | tensor2tensor/utils/beam_search.py | def compute_topk_scores_and_seq(sequences,
scores,
scores_to_gather,
flags,
beam_size,
batch_size,
prefix="default",
... | def compute_topk_scores_and_seq(sequences,
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train | beam_search | Beam search with length penalties.
Requires a function that can take the currently decoded symbols and return
the logits for the next symbol. The implementation is inspired by
https://arxiv.org/abs/1609.08144.
When running, the beam search steps can be visualized by using tfdbg to watch
the operations gener... | tensor2tensor/utils/beam_search.py | def beam_search(symbols_to_logits_fn,
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beam_size,
decode_length,
vocab_size,
alpha,
states=None,
eos_id=EOS_ID,
stop_early=True,
use_tpu=False,
use_... | def beam_search(symbols_to_logits_fn,
initial_ids,
beam_size,
decode_length,
vocab_size,
alpha,
states=None,
eos_id=EOS_ID,
stop_early=True,
use_tpu=False,
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train | video_augmentation | Augments video with optional hue, saturation and constrast.
Args:
features: dict, with keys "inputs", "targets".
features["inputs"], 4-D Tensor, shape=(THWC)
features["targets"], 4-D Tensor, shape=(THWC)
hue: bool, apply hue_transform.
saturate: bool, apply saturation transfor... | tensor2tensor/data_generators/video_utils.py | def video_augmentation(features, hue=False, saturate=False, contrast=False):
"""Augments video with optional hue, saturation and constrast.
Args:
features: dict, with keys "inputs", "targets".
features["inputs"], 4-D Tensor, shape=(THWC)
features["targets"], 4-D Tensor, shape=(THWC)... | def video_augmentation(features, hue=False, saturate=False, contrast=False):
"""Augments video with optional hue, saturation and constrast.
Args:
features: dict, with keys "inputs", "targets".
features["inputs"], 4-D Tensor, shape=(THWC)
features["targets"], 4-D Tensor, shape=(THWC)... | [
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train | create_border | Creates a border around each frame to differentiate input and target.
Args:
video: 5-D NumPy array.
color: string, "blue", "red" or "green".
border_percent: Percentarge of the frame covered by the border.
Returns:
video: 5-D NumPy array. | tensor2tensor/data_generators/video_utils.py | def create_border(video, color="blue", border_percent=2):
"""Creates a border around each frame to differentiate input and target.
Args:
video: 5-D NumPy array.
color: string, "blue", "red" or "green".
border_percent: Percentarge of the frame covered by the border.
Returns:
video: 5-D NumPy array... | def create_border(video, color="blue", border_percent=2):
"""Creates a border around each frame to differentiate input and target.
Args:
video: 5-D NumPy array.
color: string, "blue", "red" or "green".
border_percent: Percentarge of the frame covered by the border.
Returns:
video: 5-D NumPy array... | [
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train | convert_videos_to_summaries | Converts input, output and target videos into video summaries.
Args:
input_videos: 5-D NumPy array, (NTHWC) conditioning frames.
output_videos: 5-D NumPy array, (NTHWC) model predictions.
target_videos: 5-D NumPy array, (NTHWC) target frames.
tag: tf summary tag.
decode_hparams: HParams.
disp... | tensor2tensor/data_generators/video_utils.py | def convert_videos_to_summaries(input_videos, output_videos, target_videos,
tag, decode_hparams,
display_ground_truth=False):
"""Converts input, output and target videos into video summaries.
Args:
input_videos: 5-D NumPy array, (NTHWC) conditioni... | def convert_videos_to_summaries(input_videos, output_videos, target_videos,
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display_ground_truth=False):
"""Converts input, output and target videos into video summaries.
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train | display_video_hooks | Hooks to display videos at decode time. | tensor2tensor/data_generators/video_utils.py | def display_video_hooks(hook_args):
"""Hooks to display videos at decode time."""
predictions = hook_args.predictions
max_outputs = hook_args.decode_hparams.max_display_outputs
max_decodes = hook_args.decode_hparams.max_display_decodes
with tf.Graph().as_default():
_, best_decodes = video_metrics.compute... | def display_video_hooks(hook_args):
"""Hooks to display videos at decode time."""
predictions = hook_args.predictions
max_outputs = hook_args.decode_hparams.max_display_outputs
max_decodes = hook_args.decode_hparams.max_display_decodes
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train | summarize_video_metrics | Computes video metrics summaries using the decoder output. | tensor2tensor/data_generators/video_utils.py | def summarize_video_metrics(hook_args):
"""Computes video metrics summaries using the decoder output."""
problem_name = hook_args.problem.name
current_problem = hook_args.problem
hparams = hook_args.hparams
output_dirs = hook_args.output_dirs
predictions = hook_args.predictions
frame_shape = [
curre... | def summarize_video_metrics(hook_args):
"""Computes video metrics summaries using the decoder output."""
problem_name = hook_args.problem.name
current_problem = hook_args.problem
hparams = hook_args.hparams
output_dirs = hook_args.output_dirs
predictions = hook_args.predictions
frame_shape = [
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train | debug_video_writer_factory | Creates a VideoWriter for debug videos. | tensor2tensor/data_generators/video_utils.py | def debug_video_writer_factory(output_dir):
"""Creates a VideoWriter for debug videos."""
if FLAGS.disable_ffmpeg:
return common_video.IndividualFrameWriter(output_dir)
else:
output_path = os.path.join(output_dir, "video.avi")
return common_video.WholeVideoWriter(
fps=10, output_path=output_pa... | def debug_video_writer_factory(output_dir):
"""Creates a VideoWriter for debug videos."""
if FLAGS.disable_ffmpeg:
return common_video.IndividualFrameWriter(output_dir)
else:
output_path = os.path.join(output_dir, "video.avi")
return common_video.WholeVideoWriter(
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train | VideoProblem.preprocess_example | Runtime preprocessing, e.g., resize example["frame"]. | tensor2tensor/data_generators/video_utils.py | def preprocess_example(self, example, mode, hparams):
"""Runtime preprocessing, e.g., resize example["frame"]."""
if getattr(hparams, "preprocess_resize_frames", None) is not None:
example["frame"] = tf.image.resize_images(
example["frame"], hparams.preprocess_resize_frames,
tf.image.R... | def preprocess_example(self, example, mode, hparams):
"""Runtime preprocessing, e.g., resize example["frame"]."""
if getattr(hparams, "preprocess_resize_frames", None) is not None:
example["frame"] = tf.image.resize_images(
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train | VideoProblem.serving_input_fn | For serving/predict, assume that only video frames are provided. | tensor2tensor/data_generators/video_utils.py | def serving_input_fn(self, hparams):
"""For serving/predict, assume that only video frames are provided."""
video_input_frames = tf.placeholder(
dtype=tf.float32,
shape=[
None, hparams.video_num_input_frames, self.frame_width,
self.frame_height, self.num_channels
... | def serving_input_fn(self, hparams):
"""For serving/predict, assume that only video frames are provided."""
video_input_frames = tf.placeholder(
dtype=tf.float32,
shape=[
None, hparams.video_num_input_frames, self.frame_width,
self.frame_height, self.num_channels
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train | VideoProblem.generate_encoded_samples | Generate samples of the encoded frames with possible extra data.
By default this function just encodes the numpy array returned as "frame"
from `self.generate_samples` into a PNG image. Override this function to
get other encodings on disk.
Args:
data_dir: final data directory. Typically only us... | tensor2tensor/data_generators/video_utils.py | def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
"""Generate samples of the encoded frames with possible extra data.
By default this function just encodes the numpy array returned as "frame"
from `self.generate_samples` into a PNG image. Override this function to
get other encoding... | def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
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train | VideoProblem.generate_data | The function generating the data. | tensor2tensor/data_generators/video_utils.py | def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""The function generating the data."""
filepath_fns = {
problem.DatasetSplit.TRAIN: self.training_filepaths,
problem.DatasetSplit.EVAL: self.dev_filepaths,
problem.DatasetSplit.TEST: self.test_filepaths,
}
# We set shuffle... | def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""The function generating the data."""
filepath_fns = {
problem.DatasetSplit.TRAIN: self.training_filepaths,
problem.DatasetSplit.EVAL: self.dev_filepaths,
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train | add_scope | Return a decorator which add a TF name/variable scope to a function.
Note that the function returned by the decorator accept an additional 'name'
parameter, which can overwrite the name scope given when the function is
created.
Args:
scope (str): name of the scope. If None, the function name is used.
... | tensor2tensor/utils/expert_utils.py | def add_scope(scope=None, scope_fn=None):
"""Return a decorator which add a TF name/variable scope to a function.
Note that the function returned by the decorator accept an additional 'name'
parameter, which can overwrite the name scope given when the function is
created.
Args:
scope (str): name of the ... | def add_scope(scope=None, scope_fn=None):
"""Return a decorator which add a TF name/variable scope to a function.
Note that the function returned by the decorator accept an additional 'name'
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train | _add_variable_proxy_methods | Proxy methods of underlying variable.
This enables our custom getters to still work with, e.g., batch norm.
Args:
var: Variable to proxy
proxy_tensor: Tensor that is identity of var | tensor2tensor/utils/expert_utils.py | def _add_variable_proxy_methods(var, proxy_tensor):
"""Proxy methods of underlying variable.
This enables our custom getters to still work with, e.g., batch norm.
Args:
var: Variable to proxy
proxy_tensor: Tensor that is identity of var
"""
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Args:
var: Variable to proxy
proxy_tensor: Tensor that is identity of var
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train | _rowwise_unsorted_segment_sum | UnsortedSegmentSum on each row.
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values: a `Tensor` with shape `[batch_size, k]`.
indices: an integer `Tensor` with shape `[batch_size, k]`.
n: an integer.
Returns:
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"""UnsortedSegmentSum on each row.
Args:
values: a `Tensor` with shape `[batch_size, k]`.
indices: an integer `Tensor` with shape `[batch_size, k]`.
n: an integer.
Returns:
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values: a `Tensor` with shape `[batch_size, k]`.
indices: an integer `Tensor` with shape `[batch_size, k]`.
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train | _prob_in_top_k | Helper function to NoisyTopKGating.
Computes the probability that value is in top k, given different random noise.
This gives us a way of backpropagating from a loss that balances the number
of times each expert is in the top k experts per example.
In the case of no noise, pass in None for noise_stddev, and ... | tensor2tensor/utils/expert_utils.py | def _prob_in_top_k(
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"""Helper function to NoisyTopKGating.
Computes the probability that value is in top k, given different random noise.
This gives us a way of backpropagating from a loss that balances the number
of times each expert is in t... | def _prob_in_top_k(
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train | cv_squared | The squared coefficient of variation of a sample.
Useful as a loss to encourage a positive distribution to be more uniform.
Epsilons added for numerical stability.
Returns 0 for an empty Tensor.
Args:
x: a `Tensor`.
Returns:
a `Scalar`. | tensor2tensor/utils/expert_utils.py | def cv_squared(x):
"""The squared coefficient of variation of a sample.
Useful as a loss to encourage a positive distribution to be more uniform.
Epsilons added for numerical stability.
Returns 0 for an empty Tensor.
Args:
x: a `Tensor`.
Returns:
a `Scalar`.
"""
epsilon = 1e-10
float_size =... | def cv_squared(x):
"""The squared coefficient of variation of a sample.
Useful as a loss to encourage a positive distribution to be more uniform.
Epsilons added for numerical stability.
Returns 0 for an empty Tensor.
Args:
x: a `Tensor`.
Returns:
a `Scalar`.
"""
epsilon = 1e-10
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train | update_hparams_for_vq_gating | VQ Gating hparams. | tensor2tensor/utils/expert_utils.py | def update_hparams_for_vq_gating(hparams):
"""VQ Gating hparams."""
hparams.add_hparam("z_size", 4)
hparams.add_hparam("noise_dev", 0.5)
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hparams.add_hparam("bottleneck_kind", "dvq")
hparams.add_hparam("num_blocks", 1)
hparams.add_hparam("num_residuals", 1)
... | def update_hparams_for_vq_gating(hparams):
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hparams.add_hparam("z_size", 4)
hparams.add_hparam("noise_dev", 0.5)
# Bottleneck kinds supported: dense, vae, dvq.
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train | _my_top_k | GPU-compatible version of top-k that works for very small constant k.
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Calls argmax repeatedly.
tf.nn.top_k is implemented for GPU, but the gradient, sparse_to_dense,
seems not to be, so if we use tf.nn.top_k, then both the top_k and its
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Calls argmax repeatedly.
tf.nn.top_k is implemented for GPU, but the gradient, sparse_to_dense,
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train | vq_gating | VQ gating.
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x: input Tensor with shape [batch_size, input_size]
num_experts: an integer
k: an integer - number of experts per example
bneck: a bottleneck object
hparams: optional hparams
name: an optional string
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k,
bneck,
hparams=None,
name="vq_gating"):
"""VQ gating.
Args:
x: input Tensor with shape [batch_size, input_size]
num_experts: an integer
k: an integer - number of experts per example
bneck: a bottl... | def vq_gating(x,
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hparams=None,
name="vq_gating"):
"""VQ gating.
Args:
x: input Tensor with shape [batch_size, input_size]
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train | noisy_top_k_gating | Noisy top-k gating.
See paper: https://arxiv.org/abs/1701.06538.
Args:
x: input Tensor with shape [batch_size, input_size]
num_experts: an integer
train: a boolean - we only add noise at training time.
k: an integer - number of experts per example
initializer: an initializer
noisy_gating: ... | tensor2tensor/utils/expert_utils.py | def noisy_top_k_gating(x,
num_experts,
train,
k=2,
initializer=tf.zeros_initializer(),
noisy_gating=True,
noise_epsilon=1e-2,
name=None):
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train | map_ids | Apply a function to each coordinate ids of a multidimensional tensor.
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similar to tf.map_fn but with tensor where the batch dim has been flatten.
Warning: The indices ids have to be contiguous and ordered in memory as the
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"""Apply a function to each coordinate ids of a multidimensional tensor.
This allows to process each sequence of a batch independently. This is
similar to tf.map_fn but with tensor where the batch dim has been flatten.
Warning: The indices ids have to be contiguous and ordered... | def map_ids(x, indices, map_fn):
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train | ffn_expert_fn | Returns a function that creates a feed-forward network.
Use this function to create the expert_fn argument to distributed_moe.
Args:
input_size: an integer
hidden_sizes: a list of integers
output_size: an integer
hidden_activation: a unary function.
Returns:
a unary function | tensor2tensor/utils/expert_utils.py | def ffn_expert_fn(input_size,
hidden_sizes,
output_size,
hidden_activation=tf.nn.relu):
"""Returns a function that creates a feed-forward network.
Use this function to create the expert_fn argument to distributed_moe.
Args:
input_size: an integer
hid... | def ffn_expert_fn(input_size,
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output_size,
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Use this function to create the expert_fn argument to distributed_moe.
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input_size: an integer
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"[",
"input_size",
"]",
"+",
"hidden_sizes",
... | 272500b6efe353aeb638d2745ed56e519462ca31 |
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