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values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | HParams.set_hparam | Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
Raises:
KeyError: If the hyperparameter doesn't exist.
... | tensor2tensor/utils/hparam.py | def set_hparam(self, name, value):
"""Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
Raises:
KeyError:... | def set_hparam(self, name, value):
"""Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
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KeyError:... | [
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train | HParams.del_hparam | Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter. | tensor2tensor/utils/hparam.py | def del_hparam(self, name):
"""Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter.
"""
if hasattr(self, name):
delattr(self, name)
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"""Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter.
"""
if hasattr(self, name):
delattr(self, name)
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train | HParams.parse | Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
must follow the syntax described above.
Returns:
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"""Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
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... | def parse(self, values):
"""Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
must follow the syntax described above.
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train | HParams.override_from_dict | Override existing hyperparameter values, parsing new values from a dictionary.
Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
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"""Override existing hyperparameter values, parsing new values from a dictionary.
Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
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Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
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train | HParams.to_json | Serializes the hyperparameters into JSON.
Args:
indent: If a non-negative integer, JSON array elements and object members
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negative, will only insert newlines. `None` (the default) selects the
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"""Serializes the hyperparameters into JSON.
Args:
indent: If a non-negative integer, JSON array elements and object members
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indent: If a non-negative integer, JSON array elements and object members
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train | HParams.parse_json | Override existing hyperparameter values, parsing new values from a json object.
Args:
values_json: String containing a json object of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_json` doesn't exist.
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"""Override existing hyperparameter values, parsing new values from a json object.
Args:
values_json: String containing a json object of name:value pairs.
Returns:
The `HParams` instance.
Raises:
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values_json: String containing a json object of name:value pairs.
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train | HParams.values | Return the hyperparameter values as a Python dictionary.
Returns:
A dictionary with hyperparameter names as keys. The values are the
hyperparameter values. | tensor2tensor/utils/hparam.py | def values(self):
"""Return the hyperparameter values as a Python dictionary.
Returns:
A dictionary with hyperparameter names as keys. The values are the
hyperparameter values.
"""
return {n: getattr(self, n) for n in self._hparam_types.keys()} | def values(self):
"""Return the hyperparameter values as a Python dictionary.
Returns:
A dictionary with hyperparameter names as keys. The values are the
hyperparameter values.
"""
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train | HParams.get | Returns the value of `key` if it exists, else `default`. | tensor2tensor/utils/hparam.py | def get(self, key, default=None):
"""Returns the value of `key` if it exists, else `default`."""
if key in self._hparam_types:
# Ensure that default is compatible with the parameter type.
if default is not None:
param_type, is_param_list = self._hparam_types[key]
type_str = 'list<%s>... | def get(self, key, default=None):
"""Returns the value of `key` if it exists, else `default`."""
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# Ensure that default is compatible with the parameter type.
if default is not None:
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train | HParams._get_kind_name | Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
Raises:
ValueError: If parameter type is not recognized. | tensor2tensor/utils/hparam.py | def _get_kind_name(param_type, is_list):
"""Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
Raises:
ValueError: If parameter type is not rec... | def _get_kind_name(param_type, is_list):
"""Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
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train | TransformerModel.process | Returns the visualizations for query.
Args:
query: The query to process.
Returns:
A dictionary of results with processing and graph visualizations. | tensor2tensor/insights/transformer_model.py | def process(self, query):
"""Returns the visualizations for query.
Args:
query: The query to process.
Returns:
A dictionary of results with processing and graph visualizations.
"""
tf.logging.info("Processing new query [%s]" %query)
# Create the new TFDBG hook directory.
hook_... | def process(self, query):
"""Returns the visualizations for query.
Args:
query: The query to process.
Returns:
A dictionary of results with processing and graph visualizations.
"""
tf.logging.info("Processing new query [%s]" %query)
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train | _default_output_dir | Default output directory. | tensor2tensor/trax/trainer.py | def _default_output_dir():
"""Default output directory."""
try:
dataset_name = gin.query_parameter("inputs.dataset_name")
except ValueError:
dataset_name = "random"
dir_name = "{model_name}_{dataset_name}_{timestamp}".format(
model_name=gin.query_parameter("train.model").configurable.name,
d... | def _default_output_dir():
"""Default output directory."""
try:
dataset_name = gin.query_parameter("inputs.dataset_name")
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dataset_name = "random"
dir_name = "{model_name}_{dataset_name}_{timestamp}".format(
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train | _setup_gin | Setup gin configuration. | tensor2tensor/trax/trainer.py | def _setup_gin():
"""Setup gin configuration."""
# Imports for configurables
# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order,reimported,unused-variable
from tensor2tensor.trax import models as _trax_models
from tensor2tensor.trax import optimizers as _trax_opt
# pylint: disable=g-impo... | def _setup_gin():
"""Setup gin configuration."""
# Imports for configurables
# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order,reimported,unused-variable
from tensor2tensor.trax import models as _trax_models
from tensor2tensor.trax import optimizers as _trax_opt
# pylint: disable=g-impo... | [
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train | train_and_eval_dataset | Return train and evaluation datasets, feature info and supervised keys.
Args:
dataset_name: a string, the name of the dataset; if it starts with "v1_"
then we'll search T2T Problem registry for it, otherwise we assume it
is a dataset from TFDS and load it from there.
data_dir: directory where the... | tensor2tensor/v2/t2t.py | def train_and_eval_dataset(dataset_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys.
Args:
dataset_name: a string, the name of the dataset; if it starts with "v1_"
then we'll search T2T Problem registry for it, otherwise we assume it
is a dataset from TFDS ... | def train_and_eval_dataset(dataset_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys.
Args:
dataset_name: a string, the name of the dataset; if it starts with "v1_"
then we'll search T2T Problem registry for it, otherwise we assume it
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train | _make_info | Create an info-like tuple for feature given some shapes and vocab size. | tensor2tensor/v2/t2t.py | def _make_info(shape_list, num_classes):
"""Create an info-like tuple for feature given some shapes and vocab size."""
feature_info = collections.namedtuple("FeatureInfo", ["shape", "num_classes"])
cur_shape = list(shape_list[0])
# We need to merge the provided shapes, put None where they disagree.
for shape ... | def _make_info(shape_list, num_classes):
"""Create an info-like tuple for feature given some shapes and vocab size."""
feature_info = collections.namedtuple("FeatureInfo", ["shape", "num_classes"])
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train | _select_features | Select a subset of features from the example dict. | tensor2tensor/v2/t2t.py | def _select_features(example, feature_list=None):
"""Select a subset of features from the example dict."""
feature_list = feature_list or ["inputs", "targets"]
return {f: example[f] for f in feature_list} | def _select_features(example, feature_list=None):
"""Select a subset of features from the example dict."""
feature_list = feature_list or ["inputs", "targets"]
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train | _train_and_eval_dataset_v1 | Return train and evaluation datasets, feature info and supervised keys. | tensor2tensor/v2/t2t.py | def _train_and_eval_dataset_v1(problem_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys."""
problem = problems.problem(problem_name)
train_dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, data_dir)
train_dataset = train_dataset.map(_select_features)
eval_dataset... | def _train_and_eval_dataset_v1(problem_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys."""
problem = problems.problem(problem_name)
train_dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, data_dir)
train_dataset = train_dataset.map(_select_features)
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train | batch_fn | Batching function. | tensor2tensor/v2/t2t.py | def batch_fn(dataset, training, shapes, target_names,
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train | shuffle_and_batch_data | Shuffle and batch the given dataset. | tensor2tensor/v2/t2t.py | def shuffle_and_batch_data(dataset, target_names, features_info, training):
"""Shuffle and batch the given dataset."""
def append_targets(example):
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if len(target_names) == 1:
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"""Append targets to the example dictionary. Needed for Keras."""
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train | optimize_fn | Compile the model in Keras. | tensor2tensor/v2/t2t.py | def optimize_fn(model,
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loss=None,
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"""Compile the model in Keras."""
learning_rate_schedule = learning_rate_schedule or T2TLearningRateSchedule()
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train | train_fn | Train the given model on the given dataset.
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data_dir: Directory where the data is located.
output_dir: Directory where to put the logs and checkpoints.
model_class: The model class to train.
dataset: The name of the dataset to train on.
input_names: List of strings with the names of the fea... | tensor2tensor/v2/t2t.py | def train_fn(data_dir=None, output_dir=None,
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input_names=None, target_names=None,
train_steps=1000, eval_steps=1, eval_frequency=100):
"""Train the given model on the given dataset.
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train | t2t_train | Main function to train the given model on the given dataset.
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model_name: The name of the model to train.
dataset_name: The name of the dataset to train on.
data_dir: Directory where the data is located.
output_dir: Directory where to put the logs and checkpoints.
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"""Main function to train the given model on the given dataset.
Args:
model_name: The name of the model to train.
dataset_name: The name of the dataset to train on.
data_dir: Directory ... | def t2t_train(model_name, dataset_name,
data_dir=None, output_dir=None, config_file=None, config=None):
"""Main function to train the given model on the given dataset.
Args:
model_name: The name of the model to train.
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train | decode | Decode from estimator. Interactive, from file, or from dataset. | tensor2tensor/bin/t2t_decoder.py | def decode(estimator, hparams, decode_hp):
"""Decode from estimator. Interactive, from file, or from dataset."""
if FLAGS.decode_interactive:
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raise ValueError("TPU can only decode from dataset.")
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"""Decode from estimator. Interactive, from file, or from dataset."""
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raise ValueError("TPU can only decode from dataset.")
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train | score_file | Score each line in a file and return the scores. | tensor2tensor/bin/t2t_decoder.py | def score_file(filename):
"""Score each line in a file and return the scores."""
# Prepare model.
hparams = create_hparams()
encoders = registry.problem(FLAGS.problem).feature_encoders(FLAGS.data_dir)
has_inputs = "inputs" in encoders
# Prepare features for feeding into the model.
if has_inputs:
inpu... | def score_file(filename):
"""Score each line in a file and return the scores."""
# Prepare model.
hparams = create_hparams()
encoders = registry.problem(FLAGS.problem).feature_encoders(FLAGS.data_dir)
has_inputs = "inputs" in encoders
# Prepare features for feeding into the model.
if has_inputs:
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train | time_to_channels | Put time dimension on channels in an embedded video. | tensor2tensor/models/research/autoencoders.py | def time_to_channels(embedded_video):
"""Put time dimension on channels in an embedded video."""
video_shape = common_layers.shape_list(embedded_video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
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"""Put time dimension on channels in an embedded video."""
video_shape = common_layers.shape_list(embedded_video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
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train | autoencoder_basic | Basic autoencoder model. | tensor2tensor/models/research/autoencoders.py | def autoencoder_basic():
"""Basic autoencoder model."""
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
hparams.b... | def autoencoder_basic():
"""Basic autoencoder model."""
hparams = common_hparams.basic_params1()
hparams.optimizer = "adam"
hparams.learning_rate_constant = 0.0002
hparams.learning_rate_warmup_steps = 500
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train | autoencoder_autoregressive | Autoregressive autoencoder model. | tensor2tensor/models/research/autoencoders.py | def autoencoder_autoregressive():
"""Autoregressive autoencoder model."""
hparams = autoencoder_basic()
hparams.add_hparam("autoregressive_forget_base", False)
hparams.add_hparam("autoregressive_mode", "none")
hparams.add_hparam("autoregressive_decode_steps", 0)
hparams.add_hparam("autoregressive_eval_pure_... | def autoencoder_autoregressive():
"""Autoregressive autoencoder model."""
hparams = autoencoder_basic()
hparams.add_hparam("autoregressive_forget_base", False)
hparams.add_hparam("autoregressive_mode", "none")
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train | autoencoder_residual | Residual autoencoder model. | tensor2tensor/models/research/autoencoders.py | def autoencoder_residual():
"""Residual autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.optimizer = "Adafactor"
hparams.clip_grad_norm = 1.0
hparams.learning_rate_constant = 0.5
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup * rsqrt... | def autoencoder_residual():
"""Residual autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.optimizer = "Adafactor"
hparams.clip_grad_norm = 1.0
hparams.learning_rate_constant = 0.5
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train | autoencoder_residual_text | Residual autoencoder model for text. | tensor2tensor/models/research/autoencoders.py | def autoencoder_residual_text():
"""Residual autoencoder model for text."""
hparams = autoencoder_residual()
hparams.bottleneck_bits = 32
hparams.batch_size = 1024
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
hparams.bottom = {
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"""Residual autoencoder model for text."""
hparams = autoencoder_residual()
hparams.bottleneck_bits = 32
hparams.batch_size = 1024
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
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train | autoencoder_basic_discrete | Basic autoencoder model. | tensor2tensor/models/research/autoencoders.py | def autoencoder_basic_discrete():
"""Basic autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.num_hidden_layers = 5
hparams.hidden_size = 64
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.1
hparams.add_hparam("discretize_warmup_steps", 16000)
return hparams | def autoencoder_basic_discrete():
"""Basic autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.num_hidden_layers = 5
hparams.hidden_size = 64
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.1
hparams.add_hparam("discretize_warmup_steps", 16000)
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train | autoencoder_residual_discrete | Residual discrete autoencoder model. | tensor2tensor/models/research/autoencoders.py | def autoencoder_residual_discrete():
"""Residual discrete autoencoder model."""
hparams = autoencoder_residual()
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.05
hparams.add_hparam("discretize_warmup_steps", 16000)
hparams.add_hparam("bottleneck_kind", "tanh_discrete")
hparams.add_hparam("is... | def autoencoder_residual_discrete():
"""Residual discrete autoencoder model."""
hparams = autoencoder_residual()
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hparams.bottleneck_noise = 0.05
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train | autoencoder_residual_discrete_big | Residual discrete autoencoder model, big version. | tensor2tensor/models/research/autoencoders.py | def autoencoder_residual_discrete_big():
"""Residual discrete autoencoder model, big version."""
hparams = autoencoder_residual_discrete()
hparams.hidden_size = 128
hparams.max_hidden_size = 4096
hparams.bottleneck_noise = 0.1
hparams.residual_dropout = 0.4
return hparams | def autoencoder_residual_discrete_big():
"""Residual discrete autoencoder model, big version."""
hparams = autoencoder_residual_discrete()
hparams.hidden_size = 128
hparams.max_hidden_size = 4096
hparams.bottleneck_noise = 0.1
hparams.residual_dropout = 0.4
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train | autoencoder_ordered_discrete | Ordered discrete autoencoder model. | tensor2tensor/models/research/autoencoders.py | def autoencoder_ordered_discrete():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_residual_discrete()
hparams.bottleneck_noise = 0.05 # Use 0.8 for ordered.
hparams.gan_loss_factor = 0.05
hparams.add_hparam("unordered", True)
return hparams | def autoencoder_ordered_discrete():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_residual_discrete()
hparams.bottleneck_noise = 0.05 # Use 0.8 for ordered.
hparams.gan_loss_factor = 0.05
hparams.add_hparam("unordered", True)
return hparams | [
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train | autoencoder_ordered_discrete_image64 | Ordered discrete autoencoder model. | tensor2tensor/models/research/autoencoders.py | def autoencoder_ordered_discrete_image64():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_ordered_discrete()
hparams.batch_size = 32
hparams.num_hidden_layers = 6
hparams.bottleneck_warmup_steps *= 2
hparams.gan_codes_warmup_steps *= 2
return hparams | def autoencoder_ordered_discrete_image64():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_ordered_discrete()
hparams.batch_size = 32
hparams.num_hidden_layers = 6
hparams.bottleneck_warmup_steps *= 2
hparams.gan_codes_warmup_steps *= 2
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train | autoencoder_ordered_text | Ordered discrete autoencoder model for text. | tensor2tensor/models/research/autoencoders.py | def autoencoder_ordered_text():
"""Ordered discrete autoencoder model for text."""
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_bits = 1024
hparams.bottleneck_shared_bits = 1024-64
hparams.bottleneck_shared_bits_start_warmup = 75000
hparams.bottleneck_shared_bits_stop_warmup = 275000
hparam... | def autoencoder_ordered_text():
"""Ordered discrete autoencoder model for text."""
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_bits = 1024
hparams.bottleneck_shared_bits = 1024-64
hparams.bottleneck_shared_bits_start_warmup = 75000
hparams.bottleneck_shared_bits_stop_warmup = 275000
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train | autoencoder_ordered_text_small | Ordered discrete autoencoder model for text, small version. | tensor2tensor/models/research/autoencoders.py | def autoencoder_ordered_text_small():
"""Ordered discrete autoencoder model for text, small version."""
hparams = autoencoder_ordered_text()
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hparams.num_hidden_layers = 3
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"""Ordered discrete autoencoder model for text, small version."""
hparams = autoencoder_ordered_text()
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train | autoencoder_discrete_pong | Discrete autoencoder model for compressing pong frames. | tensor2tensor/models/research/autoencoders.py | def autoencoder_discrete_pong():
"""Discrete autoencoder model for compressing pong frames."""
hparams = autoencoder_ordered_discrete()
hparams.num_hidden_layers = 3
hparams.bottleneck_bits = 24
hparams.batch_size = 2
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hparams.bottleneck_l2_factor = 0.001
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"""Discrete autoencoder model for compressing pong frames."""
hparams = autoencoder_ordered_discrete()
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hparams.batch_size = 2
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train | autoencoder_discrete_tiny | Discrete autoencoder model for compressing pong frames for testing. | tensor2tensor/models/research/autoencoders.py | def autoencoder_discrete_tiny():
"""Discrete autoencoder model for compressing pong frames for testing."""
hparams = autoencoder_ordered_discrete()
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"""Discrete autoencoder model for compressing pong frames for testing."""
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train | autoencoder_discrete_cifar | Discrete autoencoder model for compressing cifar. | tensor2tensor/models/research/autoencoders.py | def autoencoder_discrete_cifar():
"""Discrete autoencoder model for compressing cifar."""
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_noise = 0.0
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hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.num_residual_layers = 4
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"""Discrete autoencoder model for compressing cifar."""
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train | autoencoder_range | Tuning grid of the main autoencoder params. | tensor2tensor/models/research/autoencoders.py | def autoencoder_range(rhp):
"""Tuning grid of the main autoencoder params."""
rhp.set_float("dropout", 0.01, 0.3)
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"""Tuning grid of the main autoencoder params."""
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train | image_encoder | A stack of self attention layers. | tensor2tensor/models/research/vqa_attention.py | def image_encoder(image_feat,
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"""A stack of self attention layers."""
x = image_feat
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train | question_encoder | Question encoder, run LSTM encoder and get the last output as encoding. | tensor2tensor/models/research/vqa_attention.py | def question_encoder(question, hparams, name="encoder"):
"""Question encoder, run LSTM encoder and get the last output as encoding."""
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train | attn | Attention on image feature with question as query. | tensor2tensor/models/research/vqa_attention.py | def attn(image_feat, query, hparams, name="attn"):
"""Attention on image feature with question as query."""
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"""Attention on image feature with question as query."""
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train | mlp | Multi layer perceptron with dropout and relu activation. | tensor2tensor/models/research/vqa_attention.py | def mlp(feature, hparams, name="mlp"):
"""Multi layer perceptron with dropout and relu activation."""
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"""Multi layer perceptron with dropout and relu activation."""
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train | vqa_attention_base | VQA attention baseline hparams. | tensor2tensor/models/research/vqa_attention.py | def vqa_attention_base():
"""VQA attention baseline hparams."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.use_fixed_batch_size = True,
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hparams.optimizer_adam_beta1 = 0.9
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hparams.optimizer_adam_epsilon = 1e-8
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"""VQA attention baseline hparams."""
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hparams.batch_size = 128
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train | vqa_attention_base_range | Small range of hyperparameters. | tensor2tensor/models/research/vqa_attention.py | def vqa_attention_base_range(rhp):
"""Small range of hyperparameters."""
# After starting from base, set intervals for some parameters.
rhp.set_float("learning_rate", 0.1, 1.0, scale=rhp.LOG_SCALE)
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rhp.set_float("learning_rate", 0.1, 1.0, scale=rhp.LOG_SCALE)
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train | History.append | Append (step, value) pair to history for the given mode and metric. | tensor2tensor/trax/history.py | def append(self, mode, metric, step, value):
"""Append (step, value) pair to history for the given mode and metric."""
if mode not in self._values:
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train | History.get | Get the history for the given metric and mode. | tensor2tensor/trax/history.py | def get(self, mode, metric):
"""Get the history for the given metric and mode."""
if mode not in self._values:
logging.info("Metric %s not found for mode %s", metric, mode)
return []
return list(self._values[mode][metric]) | def get(self, mode, metric):
"""Get the history for the given metric and mode."""
if mode not in self._values:
logging.info("Metric %s not found for mode %s", metric, mode)
return []
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train | History.metrics_for_mode | Metrics available for a given mode. | tensor2tensor/trax/history.py | def metrics_for_mode(self, mode):
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train | batch_norm_relu | Performs a batch normalization followed by a ReLU.
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is_training: `bool` for whether the model is training.
relu: `bool` if False, omits the ReLU operation.
init_zero: `bool` if True, initializes scale parameter of batch
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is_training,
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init_zero=False,
data_format="channels_first"):
"""Performs a batch normalization followed by a ReLU.
Args:
inputs: `Tensor` of shape `[batch, channels, ...]`.
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relu=True,
init_zero=False,
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train | conv2d_fixed_padding | Strided 2-D convolution with explicit padding.
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Args:
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targeting_rate=None,
keep_prob=None,
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train | residual_block | Standard building block for residual networks with BN before convolutions.
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inputs: `Tensor` of size `[batch, channels, height, width]`.
filters: `int` number of filters for the first two convolutions. Note that
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strides,
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use_td=False,
targeting_rate=None,
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use_td=False,
targeting_rate=None,
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train | bottleneck_block | Bottleneck block variant for residual networks with BN after convolutions.
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inputs: `Tensor` of size `[batch, channels, height, width]`.
filters: `int` number of filters for the first two convolutions. Note that
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train | block_layer | Creates one layer of blocks for the ResNet model.
Args:
inputs: `Tensor` of size `[batch, channels, height, width]`.
filters: `int` number of filters for the first convolution of the layer.
block_fn: `function` for the block to use within the model
blocks: `int` number of blocks contained in the laye... | tensor2tensor/models/resnet.py | def block_layer(inputs,
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use_td=False,
targeting_rate=None,
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train | resnet_v2 | Resnet model.
Args:
inputs: `Tensor` images.
block_fn: `function` for the block to use within the model. Either
`residual_block` or `bottleneck_block`.
layer_blocks: list of 3 or 4 `int`s denoting the number of blocks to include
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data_format="channels_first",
is_training=False,
is_cifar=False,
use_td=False,
targeting_rate=None,
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"""Resnet model.
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layer_blocks,
filters,
data_format="channels_first",
is_training=False,
is_cifar=False,
use_td=False,
targeting_rate=None,
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"""Resnet model.
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train | resnet_imagenet_34_td_weight_05_05 | Set of hyperparameters. | tensor2tensor/models/resnet.py | def resnet_imagenet_34_td_weight_05_05():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "weight"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
return hp | def resnet_imagenet_34_td_weight_05_05():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "weight"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
return hp | [
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train | resnet_imagenet_34_td_unit_05_05 | Set of hyperparameters. | tensor2tensor/models/resnet.py | def resnet_imagenet_34_td_unit_05_05():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "unit"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
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"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "unit"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
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train | resnet_imagenet_34_td_unit_no_drop | Set of hyperparameters. | tensor2tensor/models/resnet.py | def resnet_imagenet_34_td_unit_no_drop():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "unit"
hp.targeting_rate = 0.0
hp.keep_prob = 1.0
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"""Set of hyperparameters."""
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hp.targeting_rate = 0.0
hp.keep_prob = 1.0
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train | resnet_cifar_15 | Set of hyperparameters. | tensor2tensor/models/resnet.py | def resnet_cifar_15():
"""Set of hyperparameters."""
hp = resnet_base()
hp.block_fn = "residual"
hp.is_cifar = True
hp.layer_sizes = [2, 2, 2]
hp.filter_sizes = [16, 32, 64, 128]
return hp | def resnet_cifar_15():
"""Set of hyperparameters."""
hp = resnet_base()
hp.block_fn = "residual"
hp.is_cifar = True
hp.layer_sizes = [2, 2, 2]
hp.filter_sizes = [16, 32, 64, 128]
return hp | [
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train | _len_lcs | Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y | tensor2tensor/utils/rouge.py | def _len_lcs(x, y):
"""Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y
"""
table = _lcs(x, y)
n, m =... | def _len_lcs(x, y):
"""Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
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train | _lcs | Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
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Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of words
Returns:
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"""Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
in O(nm) time where n = len(x) and m = len(y).
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of ... | def _lcs(x, y):
"""Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
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train | rouge_l_sentence_level | Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Calculated according to:
R_lcs = LCS(X,Y)/m
P_lcs = LCS(X,Y)/n
F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_... | tensor2tensor/utils/rouge.py | def rouge_l_sentence_level(eval_sentences, ref_sentences):
"""Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Calculated according to:
R_lcs = LCS(X,Y)/m
P_lcs = LCS(X,Y)... | def rouge_l_sentence_level(eval_sentences, ref_sentences):
"""Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
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train | rouge_l_fscore | ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
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Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge_l_fscore: approx rouge-l f1 sco... | tensor2tensor/utils/rouge.py | def rouge_l_fscore(predictions, labels, **unused_kwargs):
"""ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gol... | def rouge_l_fscore(predictions, labels, **unused_kwargs):
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train | _get_ngrams | Calculates n-grams.
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train | rouge_2_fscore | ROUGE-2 F1 score computation between labels and predictions.
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train | normalize_example_nlp | Normalize the examples from different tasks so they can be merged.
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train | flatten_zip_dataset | A list of examples to a dataset containing mixed examples.
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Args:
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train | aggregate_task_losses | Multiproblem loss function. | tensor2tensor/data_generators/multi_problem.py | def aggregate_task_losses(hparams,
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train | aggregate_task_lm_losses | LM loss for multiproblems. | tensor2tensor/data_generators/multi_problem.py | def aggregate_task_lm_losses(hparams,
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train | MultiProblem.normalize_example | Normalize the examples from different tasks so they can be merged. | tensor2tensor/data_generators/multi_problem.py | def normalize_example(self, task, example, encoder, hparams, is_infer):
"""Normalize the examples from different tasks so they can be merged."""
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train | MultiProblem.update_task_ids | Generate task_ids for each problem.
These ids correspond to the index of the task in the task_list.
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train | MultiProblem.get_max_num_classes | Compute the maximum number of classes any subtask has.
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labels for the classification tasks. Currently, labels from different tasks
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Returns:
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This is useful for modifying the size of the softmax to include the output
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Returns:
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train | RecurrentMemory.pre_attention | Called prior to self-attention, to incorporate memory items.
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segment: 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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segment: an integer Tensor with shape [batch]
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train | RecentTokensMemory.pre_attention | Called prior to self-attention, to incorporate memory items.
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train | RecentTokensMemory.post_attention | Called after self-attention. The memory can be updated here.
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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
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token: Data returned by pre_attention, which can be used to carry over
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train | TransformerMemory._norm | Compute the safe norm. | tensor2tensor/layers/transformer_memory.py | def _norm(self, x):
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train | TransformerMemory._address_content | Address the memory based on content similarity.
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x: a tensor in the shape of [batch_size, length, depth].
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the logits for each memory entry [batch_size, length, memory_size]. | tensor2tensor/layers/transformer_memory.py | def _address_content(self, x):
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x: a tensor in the shape of [batch_size, length, depth].
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x: a tensor in the shape of [batch_size, length, depth].
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train | TransformerMemory.read | 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
[batch_size, length, memor... | tensor2tensor/layers/transformer_memory.py | 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
... | 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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train | TransformerMemory.write | 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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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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train | TransformerMemory.reset | Reset the entries in the memory.
Args:
entries_to_reset: a 1D tensor.
Returns:
the reset op. | tensor2tensor/layers/transformer_memory.py | 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)
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... | 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)
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train | TransformerMemory.pre_attention | Called prior to self-attention, to incorporate memory items.
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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
Tensor with shape [batch, lengt... | tensor2tensor/layers/transformer_memory.py | def pre_attention(self, segment_number, query_antecedent,
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"""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]
... | 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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train | TransformerMemory.post_attention | 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
Returns:
a (possibly modified) version of ... | tensor2tensor/layers/transformer_memory.py | def post_attention(self, token, x):
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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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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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train | _define_train | Define the training setup. | tensor2tensor/rl/ppo_learner.py | 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 = (
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train_env,
ppo_hparams,
eval_env_fn=None,
sampling_temp=1.0,
**collect_kwargs
):
"""Define the training setup."""
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train | _run_train | Train. | tensor2tensor/rl/ppo_learner.py | 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."""
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report_fn=None,
model_save_fn=None):
"""Train."""
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train | _rollout_metadata | Metadata for rollouts. | tensor2tensor/rl/ppo_learner.py | 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... | 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 = [
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train | _define_collect | Collect trajectories.
Args:
batch_env: Batch environment.
ppo_hparams: PPO hparams, defined in tensor2tensor.models.research.rl.
scope: var scope.
frame_stack_size: Number of last observations to feed into the policy.
eval_phase: TODO(koz4k): Write docstring.
sampling_temp: Sampling temperatu... | tensor2tensor/rl/ppo_learner.py | def _define_collect(batch_env, ppo_hparams, scope, frame_stack_size, eval_phase,
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"""Collect trajectories.
Args:
batch_env: Batch environment.
ppo_hparams: PPO hparams, defined in tensor2tensor.models.research.rl.
scope: var scope.
frame_st... | 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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train | deconv2d | Deconvolution layer. | tensor2tensor/models/vanilla_gan.py | 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(
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initializer=tf.random_normal_initializer(stddev=stddev))
deconv ... | 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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train | sliced_gan | Basic parameters for a vanilla_gan. | tensor2tensor/models/vanilla_gan.py | 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... | 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
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train | AbstractGAN.discriminator | 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 logits (before sigmoid). | tensor2tensor/models/vanilla_gan.py | 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... | 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:
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train | AbstractGAN.generator | Generator outputting image in [0, 1]. | tensor2tensor/models/vanilla_gan.py | 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(
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initializer=tf.random_normal_initializer(stddev=0.02)):
net =... | 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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train | AbstractGAN.body | 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). | tensor2tensor/models/vanilla_gan.py | 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... | 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
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train | inputs | 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 "t2t_".
data_dir: data directory.
input_name: optional, name of the inputs from the dictionary.
num_chunks: optio... | tensor2tensor/trax/inputs.py | 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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"""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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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/inputs.py#L58-L95 | [
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train | random_inputs | Make random Inputs for debugging.
Args:
num_devices: how many devices to build the inputs for.
input_shape: the shape of inputs (including batch dimension).
input_dtype: the type of the inputs (int32 by default).
input_range: the range of inputs (defaults to (0, 255)).
output_shape: the shape of ... | tensor2tensor/trax/inputs.py | 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 ... | 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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train | dataset_to_stream | Takes a tf.Dataset and creates a numpy stream of ready batches. | tensor2tensor/trax/inputs.py | 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,... | 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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train | _train_and_eval_dataset_v1 | Return train and evaluation datasets, feature info and supervised keys. | tensor2tensor/trax/inputs.py | 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... | 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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train | batch_fun | Batching function. | tensor2tensor/trax/inputs.py | 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... | 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... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/inputs.py#L262-L316 | [
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train | lm1b_preprocess | Preprocessing for LM1B: filter out targets exceeding maximum length. | tensor2tensor/trax/inputs.py | 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(... | 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(... | [
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... | 272500b6efe353aeb638d2745ed56e519462ca31 |
train | shuffle_and_batch_data | Shuffle and batch the given dataset. | tensor2tensor/trax/inputs.py | def shuffle_and_batch_data(dataset,
target_names,
features_info,
training,
num_devices,
shuffle_buffer_size=1024,
preprocess_fun=no_preprocess):
"""Shuffle ... | def shuffle_and_batch_data(dataset,
target_names,
features_info,
training,
num_devices,
shuffle_buffer_size=1024,
preprocess_fun=no_preprocess):
"""Shuffle ... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/inputs.py#L357-L385 | [
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train | _train_and_eval_batches | Return train and eval batches with input name and shape. | tensor2tensor/trax/inputs.py | 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(
dataset, data_dir)
input_names, target_names = keys[0], keys[1]
train_batches = shuffle_and_batch_d... | 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(
dataset, data_dir)
input_names, target_names = keys[0], keys[1]
train_batches = shuffle_and_batch_d... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/inputs.py#L388-L405 | [
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train | get_multi_dataset | 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
dataset with. This tensor is often constructed with the global_step. If
this is None, we... | tensor2tensor/data_generators/multi_problem_v2.py | 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
dataset with. This tensor is often constructed... | 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
dataset with. This tensor is often constructed... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem_v2.py#L205-L223 | [
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train | get_schedule_distribution | 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 distribution of the global_step. | tensor2tensor/data_generators/multi_problem_v2.py | 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... | 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... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem_v2.py#L226-L253 | [
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"# py_func doesn't seem to work on TPU - at least get the cons... | 272500b6efe353aeb638d2745ed56e519462ca31 |
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