id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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21,700 | tensorflow/tensor2tensor | tensor2tensor/serving/export.py | _get_hparams_path | def _get_hparams_path():
"""Get hyper-parameters file path."""
hparams_path = None
if FLAGS.output_dir:
hparams_path = os.path.join(FLAGS.output_dir, "hparams.json")
else:
tf.logging.warning(
"--output_dir not specified. Hyper-parameters will be infered from"
"--hparams_set and --hparams... | python | def _get_hparams_path():
"""Get hyper-parameters file path."""
hparams_path = None
if FLAGS.output_dir:
hparams_path = os.path.join(FLAGS.output_dir, "hparams.json")
else:
tf.logging.warning(
"--output_dir not specified. Hyper-parameters will be infered from"
"--hparams_set and --hparams... | [
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21,701 | tensorflow/tensor2tensor | tensor2tensor/serving/export.py | export_module_spec_with_checkpoint | def export_module_spec_with_checkpoint(module_spec,
checkpoint_path,
export_path,
scope_prefix=""):
"""Exports given checkpoint as tfhub module with given spec."""
# The main requirement is that it ... | python | def export_module_spec_with_checkpoint(module_spec,
checkpoint_path,
export_path,
scope_prefix=""):
"""Exports given checkpoint as tfhub module with given spec."""
# The main requirement is that it ... | [
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21,702 | tensorflow/tensor2tensor | tensor2tensor/serving/export.py | export_as_tfhub_module | def export_as_tfhub_module(model_name,
hparams,
decode_hparams,
problem,
checkpoint_path,
export_dir):
"""Exports the last checkpoint from the directory as tfhub module.
It creates... | python | def export_as_tfhub_module(model_name,
hparams,
decode_hparams,
problem,
checkpoint_path,
export_dir):
"""Exports the last checkpoint from the directory as tfhub module.
It creates... | [
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21,703 | tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | build_model | def build_model(hparams_set, model_name, data_dir, problem_name, beam_size=1):
"""Build the graph required to fetch the attention weights.
Args:
hparams_set: HParams set to build the model with.
model_name: Name of model.
data_dir: Path to directory containing training data.
problem_name: Name of p... | python | def build_model(hparams_set, model_name, data_dir, problem_name, beam_size=1):
"""Build the graph required to fetch the attention weights.
Args:
hparams_set: HParams set to build the model with.
model_name: Name of model.
data_dir: Path to directory containing training data.
problem_name: Name of p... | [
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21,704 | tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | get_att_mats | def get_att_mats(translate_model):
"""Get's the tensors representing the attentions from a build model.
The attentions are stored in a dict on the Transformer object while building
the graph.
Args:
translate_model: Transformer object to fetch the attention weights from.
Returns:
Tuple of attention ma... | python | def get_att_mats(translate_model):
"""Get's the tensors representing the attentions from a build model.
The attentions are stored in a dict on the Transformer object while building
the graph.
Args:
translate_model: Transformer object to fetch the attention weights from.
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21,705 | tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.encode | def encode(self, input_str):
"""Input str to features dict, ready for inference."""
inputs = self.encoders["inputs"].encode(input_str) + [EOS_ID]
batch_inputs = np.reshape(inputs, [1, -1, 1, 1]) # Make it 3D.
return batch_inputs | python | def encode(self, input_str):
"""Input str to features dict, ready for inference."""
inputs = self.encoders["inputs"].encode(input_str) + [EOS_ID]
batch_inputs = np.reshape(inputs, [1, -1, 1, 1]) # Make it 3D.
return batch_inputs | [
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21,706 | tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.decode | def decode(self, integers):
"""List of ints to str."""
integers = list(np.squeeze(integers))
return self.encoders["inputs"].decode(integers) | python | def decode(self, integers):
"""List of ints to str."""
integers = list(np.squeeze(integers))
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21,707 | tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.decode_list | def decode_list(self, integers):
"""List of ints to list of str."""
integers = list(np.squeeze(integers))
return self.encoders["inputs"].decode_list(integers) | python | def decode_list(self, integers):
"""List of ints to list of str."""
integers = list(np.squeeze(integers))
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21,708 | tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.get_vis_data_from_string | def get_vis_data_from_string(self, sess, input_string):
"""Constructs the data needed for visualizing attentions.
Args:
sess: A tf.Session object.
input_string: The input sentence to be translated and visualized.
Returns:
Tuple of (
output_string: The translated sentence.
... | python | def get_vis_data_from_string(self, sess, input_string):
"""Constructs the data needed for visualizing attentions.
Args:
sess: A tf.Session object.
input_string: The input sentence to be translated and visualized.
Returns:
Tuple of (
output_string: The translated sentence.
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21,709 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow.py | glow_hparams | def glow_hparams():
"""Glow Hparams."""
hparams = common_hparams.basic_params1()
hparams.clip_grad_norm = None
hparams.weight_decay = 0.0
hparams.learning_rate_constant = 3e-4
hparams.batch_size = 32
# can be prev_level, prev_step or normal.
# see: glow_ops.merge_level_and_latent_dist
hparams.add_hpar... | python | def glow_hparams():
"""Glow Hparams."""
hparams = common_hparams.basic_params1()
hparams.clip_grad_norm = None
hparams.weight_decay = 0.0
hparams.learning_rate_constant = 3e-4
hparams.batch_size = 32
# can be prev_level, prev_step or normal.
# see: glow_ops.merge_level_and_latent_dist
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21,710 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_aux.py | shift_and_pad | def shift_and_pad(tensor, shift, axis=0):
"""Shifts and pads with zero along an axis.
Example:
shift_and_pad([1, 2, 3, 4], 2) --> [0, 0, 1, 2]
shift_and_pad([1, 2, 3, 4], -2) --> [3, 4, 0, 0]
Args:
tensor: Tensor; to be shifted and padded.
shift: int; number of positions to shift by.
axis: ... | python | def shift_and_pad(tensor, shift, axis=0):
"""Shifts and pads with zero along an axis.
Example:
shift_and_pad([1, 2, 3, 4], 2) --> [0, 0, 1, 2]
shift_and_pad([1, 2, 3, 4], -2) --> [3, 4, 0, 0]
Args:
tensor: Tensor; to be shifted and padded.
shift: int; number of positions to shift by.
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21,711 | tensorflow/tensor2tensor | tensor2tensor/models/video/base.py | pixels_from_softmax | def pixels_from_softmax(frame_logits, pure_sampling=False,
temperature=1.0, gumbel_noise_factor=0.2):
"""Given frame_logits from a per-pixel softmax, generate colors."""
# If we're purely sampling, just sample each pixel.
if pure_sampling or temperature == 0.0:
return common_layers.sam... | python | def pixels_from_softmax(frame_logits, pure_sampling=False,
temperature=1.0, gumbel_noise_factor=0.2):
"""Given frame_logits from a per-pixel softmax, generate colors."""
# If we're purely sampling, just sample each pixel.
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21,712 | tensorflow/tensor2tensor | tensor2tensor/models/video/base.py | next_frame_base | def next_frame_base():
"""Common HParams for next_frame models."""
hparams = common_hparams.basic_params1()
# Loss cutoff.
hparams.add_hparam("video_modality_loss_cutoff", 0.01)
# Additional resizing the frames before feeding them to model.
hparams.add_hparam("preprocess_resize_frames", None)
# How many d... | python | def next_frame_base():
"""Common HParams for next_frame models."""
hparams = common_hparams.basic_params1()
# Loss cutoff.
hparams.add_hparam("video_modality_loss_cutoff", 0.01)
# Additional resizing the frames before feeding them to model.
hparams.add_hparam("preprocess_resize_frames", None)
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21,713 | tensorflow/tensor2tensor | tensor2tensor/rl/gym_utils.py | remove_time_limit_wrapper | def remove_time_limit_wrapper(env):
"""Removes top level TimeLimit Wrapper.
Removes TimeLimit Wrapper from top level if exists, throws error if any other
TimeLimit Wrapper is present in stack.
Args:
env: environment
Returns:
the env with removed time limit wrapper.
"""
if isinstance(env, gym.wr... | python | def remove_time_limit_wrapper(env):
"""Removes top level TimeLimit Wrapper.
Removes TimeLimit Wrapper from top level if exists, throws error if any other
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Args:
env: environment
Returns:
the env with removed time limit wrapper.
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21,714 | tensorflow/tensor2tensor | tensor2tensor/rl/gym_utils.py | gym_env_wrapper | def gym_env_wrapper(env, rl_env_max_episode_steps, maxskip_env, rendered_env,
rendered_env_resize_to, sticky_actions):
"""Wraps a gym environment. see make_gym_env for details."""
# rl_env_max_episode_steps is None or int.
assert ((not rl_env_max_episode_steps) or
isinstance(rl_env_m... | python | def gym_env_wrapper(env, rl_env_max_episode_steps, maxskip_env, rendered_env,
rendered_env_resize_to, sticky_actions):
"""Wraps a gym environment. see make_gym_env for details."""
# rl_env_max_episode_steps is None or int.
assert ((not rl_env_max_episode_steps) or
isinstance(rl_env_m... | [
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21,715 | tensorflow/tensor2tensor | tensor2tensor/rl/gym_utils.py | make_gym_env | def make_gym_env(name,
rl_env_max_episode_steps=-1,
maxskip_env=False,
rendered_env=False,
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"""Create a gym env optionally with a time limit and maxskip wrapper.
NOTE: The returne... | python | def make_gym_env(name,
rl_env_max_episode_steps=-1,
maxskip_env=False,
rendered_env=False,
rendered_env_resize_to=None,
sticky_actions=False):
"""Create a gym env optionally with a time limit and maxskip wrapper.
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21,716 | tensorflow/tensor2tensor | tensor2tensor/rl/gym_utils.py | register_gym_env | def register_gym_env(class_entry_point, version="v0", kwargs=None):
"""Registers the class in Gym and returns the registered name and the env."""
split_on_colon = class_entry_point.split(":")
assert len(split_on_colon) == 2
class_name = split_on_colon[1]
# We have to add the version to conform to gym's API.... | python | def register_gym_env(class_entry_point, version="v0", kwargs=None):
"""Registers the class in Gym and returns the registered name and the env."""
split_on_colon = class_entry_point.split(":")
assert len(split_on_colon) == 2
class_name = split_on_colon[1]
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21,717 | tensorflow/tensor2tensor | tensor2tensor/rl/gym_utils.py | MaxAndSkipEnv.step | def step(self, action):
"""Repeat action, sum reward, and max over last observations."""
total_reward = 0.0
done = None
for i in range(self._skip):
obs, reward, done, info = self.env.step(action)
if i == self._skip - 2:
self._obs_buffer[0] = obs
if i == self._skip - 1:
... | python | def step(self, action):
"""Repeat action, sum reward, and max over last observations."""
total_reward = 0.0
done = None
for i in range(self._skip):
obs, reward, done, info = self.env.step(action)
if i == self._skip - 2:
self._obs_buffer[0] = obs
if i == self._skip - 1:
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21,718 | tensorflow/tensor2tensor | tensor2tensor/data_generators/all_problems.py | _handle_errors | def _handle_errors(errors):
"""Log out and possibly reraise errors during import."""
if not errors:
return
log_all = True # pylint: disable=unused-variable
err_msg = "T2T: skipped importing {num_missing} data_generators modules."
print(err_msg.format(num_missing=len(errors)))
for module, err in errors:... | python | def _handle_errors(errors):
"""Log out and possibly reraise errors during import."""
if not errors:
return
log_all = True # pylint: disable=unused-variable
err_msg = "T2T: skipped importing {num_missing} data_generators modules."
print(err_msg.format(num_missing=len(errors)))
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21,719 | tensorflow/tensor2tensor | tensor2tensor/utils/hparams_lib.py | create_hparams | def create_hparams(hparams_set,
hparams_overrides_str="",
data_dir=None,
problem_name=None,
hparams_path=None):
"""Create HParams with data_dir and problem hparams, if kwargs provided."""
hparams = registry.hparams(hparams_set)
if hparams... | python | def create_hparams(hparams_set,
hparams_overrides_str="",
data_dir=None,
problem_name=None,
hparams_path=None):
"""Create HParams with data_dir and problem hparams, if kwargs provided."""
hparams = registry.hparams(hparams_set)
if hparams... | [
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21,720 | tensorflow/tensor2tensor | tensor2tensor/utils/hparams_lib.py | create_hparams_from_json | def create_hparams_from_json(json_path, hparams=None):
"""Loading hparams from json; can also start from hparams if specified."""
tf.logging.info("Loading hparams from existing json %s" % json_path)
with tf.gfile.Open(json_path, "r") as f:
hparams_values = json.load(f)
# Prevent certain keys from overwrit... | python | def create_hparams_from_json(json_path, hparams=None):
"""Loading hparams from json; can also start from hparams if specified."""
tf.logging.info("Loading hparams from existing json %s" % json_path)
with tf.gfile.Open(json_path, "r") as f:
hparams_values = json.load(f)
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21,721 | tensorflow/tensor2tensor | tensor2tensor/utils/hparams_lib.py | add_problem_hparams | def add_problem_hparams(hparams, problem_name_or_instance):
"""Add problem hparams for the problems."""
if isinstance(problem_name_or_instance, problem_lib.Problem):
problem = problem_name_or_instance
else:
problem = registry.problem(problem_name_or_instance)
p_hparams = problem.get_hparams(hparams)
h... | python | def add_problem_hparams(hparams, problem_name_or_instance):
"""Add problem hparams for the problems."""
if isinstance(problem_name_or_instance, problem_lib.Problem):
problem = problem_name_or_instance
else:
problem = registry.problem(problem_name_or_instance)
p_hparams = problem.get_hparams(hparams)
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21,722 | tensorflow/tensor2tensor | tensor2tensor/data_generators/subject_verb_agreement.py | load_examples | def load_examples(tmp_dir, prop_train=0.09, prop_val=0.01):
"""Loads exampls from the tsv file.
Args:
tmp_dir: temp directory.
prop_train: proportion of the train data
prop_val: proportion of the validation data
Returns:
All examples in the dataset pluse train, test, and development splits.
"... | python | def load_examples(tmp_dir, prop_train=0.09, prop_val=0.01):
"""Loads exampls from the tsv file.
Args:
tmp_dir: temp directory.
prop_train: proportion of the train data
prop_val: proportion of the validation data
Returns:
All examples in the dataset pluse train, test, and development splits.
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21,723 | tensorflow/tensor2tensor | tensor2tensor/data_generators/cifar.py | _get_cifar | def _get_cifar(directory, url):
"""Download and extract CIFAR to directory unless it is there."""
filename = os.path.basename(url)
path = generator_utils.maybe_download(directory, filename, url)
tarfile.open(path, "r:gz").extractall(directory) | python | def _get_cifar(directory, url):
"""Download and extract CIFAR to directory unless it is there."""
filename = os.path.basename(url)
path = generator_utils.maybe_download(directory, filename, url)
tarfile.open(path, "r:gz").extractall(directory) | [
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21,724 | tensorflow/tensor2tensor | tensor2tensor/data_generators/cifar.py | cifar_generator | def cifar_generator(cifar_version, tmp_dir, training, how_many, start_from=0):
"""Image generator for CIFAR-10 and 100.
Args:
cifar_version: string; one of "cifar10" or "cifar100"
tmp_dir: path to temporary storage directory.
training: a Boolean; if true, we use the train set, otherwise the test set.
... | python | def cifar_generator(cifar_version, tmp_dir, training, how_many, start_from=0):
"""Image generator for CIFAR-10 and 100.
Args:
cifar_version: string; one of "cifar10" or "cifar100"
tmp_dir: path to temporary storage directory.
training: a Boolean; if true, we use the train set, otherwise the test set.
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21,725 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_ppo_base | def rlmb_ppo_base():
"""HParams for PPO base."""
hparams = _rlmb_base()
ppo_params = dict(
base_algo="ppo",
base_algo_params="ppo_original_params",
# Number of real environments to train on simultaneously.
real_batch_size=1,
# Number of simulated environments to train on simultaneous... | python | def rlmb_ppo_base():
"""HParams for PPO base."""
hparams = _rlmb_base()
ppo_params = dict(
base_algo="ppo",
base_algo_params="ppo_original_params",
# Number of real environments to train on simultaneously.
real_batch_size=1,
# Number of simulated environments to train on simultaneous... | [
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21,726 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_dqn_base | def rlmb_dqn_base():
"""rlmb_dqn_base params."""
hparams = _rlmb_base()
simulated_rollout_length = 10
dqn_params = dict(
base_algo="dqn",
base_algo_params="dqn_original_params",
real_batch_size=1,
simulated_batch_size=16,
dqn_agent_generates_trainable_dones=False,
eval_batch_... | python | def rlmb_dqn_base():
"""rlmb_dqn_base params."""
hparams = _rlmb_base()
simulated_rollout_length = 10
dqn_params = dict(
base_algo="dqn",
base_algo_params="dqn_original_params",
real_batch_size=1,
simulated_batch_size=16,
dqn_agent_generates_trainable_dones=False,
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21,727 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_ppo_quick | def rlmb_ppo_quick():
"""Base setting but quicker with only 2 epochs."""
hparams = rlmb_ppo_base()
hparams.epochs = 2
hparams.model_train_steps = 25000
hparams.ppo_epochs_num = 700
hparams.ppo_epoch_length = 50
return hparams | python | def rlmb_ppo_quick():
"""Base setting but quicker with only 2 epochs."""
hparams = rlmb_ppo_base()
hparams.epochs = 2
hparams.model_train_steps = 25000
hparams.ppo_epochs_num = 700
hparams.ppo_epoch_length = 50
return hparams | [
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21,728 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_base_stochastic | def rlmb_base_stochastic():
"""Base setting with a stochastic next-frame model."""
hparams = rlmb_base()
hparams.initial_epoch_train_steps_multiplier = 5
hparams.generative_model = "next_frame_basic_stochastic"
hparams.generative_model_params = "next_frame_basic_stochastic"
return hparams | python | def rlmb_base_stochastic():
"""Base setting with a stochastic next-frame model."""
hparams = rlmb_base()
hparams.initial_epoch_train_steps_multiplier = 5
hparams.generative_model = "next_frame_basic_stochastic"
hparams.generative_model_params = "next_frame_basic_stochastic"
return hparams | [
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21,729 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_long_stochastic_discrete_simulation_deterministic_starts | def rlmb_long_stochastic_discrete_simulation_deterministic_starts():
"""Long setting with stochastic discrete model & deterministic sim starts."""
hparams = rlmb_base_stochastic_discrete()
hparams.generative_model_params = "next_frame_basic_stochastic_discrete_long"
hparams.ppo_epochs_num = 1000
hparams.simul... | python | def rlmb_long_stochastic_discrete_simulation_deterministic_starts():
"""Long setting with stochastic discrete model & deterministic sim starts."""
hparams = rlmb_base_stochastic_discrete()
hparams.generative_model_params = "next_frame_basic_stochastic_discrete_long"
hparams.ppo_epochs_num = 1000
hparams.simul... | [
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21,730 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_base_sv2p | def rlmb_base_sv2p():
"""Base setting with sv2p as world model."""
hparams = rlmb_base()
hparams.learning_rate_bump = 1.0
hparams.generative_model = "next_frame_sv2p"
hparams.generative_model_params = "next_frame_sv2p_atari"
return hparams | python | def rlmb_base_sv2p():
"""Base setting with sv2p as world model."""
hparams = rlmb_base()
hparams.learning_rate_bump = 1.0
hparams.generative_model = "next_frame_sv2p"
hparams.generative_model_params = "next_frame_sv2p_atari"
return hparams | [
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21,731 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | _rlmb_tiny_overrides | def _rlmb_tiny_overrides():
"""Parameters to override for tiny setting excluding agent-related hparams."""
return dict(
epochs=1,
num_real_env_frames=128,
model_train_steps=2,
max_num_noops=1,
eval_max_num_noops=1,
generative_model_params="next_frame_tiny",
stop_loop_early=... | python | def _rlmb_tiny_overrides():
"""Parameters to override for tiny setting excluding agent-related hparams."""
return dict(
epochs=1,
num_real_env_frames=128,
model_train_steps=2,
max_num_noops=1,
eval_max_num_noops=1,
generative_model_params="next_frame_tiny",
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21,732 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_tiny_stochastic | def rlmb_tiny_stochastic():
"""Tiny setting with a stochastic next-frame model."""
hparams = rlmb_ppo_tiny()
hparams.epochs = 1 # Too slow with 2 for regular runs.
hparams.generative_model = "next_frame_basic_stochastic"
hparams.generative_model_params = "next_frame_basic_stochastic"
return hparams | python | def rlmb_tiny_stochastic():
"""Tiny setting with a stochastic next-frame model."""
hparams = rlmb_ppo_tiny()
hparams.epochs = 1 # Too slow with 2 for regular runs.
hparams.generative_model = "next_frame_basic_stochastic"
hparams.generative_model_params = "next_frame_basic_stochastic"
return hparams | [
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21,733 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_tiny_recurrent | def rlmb_tiny_recurrent():
"""Tiny setting with a recurrent next-frame model."""
hparams = rlmb_ppo_tiny()
hparams.epochs = 1 # Too slow with 2 for regular runs.
hparams.generative_model = "next_frame_basic_recurrent"
hparams.generative_model_params = "next_frame_basic_recurrent"
return hparams | python | def rlmb_tiny_recurrent():
"""Tiny setting with a recurrent next-frame model."""
hparams = rlmb_ppo_tiny()
hparams.epochs = 1 # Too slow with 2 for regular runs.
hparams.generative_model = "next_frame_basic_recurrent"
hparams.generative_model_params = "next_frame_basic_recurrent"
return hparams | [
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21,734 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_tiny_sv2p | def rlmb_tiny_sv2p():
"""Tiny setting with a tiny sv2p model."""
hparams = rlmb_ppo_tiny()
hparams.generative_model = "next_frame_sv2p"
hparams.generative_model_params = "next_frame_sv2p_tiny"
hparams.grayscale = False
return hparams | python | def rlmb_tiny_sv2p():
"""Tiny setting with a tiny sv2p model."""
hparams = rlmb_ppo_tiny()
hparams.generative_model = "next_frame_sv2p"
hparams.generative_model_params = "next_frame_sv2p_tiny"
hparams.grayscale = False
return hparams | [
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21,735 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | rlmb_grid | def rlmb_grid(rhp):
"""Grid over games and frames, and 5 runs each for variance."""
rhp.set_categorical("loop.game", ["breakout", "pong", "freeway"])
base = 100000
medium = base // 2
small = medium // 2
rhp.set_discrete("loop.num_real_env_frames", [base, medium, small])
# Dummy parameter to get 5 runs fo... | python | def rlmb_grid(rhp):
"""Grid over games and frames, and 5 runs each for variance."""
rhp.set_categorical("loop.game", ["breakout", "pong", "freeway"])
base = 100000
medium = base // 2
small = medium // 2
rhp.set_discrete("loop.num_real_env_frames", [base, medium, small])
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21,736 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | merge_unscoped_hparams | def merge_unscoped_hparams(scopes_and_hparams):
"""Merge multiple HParams into one with scopes."""
merged_values = {}
for (scope, hparams) in scopes_and_hparams:
for key, value in six.iteritems(hparams.values()):
scoped_key = "%s.%s" % (scope, key)
merged_values[scoped_key] = value
return hpara... | python | def merge_unscoped_hparams(scopes_and_hparams):
"""Merge multiple HParams into one with scopes."""
merged_values = {}
for (scope, hparams) in scopes_and_hparams:
for key, value in six.iteritems(hparams.values()):
scoped_key = "%s.%s" % (scope, key)
merged_values[scoped_key] = value
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21,737 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | split_scoped_hparams | def split_scoped_hparams(scopes, merged_hparams):
"""Split single HParams with scoped keys into multiple."""
split_values = {scope: {} for scope in scopes}
merged_values = merged_hparams.values()
for scoped_key, value in six.iteritems(merged_values):
scope = scoped_key.split(".")[0]
key = scoped_key[len... | python | def split_scoped_hparams(scopes, merged_hparams):
"""Split single HParams with scoped keys into multiple."""
split_values = {scope: {} for scope in scopes}
merged_values = merged_hparams.values()
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21,738 | tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_based_params.py | training_loop_hparams_from_scoped_overrides | def training_loop_hparams_from_scoped_overrides(scoped_overrides, trial_id):
"""Create HParams suitable for training loop from scoped HParams.
Args:
scoped_overrides: HParams, with keys all scoped by one of HP_SCOPES. These
parameters are overrides for the base HParams created by
create_loop_hparam... | python | def training_loop_hparams_from_scoped_overrides(scoped_overrides, trial_id):
"""Create HParams suitable for training loop from scoped HParams.
Args:
scoped_overrides: HParams, with keys all scoped by one of HP_SCOPES. These
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21,739 | tensorflow/tensor2tensor | tensor2tensor/rl/player.py | PlayerEnv.get_keys_to_action | def get_keys_to_action(self):
"""Get mapping from keyboard keys to actions.
Required by gym.utils.play in environment or top level wrapper.
Returns:
{
Unicode code point for keyboard key: action (formatted for step()),
...
}
"""
# Based on gym AtariEnv.get_keys_to_actio... | python | def get_keys_to_action(self):
"""Get mapping from keyboard keys to actions.
Required by gym.utils.play in environment or top level wrapper.
Returns:
{
Unicode code point for keyboard key: action (formatted for step()),
...
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21,740 | tensorflow/tensor2tensor | tensor2tensor/rl/player.py | SimAndRealEnvPlayer._player_step_tuple | def _player_step_tuple(self, envs_step_tuples):
"""Construct observation, return usual step tuple.
Args:
envs_step_tuples: tuples.
Returns:
Step tuple: ob, reward, done, info
ob: concatenated images [simulated observation, real observation,
difference], with additional inform... | python | def _player_step_tuple(self, envs_step_tuples):
"""Construct observation, return usual step tuple.
Args:
envs_step_tuples: tuples.
Returns:
Step tuple: ob, reward, done, info
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21,741 | tensorflow/tensor2tensor | tensor2tensor/rl/player.py | SimAndRealEnvPlayer.reset | def reset(self):
"""Reset simulated and real environments."""
self._frame_counter = 0
ob_real = self.real_env.reset()
# Initialize simulated environment with frames from real one.
self.sim_env.add_to_initial_stack(ob_real)
for _ in range(3):
ob_real, _, _, _ = self.real_env.step(self.name_... | python | def reset(self):
"""Reset simulated and real environments."""
self._frame_counter = 0
ob_real = self.real_env.reset()
# Initialize simulated environment with frames from real one.
self.sim_env.add_to_initial_stack(ob_real)
for _ in range(3):
ob_real, _, _, _ = self.real_env.step(self.name_... | [
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21,742 | tensorflow/tensor2tensor | tensor2tensor/rl/player.py | SingleEnvPlayer._player_step_tuple | def _player_step_tuple(self, envs_step_tuples):
"""Augment observation, return usual step tuple."""
ob, reward, done, info = envs_step_tuples["env"]
ob = self._augment_observation(ob, reward, self.cumulative_reward)
return ob, reward, done, info | python | def _player_step_tuple(self, envs_step_tuples):
"""Augment observation, return usual step tuple."""
ob, reward, done, info = envs_step_tuples["env"]
ob = self._augment_observation(ob, reward, self.cumulative_reward)
return ob, reward, done, info | [
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21,743 | tensorflow/tensor2tensor | tensor2tensor/layers/common_audio.py | add_delta_deltas | def add_delta_deltas(filterbanks, name=None):
"""Compute time first and second-order derivative channels.
Args:
filterbanks: float32 tensor with shape [batch_size, len, num_bins, 1]
name: scope name
Returns:
float32 tensor with shape [batch_size, len, num_bins, 3]
"""
delta_filter = np.array([2,... | python | def add_delta_deltas(filterbanks, name=None):
"""Compute time first and second-order derivative channels.
Args:
filterbanks: float32 tensor with shape [batch_size, len, num_bins, 1]
name: scope name
Returns:
float32 tensor with shape [batch_size, len, num_bins, 3]
"""
delta_filter = np.array([2,... | [
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21,744 | tensorflow/tensor2tensor | tensor2tensor/layers/common_audio.py | compute_mel_filterbank_features | def compute_mel_filterbank_features(
waveforms,
sample_rate=16000, dither=1.0 / np.iinfo(np.int16).max, preemphasis=0.97,
frame_length=25, frame_step=10, fft_length=None,
window_fn=functools.partial(tf.contrib.signal.hann_window, periodic=True),
lower_edge_hertz=80.0, upper_edge_hertz=7600.0, num_me... | python | def compute_mel_filterbank_features(
waveforms,
sample_rate=16000, dither=1.0 / np.iinfo(np.int16).max, preemphasis=0.97,
frame_length=25, frame_step=10, fft_length=None,
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21,745 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem_utils.py | play_env_problem_randomly | def play_env_problem_randomly(env_problem,
num_steps):
"""Plays the env problem by randomly sampling actions for `num_steps`."""
# Reset all environments.
env_problem.reset()
# Play all environments, sampling random actions each time.
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num_steps):
"""Plays the env problem by randomly sampling actions for `num_steps`."""
# Reset all environments.
env_problem.reset()
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21,746 | tensorflow/tensor2tensor | tensor2tensor/data_generators/cipher.py | generate_plaintext_random | def generate_plaintext_random(plain_vocab, distribution, train_samples,
length):
"""Generates samples of text from the provided vocabulary.
Args:
plain_vocab: vocabulary.
distribution: distribution.
train_samples: samples for training.
length: length.
Returns:
t... | python | def generate_plaintext_random(plain_vocab, distribution, train_samples,
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"""Generates samples of text from the provided vocabulary.
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plain_vocab: vocabulary.
distribution: distribution.
train_samples: samples for training.
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21,747 | tensorflow/tensor2tensor | tensor2tensor/data_generators/cipher.py | encipher_shift | def encipher_shift(plaintext, plain_vocab, shift):
"""Encrypt plain text with a single shift layer.
Args:
plaintext (list of list of Strings): a list of plain text to encrypt.
plain_vocab (list of Integer): unique vocabularies being used.
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"""Encrypt plain text with a single shift layer.
Args:
plaintext (list of list of Strings): a list of plain text to encrypt.
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21,748 | tensorflow/tensor2tensor | tensor2tensor/data_generators/cipher.py | encipher_vigenere | def encipher_vigenere(plaintext, plain_vocab, key):
"""Encrypt plain text with given key.
Args:
plaintext (list of list of Strings): a list of plain text to encrypt.
plain_vocab (list of Integer): unique vocabularies being used.
key (list of Integer): key to encrypt cipher using Vigenere table.
Retu... | python | def encipher_vigenere(plaintext, plain_vocab, key):
"""Encrypt plain text with given key.
Args:
plaintext (list of list of Strings): a list of plain text to encrypt.
plain_vocab (list of Integer): unique vocabularies being used.
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21,749 | tensorflow/tensor2tensor | tensor2tensor/models/research/super_lm.py | super_lm_moe | def super_lm_moe():
"""Add mixture of experts with ~1B params."""
hparams = super_lm_base()
hparams.layers = (
("n,att,m,d,a," "n,moe,m,d,a,") * 4 + "n,ffn,d")
hparams.moe_num_experts = 32
hparams.moe_hidden_sizes = "1024"
return hparams | python | def super_lm_moe():
"""Add mixture of experts with ~1B params."""
hparams = super_lm_base()
hparams.layers = (
("n,att,m,d,a," "n,moe,m,d,a,") * 4 + "n,ffn,d")
hparams.moe_num_experts = 32
hparams.moe_hidden_sizes = "1024"
return hparams | [
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21,750 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe_tr_dense_2k | def xmoe_tr_dense_2k():
"""Series of architectural experiments on Translation.
# run on 8-core setup
119M params, einsum=0.95e13
Returns:
a hparams
"""
hparams = mtf_transformer2.mtf_bitransformer_base()
hparams.encoder_layers = ["self_att", "drd"] * 4
hparams.decoder_layers = ["self_att", "enc_a... | python | def xmoe_tr_dense_2k():
"""Series of architectural experiments on Translation.
# run on 8-core setup
119M params, einsum=0.95e13
Returns:
a hparams
"""
hparams = mtf_transformer2.mtf_bitransformer_base()
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21,751 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe_dense_4k | def xmoe_dense_4k():
"""Series of architectural experiments on cheap language models.
For all of these architectures, we run on languagemodel_lm1b8k_packed
for 32000 steps.
All log-perplexities are per-token - multiply by 1.298 for per-word
Results:
model params(M) einsum alltoall mxu-util... | python | def xmoe_dense_4k():
"""Series of architectural experiments on cheap language models.
For all of these architectures, we run on languagemodel_lm1b8k_packed
for 32000 steps.
All log-perplexities are per-token - multiply by 1.298 for per-word
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21,752 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe_2d | def xmoe_2d():
"""Two-dimensional hierarchical mixture of 16 experts."""
hparams = xmoe_top_2()
hparams.decoder_layers = ["att", "hmoe"] * 4
hparams.mesh_shape = "b0:2;b1:4"
hparams.outer_batch_size = 4
hparams.layout = "outer_batch:b0;inner_batch:b1,expert_x:b1,expert_y:b0"
hparams.moe_num_experts = [4, ... | python | def xmoe_2d():
"""Two-dimensional hierarchical mixture of 16 experts."""
hparams = xmoe_top_2()
hparams.decoder_layers = ["att", "hmoe"] * 4
hparams.mesh_shape = "b0:2;b1:4"
hparams.outer_batch_size = 4
hparams.layout = "outer_batch:b0;inner_batch:b1,expert_x:b1,expert_y:b0"
hparams.moe_num_experts = [4, ... | [
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21,753 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe2_dense | def xmoe2_dense(sz):
"""Series of architectural experiments on language modeling.
Larger models than the ones above.
All models are trained on sequences of 1024 tokens.
We assume infinite training data, so no dropout necessary.
We process 2^36 tokens in training = 524288 steps at batch size 128
TODO(noa... | python | def xmoe2_dense(sz):
"""Series of architectural experiments on language modeling.
Larger models than the ones above.
All models are trained on sequences of 1024 tokens.
We assume infinite training data, so no dropout necessary.
We process 2^36 tokens in training = 524288 steps at batch size 128
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21,754 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe2_v1 | def xmoe2_v1():
"""Model incorporating mixture-of-experts and local-attention.
~6B parameters
32 experts in 3 hierarchichal moe layers.
Returns:
a hparams
"""
hparams = xmoe2_dense(0)
moe.set_default_moe_hparams(hparams)
hparams.decoder_layers = (
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"a... | python | def xmoe2_v1():
"""Model incorporating mixture-of-experts and local-attention.
~6B parameters
32 experts in 3 hierarchichal moe layers.
Returns:
a hparams
"""
hparams = xmoe2_dense(0)
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21,755 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe2_v1_x128 | def xmoe2_v1_x128():
"""128 experts, ~25B params - Train for 131072 steps on 8x8."""
hparams = xmoe2_v1()
hparams.moe_num_experts = [16, 8]
hparams.outer_batch_size = 8
hparams.mesh_shape = "b0:8;b1:16"
hparams.batch_size = 512
hparams.learning_rate_decay_steps = 16384
return hparams | python | def xmoe2_v1_x128():
"""128 experts, ~25B params - Train for 131072 steps on 8x8."""
hparams = xmoe2_v1()
hparams.moe_num_experts = [16, 8]
hparams.outer_batch_size = 8
hparams.mesh_shape = "b0:8;b1:16"
hparams.batch_size = 512
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return hparams | [
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21,756 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe2_tiny | def xmoe2_tiny():
"""Test on local cpu."""
hparams = xmoe2_v1()
hparams.decoder_layers = [
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hparams.d_model = 128
hparams.moe_hidden_size = 512
hparams.outer_batch_size = 0
hparams.batch_size = 2
hparams.mesh_shape = ""
hparams.activation_dtype... | python | def xmoe2_tiny():
"""Test on local cpu."""
hparams = xmoe2_v1()
hparams.decoder_layers = [
"local_att", "att", "compressed_att", "drd", "hmoe"]
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21,757 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | xmoe2_v1_l4k_compressed_c4 | def xmoe2_v1_l4k_compressed_c4():
"""With compressed attention."""
hparams = xmoe2_v1_l4k()
hparams.decoder_layers = [
"compressed_att" if l == "att" else l for l in hparams.decoder_layers]
hparams.compression_factor = 4
return hparams | python | def xmoe2_v1_l4k_compressed_c4():
"""With compressed attention."""
hparams = xmoe2_v1_l4k()
hparams.decoder_layers = [
"compressed_att" if l == "att" else l for l in hparams.decoder_layers]
hparams.compression_factor = 4
return hparams | [
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21,758 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | wiki_2x2_base | def wiki_2x2_base():
"""Set of architectural experiments - language model on wikipedia on a 2x2.
1 epoch = ~180k steps at batch size 32 - we may never finish an epoch!
Returns:
a hparams
"""
hparams = mtf_transformer.mtf_transformer_base_lm()
hparams.shared_embedding_and_softmax_weights = False
# no... | python | def wiki_2x2_base():
"""Set of architectural experiments - language model on wikipedia on a 2x2.
1 epoch = ~180k steps at batch size 32 - we may never finish an epoch!
Returns:
a hparams
"""
hparams = mtf_transformer.mtf_transformer_base_lm()
hparams.shared_embedding_and_softmax_weights = False
# no... | [
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21,759 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | denoise_z15 | def denoise_z15():
"""Replace tokens instead of masking."""
hparams = xmoe2_dense_0()
hparams.decoder_type = "denoising"
hparams.noising_spec_train = {"type": "random_zipfian", "prob": 0.15}
hparams.noising_use_eval_during_train = 0.25
return hparams | python | def denoise_z15():
"""Replace tokens instead of masking."""
hparams = xmoe2_dense_0()
hparams.decoder_type = "denoising"
hparams.noising_spec_train = {"type": "random_zipfian", "prob": 0.15}
hparams.noising_use_eval_during_train = 0.25
return hparams | [
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21,760 | tensorflow/tensor2tensor | tensor2tensor/models/research/moe_experiments.py | denoise_v1_m15 | def denoise_v1_m15():
"""Denoising experiment."""
hparams = xmoe2_v1()
# no local attention
# TODO(noam): non-masked version of local-attention
hparams.decoder_layers = [
"att" if l == "local_att" else l for l in hparams.decoder_layers]
hparams.decoder_type = "denoising"
hparams.noising_spec_train =... | python | def denoise_v1_m15():
"""Denoising experiment."""
hparams = xmoe2_v1()
# no local attention
# TODO(noam): non-masked version of local-attention
hparams.decoder_layers = [
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21,761 | tensorflow/tensor2tensor | tensor2tensor/utils/sari_hook.py | _get_ngram_counter | def _get_ngram_counter(ids, n):
"""Get a Counter with the ngrams of the given ID list.
Args:
ids: np.array or a list corresponding to a single sentence
n: n-gram size
Returns:
collections.Counter with ID tuples as keys and 1s as values.
"""
# Remove zero IDs used to pad the sequence.
ids = [to... | python | def _get_ngram_counter(ids, n):
"""Get a Counter with the ngrams of the given ID list.
Args:
ids: np.array or a list corresponding to a single sentence
n: n-gram size
Returns:
collections.Counter with ID tuples as keys and 1s as values.
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21,762 | tensorflow/tensor2tensor | tensor2tensor/utils/sari_hook.py | _get_fbeta_score | def _get_fbeta_score(true_positives, selected, relevant, beta=1):
"""Compute Fbeta score.
Args:
true_positives: Number of true positive ngrams.
selected: Number of selected ngrams.
relevant: Number of relevant ngrams.
beta: 0 gives precision only, 1 gives F1 score, and Inf gives recall only.
Ret... | python | def _get_fbeta_score(true_positives, selected, relevant, beta=1):
"""Compute Fbeta score.
Args:
true_positives: Number of true positive ngrams.
selected: Number of selected ngrams.
relevant: Number of relevant ngrams.
beta: 0 gives precision only, 1 gives F1 score, and Inf gives recall only.
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21,763 | tensorflow/tensor2tensor | tensor2tensor/utils/sari_hook.py | get_sari_score | def get_sari_score(source_ids, prediction_ids, list_of_targets,
max_gram_size=4, beta_for_deletion=0):
"""Compute the SARI score for a single prediction and one or more targets.
Args:
source_ids: a list / np.array of SentencePiece IDs
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max_gram_size=4, beta_for_deletion=0):
"""Compute the SARI score for a single prediction and one or more targets.
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21,764 | tensorflow/tensor2tensor | tensor2tensor/data_generators/mnist.py | _get_mnist | def _get_mnist(directory):
"""Download all MNIST files to directory unless they are there."""
for filename in [
_MNIST_TRAIN_DATA_FILENAME, _MNIST_TRAIN_LABELS_FILENAME,
_MNIST_TEST_DATA_FILENAME, _MNIST_TEST_LABELS_FILENAME
]:
generator_utils.maybe_download(directory, filename, _MNIST_URL + filen... | python | def _get_mnist(directory):
"""Download all MNIST files to directory unless they are there."""
for filename in [
_MNIST_TRAIN_DATA_FILENAME, _MNIST_TRAIN_LABELS_FILENAME,
_MNIST_TEST_DATA_FILENAME, _MNIST_TEST_LABELS_FILENAME
]:
generator_utils.maybe_download(directory, filename, _MNIST_URL + filen... | [
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21,765 | tensorflow/tensor2tensor | tensor2tensor/data_generators/mnist.py | _extract_mnist_images | def _extract_mnist_images(filename, num_images):
"""Extract images from an MNIST file into a numpy array.
Args:
filename: The path to an MNIST images file.
num_images: The number of images in the file.
Returns:
A numpy array of shape [number_of_images, height, width, channels].
"""
with gzip.ope... | python | def _extract_mnist_images(filename, num_images):
"""Extract images from an MNIST file into a numpy array.
Args:
filename: The path to an MNIST images file.
num_images: The number of images in the file.
Returns:
A numpy array of shape [number_of_images, height, width, channels].
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21,766 | tensorflow/tensor2tensor | tensor2tensor/data_generators/mnist.py | _extract_mnist_labels | def _extract_mnist_labels(filename, num_labels):
"""Extract labels from an MNIST file into integers.
Args:
filename: The path to an MNIST labels file.
num_labels: The number of labels in the file.
Returns:
A int64 numpy array of shape [num_labels]
"""
with gzip.open(filename) as bytestream:
... | python | def _extract_mnist_labels(filename, num_labels):
"""Extract labels from an MNIST file into integers.
Args:
filename: The path to an MNIST labels file.
num_labels: The number of labels in the file.
Returns:
A int64 numpy array of shape [num_labels]
"""
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21,767 | tensorflow/tensor2tensor | tensor2tensor/data_generators/mnist.py | _get_fashion_mnist | def _get_fashion_mnist(directory):
"""Download all FashionMNIST files to directory unless they are there."""
# Fashion mnist files have the same names as MNIST.
# We must choose a separate name (by adding 'fashion-' prefix) in the tmp_dir.
for filename in [
_MNIST_TRAIN_DATA_FILENAME, _MNIST_TRAIN_LABELS_... | python | def _get_fashion_mnist(directory):
"""Download all FashionMNIST files to directory unless they are there."""
# Fashion mnist files have the same names as MNIST.
# We must choose a separate name (by adding 'fashion-' prefix) in the tmp_dir.
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21,768 | tensorflow/tensor2tensor | tensor2tensor/data_generators/mnist.py | fashion_mnist_generator | def fashion_mnist_generator(tmp_dir, training, how_many, start_from=0):
"""Image generator for FashionMNIST.
Args:
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 images and labels to generate.
start_from: ... | python | def fashion_mnist_generator(tmp_dir, training, how_many, start_from=0):
"""Image generator for FashionMNIST.
Args:
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 images and labels to generate.
start_from: ... | [
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21,769 | tensorflow/tensor2tensor | tensor2tensor/data_generators/timeseries_data_generator.py | generate_data | def generate_data(timeseries_length, timeseries_params):
"""Generates synthetic timeseries using input parameters.
Each generated timeseries has timeseries_length data points.
Parameters for each timeseries are specified by timeseries_params.
Args:
timeseries_length: Number of data points to generate for ... | python | def generate_data(timeseries_length, timeseries_params):
"""Generates synthetic timeseries using input parameters.
Each generated timeseries has timeseries_length data points.
Parameters for each timeseries are specified by timeseries_params.
Args:
timeseries_length: Number of data points to generate for ... | [
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21,770 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_stochastic.py | next_frame_basic_stochastic_discrete | def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.batch_size = 4
hparams.video_num_target_frames = 6
hparams.scheduled_sampling_mode = "prob_inverse_lin"
hparams.scheduled_sampling_de... | python | def next_frame_basic_stochastic_discrete():
"""Basic 2-frame conv model with stochastic discrete latent."""
hparams = basic_deterministic_params.next_frame_sampling()
hparams.batch_size = 4
hparams.video_num_target_frames = 6
hparams.scheduled_sampling_mode = "prob_inverse_lin"
hparams.scheduled_sampling_de... | [
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21,771 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_stochastic.py | next_frame_stochastic_discrete_range | def next_frame_stochastic_discrete_range(rhp):
"""Next frame stochastic discrete tuning grid."""
rhp.set_float("learning_rate_constant", 0.001, 0.01)
rhp.set_float("dropout", 0.2, 0.6)
rhp.set_int("filter_double_steps", 3, 5)
rhp.set_discrete("hidden_size", [64, 96, 128])
rhp.set_discrete("bottleneck_bits",... | python | def next_frame_stochastic_discrete_range(rhp):
"""Next frame stochastic discrete tuning grid."""
rhp.set_float("learning_rate_constant", 0.001, 0.01)
rhp.set_float("dropout", 0.2, 0.6)
rhp.set_int("filter_double_steps", 3, 5)
rhp.set_discrete("hidden_size", [64, 96, 128])
rhp.set_discrete("bottleneck_bits",... | [
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21,772 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/base.py | shapes | def shapes(x):
"""Get a structure of shapes for a structure of nested arrays."""
def shape(x):
try:
return x.shape
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"""Get a structure of shapes for a structure of nested arrays."""
def shape(x):
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"""Get a structure of sizes for a structure of nested arrays."""
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except Exception: # pylint: disable=broad-except
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"""Get a structure of sizes for a structure of nested arrays."""
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21,774 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/base.py | _find_frame | def _find_frame(stack, start=0):
"""Find the frame with the caller on the stack."""
# We want to find the first place where the layer was called
# that is *not* an __init__ function of an inheriting layer.
frame = inspect.getframeinfo(stack[start][0])
# If we are in an init, move on.
if frame.function == '_... | python | def _find_frame(stack, start=0):
"""Find the frame with the caller on the stack."""
# We want to find the first place where the layer was called
# that is *not* an __init__ function of an inheriting layer.
frame = inspect.getframeinfo(stack[start][0])
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21,775 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/base.py | _shorten_file_path | def _shorten_file_path(line):
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if start < 0:
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first_quote = line.find('"', start)
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return line
second_quote = line.find('"', first_quote + 1)
if second_quote < 0:
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"""Shorten file path in error lines for more readable tracebacks."""
start = line.lower().find('file')
if start < 0:
return line
first_quote = line.find('"', start)
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"""Cleaned-up form of traceback."""
counter, res = 0, []
# Skipping 3 lines by default: the top (useless) and self-call.
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21,777 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/base.py | layer | def layer(output_shape=None, new_parameters=None):
"""Create a layer class from a function."""
def layer_decorator(call):
"""Decorating the call function."""
def output_shape_fun(self, input_shape):
if output_shape is None:
return input_shape
kwargs = self._init_kwargs # pylint: disable... | python | def layer(output_shape=None, new_parameters=None):
"""Create a layer class from a function."""
def layer_decorator(call):
"""Decorating the call function."""
def output_shape_fun(self, input_shape):
if output_shape is None:
return input_shape
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21,778 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/base.py | Layer.initialize | def initialize(self, input_shape, rng):
"""Initialize the layer given an input shape and rng.
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21,779 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/wikisum.py | _wiki_articles | def _wiki_articles(shard_id, wikis_dir=None):
"""Generates WikipediaArticles from GCS that are part of shard shard_id."""
if not wikis_dir:
wikis_dir = WIKI_CONTENT_DIR
with tf.Graph().as_default():
dataset = tf.data.TFRecordDataset(
cc_utils.readahead(
os.path.join(wikis_dir, WIKI_CON... | python | def _wiki_articles(shard_id, wikis_dir=None):
"""Generates WikipediaArticles from GCS that are part of shard shard_id."""
if not wikis_dir:
wikis_dir = WIKI_CONTENT_DIR
with tf.Graph().as_default():
dataset = tf.data.TFRecordDataset(
cc_utils.readahead(
os.path.join(wikis_dir, WIKI_CON... | [
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21,780 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/wikisum.py | rank_reference_paragraphs | def rank_reference_paragraphs(wiki_title, references_content, normalize=True):
"""Rank and return reference paragraphs by tf-idf score on title tokens."""
normalized_title = _normalize_text(wiki_title)
title_tokens = _tokens_to_score(
set(tokenizer.encode(text_encoder.native_to_unicode(normalized_title))))
... | python | def rank_reference_paragraphs(wiki_title, references_content, normalize=True):
"""Rank and return reference paragraphs by tf-idf score on title tokens."""
normalized_title = _normalize_text(wiki_title)
title_tokens = _tokens_to_score(
set(tokenizer.encode(text_encoder.native_to_unicode(normalized_title))))
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21,781 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/wikisum.py | _encode_wiki_sections | def _encode_wiki_sections(sections, vocab):
"""Encodes sections with vocab. Returns ids and section boundaries."""
ids = []
section_boundaries = []
for i, section in enumerate(sections):
if i > 0:
# Skip including article title
ids.extend(vocab.encode(_format_title(_normalize_text(section.title)... | python | def _encode_wiki_sections(sections, vocab):
"""Encodes sections with vocab. Returns ids and section boundaries."""
ids = []
section_boundaries = []
for i, section in enumerate(sections):
if i > 0:
# Skip including article title
ids.extend(vocab.encode(_format_title(_normalize_text(section.title)... | [
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21,782 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/wikisum.py | extract_references_from_wets | def extract_references_from_wets(wet_files, metadata_dir, out_dir,
tmp_dir=None):
"""Extract references from WET files into sharded output files."""
# Setup output files
shard_files = make_ref_shard_files(out_dir)
num_refs = 0
for i, wet_file in enumerate(wet_files):
num_... | python | def extract_references_from_wets(wet_files, metadata_dir, out_dir,
tmp_dir=None):
"""Extract references from WET files into sharded output files."""
# Setup output files
shard_files = make_ref_shard_files(out_dir)
num_refs = 0
for i, wet_file in enumerate(wet_files):
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21,783 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki.py | _dump_to_pages | def _dump_to_pages(dump):
"""Extract pages from an xml dump.
Args:
dump: a unicode string
Returns:
a list of unicode strings
"""
pos = 0
ret = []
start_tag = u"<page>\n"
end_tag = u"</page>\n"
while True:
start_pos = dump.find(start_tag, pos)
if start_pos == -1:
break
start_... | python | def _dump_to_pages(dump):
"""Extract pages from an xml dump.
Args:
dump: a unicode string
Returns:
a list of unicode strings
"""
pos = 0
ret = []
start_tag = u"<page>\n"
end_tag = u"</page>\n"
while True:
start_pos = dump.find(start_tag, pos)
if start_pos == -1:
break
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21,784 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki.py | _page_to_text | def _page_to_text(page):
"""Extract the text from a page.
Args:
page: a unicode string
Returns:
a unicode string
"""
# text start tag looks like "<text ..otherstuff>"
start_pos = page.find(u"<text")
assert start_pos != -1
end_tag_pos = page.find(u">", start_pos)
assert end_tag_pos != -1
end... | python | def _page_to_text(page):
"""Extract the text from a page.
Args:
page: a unicode string
Returns:
a unicode string
"""
# text start tag looks like "<text ..otherstuff>"
start_pos = page.find(u"<text")
assert start_pos != -1
end_tag_pos = page.find(u">", start_pos)
assert end_tag_pos != -1
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21,785 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki.py | _find_and_replace | def _find_and_replace(text, start_string, end_string, replace_fn):
"""Remove everything found between instances of start_string and end_string.
Replace each such instance with replace_fn(removed_text)
e.g. _find_and_replace(u"the [[fat]] cat [[sat]]", u"[[", u"]]", lambda x: x)
= u"the fat cat sat"
Args:... | python | def _find_and_replace(text, start_string, end_string, replace_fn):
"""Remove everything found between instances of start_string and end_string.
Replace each such instance with replace_fn(removed_text)
e.g. _find_and_replace(u"the [[fat]] cat [[sat]]", u"[[", u"]]", lambda x: x)
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21,786 | tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | prepare_question_encoder | def prepare_question_encoder(inputs, hparams):
"""Prepare question encoder.
Args:
inputs: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-attention
"""
encoder_input = inputs
... | python | def prepare_question_encoder(inputs, hparams):
"""Prepare question encoder.
Args:
inputs: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-attention
"""
encoder_input = inputs
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] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/vqa_self_attention.py#L316-L339 |
21,787 | tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | prepare_image_question_encoder | def prepare_image_question_encoder(image_feat, question, hparams):
"""Prepare encoder.
Args:
image_feat: a Tensor.
question: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-att... | python | def prepare_image_question_encoder(image_feat, question, hparams):
"""Prepare encoder.
Args:
image_feat: a Tensor.
question: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-att... | [
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21,788 | tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | iterative_encoder_decoder | def iterative_encoder_decoder(encoder_input,
encoder_self_attention_bias,
encoder_decoder_attention_bias,
query,
hparams):
"""Iterative encoder decoder."""
for _ in range(hparams.num_rec_steps):
... | python | def iterative_encoder_decoder(encoder_input,
encoder_self_attention_bias,
encoder_decoder_attention_bias,
query,
hparams):
"""Iterative encoder decoder."""
for _ in range(hparams.num_rec_steps):
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21,789 | tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_self_attention.py | vqa_self_attention_feature_batch1024_big | def vqa_self_attention_feature_batch1024_big():
"""Big model."""
hparams = vqa_self_attention_feature_batch1024()
hparams.learning_rate_constant = 7e-4
hparams.batch_size = 256
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_heads = 16
hparams.layer_prepostprocess_dropout = 0.3
hpara... | python | def vqa_self_attention_feature_batch1024_big():
"""Big model."""
hparams = vqa_self_attention_feature_batch1024()
hparams.learning_rate_constant = 7e-4
hparams.batch_size = 256
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_heads = 16
hparams.layer_prepostprocess_dropout = 0.3
hpara... | [
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21,790 | tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | _bucket_boundaries | def _bucket_boundaries(max_length, min_length=8, length_bucket_step=1.1):
"""A default set of length-bucket boundaries."""
assert length_bucket_step > 1.0
x = min_length
boundaries = []
while x < max_length:
boundaries.append(x)
x = max(x + 1, int(x * length_bucket_step))
return boundaries | python | def _bucket_boundaries(max_length, min_length=8, length_bucket_step=1.1):
"""A default set of length-bucket boundaries."""
assert length_bucket_step > 1.0
x = min_length
boundaries = []
while x < max_length:
boundaries.append(x)
x = max(x + 1, int(x * length_bucket_step))
return boundaries | [
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21,791 | tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | batching_scheme | def batching_scheme(batch_size,
max_length,
min_length_bucket,
length_bucket_step,
drop_long_sequences=False,
shard_multiplier=1,
length_multiplier=1,
min_length=0):
"""A batchin... | python | def batching_scheme(batch_size,
max_length,
min_length_bucket,
length_bucket_step,
drop_long_sequences=False,
shard_multiplier=1,
length_multiplier=1,
min_length=0):
"""A batchin... | [
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Every batch contains a number of sequences divisible by `shard_multiplier`.
Args:
batch_size: int, total number of tokens in a batch.
max_length: int, sequences longer than this will be skipped. Defaults to
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21,792 | tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | hparams_to_batching_scheme | def hparams_to_batching_scheme(hparams,
drop_long_sequences=False,
shard_multiplier=1,
length_multiplier=1):
"""Wrapper around _batching_scheme with hparams."""
return batching_scheme(
batch_size=hparams.batch_size,
... | python | def hparams_to_batching_scheme(hparams,
drop_long_sequences=False,
shard_multiplier=1,
length_multiplier=1):
"""Wrapper around _batching_scheme with hparams."""
return batching_scheme(
batch_size=hparams.batch_size,
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21,793 | tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | pad_for_tpu | def pad_for_tpu(shapes_dict, hparams, max_length):
"""Pads unknown features' dimensions for TPU."""
padded_shapes = {}
def get_filler(specified_max_length):
if not specified_max_length:
return max_length
return min(specified_max_length, max_length)
inputs_none_filler = get_filler(hparams.max_inp... | python | def pad_for_tpu(shapes_dict, hparams, max_length):
"""Pads unknown features' dimensions for TPU."""
padded_shapes = {}
def get_filler(specified_max_length):
if not specified_max_length:
return max_length
return min(specified_max_length, max_length)
inputs_none_filler = get_filler(hparams.max_inp... | [
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21,794 | tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | standardize_shapes | def standardize_shapes(features, batch_size=None):
"""Set the right shapes for the features."""
for fname in ["inputs", "targets"]:
if fname not in features:
continue
f = features[fname]
while len(f.get_shape()) < 4:
f = tf.expand_dims(f, axis=-1)
features[fname] = f
if batch_size:
... | python | def standardize_shapes(features, batch_size=None):
"""Set the right shapes for the features."""
for fname in ["inputs", "targets"]:
if fname not in features:
continue
f = features[fname]
while len(f.get_shape()) < 4:
f = tf.expand_dims(f, axis=-1)
features[fname] = f
if batch_size:
... | [
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21,795 | tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | _file_num_records_cached | def _file_num_records_cached(filename):
"""Return the number of TFRecords in a file."""
# Cache the result, as this is expensive to compute
if filename in _file_num_records_cache:
return _file_num_records_cache[filename]
ret = 0
for _ in tf.python_io.tf_record_iterator(filename):
ret += 1
_file_num_... | python | def _file_num_records_cached(filename):
"""Return the number of TFRecords in a file."""
# Cache the result, as this is expensive to compute
if filename in _file_num_records_cache:
return _file_num_records_cache[filename]
ret = 0
for _ in tf.python_io.tf_record_iterator(filename):
ret += 1
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21,796 | tensorflow/tensor2tensor | tensor2tensor/utils/data_reader.py | pad_batch | def pad_batch(features, batch_multiple):
"""Pad batch dim of features to nearest multiple of batch_multiple."""
feature = list(features.items())[0][1]
batch_size = tf.shape(feature)[0]
mod = batch_size % batch_multiple
has_mod = tf.cast(tf.cast(mod, tf.bool), tf.int32)
batch_padding = batch_multiple * has_m... | python | def pad_batch(features, batch_multiple):
"""Pad batch dim of features to nearest multiple of batch_multiple."""
feature = list(features.items())[0][1]
batch_size = tf.shape(feature)[0]
mod = batch_size % batch_multiple
has_mod = tf.cast(tf.cast(mod, tf.bool), tf.int32)
batch_padding = batch_multiple * has_m... | [
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21,797 | tensorflow/tensor2tensor | tensor2tensor/data_generators/gene_expression.py | generate_shard_args | def generate_shard_args(outfiles, num_examples):
"""Generate start and end indices per outfile."""
num_shards = len(outfiles)
num_examples_per_shard = num_examples // num_shards
start_idxs = [i * num_examples_per_shard for i in range(num_shards)]
end_idxs = list(start_idxs)
end_idxs.pop(0)
end_idxs.append... | python | def generate_shard_args(outfiles, num_examples):
"""Generate start and end indices per outfile."""
num_shards = len(outfiles)
num_examples_per_shard = num_examples // num_shards
start_idxs = [i * num_examples_per_shard for i in range(num_shards)]
end_idxs = list(start_idxs)
end_idxs.pop(0)
end_idxs.append... | [
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21,798 | tensorflow/tensor2tensor | tensor2tensor/data_generators/gene_expression.py | to_example_dict | def to_example_dict(encoder, inputs, mask, outputs):
"""Convert single h5 record to an example dict."""
# Inputs
bases = []
input_ids = []
last_idx = -1
for row in np.argwhere(inputs):
idx, base_id = row
idx, base_id = int(idx), int(base_id)
assert idx > last_idx # if not, means 2 True values i... | python | def to_example_dict(encoder, inputs, mask, outputs):
"""Convert single h5 record to an example dict."""
# Inputs
bases = []
input_ids = []
last_idx = -1
for row in np.argwhere(inputs):
idx, base_id = row
idx, base_id = int(idx), int(base_id)
assert idx > last_idx # if not, means 2 True values i... | [
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21,799 | tensorflow/tensor2tensor | tensor2tensor/models/research/glow_ops.py | linear_interpolate | def linear_interpolate(tensor1, tensor2, coeffs):
"""Linearly interpolate between two tensors at coeff.
Args:
tensor1: 4-D Tensor, shape=(NHWC)
tensor2: 4-D Tensor, shape=(NHWC)
coeffs: list of floats.
Returns:
interp_latents: 5-D Tensor, with interp_latents[i] representing
in... | python | def linear_interpolate(tensor1, tensor2, coeffs):
"""Linearly interpolate between two tensors at coeff.
Args:
tensor1: 4-D Tensor, shape=(NHWC)
tensor2: 4-D Tensor, shape=(NHWC)
coeffs: list of floats.
Returns:
interp_latents: 5-D Tensor, with interp_latents[i] representing
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Returns:
interp_latents: 5-D Tensor, with interp_latents[i] representing
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