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
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22,000 | tensorflow/tensor2tensor | tensor2tensor/serving/serving_utils.py | make_grpc_request_fn | def make_grpc_request_fn(servable_name, server, timeout_secs):
"""Wraps function to make grpc requests with runtime args."""
stub = _create_stub(server)
def _make_grpc_request(examples):
"""Builds and sends request to TensorFlow model server."""
request = predict_pb2.PredictRequest()
request.model_sp... | python | def make_grpc_request_fn(servable_name, server, timeout_secs):
"""Wraps function to make grpc requests with runtime args."""
stub = _create_stub(server)
def _make_grpc_request(examples):
"""Builds and sends request to TensorFlow model server."""
request = predict_pb2.PredictRequest()
request.model_sp... | [
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22,001 | tensorflow/tensor2tensor | tensor2tensor/serving/serving_utils.py | make_cloud_mlengine_request_fn | def make_cloud_mlengine_request_fn(credentials, model_name, version):
"""Wraps function to make CloudML Engine requests with runtime args."""
def _make_cloud_mlengine_request(examples):
"""Builds and sends requests to Cloud ML Engine."""
api = discovery.build("ml", "v1", credentials=credentials)
parent... | python | def make_cloud_mlengine_request_fn(credentials, model_name, version):
"""Wraps function to make CloudML Engine requests with runtime args."""
def _make_cloud_mlengine_request(examples):
"""Builds and sends requests to Cloud ML Engine."""
api = discovery.build("ml", "v1", credentials=credentials)
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22,002 | tensorflow/tensor2tensor | tensor2tensor/serving/serving_utils.py | predict | def predict(inputs_list, problem, request_fn):
"""Encodes inputs, makes request to deployed TF model, and decodes outputs."""
assert isinstance(inputs_list, list)
fname = "inputs" if problem.has_inputs else "targets"
input_encoder = problem.feature_info[fname].encoder
input_ids_list = [
_encode(inputs, ... | python | def predict(inputs_list, problem, request_fn):
"""Encodes inputs, makes request to deployed TF model, and decodes outputs."""
assert isinstance(inputs_list, list)
fname = "inputs" if problem.has_inputs else "targets"
input_encoder = problem.feature_info[fname].encoder
input_ids_list = [
_encode(inputs, ... | [
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22,003 | tensorflow/tensor2tensor | tensor2tensor/models/video/basic_recurrent.py | next_frame_basic_recurrent | def next_frame_basic_recurrent():
"""Basic 2-frame recurrent model with stochastic tower."""
hparams = basic_stochastic.next_frame_basic_stochastic_discrete()
hparams.filter_double_steps = 2
hparams.hidden_size = 64
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
hparams.concat_inte... | python | def next_frame_basic_recurrent():
"""Basic 2-frame recurrent model with stochastic tower."""
hparams = basic_stochastic.next_frame_basic_stochastic_discrete()
hparams.filter_double_steps = 2
hparams.hidden_size = 64
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
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22,004 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_distill.py | create_teacher_experiment | def create_teacher_experiment(run_config, hparams, argv):
"""Creates experiment function."""
tf.logging.info("training teacher")
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if ... | python | def create_teacher_experiment(run_config, hparams, argv):
"""Creates experiment function."""
tf.logging.info("training teacher")
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
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22,005 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | masked_mean | def masked_mean(inputs, targets, mask_id=None):
"""Mean of the inputs but counting only those where targets != mask_id."""
inputs = [x.astype(np.float32) for x in inputs]
# We assume all elements in the list contribute equally.
# TODO(lukaszkaiser): remove this assumption (e.g., when masks differ).
length = l... | python | def masked_mean(inputs, targets, mask_id=None):
"""Mean of the inputs but counting only those where targets != mask_id."""
inputs = [x.astype(np.float32) for x in inputs]
# We assume all elements in the list contribute equally.
# TODO(lukaszkaiser): remove this assumption (e.g., when masks differ).
length = l... | [
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22,006 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | neg_log_perplexity | def neg_log_perplexity(batch, model_predictions):
"""Calculate negative log perplexity."""
_, targets = batch
model_predictions, targets = _make_list(model_predictions, targets)
xent = []
for (prediction, target) in zip(model_predictions, targets):
hot_target = layers.one_hot(target, prediction.shape[-1])... | python | def neg_log_perplexity(batch, model_predictions):
"""Calculate negative log perplexity."""
_, targets = batch
model_predictions, targets = _make_list(model_predictions, targets)
xent = []
for (prediction, target) in zip(model_predictions, targets):
hot_target = layers.one_hot(target, prediction.shape[-1])... | [
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22,007 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | loss | def loss(params, batch, model_predict, rng):
"""Calculate loss."""
inputs, targets = batch
predictions = model_predict(inputs, params, rng=rng)
predictions, targets = _make_list(predictions, targets)
xent = []
for (pred, target) in zip(predictions, targets):
xent.append(np.sum(pred * layers.one_hot(targ... | python | def loss(params, batch, model_predict, rng):
"""Calculate loss."""
inputs, targets = batch
predictions = model_predict(inputs, params, rng=rng)
predictions, targets = _make_list(predictions, targets)
xent = []
for (pred, target) in zip(predictions, targets):
xent.append(np.sum(pred * layers.one_hot(targ... | [
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22,008 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | restore_state | def restore_state(output_dir):
"""Restore State."""
params_file = os.path.join(output_dir, "model.pkl")
if not gfile.exists(params_file):
return State(step=None, params=None, history=trax_history.History())
with gfile.GFile(params_file, "rb") as f:
(params, step, history) = pickle.load(f)
log("Model ... | python | def restore_state(output_dir):
"""Restore State."""
params_file = os.path.join(output_dir, "model.pkl")
if not gfile.exists(params_file):
return State(step=None, params=None, history=trax_history.History())
with gfile.GFile(params_file, "rb") as f:
(params, step, history) = pickle.load(f)
log("Model ... | [
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22,009 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | save_state | def save_state(state, output_dir, keep=False):
"""Save State and optionally gin config."""
params_file = os.path.join(output_dir, "model.pkl")
with gfile.GFile(params_file, "wb") as f:
pickle.dump((state.params, state.step, state.history), f)
if keep:
params_file = os.path.join(output_dir, "model_{}.pkl... | python | def save_state(state, output_dir, keep=False):
"""Save State and optionally gin config."""
params_file = os.path.join(output_dir, "model.pkl")
with gfile.GFile(params_file, "wb") as f:
pickle.dump((state.params, state.step, state.history), f)
if keep:
params_file = os.path.join(output_dir, "model_{}.pkl... | [
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22,010 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | evaluate_train_and_eval | def evaluate_train_and_eval(step, inputs, predict_fun, eval_steps, rng,
train_sw=None, eval_sw=None, history=None):
"""Evalaute on train and eval data, and log metrics."""
step_log(step, "Evaluation")
train_metrics, eval_metrics = [
evaluate( # pylint: disable=g-complex-comprehe... | python | def evaluate_train_and_eval(step, inputs, predict_fun, eval_steps, rng,
train_sw=None, eval_sw=None, history=None):
"""Evalaute on train and eval data, and log metrics."""
step_log(step, "Evaluation")
train_metrics, eval_metrics = [
evaluate( # pylint: disable=g-complex-comprehe... | [
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22,011 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | log_metrics | def log_metrics(metrics, summ_writer, log_prefix, step, history=None):
"""Log metrics to summary writer and history."""
rjust_len = max([len(name) for name in metrics])
for name, value in six.iteritems(metrics):
step_log(step, "%s %s | % .8f" % (
log_prefix.ljust(5), name.rjust(rjust_len), value))
... | python | def log_metrics(metrics, summ_writer, log_prefix, step, history=None):
"""Log metrics to summary writer and history."""
rjust_len = max([len(name) for name in metrics])
for name, value in six.iteritems(metrics):
step_log(step, "%s %s | % .8f" % (
log_prefix.ljust(5), name.rjust(rjust_len), value))
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22,012 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | get_random_number_generator_and_set_seed | def get_random_number_generator_and_set_seed(seed=None):
"""Get a JAX random number generator and set random seed everywhere."""
random.seed(seed)
# While python random accepts None as seed and uses time/os seed then,
# some other functions expect integers so we create one here.
if seed is None:
seed = ra... | python | def get_random_number_generator_and_set_seed(seed=None):
"""Get a JAX random number generator and set random seed everywhere."""
random.seed(seed)
# While python random accepts None as seed and uses time/os seed then,
# some other functions expect integers so we create one here.
if seed is None:
seed = ra... | [
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22,013 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | epochs | def epochs(steps=None, epoch_steps=1):
"""Iterator over epochs until steps is reached. 1-indexed.
Args:
steps: int, total number of steps. Infinite if None.
epoch_steps: int, number of steps per epoch. Can also be an iterable<int> to
enable variable length epochs.
Yields:
(epoch: int, epoch id... | python | def epochs(steps=None, epoch_steps=1):
"""Iterator over epochs until steps is reached. 1-indexed.
Args:
steps: int, total number of steps. Infinite if None.
epoch_steps: int, number of steps per epoch. Can also be an iterable<int> to
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Yields:
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22,014 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | _jit_predict_fun | def _jit_predict_fun(model_predict, num_devices):
"""Use jit on model_predict if required."""
def predict(x, params=(), rng=None):
"""Predict function jited and parallelized as requested."""
# On one device, jit and run.
if num_devices == 1:
return backend.jit(model_predict)(x, params, rng=rng)
... | python | def _jit_predict_fun(model_predict, num_devices):
"""Use jit on model_predict if required."""
def predict(x, params=(), rng=None):
"""Predict function jited and parallelized as requested."""
# On one device, jit and run.
if num_devices == 1:
return backend.jit(model_predict)(x, params, rng=rng)
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22,015 | tensorflow/tensor2tensor | tensor2tensor/trax/trax.py | _jit_update_fun | def _jit_update_fun(predict_fun, loss_fun, optimizer, lr_fun, num_devices):
"""Get jit-ed update function for loss, optimizer, learning rate function."""
if num_devices == 1: # TODO(lukaszkaiser): remove branch when not needed.
def single_update(i, opt_state, batch, rng):
rng, subrng = jax_random.split(r... | python | def _jit_update_fun(predict_fun, loss_fun, optimizer, lr_fun, num_devices):
"""Get jit-ed update function for loss, optimizer, learning rate function."""
if num_devices == 1: # TODO(lukaszkaiser): remove branch when not needed.
def single_update(i, opt_state, batch, rng):
rng, subrng = jax_random.split(r... | [
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22,016 | tensorflow/tensor2tensor | tensor2tensor/keras/initializers.py | _compute_fans | def _compute_fans(shape):
"""Computes the number of input and output units for a weight shape.
Args:
shape: Integer shape tuple or TF tensor shape.
Returns:
A tuple of scalars (fan_in, fan_out).
"""
if len(shape) < 1: # Just to avoid errors for constants.
fan_in = fan_out = 1
elif len(shape) ... | python | def _compute_fans(shape):
"""Computes the number of input and output units for a weight shape.
Args:
shape: Integer shape tuple or TF tensor shape.
Returns:
A tuple of scalars (fan_in, fan_out).
"""
if len(shape) < 1: # Just to avoid errors for constants.
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22,017 | tensorflow/tensor2tensor | tensor2tensor/keras/initializers.py | get | def get(identifier, value=None):
"""Getter for loading from strings; returns value if can't load."""
if value is None:
value = identifier
if identifier is None:
return None
elif isinstance(identifier, dict):
try:
return deserialize(identifier)
except ValueError:
return value
elif i... | python | def get(identifier, value=None):
"""Getter for loading from strings; returns value if can't load."""
if value is None:
value = identifier
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22,018 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | Trajectory.add_time_step | def add_time_step(self, **create_time_step_kwargs):
"""Creates a time-step and appends it to the list.
Args:
**create_time_step_kwargs: Forwarded to
time_step.TimeStep.create_time_step.
"""
ts = time_step.TimeStep.create_time_step(**create_time_step_kwargs)
assert isinstance(ts, time_... | python | def add_time_step(self, **create_time_step_kwargs):
"""Creates a time-step and appends it to the list.
Args:
**create_time_step_kwargs: Forwarded to
time_step.TimeStep.create_time_step.
"""
ts = time_step.TimeStep.create_time_step(**create_time_step_kwargs)
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22,019 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | Trajectory.change_last_time_step | def change_last_time_step(self, **replace_time_step_kwargs):
"""Replace the last time-steps with the given kwargs."""
# Pre-conditions: self._time_steps shouldn't be empty.
assert self._time_steps
self._time_steps[-1] = self._time_steps[-1].replace(
**replace_time_step_kwargs) | python | def change_last_time_step(self, **replace_time_step_kwargs):
"""Replace the last time-steps with the given kwargs."""
# Pre-conditions: self._time_steps shouldn't be empty.
assert self._time_steps
self._time_steps[-1] = self._time_steps[-1].replace(
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22,020 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | Trajectory.reward | def reward(self):
"""Returns a tuple of sum of raw and processed rewards."""
raw_rewards, processed_rewards = 0, 0
for ts in self.time_steps:
# NOTE: raw_reward and processed_reward are None for the first time-step.
if ts.raw_reward is not None:
raw_rewards += ts.raw_reward
if ts.p... | python | def reward(self):
"""Returns a tuple of sum of raw and processed rewards."""
raw_rewards, processed_rewards = 0, 0
for ts in self.time_steps:
# NOTE: raw_reward and processed_reward are None for the first time-step.
if ts.raw_reward is not None:
raw_rewards += ts.raw_reward
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22,021 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | BatchTrajectory._complete_trajectory | def _complete_trajectory(self, trajectory, index):
"""Completes the given trajectory at the given index."""
assert isinstance(trajectory, Trajectory)
# This *should* be the case.
assert trajectory.last_time_step.action is None
# Add to completed trajectories.
self._completed_trajectories.appe... | python | def _complete_trajectory(self, trajectory, index):
"""Completes the given trajectory at the given index."""
assert isinstance(trajectory, Trajectory)
# This *should* be the case.
assert trajectory.last_time_step.action is None
# Add to completed trajectories.
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22,022 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | BatchTrajectory.reset | def reset(self, indices, observations):
"""Resets trajectories at given indices and populates observations.
Reset can either be called right at the beginning, when there are no
time-steps, or to reset a currently active trajectory.
If resetting a currently active trajectory then we save it in
self... | python | def reset(self, indices, observations):
"""Resets trajectories at given indices and populates observations.
Reset can either be called right at the beginning, when there are no
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If resetting a currently active trajectory then we save it in
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22,023 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | BatchTrajectory.complete_all_trajectories | def complete_all_trajectories(self):
"""Essentially same as reset, but we don't have observations."""
for index in range(self.batch_size):
trajectory = self._trajectories[index]
assert trajectory.is_active
self._complete_trajectory(trajectory, index) | python | def complete_all_trajectories(self):
"""Essentially same as reset, but we don't have observations."""
for index in range(self.batch_size):
trajectory = self._trajectories[index]
assert trajectory.is_active
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22,024 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | BatchTrajectory.step | def step(self, observations, raw_rewards, processed_rewards, dones, actions):
"""Record the information obtained from taking a step in all envs.
Records (observation, rewards, done) in a new time-step and actions in the
current time-step.
If any trajectory gets done, we move that trajectory to
com... | python | def step(self, observations, raw_rewards, processed_rewards, dones, actions):
"""Record the information obtained from taking a step in all envs.
Records (observation, rewards, done) in a new time-step and actions in the
current time-step.
If any trajectory gets done, we move that trajectory to
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22,025 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | BatchTrajectory.num_time_steps | def num_time_steps(self):
"""Returns the number of time-steps in completed and incomplete trajectories."""
num_time_steps = sum(t.num_time_steps for t in self.trajectories)
return num_time_steps + self.num_completed_time_steps | python | def num_time_steps(self):
"""Returns the number of time-steps in completed and incomplete trajectories."""
num_time_steps = sum(t.num_time_steps for t in self.trajectories)
return num_time_steps + self.num_completed_time_steps | [
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22,026 | tensorflow/tensor2tensor | tensor2tensor/envs/trajectory.py | BatchTrajectory.observations_np | def observations_np(self, boundary=20):
"""Pads the observations in all the trajectories and returns them.
Args:
boundary: integer, Observations will be padded to (n * boundary) + 1 where
n is an integer.
Returns:
a tuple(padded_observations, time_steps), with shapes:
padded_ob... | python | def observations_np(self, boundary=20):
"""Pads the observations in all the trajectories and returns them.
Args:
boundary: integer, Observations will be padded to (n * boundary) + 1 where
n is an integer.
Returns:
a tuple(padded_observations, time_steps), with shapes:
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22,027 | tensorflow/tensor2tensor | tensor2tensor/data_generators/squad.py | _generate_examples | def _generate_examples(tmp_dir, dataset_split):
"""Generate squad examples.
Args:
tmp_dir: a string
dataset_split: problem.DatasetSplit.TRAIN or problem.DatasetSplit.EVAL
Yields:
dictionaries representing examples
"""
if dataset_split == problem.DatasetSplit.TRAIN:
file_name = _TRAINING_SET
... | python | def _generate_examples(tmp_dir, dataset_split):
"""Generate squad examples.
Args:
tmp_dir: a string
dataset_split: problem.DatasetSplit.TRAIN or problem.DatasetSplit.EVAL
Yields:
dictionaries representing examples
"""
if dataset_split == problem.DatasetSplit.TRAIN:
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22,028 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_transformer2.py | layer_stack_from_hparams | def layer_stack_from_hparams(hparams, prefix):
"""Create a layer stack based on the hyperparameter values."""
layers = hparams.get(prefix + "layers")
return transformer.LayerStack(
[layers_registry[l](hparams, prefix) for l in layers],
dropout_rate=hparams.layer_prepostprocess_dropout,
norm_epsi... | python | def layer_stack_from_hparams(hparams, prefix):
"""Create a layer stack based on the hyperparameter values."""
layers = hparams.get(prefix + "layers")
return transformer.LayerStack(
[layers_registry[l](hparams, prefix) for l in layers],
dropout_rate=hparams.layer_prepostprocess_dropout,
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22,029 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_transformer2.py | mtf_unitransformer_base | def mtf_unitransformer_base():
"""Hyperparameters for single-stack Transformer."""
hparams = mtf_transformer2_base()
hparams.add_hparam("autoregressive", True)
# HYPERPARAMETERS FOR THE SINGLE LAYER STACK
hparams.add_hparam("layers", ["self_att", "drd"] * 6)
# number of heads in multihead attention
hparam... | python | def mtf_unitransformer_base():
"""Hyperparameters for single-stack Transformer."""
hparams = mtf_transformer2_base()
hparams.add_hparam("autoregressive", True)
# HYPERPARAMETERS FOR THE SINGLE LAYER STACK
hparams.add_hparam("layers", ["self_att", "drd"] * 6)
# number of heads in multihead attention
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22,030 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_transformer2.py | mtf_bitransformer_base | def mtf_bitransformer_base():
"""Machine translation base configuration."""
hparams = mtf_transformer2_base()
hparams.max_length = 256
hparams.shared_embedding = True
# HYPERPARAMETERS FOR THE LAYER STACKS
hparams.add_hparam("encoder_layers", ["self_att", "drd"] * 6)
hparams.add_hparam("decoder_layers", [... | python | def mtf_bitransformer_base():
"""Machine translation base configuration."""
hparams = mtf_transformer2_base()
hparams.max_length = 256
hparams.shared_embedding = True
# HYPERPARAMETERS FOR THE LAYER STACKS
hparams.add_hparam("encoder_layers", ["self_att", "drd"] * 6)
hparams.add_hparam("decoder_layers", [... | [
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22,031 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_transformer2.py | mtf_bitransformer_tiny | def mtf_bitransformer_tiny():
"""Small encoder-decoder model for testing."""
hparams = mtf_bitransformer_base()
hparams.batch_size = 2
hparams.mesh_shape = ""
hparams.d_model = 128
hparams.encoder_layers = ["self_att", "drd"] * 2
hparams.decoder_layers = ["self_att", "enc_att", "drd"] * 2
hparams.num_he... | python | def mtf_bitransformer_tiny():
"""Small encoder-decoder model for testing."""
hparams = mtf_bitransformer_base()
hparams.batch_size = 2
hparams.mesh_shape = ""
hparams.d_model = 128
hparams.encoder_layers = ["self_att", "drd"] * 2
hparams.decoder_layers = ["self_att", "enc_att", "drd"] * 2
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22,032 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_transformer2.py | mtr_lm_v1 | def mtr_lm_v1():
"""Model incorporating mixture-of-experts, local and global attention.
~6B parameters
32 experts in 3 hierarchichal moe layers.
Returns:
a hparams
"""
hparams = mtr_lm_dense(0)
hparams.layers = (["local_self_att", "local_self_att", "drd",
"self_att", "drd", "lo... | python | def mtr_lm_v1():
"""Model incorporating mixture-of-experts, local and global attention.
~6B parameters
32 experts in 3 hierarchichal moe layers.
Returns:
a hparams
"""
hparams = mtr_lm_dense(0)
hparams.layers = (["local_self_att", "local_self_att", "drd",
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22,033 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_transformer2.py | mtr_tr_dense | def mtr_tr_dense(sz):
"""Series of machine translation models.
All models are trained on sequences of 256 tokens.
You can use the dataset translate_enfr_wmt32k_packed.
154000 steps = 3 epochs.
Args:
sz: an integer
Returns:
a hparams
"""
n = 2 ** sz
hparams = mtf_bitransformer_base()
hpar... | python | def mtr_tr_dense(sz):
"""Series of machine translation models.
All models are trained on sequences of 256 tokens.
You can use the dataset translate_enfr_wmt32k_packed.
154000 steps = 3 epochs.
Args:
sz: an integer
Returns:
a hparams
"""
n = 2 ** sz
hparams = mtf_bitransformer_base()
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22,034 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_transformer2.py | mtr_tr_dense_local | def mtr_tr_dense_local(sz):
"""With local self-attention in the decoder."""
hparams = mtr_tr_dense(sz)
hparams.decoder_layers = ["local_self_att", "enc_att", "drd"] * 6
hparams.local_attention_radius = 32
return hparams | python | def mtr_tr_dense_local(sz):
"""With local self-attention in the decoder."""
hparams = mtr_tr_dense(sz)
hparams.decoder_layers = ["local_self_att", "enc_att", "drd"] * 6
hparams.local_attention_radius = 32
return hparams | [
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22,035 | tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_recurrent_self_attention.py | recurrent_transformer_decoder | def recurrent_transformer_decoder(
decoder_input,
encoder_output,
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encoder_decoder_attention_bias,
hparams,
name="decoder",
nonpadding=None,
save_weights_to=None,
make_image_summary=True):
"""Recurrent decoder function."""
x = decoder_input
attention... | python | def recurrent_transformer_decoder(
decoder_input,
encoder_output,
decoder_self_attention_bias,
encoder_decoder_attention_bias,
hparams,
name="decoder",
nonpadding=None,
save_weights_to=None,
make_image_summary=True):
"""Recurrent decoder function."""
x = decoder_input
attention... | [
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22,036 | tensorflow/tensor2tensor | tensor2tensor/models/mtf_resnet.py | batch_norm_relu | def batch_norm_relu(inputs, is_training, relu=True):
"""Block of batch norm and relu."""
inputs = mtf.layers.batch_norm(
inputs,
is_training,
BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON,
init_zero=(not relu))
if relu:
inputs = mtf.relu(inputs)
return inputs | python | def batch_norm_relu(inputs, is_training, relu=True):
"""Block of batch norm and relu."""
inputs = mtf.layers.batch_norm(
inputs,
is_training,
BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON,
init_zero=(not relu))
if relu:
inputs = mtf.relu(inputs)
return inputs | [
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22,037 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | universal_transformer_encoder | def universal_transformer_encoder(encoder_input,
encoder_self_attention_bias,
hparams,
name="encoder",
nonpadding=None,
save_weights_to=None,
... | python | def universal_transformer_encoder(encoder_input,
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hparams,
name="encoder",
nonpadding=None,
save_weights_to=None,
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22,038 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | universal_transformer_layer | def universal_transformer_layer(x,
hparams,
ffn_unit,
attention_unit,
pad_remover=None):
"""Core function applying the universal transformer layer.
Args:
x: input
hparams: model h... | python | def universal_transformer_layer(x,
hparams,
ffn_unit,
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pad_remover=None):
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Args:
x: input
hparams: model h... | [
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22,039 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | get_ut_layer | def get_ut_layer(x,
hparams,
ffn_unit,
attention_unit,
pad_remover=None):
"""Provides the function that is used in universal transforemr steps.
Args:
x: input
hparams: model hyper-parameters
ffn_unit: feed-forward unit
attention_un... | python | def get_ut_layer(x,
hparams,
ffn_unit,
attention_unit,
pad_remover=None):
"""Provides the function that is used in universal transforemr steps.
Args:
x: input
hparams: model hyper-parameters
ffn_unit: feed-forward unit
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22,040 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | transformer_encoder_ffn_unit | def transformer_encoder_ffn_unit(x,
hparams,
nonpadding_mask=None,
pad_remover=None):
"""Applies a feed-forward function which is parametrised for encoding.
Args:
x: input
hparams: model hyper-parameters
... | python | def transformer_encoder_ffn_unit(x,
hparams,
nonpadding_mask=None,
pad_remover=None):
"""Applies a feed-forward function which is parametrised for encoding.
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x: input
hparams: model hyper-parameters
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22,041 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | transformer_encoder_attention_unit | def transformer_encoder_attention_unit(x,
hparams,
encoder_self_attention_bias,
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save_weights_to=None,
... | python | def transformer_encoder_attention_unit(x,
hparams,
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save_weights_to=None,
... | [
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22,042 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | transformer_decoder_attention_unit | def transformer_decoder_attention_unit(x,
hparams,
encoder_output,
decoder_self_attention_bias,
encoder_decoder_attention_bias,
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22,043 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | universal_transformer_basic | def universal_transformer_basic(layer_inputs,
step, hparams,
ffn_unit,
attention_unit):
"""Basic Universal Transformer.
This model is pretty similar to the vanilla transformer in which weights are
shared between layer... | python | def universal_transformer_basic(layer_inputs,
step, hparams,
ffn_unit,
attention_unit):
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22,044 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | universal_transformer_highway | def universal_transformer_highway(layer_inputs,
step, hparams,
ffn_unit,
attention_unit,
pad_remover=None):
"""Universal Transformer with highway connection.
It transforms the st... | python | def universal_transformer_highway(layer_inputs,
step, hparams,
ffn_unit,
attention_unit,
pad_remover=None):
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22,045 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | universal_transformer_depthwise_attention | def universal_transformer_depthwise_attention(layer_inputs,
step, hparams,
ffn_unit,
attention_unit):
"""universal_transformer with depth-wise attention.
It uses an attention me... | python | def universal_transformer_depthwise_attention(layer_inputs,
step, hparams,
ffn_unit,
attention_unit):
"""universal_transformer with depth-wise attention.
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22,046 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | universal_transformer_with_gru_as_transition_function | def universal_transformer_with_gru_as_transition_function(
layer_inputs, step, hparams, ffn_unit, attention_unit, pad_remover=None):
"""Universal Transformer which uses a gru as transition function.
It's kind of like having a gru, filliped vertically next to the Universal
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layer_inputs, step, hparams, ffn_unit, attention_unit, pad_remover=None):
"""Universal Transformer which uses a gru as transition function.
It's kind of like having a gru, filliped vertically next to the Universal
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22,047 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | universal_transformer_with_lstm_as_transition_function | def universal_transformer_with_lstm_as_transition_function(
layer_inputs, step, hparams, ffn_unit, attention_unit, pad_remover=None):
"""Universal Transformer which uses a lstm as transition function.
It's kind of like having a lstm, filliped vertically next to the Universal
Transformer that controls the flo... | python | def universal_transformer_with_lstm_as_transition_function(
layer_inputs, step, hparams, ffn_unit, attention_unit, pad_remover=None):
"""Universal Transformer which uses a lstm as transition function.
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22,048 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | _ffn_layer_multi_inputs | def _ffn_layer_multi_inputs(inputs_list,
hparams,
ffn_layer_type="dense",
name="ffn",
kernel_initializer=None,
bias_initializer=None,
activation=None,
... | python | def _ffn_layer_multi_inputs(inputs_list,
hparams,
ffn_layer_type="dense",
name="ffn",
kernel_initializer=None,
bias_initializer=None,
activation=None,
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22,049 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | fill_memory_slot | def fill_memory_slot(memory, value, index):
"""Fills the memory slot at a particular index with the given value.
Args:
memory: a 4-d tensor [memory_size, batch, length, channel] containing
the state of all steps
value: a 3-d tensor [batch, length, channel] as the sate
index: integer in [0, memory... | python | def fill_memory_slot(memory, value, index):
"""Fills the memory slot at a particular index with the given value.
Args:
memory: a 4-d tensor [memory_size, batch, length, channel] containing
the state of all steps
value: a 3-d tensor [batch, length, channel] as the sate
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22,050 | tensorflow/tensor2tensor | tensor2tensor/models/research/universal_transformer_util.py | step_preprocess | def step_preprocess(x, step, hparams):
"""Preprocess the input at the beginning of each step.
Args:
x: input tensor
step: step
hparams: model hyper-parameters
Returns:
preprocessed input.
"""
original_channel_size = common_layers.shape_list(x)[-1]
if hparams.add_position_timing_signal:
... | python | def step_preprocess(x, step, hparams):
"""Preprocess the input at the beginning of each step.
Args:
x: input tensor
step: step
hparams: model hyper-parameters
Returns:
preprocessed input.
"""
original_channel_size = common_layers.shape_list(x)[-1]
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22,051 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/utils.py | wet_records_from_file_obj | def wet_records_from_file_obj(f, take_ownership=False):
"""Iterate through records in WET file object."""
while True:
record = WETRecord.read(f)
if record is None:
break
if not record.url:
continue
yield record
if take_ownership:
f.close() | python | def wet_records_from_file_obj(f, take_ownership=False):
"""Iterate through records in WET file object."""
while True:
record = WETRecord.read(f)
if record is None:
break
if not record.url:
continue
yield record
if take_ownership:
f.close() | [
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22,052 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/utils.py | wet_records | def wet_records(wet_filepath):
"""Generate WETRecords from filepath."""
if wet_filepath.endswith('.gz'):
fopen = gzip.open
else:
fopen = tf.gfile.GFile
with fopen(wet_filepath) as f:
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"""Generate WETRecords from filepath."""
if wet_filepath.endswith('.gz'):
fopen = gzip.open
else:
fopen = tf.gfile.GFile
with fopen(wet_filepath) as f:
for record in wet_records_from_file_obj(f):
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22,053 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/utils.py | timing | def timing(name=''):
"""Log start, end, and duration."""
start = datetime.datetime.now()
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timestamp = end.strftime('%H:%M')
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"""Log start, end, and duration."""
start = datetime.datetime.now()
timestamp = start.strftime('%H:%M')
tf.logging.info('Starting job [%s] at %s', name, timestamp)
yield
end = datetime.datetime.now()
timestamp = end.strftime('%H:%M')
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22,054 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/utils.py | WETHeader.read | def read(cls, f):
"""Read header from file. Headers end with length and then 1 blank line."""
url = None
line = f.readline()
if not line:
# EOF
return None
while not line.startswith(cls.LENGTH_HEADER):
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url = line[len(cls.URI_HEADER):].st... | python | def read(cls, f):
"""Read header from file. Headers end with length and then 1 blank line."""
url = None
line = f.readline()
if not line:
# EOF
return None
while not line.startswith(cls.LENGTH_HEADER):
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22,055 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/utils.py | WETRecord.read | def read(cls, f):
"""Read WETRecord from file. Records end with 2 blank lines."""
header = WETHeader.read(f)
if header is None:
# EOF
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content = f.read(header.length)
# Consume empty separators
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f.readline()
return cls(header.url, content) | python | def read(cls, f):
"""Read WETRecord from file. Records end with 2 blank lines."""
header = WETHeader.read(f)
if header is None:
# EOF
return None
content = f.read(header.length)
# Consume empty separators
f.readline()
f.readline()
return cls(header.url, content) | [
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22,056 | tensorflow/tensor2tensor | tensor2tensor/trax/models/mlp.py | MLP | def MLP(num_hidden_layers=2,
hidden_size=512,
activation_fn=layers.Relu,
num_output_classes=10,
mode="train"):
"""Multi-layer feed-forward neural network with non-linear activations."""
del mode
cur_layers = [layers.Flatten()]
for _ in range(num_hidden_layers):
cur_layers += ... | python | def MLP(num_hidden_layers=2,
hidden_size=512,
activation_fn=layers.Relu,
num_output_classes=10,
mode="train"):
"""Multi-layer feed-forward neural network with non-linear activations."""
del mode
cur_layers = [layers.Flatten()]
for _ in range(num_hidden_layers):
cur_layers += ... | [
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22,057 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem._verify_same_spaces | def _verify_same_spaces(self):
"""Verifies that all the envs have the same observation and action space."""
# Pre-conditions: self._envs is initialized.
if self._envs is None:
raise ValueError("Environments not initialized.")
if not isinstance(self._envs, list):
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"""Verifies that all the envs have the same observation and action space."""
# Pre-conditions: self._envs is initialized.
if self._envs is None:
raise ValueError("Environments not initialized.")
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22,058 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem.initialize_environments | def initialize_environments(self, batch_size=1):
"""Initializes the environments and trajectories.
Subclasses can override this if they don't want a default implementation
which initializes `batch_size` environments, but must take care to
initialize self._trajectories (this is checked in __init__ anywa... | python | def initialize_environments(self, batch_size=1):
"""Initializes the environments and trajectories.
Subclasses can override this if they don't want a default implementation
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22,059 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem.process_rewards | def process_rewards(self, rewards):
"""Clips, rounds, and changes to integer type.
Args:
rewards: numpy array of raw (float) rewards.
Returns:
processed_rewards: numpy array of np.int64
"""
min_reward, max_reward = self.reward_range
# Clips at min and max reward.
rewards = np... | python | def process_rewards(self, rewards):
"""Clips, rounds, and changes to integer type.
Args:
rewards: numpy array of raw (float) rewards.
Returns:
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min_reward, max_reward = self.reward_range
# Clips at min and max reward.
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22,060 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem.num_rewards | def num_rewards(self):
"""Returns the number of distinct rewards.
Returns:
Returns None if the reward range is infinite or the processed rewards
aren't discrete, otherwise returns the number of distinct rewards.
"""
# Pre-conditions: reward range is finite.
# : processed ... | python | def num_rewards(self):
"""Returns the number of distinct rewards.
Returns:
Returns None if the reward range is infinite or the processed rewards
aren't discrete, otherwise returns the number of distinct rewards.
"""
# Pre-conditions: reward range is finite.
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22,061 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem._reset | def _reset(self, indices):
"""Resets environments at indices shouldn't pre-process or record.
Subclasses should override this to do the actual reset if something other
than the default implementation is desired.
Args:
indices: list of indices of underlying envs to call reset on.
Returns:
... | python | def _reset(self, indices):
"""Resets environments at indices shouldn't pre-process or record.
Subclasses should override this to do the actual reset if something other
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Args:
indices: list of indices of underlying envs to call reset on.
Returns:
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22,062 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem._step | def _step(self, actions):
"""Takes a step in all environments, shouldn't pre-process or record.
Subclasses should override this to do the actual step if something other
than the default implementation is desired.
Args:
actions: (np.ndarray) with first dimension equal to the batch size.
Retu... | python | def _step(self, actions):
"""Takes a step in all environments, shouldn't pre-process or record.
Subclasses should override this to do the actual step if something other
than the default implementation is desired.
Args:
actions: (np.ndarray) with first dimension equal to the batch size.
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22,063 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem.step | def step(self, actions):
"""Takes a step in all environments.
Subclasses should override _step to do the actual reset if something other
than the default implementation is desired.
Args:
actions: Batch of actions.
Returns:
(preprocessed_observations, processed_rewards, dones, infos).
... | python | def step(self, actions):
"""Takes a step in all environments.
Subclasses should override _step to do the actual reset if something other
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Args:
actions: Batch of actions.
Returns:
(preprocessed_observations, processed_rewards, dones, infos).
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22,064 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem.example_reading_spec | def example_reading_spec(self):
"""Data fields to store on disk and their decoders."""
# Subclasses can override and/or extend.
processed_reward_type = tf.float32
if self.is_processed_rewards_discrete:
processed_reward_type = tf.int64
data_fields = {
TIMESTEP_FIELD: tf.FixedLenFeatu... | python | def example_reading_spec(self):
"""Data fields to store on disk and their decoders."""
# Subclasses can override and/or extend.
processed_reward_type = tf.float32
if self.is_processed_rewards_discrete:
processed_reward_type = tf.int64
data_fields = {
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22,065 | tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem._generate_time_steps | def _generate_time_steps(self, trajectory_list):
"""A generator to yield single time-steps from a list of trajectories."""
for single_trajectory in trajectory_list:
assert isinstance(single_trajectory, trajectory.Trajectory)
# Skip writing trajectories that have only a single time-step -- this
... | python | def _generate_time_steps(self, trajectory_list):
"""A generator to yield single time-steps from a list of trajectories."""
for single_trajectory in trajectory_list:
assert isinstance(single_trajectory, trajectory.Trajectory)
# Skip writing trajectories that have only a single time-step -- this
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22,066 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | decompress_step | def decompress_step(source, hparams, first_relu, name):
"""Decompression function."""
with tf.variable_scope(name):
shape = common_layers.shape_list(source)
multiplier = 2
kernel = (1, 1)
thicker = common_layers.conv_block(
source,
hparams.hidden_size * multiplier, [((1, 1), kernel)]... | python | def decompress_step(source, hparams, first_relu, name):
"""Decompression function."""
with tf.variable_scope(name):
shape = common_layers.shape_list(source)
multiplier = 2
kernel = (1, 1)
thicker = common_layers.conv_block(
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22,067 | tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | encode | def encode(x, x_space, hparams, name):
"""Transformer preparations and encoder."""
with tf.variable_scope(name):
(encoder_input, encoder_self_attention_bias,
ed) = transformer.transformer_prepare_encoder(x, x_space, hparams)
encoder_input = tf.nn.dropout(encoder_input, 1.0 - hparams.dropout)
return... | python | def encode(x, x_space, hparams, name):
"""Transformer preparations and encoder."""
with tf.variable_scope(name):
(encoder_input, encoder_self_attention_bias,
ed) = transformer.transformer_prepare_encoder(x, x_space, hparams)
encoder_input = tf.nn.dropout(encoder_input, 1.0 - hparams.dropout)
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22,068 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | policy_net | def policy_net(rng_key,
batch_observations_shape,
num_actions,
bottom_layers=None):
"""A policy net function."""
# Use the bottom_layers as the bottom part of the network and just add the
# required layers on top of it.
if bottom_layers is None:
bottom_layers = [... | python | def policy_net(rng_key,
batch_observations_shape,
num_actions,
bottom_layers=None):
"""A policy net function."""
# Use the bottom_layers as the bottom part of the network and just add the
# required layers on top of it.
if bottom_layers is None:
bottom_layers = [... | [
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22,069 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | value_net | def value_net(rng_key,
batch_observations_shape,
num_actions,
bottom_layers=None):
"""A value net function."""
del num_actions
if bottom_layers is None:
bottom_layers = []
bottom_layers.extend([
layers.Dense(1),
])
net = layers.Serial(*bottom_layers)
re... | python | def value_net(rng_key,
batch_observations_shape,
num_actions,
bottom_layers=None):
"""A value net function."""
del num_actions
if bottom_layers is None:
bottom_layers = []
bottom_layers.extend([
layers.Dense(1),
])
net = layers.Serial(*bottom_layers)
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22,070 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | policy_and_value_net | def policy_and_value_net(rng_key,
batch_observations_shape,
num_actions,
bottom_layers=None):
"""A policy and value net function."""
# Layers.
cur_layers = []
if bottom_layers is not None:
cur_layers.extend(bottom_layers)
# Now, ... | python | def policy_and_value_net(rng_key,
batch_observations_shape,
num_actions,
bottom_layers=None):
"""A policy and value net function."""
# Layers.
cur_layers = []
if bottom_layers is not None:
cur_layers.extend(bottom_layers)
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22,071 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | log_params | def log_params(params, name="params"):
"""Dumps the params with `logging.error`."""
for i, param in enumerate(params):
if not param:
# Empty tuple.
continue
if not isinstance(param, (list, tuple)):
logging.error(
"%s[%d] : (%s) = [%s]", name, i, param.shape, onp.array(param))
... | python | def log_params(params, name="params"):
"""Dumps the params with `logging.error`."""
for i, param in enumerate(params):
if not param:
# Empty tuple.
continue
if not isinstance(param, (list, tuple)):
logging.error(
"%s[%d] : (%s) = [%s]", name, i, param.shape, onp.array(param))
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22,072 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | collect_trajectories | def collect_trajectories(env,
policy_fun,
num_trajectories=1,
policy="greedy",
max_timestep=None,
epsilon=0.1):
"""Collect trajectories with the given policy net and behaviour.
Args:
env... | python | def collect_trajectories(env,
policy_fun,
num_trajectories=1,
policy="greedy",
max_timestep=None,
epsilon=0.1):
"""Collect trajectories with the given policy net and behaviour.
Args:
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22,073 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | get_padding_value | def get_padding_value(dtype):
"""Returns the padding value given a dtype."""
padding_value = None
if dtype == np.uint8:
padding_value = np.uint8(0)
elif dtype == np.uint16:
padding_value = np.uint16(0)
elif dtype == np.float32:
padding_value = 0.0
else:
padding_value = 0
assert padding_val... | python | def get_padding_value(dtype):
"""Returns the padding value given a dtype."""
padding_value = None
if dtype == np.uint8:
padding_value = np.uint8(0)
elif dtype == np.uint16:
padding_value = np.uint16(0)
elif dtype == np.float32:
padding_value = 0.0
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22,074 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | pad_trajectories | def pad_trajectories(trajectories, boundary=20):
"""Pad trajectories to a bucket length that is a multiple of boundary.
Args:
trajectories: list[(observation, actions, rewards)], where each observation
is shaped (t+1,) + OBS and actions & rewards are shaped (t,), with the
length of the list being B... | python | def pad_trajectories(trajectories, boundary=20):
"""Pad trajectories to a bucket length that is a multiple of boundary.
Args:
trajectories: list[(observation, actions, rewards)], where each observation
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22,075 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | rewards_to_go | def rewards_to_go(rewards, mask, gamma=0.99):
r"""Computes rewards to go.
Reward to go is defined as follows, the discounted reward that we have to
yet collect, going forward from this point, i.e.:
r2g_t = \sum_{l=0}^{\infty} (\gamma^{l} * reward_{t+l})
Args:
rewards: np.ndarray of shape (B, T) of rewa... | python | def rewards_to_go(rewards, mask, gamma=0.99):
r"""Computes rewards to go.
Reward to go is defined as follows, the discounted reward that we have to
yet collect, going forward from this point, i.e.:
r2g_t = \sum_{l=0}^{\infty} (\gamma^{l} * reward_{t+l})
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rewards: np.ndarray of shape (B, T) of rewa... | [
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22,076 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | value_loss | def value_loss(value_net_apply,
value_net_params,
observations,
rewards,
reward_mask,
gamma=0.99):
"""Computes the value loss.
Args:
value_net_apply: value net apply function with signature (params, ndarray of
shape (B, T+1) + OBS... | python | def value_loss(value_net_apply,
value_net_params,
observations,
rewards,
reward_mask,
gamma=0.99):
"""Computes the value loss.
Args:
value_net_apply: value net apply function with signature (params, ndarray of
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22,077 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | value_loss_given_predictions | def value_loss_given_predictions(value_prediction,
rewards,
reward_mask,
gamma=0.99):
"""Computes the value loss given the prediction of the value function.
Args:
value_prediction: np.ndarray of shape (B, T+1, 1)... | python | def value_loss_given_predictions(value_prediction,
rewards,
reward_mask,
gamma=0.99):
"""Computes the value loss given the prediction of the value function.
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22,078 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | gae_advantages | def gae_advantages(td_deltas, mask, lambda_=0.95, gamma=0.99):
r"""Computes the GAE advantages given the one step TD-residuals.
The formula for a GAE advantage estimator is as follows:
A_{bt} = \sum_{l=0}^{\infty}(\gamma * \lambda)^{l}(\delta_{b,t+l}).
Internally we just call rewards_to_go, since it is the s... | python | def gae_advantages(td_deltas, mask, lambda_=0.95, gamma=0.99):
r"""Computes the GAE advantages given the one step TD-residuals.
The formula for a GAE advantage estimator is as follows:
A_{bt} = \sum_{l=0}^{\infty}(\gamma * \lambda)^{l}(\delta_{b,t+l}).
Internally we just call rewards_to_go, since it is the s... | [
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Internally we just call rewards_to_go, since it is the same computation.
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22,079 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | chosen_probabs | def chosen_probabs(probab_observations, actions):
"""Picks out the probabilities of the actions along batch and time-steps.
Args:
probab_observations: ndarray of shape `[B, T+1, A]`, where
probab_observations[b, t, i] contains the log-probability of action = i at
the t^th time-step in the b^th traj... | python | def chosen_probabs(probab_observations, actions):
"""Picks out the probabilities of the actions along batch and time-steps.
Args:
probab_observations: ndarray of shape `[B, T+1, A]`, where
probab_observations[b, t, i] contains the log-probability of action = i at
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22,080 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | compute_probab_ratios | def compute_probab_ratios(p_new, p_old, actions, reward_mask):
"""Computes the probability ratios for each time-step in a trajectory.
Args:
p_new: ndarray of shape [B, T+1, A] of the log-probabilities that the policy
network assigns to all the actions at each time-step in each batch using
the old p... | python | def compute_probab_ratios(p_new, p_old, actions, reward_mask):
"""Computes the probability ratios for each time-step in a trajectory.
Args:
p_new: ndarray of shape [B, T+1, A] of the log-probabilities that the policy
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22,081 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | ppo_loss | def ppo_loss(policy_net_apply,
new_policy_params,
old_policy_params,
value_net_apply,
value_net_params,
padded_observations,
padded_actions,
padded_rewards,
reward_mask,
gamma=0.99,
lambda_=... | python | def ppo_loss(policy_net_apply,
new_policy_params,
old_policy_params,
value_net_apply,
value_net_params,
padded_observations,
padded_actions,
padded_rewards,
reward_mask,
gamma=0.99,
lambda_=... | [
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22,082 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | ppo_loss_given_predictions | def ppo_loss_given_predictions(log_probab_actions_new,
log_probab_actions_old,
predicted_values,
padded_actions,
padded_rewards,
reward_mask,
... | python | def ppo_loss_given_predictions(log_probab_actions_new,
log_probab_actions_old,
predicted_values,
padded_actions,
padded_rewards,
reward_mask,
... | [
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22,083 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | ppo_opt_step | def ppo_opt_step(i,
opt_state,
ppo_opt_update,
policy_net_apply,
old_policy_params,
value_net_apply,
value_net_params,
padded_observations,
padded_actions,
padded_rewa... | python | def ppo_opt_step(i,
opt_state,
ppo_opt_update,
policy_net_apply,
old_policy_params,
value_net_apply,
value_net_params,
padded_observations,
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22,084 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | value_opt_step | def value_opt_step(i,
opt_state,
opt_update,
value_net_apply,
padded_observations,
padded_rewards,
reward_mask,
gamma=0.99):
"""Value optimizer step."""
value_params = trax_opt.get_pa... | python | def value_opt_step(i,
opt_state,
opt_update,
value_net_apply,
padded_observations,
padded_rewards,
reward_mask,
gamma=0.99):
"""Value optimizer step."""
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22,085 | tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | policy_and_value_opt_step | def policy_and_value_opt_step(i,
opt_state,
opt_update,
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old_params,
padded_observations,
padded_actions,
... | python | def policy_and_value_opt_step(i,
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22,086 | tensorflow/tensor2tensor | tensor2tensor/data_generators/multinli.py | _maybe_download_corpora | def _maybe_download_corpora(tmp_dir):
"""Download corpora for multinli.
Args:
tmp_dir: a string
Returns:
a string
"""
mnli_filename = "MNLI.zip"
mnli_finalpath = os.path.join(tmp_dir, "MNLI")
if not tf.gfile.Exists(mnli_finalpath):
zip_filepath = generator_utils.maybe_download(
tmp_di... | python | def _maybe_download_corpora(tmp_dir):
"""Download corpora for multinli.
Args:
tmp_dir: a string
Returns:
a string
"""
mnli_filename = "MNLI.zip"
mnli_finalpath = os.path.join(tmp_dir, "MNLI")
if not tf.gfile.Exists(mnli_finalpath):
zip_filepath = generator_utils.maybe_download(
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22,087 | tensorflow/tensor2tensor | tensor2tensor/models/shake_shake.py | shake_shake_skip_connection | def shake_shake_skip_connection(x, output_filters, stride, is_training):
"""Adds a residual connection to the filter x for the shake-shake model."""
curr_filters = common_layers.shape_list(x)[-1]
if curr_filters == output_filters:
return x
stride_spec = [1, stride, stride, 1]
# Skip path 1.
path1 = tf.n... | python | def shake_shake_skip_connection(x, output_filters, stride, is_training):
"""Adds a residual connection to the filter x for the shake-shake model."""
curr_filters = common_layers.shape_list(x)[-1]
if curr_filters == output_filters:
return x
stride_spec = [1, stride, stride, 1]
# Skip path 1.
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22,088 | tensorflow/tensor2tensor | tensor2tensor/models/shake_shake.py | shake_shake_branch | def shake_shake_branch(x, output_filters, stride, rand_forward, rand_backward,
hparams):
"""Building a 2 branching convnet."""
is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
x = tf.nn.relu(x)
x = tf.layers.conv2d(
x,
output_filters, (3, 3),
strides=(stride, st... | python | def shake_shake_branch(x, output_filters, stride, rand_forward, rand_backward,
hparams):
"""Building a 2 branching convnet."""
is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
x = tf.nn.relu(x)
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22,089 | tensorflow/tensor2tensor | tensor2tensor/models/shake_shake.py | shake_shake_block | def shake_shake_block(x, output_filters, stride, hparams):
"""Builds a full shake-shake sub layer."""
is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
batch_size = common_layers.shape_list(x)[0]
# Generate random numbers for scaling the branches.
rand_forward = [
tf.random_uniform(
[... | python | def shake_shake_block(x, output_filters, stride, hparams):
"""Builds a full shake-shake sub layer."""
is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
batch_size = common_layers.shape_list(x)[0]
# Generate random numbers for scaling the branches.
rand_forward = [
tf.random_uniform(
[... | [
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22,090 | tensorflow/tensor2tensor | tensor2tensor/models/shake_shake.py | shake_shake_layer | def shake_shake_layer(x, output_filters, num_blocks, stride, hparams):
"""Builds many sub layers into one full layer."""
for block_num in range(num_blocks):
curr_stride = stride if (block_num == 0) else 1
with tf.variable_scope("layer_{}".format(block_num)):
x = shake_shake_block(x, output_filters, cu... | python | def shake_shake_layer(x, output_filters, num_blocks, stride, hparams):
"""Builds many sub layers into one full layer."""
for block_num in range(num_blocks):
curr_stride = stride if (block_num == 0) else 1
with tf.variable_scope("layer_{}".format(block_num)):
x = shake_shake_block(x, output_filters, cu... | [
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22,091 | tensorflow/tensor2tensor | tensor2tensor/models/shake_shake.py | shakeshake_small | def shakeshake_small():
"""Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.hidden_size = 32
hparams.layer_prepostprocess_dropout = 0.0
hparams.dropout = 0
hparams.label_smoothing = 0.0
hparams.clip_grad_n... | python | def shakeshake_small():
"""Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.hidden_size = 32
hparams.layer_prepostprocess_dropout = 0.0
hparams.dropout = 0
hparams.label_smoothing = 0.0
hparams.clip_grad_n... | [
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22,092 | tensorflow/tensor2tensor | tensor2tensor/utils/metrics_hook.py | has_metric_plateaued | def has_metric_plateaued(steps, values, num_steps=100, delta=0.1,
decrease=True):
"""Check if metric has plateaued.
A metric has plateaued if the value has not increased/decreased (depending on
`decrease`) by `delta` for at least `num_steps`.
Args:
steps: list<int> list of global ... | python | def has_metric_plateaued(steps, values, num_steps=100, delta=0.1,
decrease=True):
"""Check if metric has plateaued.
A metric has plateaued if the value has not increased/decreased (depending on
`decrease`) by `delta` for at least `num_steps`.
Args:
steps: list<int> list of global ... | [
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22,093 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp_params.py | next_frame_savp | def next_frame_savp():
"""SAVP model hparams."""
hparams = sv2p_params.next_frame_sv2p()
hparams.add_hparam("z_dim", 8)
hparams.add_hparam("num_discriminator_filters", 32)
hparams.add_hparam("use_vae", True)
hparams.add_hparam("use_gan", False)
hparams.add_hparam("use_spectral_norm", True)
hparams.add_h... | python | def next_frame_savp():
"""SAVP model hparams."""
hparams = sv2p_params.next_frame_sv2p()
hparams.add_hparam("z_dim", 8)
hparams.add_hparam("num_discriminator_filters", 32)
hparams.add_hparam("use_vae", True)
hparams.add_hparam("use_gan", False)
hparams.add_hparam("use_spectral_norm", True)
hparams.add_h... | [
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22,094 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp_params.py | next_frame_savp_vae | def next_frame_savp_vae():
"""SAVP - VAE only model."""
hparams = next_frame_savp()
hparams.use_vae = True
hparams.use_gan = False
hparams.latent_loss_multiplier = 1e-3
hparams.latent_loss_multiplier_schedule = "linear_anneal"
return hparams | python | def next_frame_savp_vae():
"""SAVP - VAE only model."""
hparams = next_frame_savp()
hparams.use_vae = True
hparams.use_gan = False
hparams.latent_loss_multiplier = 1e-3
hparams.latent_loss_multiplier_schedule = "linear_anneal"
return hparams | [
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22,095 | tensorflow/tensor2tensor | tensor2tensor/models/video/savp_params.py | next_frame_savp_gan | def next_frame_savp_gan():
"""SAVP - GAN only model."""
hparams = next_frame_savp()
hparams.use_gan = True
hparams.use_vae = False
hparams.gan_loss_multiplier = 0.001
hparams.optimizer_adam_beta1 = 0.5
hparams.learning_rate_constant = 2e-4
hparams.gan_loss = "cross_entropy"
hparams.learning_rate_decay... | python | def next_frame_savp_gan():
"""SAVP - GAN only model."""
hparams = next_frame_savp()
hparams.use_gan = True
hparams.use_vae = False
hparams.gan_loss_multiplier = 0.001
hparams.optimizer_adam_beta1 = 0.5
hparams.learning_rate_constant = 2e-4
hparams.gan_loss = "cross_entropy"
hparams.learning_rate_decay... | [
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22,096 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | diet_adam_optimizer_params | def diet_adam_optimizer_params():
"""Default hyperparameters for a DietAdamOptimizer.
Returns:
a hyperparameters object.
"""
return hparam.HParams(
quantize=True, # use 16-bit fixed-point
quantization_scale=10.0 / tf.int16.max,
optimizer="DietAdam",
learning_rate=1.0,
learnin... | python | def diet_adam_optimizer_params():
"""Default hyperparameters for a DietAdamOptimizer.
Returns:
a hyperparameters object.
"""
return hparam.HParams(
quantize=True, # use 16-bit fixed-point
quantization_scale=10.0 / tf.int16.max,
optimizer="DietAdam",
learning_rate=1.0,
learnin... | [
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Returns:
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22,097 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | diet_expert | def diet_expert(x, hidden_size, params):
"""A two-layer feed-forward network with relu activation on hidden layer.
Uses diet variables.
Recomputes hidden layer on backprop to save activation memory.
Args:
x: a Tensor with shape [batch, io_size]
hidden_size: an integer
params: a diet variable HPara... | python | def diet_expert(x, hidden_size, params):
"""A two-layer feed-forward network with relu activation on hidden layer.
Uses diet variables.
Recomputes hidden layer on backprop to save activation memory.
Args:
x: a Tensor with shape [batch, io_size]
hidden_size: an integer
params: a diet variable HPara... | [
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Args:
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22,098 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | _quantize | def _quantize(x, params, randomize=True):
"""Quantize x according to params, optionally randomizing the rounding."""
if not params.quantize:
return x
if not randomize:
return tf.bitcast(
tf.cast(x / params.quantization_scale, tf.int16), tf.float16)
abs_x = tf.abs(x)
sign_x = tf.sign(x)
y =... | python | def _quantize(x, params, randomize=True):
"""Quantize x according to params, optionally randomizing the rounding."""
if not params.quantize:
return x
if not randomize:
return tf.bitcast(
tf.cast(x / params.quantization_scale, tf.int16), tf.float16)
abs_x = tf.abs(x)
sign_x = tf.sign(x)
y =... | [
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22,099 | tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | _dequantize | def _dequantize(q, params):
"""Dequantize q according to params."""
if not params.quantize:
return q
return tf.to_float(tf.bitcast(q, tf.int16)) * params.quantization_scale | python | def _dequantize(q, params):
"""Dequantize q according to params."""
if not params.quantize:
return q
return tf.to_float(tf.bitcast(q, tf.int16)) * params.quantization_scale | [
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