repo stringlengths 7 55 | path stringlengths 4 223 | func_name stringlengths 1 134 | original_string stringlengths 75 104k | language stringclasses 1
value | code stringlengths 75 104k | code_tokens listlengths 19 28.4k | docstring stringlengths 1 46.9k | docstring_tokens listlengths 1 1.97k | sha stringlengths 40 40 | url stringlengths 87 315 | partition stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|
tensorflow/tensor2tensor | tensor2tensor/envs/env_problem.py | EnvProblem.reset | def reset(self, indices=None):
"""Resets environments at given indices.
Subclasses should override _reset to do the actual reset if something other
than the default implementation is desired.
Args:
indices: Indices of environments to reset. If None all envs are reset.
Returns:
Batch o... | python | def reset(self, indices=None):
"""Resets environments at given indices.
Subclasses should override _reset to do the actual reset if something other
than the default implementation is desired.
Args:
indices: Indices of environments to reset. If None all envs are reset.
Returns:
Batch o... | [
"def",
"reset",
"(",
"self",
",",
"indices",
"=",
"None",
")",
":",
"if",
"indices",
"is",
"None",
":",
"indices",
"=",
"np",
".",
"arange",
"(",
"self",
".",
"trajectories",
".",
"batch_size",
")",
"# If this is empty (not None) then don't do anything, no env w... | Resets environments at given indices.
Subclasses should override _reset to do the actual reset if something other
than the default implementation is desired.
Args:
indices: Indices of environments to reset. If None all envs are reset.
Returns:
Batch of initial observations of reset enviro... | [
"Resets",
"environments",
"at",
"given",
"indices",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/envs/env_problem.py#L474-L502 | train |
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.
Retu... | [
"def",
"_step",
"(",
"self",
",",
"actions",
")",
":",
"# Pre-conditions: common_preconditions, see `assert_common_preconditions`.",
"# : len(actions) == len(self._envs)",
"self",
".",
"assert_common_preconditions",
"(",
")",
"assert",
"len",
"(",
"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.
Returns:
a tuple of stacked raw... | [
"Takes",
"a",
"step",
"in",
"all",
"environments",
"shouldn",
"t",
"pre",
"-",
"process",
"or",
"record",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/envs/env_problem.py#L504-L538 | train |
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
than the default implementation is desired.
Args:
actions: Batch of actions.
Returns:
(preprocessed_observations, processed_rewards, dones, infos).
... | [
"def",
"step",
"(",
"self",
",",
"actions",
")",
":",
"observations",
",",
"raw_rewards",
",",
"dones",
",",
"infos",
"=",
"self",
".",
"_step",
"(",
"actions",
")",
"# Process rewards.",
"raw_rewards",
"=",
"raw_rewards",
".",
"astype",
"(",
"np",
".",
... | 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). | [
"Takes",
"a",
"step",
"in",
"all",
"environments",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/envs/env_problem.py#L540-L566 | train |
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 = {
TIMESTEP_FIELD: tf.FixedLenFeatu... | [
"def",
"example_reading_spec",
"(",
"self",
")",
":",
"# Subclasses can override and/or extend.",
"processed_reward_type",
"=",
"tf",
".",
"float32",
"if",
"self",
".",
"is_processed_rewards_discrete",
":",
"processed_reward_type",
"=",
"tf",
".",
"int64",
"data_fields",
... | Data fields to store on disk and their decoders. | [
"Data",
"fields",
"to",
"store",
"on",
"disk",
"and",
"their",
"decoders",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/envs/env_problem.py#L568-L594 | train |
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
... | [
"def",
"_generate_time_steps",
"(",
"self",
",",
"trajectory_list",
")",
":",
"for",
"single_trajectory",
"in",
"trajectory_list",
":",
"assert",
"isinstance",
"(",
"single_trajectory",
",",
"trajectory",
".",
"Trajectory",
")",
"# Skip writing trajectories that have only... | A generator to yield single time-steps from a list of trajectories. | [
"A",
"generator",
"to",
"yield",
"single",
"time",
"-",
"steps",
"from",
"a",
"list",
"of",
"trajectories",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/envs/env_problem.py#L656-L713 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | init_vq_bottleneck | def init_vq_bottleneck(bottleneck_size, hidden_size):
"""Get lookup table for VQ bottleneck."""
means = tf.get_variable(
name="means",
shape=[bottleneck_size, hidden_size],
initializer=tf.uniform_unit_scaling_initializer())
ema_count = tf.get_variable(
name="ema_count",
shape=[bottle... | python | def init_vq_bottleneck(bottleneck_size, hidden_size):
"""Get lookup table for VQ bottleneck."""
means = tf.get_variable(
name="means",
shape=[bottleneck_size, hidden_size],
initializer=tf.uniform_unit_scaling_initializer())
ema_count = tf.get_variable(
name="ema_count",
shape=[bottle... | [
"def",
"init_vq_bottleneck",
"(",
"bottleneck_size",
",",
"hidden_size",
")",
":",
"means",
"=",
"tf",
".",
"get_variable",
"(",
"name",
"=",
"\"means\"",
",",
"shape",
"=",
"[",
"bottleneck_size",
",",
"hidden_size",
"]",
",",
"initializer",
"=",
"tf",
".",... | Get lookup table for VQ bottleneck. | [
"Get",
"lookup",
"table",
"for",
"VQ",
"bottleneck",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L31-L48 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | vq_nearest_neighbor | def vq_nearest_neighbor(x, hparams):
"""Find the nearest element in means to elements in x."""
bottleneck_size = 2**hparams.bottleneck_bits
means = hparams.means
x_norm_sq = tf.reduce_sum(tf.square(x), axis=-1, keepdims=True)
means_norm_sq = tf.reduce_sum(tf.square(means), axis=-1, keepdims=True)
scalar_pro... | python | def vq_nearest_neighbor(x, hparams):
"""Find the nearest element in means to elements in x."""
bottleneck_size = 2**hparams.bottleneck_bits
means = hparams.means
x_norm_sq = tf.reduce_sum(tf.square(x), axis=-1, keepdims=True)
means_norm_sq = tf.reduce_sum(tf.square(means), axis=-1, keepdims=True)
scalar_pro... | [
"def",
"vq_nearest_neighbor",
"(",
"x",
",",
"hparams",
")",
":",
"bottleneck_size",
"=",
"2",
"**",
"hparams",
".",
"bottleneck_bits",
"means",
"=",
"hparams",
".",
"means",
"x_norm_sq",
"=",
"tf",
".",
"reduce_sum",
"(",
"tf",
".",
"square",
"(",
"x",
... | Find the nearest element in means to elements in x. | [
"Find",
"the",
"nearest",
"element",
"in",
"means",
"to",
"elements",
"in",
"x",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L51-L69 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | vq_discrete_bottleneck | def vq_discrete_bottleneck(x, hparams):
"""Simple vector quantized discrete bottleneck."""
tf.logging.info("Using EMA with beta = {}".format(hparams.beta))
bottleneck_size = 2**hparams.bottleneck_bits
x_shape = common_layers.shape_list(x)
x = tf.reshape(x, [-1, hparams.hidden_size])
x_means_hot, e_loss = vq... | python | def vq_discrete_bottleneck(x, hparams):
"""Simple vector quantized discrete bottleneck."""
tf.logging.info("Using EMA with beta = {}".format(hparams.beta))
bottleneck_size = 2**hparams.bottleneck_bits
x_shape = common_layers.shape_list(x)
x = tf.reshape(x, [-1, hparams.hidden_size])
x_means_hot, e_loss = vq... | [
"def",
"vq_discrete_bottleneck",
"(",
"x",
",",
"hparams",
")",
":",
"tf",
".",
"logging",
".",
"info",
"(",
"\"Using EMA with beta = {}\"",
".",
"format",
"(",
"hparams",
".",
"beta",
")",
")",
"bottleneck_size",
"=",
"2",
"**",
"hparams",
".",
"bottleneck_... | Simple vector quantized discrete bottleneck. | [
"Simple",
"vector",
"quantized",
"discrete",
"bottleneck",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L72-L107 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | vq_discrete_unbottleneck | def vq_discrete_unbottleneck(x, hparams):
"""Simple undiscretization from vector quantized representation."""
x_shape = common_layers.shape_list(x)
bottleneck_size = 2**hparams.bottleneck_bits
means = hparams.means
x_flat = tf.reshape(x, [-1, bottleneck_size])
result = tf.matmul(x_flat, means)
result = tf... | python | def vq_discrete_unbottleneck(x, hparams):
"""Simple undiscretization from vector quantized representation."""
x_shape = common_layers.shape_list(x)
bottleneck_size = 2**hparams.bottleneck_bits
means = hparams.means
x_flat = tf.reshape(x, [-1, bottleneck_size])
result = tf.matmul(x_flat, means)
result = tf... | [
"def",
"vq_discrete_unbottleneck",
"(",
"x",
",",
"hparams",
")",
":",
"x_shape",
"=",
"common_layers",
".",
"shape_list",
"(",
"x",
")",
"bottleneck_size",
"=",
"2",
"**",
"hparams",
".",
"bottleneck_bits",
"means",
"=",
"hparams",
".",
"means",
"x_flat",
"... | Simple undiscretization from vector quantized representation. | [
"Simple",
"undiscretization",
"from",
"vector",
"quantized",
"representation",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L110-L118 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | residual_conv | def residual_conv(x, repeat, k, hparams, name, reuse=None):
"""A stack of convolution blocks with residual connections."""
with tf.variable_scope(name, reuse=reuse):
dilations_and_kernels = [((1, 1), k) for _ in range(3)]
for i in range(repeat):
with tf.variable_scope("repeat_%d" % i):
y = com... | python | def residual_conv(x, repeat, k, hparams, name, reuse=None):
"""A stack of convolution blocks with residual connections."""
with tf.variable_scope(name, reuse=reuse):
dilations_and_kernels = [((1, 1), k) for _ in range(3)]
for i in range(repeat):
with tf.variable_scope("repeat_%d" % i):
y = com... | [
"def",
"residual_conv",
"(",
"x",
",",
"repeat",
",",
"k",
",",
"hparams",
",",
"name",
",",
"reuse",
"=",
"None",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
",",
"reuse",
"=",
"reuse",
")",
":",
"dilations_and_kernels",
"=",
"[",
"(... | A stack of convolution blocks with residual connections. | [
"A",
"stack",
"of",
"convolution",
"blocks",
"with",
"residual",
"connections",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L121-L135 | train |
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(
source,
hparams.hidden_size * multiplier, [((1, 1), kernel)]... | [
"def",
"decompress_step",
"(",
"source",
",",
"hparams",
",",
"first_relu",
",",
"name",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"shape",
"=",
"common_layers",
".",
"shape_list",
"(",
"source",
")",
"multiplier",
"=",
"2",
"... | Decompression function. | [
"Decompression",
"function",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L138-L149 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | compress | def compress(x, hparams, name):
"""Compress."""
with tf.variable_scope(name):
# Run compression by strided convs.
cur = x
k1 = (3, 1)
k2 = (2, 1)
cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams, "rc")
for i in range(hparams.num_compress_steps):
cur = common_layers.conv... | python | def compress(x, hparams, name):
"""Compress."""
with tf.variable_scope(name):
# Run compression by strided convs.
cur = x
k1 = (3, 1)
k2 = (2, 1)
cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams, "rc")
for i in range(hparams.num_compress_steps):
cur = common_layers.conv... | [
"def",
"compress",
"(",
"x",
",",
"hparams",
",",
"name",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"# Run compression by strided convs.",
"cur",
"=",
"x",
"k1",
"=",
"(",
"3",
",",
"1",
")",
"k2",
"=",
"(",
"2",
",",
"1... | Compress. | [
"Compress",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L152-L166 | train |
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)
return... | [
"def",
"encode",
"(",
"x",
",",
"x_space",
",",
"hparams",
",",
"name",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"(",
"encoder_input",
",",
"encoder_self_attention_bias",
",",
"ed",
")",
"=",
"transformer",
".",
"transformer_pr... | Transformer preparations and encoder. | [
"Transformer",
"preparations",
"and",
"encoder",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L169-L176 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | decode_transformer | def decode_transformer(encoder_output, encoder_decoder_attention_bias, targets,
hparams, name):
"""Original Transformer decoder."""
with tf.variable_scope(name):
targets = common_layers.flatten4d3d(targets)
decoder_input, decoder_self_bias = (
transformer.transformer_prepare_... | python | def decode_transformer(encoder_output, encoder_decoder_attention_bias, targets,
hparams, name):
"""Original Transformer decoder."""
with tf.variable_scope(name):
targets = common_layers.flatten4d3d(targets)
decoder_input, decoder_self_bias = (
transformer.transformer_prepare_... | [
"def",
"decode_transformer",
"(",
"encoder_output",
",",
"encoder_decoder_attention_bias",
",",
"targets",
",",
"hparams",
",",
"name",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"targets",
"=",
"common_layers",
".",
"flatten4d3d",
"("... | Original Transformer decoder. | [
"Original",
"Transformer",
"decoder",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L179-L199 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | get_latent_pred_loss | def get_latent_pred_loss(latents_pred, latents_discrete_hot, hparams):
"""Latent prediction and loss."""
latents_logits = tf.layers.dense(
latents_pred, 2**hparams.bottleneck_bits, name="extra_logits")
loss = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=tf.stop_gradient(latents_discrete_hot), lo... | python | def get_latent_pred_loss(latents_pred, latents_discrete_hot, hparams):
"""Latent prediction and loss."""
latents_logits = tf.layers.dense(
latents_pred, 2**hparams.bottleneck_bits, name="extra_logits")
loss = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=tf.stop_gradient(latents_discrete_hot), lo... | [
"def",
"get_latent_pred_loss",
"(",
"latents_pred",
",",
"latents_discrete_hot",
",",
"hparams",
")",
":",
"latents_logits",
"=",
"tf",
".",
"layers",
".",
"dense",
"(",
"latents_pred",
",",
"2",
"**",
"hparams",
".",
"bottleneck_bits",
",",
"name",
"=",
"\"ex... | Latent prediction and loss. | [
"Latent",
"prediction",
"and",
"loss",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L202-L208 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | ae_transformer_internal | def ae_transformer_internal(inputs, targets, target_space, hparams, cache=None):
"""Main step used for training."""
# Encoder.
inputs = common_layers.flatten4d3d(inputs)
inputs, ed = encode(inputs, target_space, hparams, "input_enc")
# Autoencoding.
losses = {"extra": tf.constant(0.0), "latent_pred": tf.co... | python | def ae_transformer_internal(inputs, targets, target_space, hparams, cache=None):
"""Main step used for training."""
# Encoder.
inputs = common_layers.flatten4d3d(inputs)
inputs, ed = encode(inputs, target_space, hparams, "input_enc")
# Autoencoding.
losses = {"extra": tf.constant(0.0), "latent_pred": tf.co... | [
"def",
"ae_transformer_internal",
"(",
"inputs",
",",
"targets",
",",
"target_space",
",",
"hparams",
",",
"cache",
"=",
"None",
")",
":",
"# Encoder.",
"inputs",
"=",
"common_layers",
".",
"flatten4d3d",
"(",
"inputs",
")",
"inputs",
",",
"ed",
"=",
"encode... | Main step used for training. | [
"Main",
"step",
"used",
"for",
"training",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L245-L316 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | transformer_nat_small | def transformer_nat_small():
"""Set of hyperparameters."""
hparams = transformer.transformer_small()
hparams.batch_size = 2048
hparams.learning_rate = 0.2
hparams.learning_rate_warmup_steps = 4000
hparams.num_hidden_layers = 3
hparams.hidden_size = 384
hparams.filter_size = 2048
hparams.label_smoothin... | python | def transformer_nat_small():
"""Set of hyperparameters."""
hparams = transformer.transformer_small()
hparams.batch_size = 2048
hparams.learning_rate = 0.2
hparams.learning_rate_warmup_steps = 4000
hparams.num_hidden_layers = 3
hparams.hidden_size = 384
hparams.filter_size = 2048
hparams.label_smoothin... | [
"def",
"transformer_nat_small",
"(",
")",
":",
"hparams",
"=",
"transformer",
".",
"transformer_small",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"2048",
"hparams",
".",
"learning_rate",
"=",
"0.2",
"hparams",
".",
"learning_rate_warmup_steps",
"=",
"4000",
"... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L384-L407 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | transformer_nat_base | def transformer_nat_base():
"""Set of hyperparameters."""
hparams = transformer_nat_small()
hparams.batch_size = 2048
hparams.hidden_size = 512
hparams.filter_size = 4096
hparams.num_hidden_layers = 6
return hparams | python | def transformer_nat_base():
"""Set of hyperparameters."""
hparams = transformer_nat_small()
hparams.batch_size = 2048
hparams.hidden_size = 512
hparams.filter_size = 4096
hparams.num_hidden_layers = 6
return hparams | [
"def",
"transformer_nat_base",
"(",
")",
":",
"hparams",
"=",
"transformer_nat_small",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"2048",
"hparams",
".",
"hidden_size",
"=",
"512",
"hparams",
".",
"filter_size",
"=",
"4096",
"hparams",
".",
"num_hidden_layers"... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L411-L418 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_nat.py | transformer_nat_big | def transformer_nat_big():
"""Set of hyperparameters."""
hparams = transformer_nat_small()
hparams.batch_size = 2048
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_hidden_layers = 6
hparams.num_heads = 16
hparams.layer_prepostprocess_dropout = 0.3
return hparams | python | def transformer_nat_big():
"""Set of hyperparameters."""
hparams = transformer_nat_small()
hparams.batch_size = 2048
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_hidden_layers = 6
hparams.num_heads = 16
hparams.layer_prepostprocess_dropout = 0.3
return hparams | [
"def",
"transformer_nat_big",
"(",
")",
":",
"hparams",
"=",
"transformer_nat_small",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"2048",
"hparams",
".",
"hidden_size",
"=",
"1024",
"hparams",
".",
"filter_size",
"=",
"4096",
"hparams",
".",
"num_hidden_layers"... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_nat.py#L422-L431 | train |
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 = [... | [
"def",
"policy_net",
"(",
"rng_key",
",",
"batch_observations_shape",
",",
"num_actions",
",",
"bottom_layers",
"=",
"None",
")",
":",
"# 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",
"... | A policy net function. | [
"A",
"policy",
"net",
"function",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L78-L92 | train |
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)
re... | [
"def",
"value_net",
"(",
"rng_key",
",",
"batch_observations_shape",
",",
"num_actions",
",",
"bottom_layers",
"=",
"None",
")",
":",
"del",
"num_actions",
"if",
"bottom_layers",
"is",
"None",
":",
"bottom_layers",
"=",
"[",
"]",
"bottom_layers",
".",
"extend",
... | A value net function. | [
"A",
"value",
"net",
"function",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L95-L108 | train |
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)
# Now, ... | [
"def",
"policy_and_value_net",
"(",
"rng_key",
",",
"batch_observations_shape",
",",
"num_actions",
",",
"bottom_layers",
"=",
"None",
")",
":",
"# Layers.",
"cur_layers",
"=",
"[",
"]",
"if",
"bottom_layers",
"is",
"not",
"None",
":",
"cur_layers",
".",
"extend... | A policy and value net function. | [
"A",
"policy",
"and",
"value",
"net",
"function",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L111-L130 | train |
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))
... | [
"def",
"log_params",
"(",
"params",
",",
"name",
"=",
"\"params\"",
")",
":",
"for",
"i",
",",
"param",
"in",
"enumerate",
"(",
"params",
")",
":",
"if",
"not",
"param",
":",
"# Empty tuple.",
"continue",
"if",
"not",
"isinstance",
"(",
"param",
",",
"... | Dumps the params with `logging.error`. | [
"Dumps",
"the",
"params",
"with",
"logging",
".",
"error",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L140-L152 | train |
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:
env... | [
"def",
"collect_trajectories",
"(",
"env",
",",
"policy_fun",
",",
"num_trajectories",
"=",
"1",
",",
"policy",
"=",
"\"greedy\"",
",",
"max_timestep",
"=",
"None",
",",
"epsilon",
"=",
"0.1",
")",
":",
"trajectories",
"=",
"[",
"]",
"for",
"t",
"in",
"r... | Collect trajectories with the given policy net and behaviour.
Args:
env: A gym env interface, for now this is not-batched.
policy_fun: observations(B,T+1) -> log-probabs(B,T+1, A) callable.
num_trajectories: int, number of trajectories.
policy: string, "greedy", "epsilon-greedy", or "categorical-samp... | [
"Collect",
"trajectories",
"with",
"the",
"given",
"policy",
"net",
"and",
"behaviour",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L159-L275 | train |
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
else:
padding_value = 0
assert padding_val... | [
"def",
"get_padding_value",
"(",
"dtype",
")",
":",
"padding_value",
"=",
"None",
"if",
"dtype",
"==",
"np",
".",
"uint8",
":",
"padding_value",
"=",
"np",
".",
"uint8",
"(",
"0",
")",
"elif",
"dtype",
"==",
"np",
".",
"uint16",
":",
"padding_value",
"... | Returns the padding value given a dtype. | [
"Returns",
"the",
"padding",
"value",
"given",
"a",
"dtype",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L283-L295 | train |
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
is shaped (t+1,) + OBS and actions & rewards are shaped (t,), with the
length of the list being B... | [
"def",
"pad_trajectories",
"(",
"trajectories",
",",
"boundary",
"=",
"20",
")",
":",
"# Let's compute max(t) over all trajectories.",
"t_max",
"=",
"max",
"(",
"r",
".",
"shape",
"[",
"0",
"]",
"for",
"(",
"_",
",",
"_",
",",
"r",
")",
"in",
"trajectories... | 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 (batch size).
boundary: int, bucket length, the a... | [
"Pad",
"trajectories",
"to",
"a",
"bucket",
"length",
"that",
"is",
"a",
"multiple",
"of",
"boundary",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L299-L369 | train |
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})
Args:
rewards: np.ndarray of shape (B, T) of rewa... | [
"def",
"rewards_to_go",
"(",
"rewards",
",",
"mask",
",",
"gamma",
"=",
"0.99",
")",
":",
"B",
",",
"T",
"=",
"rewards",
".",
"shape",
"# pylint: disable=invalid-name,unused-variable",
"masked_rewards",
"=",
"rewards",
"*",
"mask",
"# (B, T)",
"# We use the follow... | 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 rewards.
mask: np.ndarray of shape (B, T) of mas... | [
"r",
"Computes",
"rewards",
"to",
"go",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L373-L421 | train |
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
shape (B, T+1) + OBS... | [
"def",
"value_loss",
"(",
"value_net_apply",
",",
"value_net_params",
",",
"observations",
",",
"rewards",
",",
"reward_mask",
",",
"gamma",
"=",
"0.99",
")",
":",
"B",
",",
"T",
"=",
"rewards",
".",
"shape",
"# pylint: disable=invalid-name",
"assert",
"(",
"B... | Computes the value loss.
Args:
value_net_apply: value net apply function with signature (params, ndarray of
shape (B, T+1) + OBS) -> ndarray(B, T+1, 1)
value_net_params: params of value_net_apply.
observations: np.ndarray of shape (B, T+1) + OBS
rewards: np.ndarray of shape (B, T) of rewards.
... | [
"Computes",
"the",
"value",
"loss",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L425-L454 | train |
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.
Args:
value_prediction: np.ndarray of shape (B, T+1, 1)... | [
"def",
"value_loss_given_predictions",
"(",
"value_prediction",
",",
"rewards",
",",
"reward_mask",
",",
"gamma",
"=",
"0.99",
")",
":",
"B",
",",
"T",
"=",
"rewards",
".",
"shape",
"# pylint: disable=invalid-name",
"assert",
"(",
"B",
",",
"T",
")",
"==",
"... | Computes the value loss given the prediction of the value function.
Args:
value_prediction: np.ndarray of shape (B, T+1, 1)
rewards: np.ndarray of shape (B, T) of rewards.
reward_mask: np.ndarray of shape (B, T), the mask over rewards.
gamma: float, discount factor.
Returns:
The average L2 val... | [
"Computes",
"the",
"value",
"loss",
"given",
"the",
"prediction",
"of",
"the",
"value",
"function",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L458-L484 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | deltas | def deltas(predicted_values, rewards, mask, gamma=0.99):
r"""Computes TD-residuals from V(s) and rewards.
Where a `delta`, i.e. a td-residual is defined as:
delta_{b,t} = r_{b,t} + \gamma * v_{b,t+1} - v_{b,t}.
Args:
predicted_values: ndarray of shape (B, T+1). NOTE: Expects axis 2 was
squeezed. Th... | python | def deltas(predicted_values, rewards, mask, gamma=0.99):
r"""Computes TD-residuals from V(s) and rewards.
Where a `delta`, i.e. a td-residual is defined as:
delta_{b,t} = r_{b,t} + \gamma * v_{b,t+1} - v_{b,t}.
Args:
predicted_values: ndarray of shape (B, T+1). NOTE: Expects axis 2 was
squeezed. Th... | [
"def",
"deltas",
"(",
"predicted_values",
",",
"rewards",
",",
"mask",
",",
"gamma",
"=",
"0.99",
")",
":",
"# `d`s are basically one-step TD residuals.",
"d",
"=",
"[",
"]",
"_",
",",
"T",
"=",
"rewards",
".",
"shape",
"# pylint: disable=invalid-name",
"for",
... | r"""Computes TD-residuals from V(s) and rewards.
Where a `delta`, i.e. a td-residual is defined as:
delta_{b,t} = r_{b,t} + \gamma * v_{b,t+1} - v_{b,t}.
Args:
predicted_values: ndarray of shape (B, T+1). NOTE: Expects axis 2 was
squeezed. These represent V(s_bt) for b < B and t < T+1
rewards: nd... | [
"r",
"Computes",
"TD",
"-",
"residuals",
"from",
"V",
"(",
"s",
")",
"and",
"rewards",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L488-L513 | train |
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... | [
"def",
"gae_advantages",
"(",
"td_deltas",
",",
"mask",
",",
"lambda_",
"=",
"0.95",
",",
"gamma",
"=",
"0.99",
")",
":",
"return",
"rewards_to_go",
"(",
"td_deltas",
",",
"mask",
",",
"lambda_",
"*",
"gamma",
")"
] | 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 same computation.
Args:
td_deltas: np.ndarray of shape (B, ... | [
"r",
"Computes",
"the",
"GAE",
"advantages",
"given",
"the",
"one",
"step",
"TD",
"-",
"residuals",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L516-L537 | train |
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
the t^th time-step in the b^th traj... | [
"def",
"chosen_probabs",
"(",
"probab_observations",
",",
"actions",
")",
":",
"B",
",",
"T",
"=",
"actions",
".",
"shape",
"# pylint: disable=invalid-name",
"assert",
"(",
"B",
",",
"T",
"+",
"1",
")",
"==",
"probab_observations",
".",
"shape",
"[",
":",
... | 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 trajectory.
actions: ndarray of shape `[B, T]`, with ea... | [
"Picks",
"out",
"the",
"probabilities",
"of",
"the",
"actions",
"along",
"batch",
"and",
"time",
"-",
"steps",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L540-L555 | train |
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
network assigns to all the actions at each time-step in each batch using
the old p... | [
"def",
"compute_probab_ratios",
"(",
"p_new",
",",
"p_old",
",",
"actions",
",",
"reward_mask",
")",
":",
"B",
",",
"T",
"=",
"actions",
".",
"shape",
"# pylint: disable=invalid-name",
"assert",
"(",
"B",
",",
"T",
"+",
"1",
")",
"==",
"p_old",
".",
"sha... | 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 parameters.
p_old: ndarray of shape [B, T+1, A], same as above, b... | [
"Computes",
"the",
"probability",
"ratios",
"for",
"each",
"time",
"-",
"step",
"in",
"a",
"trajectory",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L558-L588 | train |
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_=... | [
"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",
",... | PPO objective, with an eventual minus sign, given observations. | [
"PPO",
"objective",
"with",
"an",
"eventual",
"minus",
"sign",
"given",
"observations",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L602-L645 | train |
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,
... | [
"def",
"ppo_loss_given_predictions",
"(",
"log_probab_actions_new",
",",
"log_probab_actions_old",
",",
"predicted_values",
",",
"padded_actions",
",",
"padded_rewards",
",",
"reward_mask",
",",
"gamma",
"=",
"0.99",
",",
"lambda_",
"=",
"0.95",
",",
"epsilon",
"=",
... | PPO objective, with an eventual minus sign, given predictions. | [
"PPO",
"objective",
"with",
"an",
"eventual",
"minus",
"sign",
"given",
"predictions",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L649-L695 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | combined_loss_given_predictions | def combined_loss_given_predictions(log_probab_actions_new,
log_probab_actions_old,
value_prediction,
padded_actions,
padded_rewards,
reward... | python | def combined_loss_given_predictions(log_probab_actions_new,
log_probab_actions_old,
value_prediction,
padded_actions,
padded_rewards,
reward... | [
"def",
"combined_loss_given_predictions",
"(",
"log_probab_actions_new",
",",
"log_probab_actions_old",
",",
"value_prediction",
",",
"padded_actions",
",",
"padded_rewards",
",",
"reward_mask",
",",
"gamma",
"=",
"0.99",
",",
"lambda_",
"=",
"0.95",
",",
"epsilon",
"... | Computes the combined (clipped loss + value loss) given predictions. | [
"Computes",
"the",
"combined",
"(",
"clipped",
"loss",
"+",
"value",
"loss",
")",
"given",
"predictions",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L699-L726 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | combined_loss | def combined_loss(new_params,
old_params,
policy_and_value_net_apply,
padded_observations,
padded_actions,
padded_rewards,
reward_mask,
gamma=0.99,
lambda_=0.95,
... | python | def combined_loss(new_params,
old_params,
policy_and_value_net_apply,
padded_observations,
padded_actions,
padded_rewards,
reward_mask,
gamma=0.99,
lambda_=0.95,
... | [
"def",
"combined_loss",
"(",
"new_params",
",",
"old_params",
",",
"policy_and_value_net_apply",
",",
"padded_observations",
",",
"padded_actions",
",",
"padded_rewards",
",",
"reward_mask",
",",
"gamma",
"=",
"0.99",
",",
"lambda_",
"=",
"0.95",
",",
"epsilon",
"... | Computes the combined (clipped loss + value loss) given observations. | [
"Computes",
"the",
"combined",
"(",
"clipped",
"loss",
"+",
"value",
"loss",
")",
"given",
"observations",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L731-L761 | train |
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,
padded_actions,
padded_rewa... | [
"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_rewards",
",",
"reward_mask",
... | PPO optimizer step. | [
"PPO",
"optimizer",
"step",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L765-L795 | train |
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."""
value_params = trax_opt.get_pa... | [
"def",
"value_opt_step",
"(",
"i",
",",
"opt_state",
",",
"opt_update",
",",
"value_net_apply",
",",
"padded_observations",
",",
"padded_rewards",
",",
"reward_mask",
",",
"gamma",
"=",
"0.99",
")",
":",
"value_params",
"=",
"trax_opt",
".",
"get_params",
"(",
... | Value optimizer step. | [
"Value",
"optimizer",
"step",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L799-L816 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | policy_and_value_opt_step | def policy_and_value_opt_step(i,
opt_state,
opt_update,
policy_and_value_net_apply,
old_params,
padded_observations,
padded_actions,
... | python | def policy_and_value_opt_step(i,
opt_state,
opt_update,
policy_and_value_net_apply,
old_params,
padded_observations,
padded_actions,
... | [
"def",
"policy_and_value_opt_step",
"(",
"i",
",",
"opt_state",
",",
"opt_update",
",",
"policy_and_value_net_apply",
",",
"old_params",
",",
"padded_observations",
",",
"padded_actions",
",",
"padded_rewards",
",",
"reward_mask",
",",
"c1",
"=",
"1.0",
",",
"c2",
... | Policy and Value optimizer step. | [
"Policy",
"and",
"Value",
"optimizer",
"step",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L820-L855 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/rlax/ppo.py | training_loop | def training_loop(env=None,
env_name="CartPole-v0",
epochs=EPOCHS,
policy_net_fun=None,
value_net_fun=None,
policy_and_value_net_fun=None,
policy_optimizer_fun=None,
value_optimizer_fun=None,
... | python | def training_loop(env=None,
env_name="CartPole-v0",
epochs=EPOCHS,
policy_net_fun=None,
value_net_fun=None,
policy_and_value_net_fun=None,
policy_optimizer_fun=None,
value_optimizer_fun=None,
... | [
"def",
"training_loop",
"(",
"env",
"=",
"None",
",",
"env_name",
"=",
"\"CartPole-v0\"",
",",
"epochs",
"=",
"EPOCHS",
",",
"policy_net_fun",
"=",
"None",
",",
"value_net_fun",
"=",
"None",
",",
"policy_and_value_net_fun",
"=",
"None",
",",
"policy_optimizer_fu... | Runs the training loop for PPO, with fixed policy and value nets. | [
"Runs",
"the",
"training",
"loop",
"for",
"PPO",
"with",
"fixed",
"policy",
"and",
"value",
"nets",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/rlax/ppo.py#L864-L1215 | train |
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(
tmp_di... | [
"def",
"_maybe_download_corpora",
"(",
"tmp_dir",
")",
":",
"mnli_filename",
"=",
"\"MNLI.zip\"",
"mnli_finalpath",
"=",
"os",
".",
"path",
".",
"join",
"(",
"tmp_dir",
",",
"\"MNLI\"",
")",
"if",
"not",
"tf",
".",
"gfile",
".",
"Exists",
"(",
"mnli_finalpat... | Download corpora for multinli.
Args:
tmp_dir: a string
Returns:
a string | [
"Download",
"corpora",
"for",
"multinli",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multinli.py#L42-L59 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multinli.py | _example_generator | def _example_generator(filename):
"""Generate mnli examples.
Args:
filename: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings
"""
for idx, line in enumerate(tf.gfile.Open(filename, "rb")):
if idx == 0: continue # skip header
line = text_encoder.to_unicode_... | python | def _example_generator(filename):
"""Generate mnli examples.
Args:
filename: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings
"""
for idx, line in enumerate(tf.gfile.Open(filename, "rb")):
if idx == 0: continue # skip header
line = text_encoder.to_unicode_... | [
"def",
"_example_generator",
"(",
"filename",
")",
":",
"for",
"idx",
",",
"line",
"in",
"enumerate",
"(",
"tf",
".",
"gfile",
".",
"Open",
"(",
"filename",
",",
"\"rb\"",
")",
")",
":",
"if",
"idx",
"==",
"0",
":",
"continue",
"# skip header",
"line",... | Generate mnli examples.
Args:
filename: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings | [
"Generate",
"mnli",
"examples",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multinli.py#L62-L79 | train |
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.
path1 = tf.n... | [
"def",
"shake_shake_skip_connection",
"(",
"x",
",",
"output_filters",
",",
"stride",
",",
"is_training",
")",
":",
"curr_filters",
"=",
"common_layers",
".",
"shape_list",
"(",
"x",
")",
"[",
"-",
"1",
"]",
"if",
"curr_filters",
"==",
"output_filters",
":",
... | Adds a residual connection to the filter x for the shake-shake model. | [
"Adds",
"a",
"residual",
"connection",
"to",
"the",
"filter",
"x",
"for",
"the",
"shake",
"-",
"shake",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/shake_shake.py#L30-L52 | train |
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)
x = tf.layers.conv2d(
x,
output_filters, (3, 3),
strides=(stride, st... | [
"def",
"shake_shake_branch",
"(",
"x",
",",
"output_filters",
",",
"stride",
",",
"rand_forward",
",",
"rand_backward",
",",
"hparams",
")",
":",
"is_training",
"=",
"hparams",
".",
"mode",
"==",
"tf",
".",
"estimator",
".",
"ModeKeys",
".",
"TRAIN",
"x",
... | Building a 2 branching convnet. | [
"Building",
"a",
"2",
"branching",
"convnet",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/shake_shake.py#L55-L75 | train |
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(
[... | [
"def",
"shake_shake_block",
"(",
"x",
",",
"output_filters",
",",
"stride",
",",
"hparams",
")",
":",
"is_training",
"=",
"hparams",
".",
"mode",
"==",
"tf",
".",
"estimator",
".",
"ModeKeys",
".",
"TRAIN",
"batch_size",
"=",
"common_layers",
".",
"shape_lis... | Builds a full shake-shake sub layer. | [
"Builds",
"a",
"full",
"shake",
"-",
"shake",
"sub",
"layer",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/shake_shake.py#L78-L119 | train |
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... | [
"def",
"shake_shake_layer",
"(",
"x",
",",
"output_filters",
",",
"num_blocks",
",",
"stride",
",",
"hparams",
")",
":",
"for",
"block_num",
"in",
"range",
"(",
"num_blocks",
")",
":",
"curr_stride",
"=",
"stride",
"if",
"(",
"block_num",
"==",
"0",
")",
... | Builds many sub layers into one full layer. | [
"Builds",
"many",
"sub",
"layers",
"into",
"one",
"full",
"layer",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/shake_shake.py#L122-L128 | train |
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... | [
"def",
"shakeshake_small",
"(",
")",
":",
"hparams",
"=",
"common_hparams",
".",
"basic_params1",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"128",
"hparams",
".",
"hidden_size",
"=",
"32",
"hparams",
".",
"layer_prepostprocess_dropout",
"=",
"0.0",
"hparams",... | Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU. | [
"Parameters",
"for",
"CIFAR",
"-",
"10",
".",
"Gets",
"to",
"about",
"96%",
"accuracy"
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/shake_shake.py#L165-L187 | train |
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 ... | [
"def",
"has_metric_plateaued",
"(",
"steps",
",",
"values",
",",
"num_steps",
"=",
"100",
",",
"delta",
"=",
"0.1",
",",
"decrease",
"=",
"True",
")",
":",
"assert",
"num_steps",
">",
"0",
"if",
"len",
"(",
"steps",
")",
"<",
"2",
":",
"return",
"Fal... | 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 steps for values.
values: list<float> list of metric values.
num_steps: int, number of steps the metric ... | [
"Check",
"if",
"metric",
"has",
"plateaued",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/metrics_hook.py#L249-L290 | train |
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... | [
"def",
"next_frame_savp",
"(",
")",
":",
"hparams",
"=",
"sv2p_params",
".",
"next_frame_sv2p",
"(",
")",
"hparams",
".",
"add_hparam",
"(",
"\"z_dim\"",
",",
"8",
")",
"hparams",
".",
"add_hparam",
"(",
"\"num_discriminator_filters\"",
",",
"32",
")",
"hparam... | SAVP model hparams. | [
"SAVP",
"model",
"hparams",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/video/savp_params.py#L27-L56 | train |
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 | [
"def",
"next_frame_savp_vae",
"(",
")",
":",
"hparams",
"=",
"next_frame_savp",
"(",
")",
"hparams",
".",
"use_vae",
"=",
"True",
"hparams",
".",
"use_gan",
"=",
"False",
"hparams",
".",
"latent_loss_multiplier",
"=",
"1e-3",
"hparams",
".",
"latent_loss_multipl... | SAVP - VAE only model. | [
"SAVP",
"-",
"VAE",
"only",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/video/savp_params.py#L70-L77 | train |
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... | [
"def",
"next_frame_savp_gan",
"(",
")",
":",
"hparams",
"=",
"next_frame_savp",
"(",
")",
"hparams",
".",
"use_gan",
"=",
"True",
"hparams",
".",
"use_vae",
"=",
"False",
"hparams",
".",
"gan_loss_multiplier",
"=",
"0.001",
"hparams",
".",
"optimizer_adam_beta1"... | SAVP - GAN only model. | [
"SAVP",
"-",
"GAN",
"only",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/video/savp_params.py#L81-L92 | train |
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... | [
"def",
"diet_adam_optimizer_params",
"(",
")",
":",
"return",
"hparam",
".",
"HParams",
"(",
"quantize",
"=",
"True",
",",
"# use 16-bit fixed-point",
"quantization_scale",
"=",
"10.0",
"/",
"tf",
".",
"int16",
".",
"max",
",",
"optimizer",
"=",
"\"DietAdam\"",
... | Default hyperparameters for a DietAdamOptimizer.
Returns:
a hyperparameters object. | [
"Default",
"hyperparameters",
"for",
"a",
"DietAdamOptimizer",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L34-L51 | train |
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... | [
"def",
"diet_expert",
"(",
"x",
",",
"hidden_size",
",",
"params",
")",
":",
"@",
"fn_with_diet_vars",
"(",
"params",
")",
"def",
"diet_expert_internal",
"(",
"x",
")",
":",
"dim",
"=",
"x",
".",
"get_shape",
"(",
")",
".",
"as_list",
"(",
")",
"[",
... | 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 HParams object.
Returns:
a Tensor with shape... | [
"A",
"two",
"-",
"layer",
"feed",
"-",
"forward",
"network",
"with",
"relu",
"activation",
"on",
"hidden",
"layer",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L54-L77 | train |
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 =... | [
"def",
"_quantize",
"(",
"x",
",",
"params",
",",
"randomize",
"=",
"True",
")",
":",
"if",
"not",
"params",
".",
"quantize",
":",
"return",
"x",
"if",
"not",
"randomize",
":",
"return",
"tf",
".",
"bitcast",
"(",
"tf",
".",
"cast",
"(",
"x",
"/",
... | Quantize x according to params, optionally randomizing the rounding. | [
"Quantize",
"x",
"according",
"to",
"params",
"optionally",
"randomizing",
"the",
"rounding",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L235-L250 | train |
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 | [
"def",
"_dequantize",
"(",
"q",
",",
"params",
")",
":",
"if",
"not",
"params",
".",
"quantize",
":",
"return",
"q",
"return",
"tf",
".",
"to_float",
"(",
"tf",
".",
"bitcast",
"(",
"q",
",",
"tf",
".",
"int16",
")",
")",
"*",
"params",
".",
"qua... | Dequantize q according to params. | [
"Dequantize",
"q",
"according",
"to",
"params",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L253-L257 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | make_diet_var_getter | def make_diet_var_getter(params):
"""Create a custom variable getter for diet variables according to params."""
def diet_var_initializer(shape, dtype, partition_info=None):
"""Initializer for a diet variable."""
del dtype
del partition_info
with common_layers.fn_device_dependency("diet_init") as o... | python | def make_diet_var_getter(params):
"""Create a custom variable getter for diet variables according to params."""
def diet_var_initializer(shape, dtype, partition_info=None):
"""Initializer for a diet variable."""
del dtype
del partition_info
with common_layers.fn_device_dependency("diet_init") as o... | [
"def",
"make_diet_var_getter",
"(",
"params",
")",
":",
"def",
"diet_var_initializer",
"(",
"shape",
",",
"dtype",
",",
"partition_info",
"=",
"None",
")",
":",
"\"\"\"Initializer for a diet variable.\"\"\"",
"del",
"dtype",
"del",
"partition_info",
"with",
"common_la... | Create a custom variable getter for diet variables according to params. | [
"Create",
"a",
"custom",
"variable",
"getter",
"for",
"diet",
"variables",
"according",
"to",
"params",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L260-L293 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | _fn_with_diet_vars | def _fn_with_diet_vars(fn, args, params):
"""Call function with args; use diet variables according to params."""
vs_ctr = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Custom gradient function."""
del outputs # recomputing below
with common_layers.fn_device_dependency("diet_grad",
... | python | def _fn_with_diet_vars(fn, args, params):
"""Call function with args; use diet variables according to params."""
vs_ctr = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Custom gradient function."""
del outputs # recomputing below
with common_layers.fn_device_dependency("diet_grad",
... | [
"def",
"_fn_with_diet_vars",
"(",
"fn",
",",
"args",
",",
"params",
")",
":",
"vs_ctr",
"=",
"[",
"]",
"def",
"grad_fn",
"(",
"inputs",
",",
"variables",
",",
"outputs",
",",
"output_grads",
")",
":",
"\"\"\"Custom gradient function.\"\"\"",
"del",
"outputs",
... | Call function with args; use diet variables according to params. | [
"Call",
"function",
"with",
"args",
";",
"use",
"diet",
"variables",
"according",
"to",
"params",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L296-L349 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | fn_with_diet_vars | def fn_with_diet_vars(params):
"""Decorator for graph-building function to use diet variables."""
params = copy.copy(params)
def dec(fn):
def wrapped(*args):
return _fn_with_diet_vars(fn, args, params)
return wrapped
return dec | python | def fn_with_diet_vars(params):
"""Decorator for graph-building function to use diet variables."""
params = copy.copy(params)
def dec(fn):
def wrapped(*args):
return _fn_with_diet_vars(fn, args, params)
return wrapped
return dec | [
"def",
"fn_with_diet_vars",
"(",
"params",
")",
":",
"params",
"=",
"copy",
".",
"copy",
"(",
"params",
")",
"def",
"dec",
"(",
"fn",
")",
":",
"def",
"wrapped",
"(",
"*",
"args",
")",
":",
"return",
"_fn_with_diet_vars",
"(",
"fn",
",",
"args",
",",... | Decorator for graph-building function to use diet variables. | [
"Decorator",
"for",
"graph",
"-",
"building",
"function",
"to",
"use",
"diet",
"variables",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L352-L363 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | DietAdamOptimizer.create_slots | def create_slots(self, var):
"""Create the factorized Adam accumulators for diet variables."""
params = self.params
shape = var.get_shape().as_list()
if not hasattr(params, "slots"):
params.slots = defaultdict(dict)
name = var.op.name
slots = params.slots[name]
if params.factored_se... | python | def create_slots(self, var):
"""Create the factorized Adam accumulators for diet variables."""
params = self.params
shape = var.get_shape().as_list()
if not hasattr(params, "slots"):
params.slots = defaultdict(dict)
name = var.op.name
slots = params.slots[name]
if params.factored_se... | [
"def",
"create_slots",
"(",
"self",
",",
"var",
")",
":",
"params",
"=",
"self",
".",
"params",
"shape",
"=",
"var",
".",
"get_shape",
"(",
")",
".",
"as_list",
"(",
")",
"if",
"not",
"hasattr",
"(",
"params",
",",
"\"slots\"",
")",
":",
"params",
... | Create the factorized Adam accumulators for diet variables. | [
"Create",
"the",
"factorized",
"Adam",
"accumulators",
"for",
"diet",
"variables",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L144-L175 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/diet.py | DietAdamOptimizer.update_variable | def update_variable(self, var, grad_var):
"""Update the variable and its slots."""
params = self.params
global_step = tf.to_float(self.global_step) + 1
# compute learning rate
lrate = params.learning_rate
if params.learning_rate_decay_scheme == "noam":
lrate *= tf.minimum(global_step * pa... | python | def update_variable(self, var, grad_var):
"""Update the variable and its slots."""
params = self.params
global_step = tf.to_float(self.global_step) + 1
# compute learning rate
lrate = params.learning_rate
if params.learning_rate_decay_scheme == "noam":
lrate *= tf.minimum(global_step * pa... | [
"def",
"update_variable",
"(",
"self",
",",
"var",
",",
"grad_var",
")",
":",
"params",
"=",
"self",
".",
"params",
"global_step",
"=",
"tf",
".",
"to_float",
"(",
"self",
".",
"global_step",
")",
"+",
"1",
"# compute learning rate",
"lrate",
"=",
"params"... | Update the variable and its slots. | [
"Update",
"the",
"variable",
"and",
"its",
"slots",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/diet.py#L177-L225 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/mtf_model.py | MtfModel.estimator_spec_eval | def estimator_spec_eval(
self, features, logits, labels, loss, restore_hook, use_tpu):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
problem = hparams.problem
if logits.get_shape().ndims == 3:
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
# Support for mult... | python | def estimator_spec_eval(
self, features, logits, labels, loss, restore_hook, use_tpu):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
problem = hparams.problem
if logits.get_shape().ndims == 3:
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
# Support for mult... | [
"def",
"estimator_spec_eval",
"(",
"self",
",",
"features",
",",
"logits",
",",
"labels",
",",
"loss",
",",
"restore_hook",
",",
"use_tpu",
")",
":",
"hparams",
"=",
"self",
".",
"hparams",
"problem",
"=",
"hparams",
".",
"problem",
"if",
"logits",
".",
... | Construct EstimatorSpec for EVAL mode. | [
"Construct",
"EstimatorSpec",
"for",
"EVAL",
"mode",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/mtf_model.py#L188-L229 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/desc2code.py | generator_samples | def generator_samples(tmp_dir, pb_cst):
"""Generator for the dataset samples.
If not present, download and extract the dataset.
Args:
tmp_dir: path to the directory where to download the dataset.
pb_cst: CodingPbConstants object defining paths
Yields:
A CodingPbInfo object containing the next cha... | python | def generator_samples(tmp_dir, pb_cst):
"""Generator for the dataset samples.
If not present, download and extract the dataset.
Args:
tmp_dir: path to the directory where to download the dataset.
pb_cst: CodingPbConstants object defining paths
Yields:
A CodingPbInfo object containing the next cha... | [
"def",
"generator_samples",
"(",
"tmp_dir",
",",
"pb_cst",
")",
":",
"# Step1: Download dataset (eventually)",
"data_zip_path",
"=",
"generator_utils",
".",
"maybe_download_from_drive",
"(",
"directory",
"=",
"tmp_dir",
",",
"filename",
"=",
"_DATASET_FILENAME",
",",
"u... | Generator for the dataset samples.
If not present, download and extract the dataset.
Args:
tmp_dir: path to the directory where to download the dataset.
pb_cst: CodingPbConstants object defining paths
Yields:
A CodingPbInfo object containing the next challenge informations. | [
"Generator",
"for",
"the",
"dataset",
"samples",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/desc2code.py#L240-L308 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm | def lstm(inputs, sequence_length, hparams, train, name, initial_state=None):
"""Adds a stack of LSTM layers on top of input.
Args:
inputs: The input `Tensor`, shaped `[batch_size, time_steps, hidden_size]`.
sequence_length: Lengths of the actual input sequence, excluding padding; a
`Tensor` shaped ... | python | def lstm(inputs, sequence_length, hparams, train, name, initial_state=None):
"""Adds a stack of LSTM layers on top of input.
Args:
inputs: The input `Tensor`, shaped `[batch_size, time_steps, hidden_size]`.
sequence_length: Lengths of the actual input sequence, excluding padding; a
`Tensor` shaped ... | [
"def",
"lstm",
"(",
"inputs",
",",
"sequence_length",
",",
"hparams",
",",
"train",
",",
"name",
",",
"initial_state",
"=",
"None",
")",
":",
"layers",
"=",
"[",
"_dropout_lstm_cell",
"(",
"hparams",
",",
"train",
")",
"for",
"_",
"in",
"range",
"(",
"... | Adds a stack of LSTM layers on top of input.
Args:
inputs: The input `Tensor`, shaped `[batch_size, time_steps, hidden_size]`.
sequence_length: Lengths of the actual input sequence, excluding padding; a
`Tensor` shaped `[batch_size]`.
hparams: HParams; hyperparameters.
train: bool; `True` whe... | [
"Adds",
"a",
"stack",
"of",
"LSTM",
"layers",
"on",
"top",
"of",
"input",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L38-L67 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_attention_decoder | def lstm_attention_decoder(inputs, hparams, train, name, initial_state,
encoder_outputs, encoder_output_length,
decoder_input_length):
"""Run LSTM cell with attention on inputs of shape [batch x time x size].
Args:
inputs: The decoder input `Tensor`, shaped... | python | def lstm_attention_decoder(inputs, hparams, train, name, initial_state,
encoder_outputs, encoder_output_length,
decoder_input_length):
"""Run LSTM cell with attention on inputs of shape [batch x time x size].
Args:
inputs: The decoder input `Tensor`, shaped... | [
"def",
"lstm_attention_decoder",
"(",
"inputs",
",",
"hparams",
",",
"train",
",",
"name",
",",
"initial_state",
",",
"encoder_outputs",
",",
"encoder_output_length",
",",
"decoder_input_length",
")",
":",
"layers",
"=",
"[",
"_dropout_lstm_cell",
"(",
"hparams",
... | Run LSTM cell with attention on inputs of shape [batch x time x size].
Args:
inputs: The decoder input `Tensor`, shaped `[batch_size, decoder_steps,
hidden_size]`.
hparams: HParams; hyperparameters.
train: bool; `True` when constructing training graph to enable dropout.
name: string; Create v... | [
"Run",
"LSTM",
"cell",
"with",
"attention",
"on",
"inputs",
"of",
"shape",
"[",
"batch",
"x",
"time",
"x",
"size",
"]",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L70-L174 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq_internal | def lstm_seq2seq_internal(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model, main step used for training."""
with tf.variable_scope("lstm_seq2seq"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs = common_layers.flatt... | python | def lstm_seq2seq_internal(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model, main step used for training."""
with tf.variable_scope("lstm_seq2seq"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs = common_layers.flatt... | [
"def",
"lstm_seq2seq_internal",
"(",
"inputs",
",",
"targets",
",",
"hparams",
",",
"train",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"\"lstm_seq2seq\"",
")",
":",
"if",
"inputs",
"is",
"not",
"None",
":",
"inputs_length",
"=",
"common_layers",
".... | The basic LSTM seq2seq model, main step used for training. | [
"The",
"basic",
"LSTM",
"seq2seq",
"model",
"main",
"step",
"used",
"for",
"training",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L177-L203 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq_internal_attention | def lstm_seq2seq_internal_attention(inputs, targets, hparams, train,
inputs_length, targets_length):
"""LSTM seq2seq model with attention, main step used for training."""
with tf.variable_scope("lstm_seq2seq_attention"):
# Flatten inputs.
inputs = common_layers.flatten4d3... | python | def lstm_seq2seq_internal_attention(inputs, targets, hparams, train,
inputs_length, targets_length):
"""LSTM seq2seq model with attention, main step used for training."""
with tf.variable_scope("lstm_seq2seq_attention"):
# Flatten inputs.
inputs = common_layers.flatten4d3... | [
"def",
"lstm_seq2seq_internal_attention",
"(",
"inputs",
",",
"targets",
",",
"hparams",
",",
"train",
",",
"inputs_length",
",",
"targets_length",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"\"lstm_seq2seq_attention\"",
")",
":",
"# Flatten inputs.",
"inpu... | LSTM seq2seq model with attention, main step used for training. | [
"LSTM",
"seq2seq",
"model",
"with",
"attention",
"main",
"step",
"used",
"for",
"training",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L206-L225 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_bid_encoder | def lstm_bid_encoder(inputs, sequence_length, hparams, train, name):
"""Bidirectional LSTM for encoding inputs that are [batch x time x size]."""
with tf.variable_scope(name):
cell_fw = tf.nn.rnn_cell.MultiRNNCell(
[_dropout_lstm_cell(hparams, train)
for _ in range(hparams.num_hidden_layers)])... | python | def lstm_bid_encoder(inputs, sequence_length, hparams, train, name):
"""Bidirectional LSTM for encoding inputs that are [batch x time x size]."""
with tf.variable_scope(name):
cell_fw = tf.nn.rnn_cell.MultiRNNCell(
[_dropout_lstm_cell(hparams, train)
for _ in range(hparams.num_hidden_layers)])... | [
"def",
"lstm_bid_encoder",
"(",
"inputs",
",",
"sequence_length",
",",
"hparams",
",",
"train",
",",
"name",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"cell_fw",
"=",
"tf",
".",
"nn",
".",
"rnn_cell",
".",
"MultiRNNCell",
"(",... | Bidirectional LSTM for encoding inputs that are [batch x time x size]. | [
"Bidirectional",
"LSTM",
"for",
"encoding",
"inputs",
"that",
"are",
"[",
"batch",
"x",
"time",
"x",
"size",
"]",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L228-L273 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq_internal_bid_encoder | def lstm_seq2seq_internal_bid_encoder(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model with bidirectional encoder."""
with tf.variable_scope("lstm_seq2seq_bid_encoder"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs... | python | def lstm_seq2seq_internal_bid_encoder(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model with bidirectional encoder."""
with tf.variable_scope("lstm_seq2seq_bid_encoder"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs... | [
"def",
"lstm_seq2seq_internal_bid_encoder",
"(",
"inputs",
",",
"targets",
",",
"hparams",
",",
"train",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"\"lstm_seq2seq_bid_encoder\"",
")",
":",
"if",
"inputs",
"is",
"not",
"None",
":",
"inputs_length",
"=",... | The basic LSTM seq2seq model with bidirectional encoder. | [
"The",
"basic",
"LSTM",
"seq2seq",
"model",
"with",
"bidirectional",
"encoder",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L276-L302 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq_internal_attention_bid_encoder | def lstm_seq2seq_internal_attention_bid_encoder(inputs, targets, hparams,
train):
"""LSTM seq2seq model with attention, main step used for training."""
with tf.variable_scope("lstm_seq2seq_attention_bid_encoder"):
inputs_length = common_layers.length_from_embeddin... | python | def lstm_seq2seq_internal_attention_bid_encoder(inputs, targets, hparams,
train):
"""LSTM seq2seq model with attention, main step used for training."""
with tf.variable_scope("lstm_seq2seq_attention_bid_encoder"):
inputs_length = common_layers.length_from_embeddin... | [
"def",
"lstm_seq2seq_internal_attention_bid_encoder",
"(",
"inputs",
",",
"targets",
",",
"hparams",
",",
"train",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"\"lstm_seq2seq_attention_bid_encoder\"",
")",
":",
"inputs_length",
"=",
"common_layers",
".",
"leng... | LSTM seq2seq model with attention, main step used for training. | [
"LSTM",
"seq2seq",
"model",
"with",
"attention",
"main",
"step",
"used",
"for",
"training",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L305-L325 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_seq2seq | def lstm_seq2seq():
"""hparams for LSTM."""
hparams = common_hparams.basic_params1()
hparams.daisy_chain_variables = False
hparams.batch_size = 1024
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_decay ... | python | def lstm_seq2seq():
"""hparams for LSTM."""
hparams = common_hparams.basic_params1()
hparams.daisy_chain_variables = False
hparams.batch_size = 1024
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_decay ... | [
"def",
"lstm_seq2seq",
"(",
")",
":",
"hparams",
"=",
"common_hparams",
".",
"basic_params1",
"(",
")",
"hparams",
".",
"daisy_chain_variables",
"=",
"False",
"hparams",
".",
"batch_size",
"=",
"1024",
"hparams",
".",
"hidden_size",
"=",
"128",
"hparams",
".",... | hparams for LSTM. | [
"hparams",
"for",
"LSTM",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L410-L420 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_attention_base | def lstm_attention_base():
"""Base attention params."""
hparams = lstm_seq2seq()
hparams.add_hparam("attention_layer_size", hparams.hidden_size)
hparams.add_hparam("output_attention", True)
hparams.add_hparam("num_heads", 1)
return hparams | python | def lstm_attention_base():
"""Base attention params."""
hparams = lstm_seq2seq()
hparams.add_hparam("attention_layer_size", hparams.hidden_size)
hparams.add_hparam("output_attention", True)
hparams.add_hparam("num_heads", 1)
return hparams | [
"def",
"lstm_attention_base",
"(",
")",
":",
"hparams",
"=",
"lstm_seq2seq",
"(",
")",
"hparams",
".",
"add_hparam",
"(",
"\"attention_layer_size\"",
",",
"hparams",
".",
"hidden_size",
")",
"hparams",
".",
"add_hparam",
"(",
"\"output_attention\"",
",",
"True",
... | Base attention params. | [
"Base",
"attention",
"params",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L423-L429 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_asr_v1 | def lstm_asr_v1():
"""Basic LSTM Params."""
hparams = lstm_bahdanau_attention()
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.batch_size = 36
hparams.max_input_seq_length = 600000
hparams.max_target_seq_length = 350
hparams.max_length = hparams.max_input_seq_length
hparams.min_length... | python | def lstm_asr_v1():
"""Basic LSTM Params."""
hparams = lstm_bahdanau_attention()
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.batch_size = 36
hparams.max_input_seq_length = 600000
hparams.max_target_seq_length = 350
hparams.max_length = hparams.max_input_seq_length
hparams.min_length... | [
"def",
"lstm_asr_v1",
"(",
")",
":",
"hparams",
"=",
"lstm_bahdanau_attention",
"(",
")",
"hparams",
".",
"num_hidden_layers",
"=",
"2",
"hparams",
".",
"hidden_size",
"=",
"256",
"hparams",
".",
"batch_size",
"=",
"36",
"hparams",
".",
"max_input_seq_length",
... | Basic LSTM Params. | [
"Basic",
"LSTM",
"Params",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L471-L482 | train |
tensorflow/tensor2tensor | tensor2tensor/models/lstm.py | lstm_area_attention_base | def lstm_area_attention_base():
"""Hparams for LSTM with area attention."""
hparams = lstm_luong_attention()
hparams.batch_size = 16384
hparams.num_hidden_layers = 2
hparams.hidden_size = 1024
hparams.num_heads = 4
hparams.dropout = 0.2
hparams.learning_rate = 0.1
hparams.max_area_width = 2
hparams.... | python | def lstm_area_attention_base():
"""Hparams for LSTM with area attention."""
hparams = lstm_luong_attention()
hparams.batch_size = 16384
hparams.num_hidden_layers = 2
hparams.hidden_size = 1024
hparams.num_heads = 4
hparams.dropout = 0.2
hparams.learning_rate = 0.1
hparams.max_area_width = 2
hparams.... | [
"def",
"lstm_area_attention_base",
"(",
")",
":",
"hparams",
"=",
"lstm_luong_attention",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"16384",
"hparams",
".",
"num_hidden_layers",
"=",
"2",
"hparams",
".",
"hidden_size",
"=",
"1024",
"hparams",
".",
"num_heads"... | Hparams for LSTM with area attention. | [
"Hparams",
"for",
"LSTM",
"with",
"area",
"attention",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/lstm.py#L486-L498 | train |
tensorflow/tensor2tensor | tensor2tensor/bin/t2t_attack.py | create_surrogate_run_config | def create_surrogate_run_config(hp):
"""Create a run config.
Args:
hp: model hyperparameters
Returns:
a run config
"""
save_ckpt_steps = max(FLAGS.iterations_per_loop, FLAGS.local_eval_frequency)
save_ckpt_secs = FLAGS.save_checkpoints_secs or None
if save_ckpt_secs:
save_ckpt_steps = None
... | python | def create_surrogate_run_config(hp):
"""Create a run config.
Args:
hp: model hyperparameters
Returns:
a run config
"""
save_ckpt_steps = max(FLAGS.iterations_per_loop, FLAGS.local_eval_frequency)
save_ckpt_secs = FLAGS.save_checkpoints_secs or None
if save_ckpt_secs:
save_ckpt_steps = None
... | [
"def",
"create_surrogate_run_config",
"(",
"hp",
")",
":",
"save_ckpt_steps",
"=",
"max",
"(",
"FLAGS",
".",
"iterations_per_loop",
",",
"FLAGS",
".",
"local_eval_frequency",
")",
"save_ckpt_secs",
"=",
"FLAGS",
".",
"save_checkpoints_secs",
"or",
"None",
"if",
"s... | Create a run config.
Args:
hp: model hyperparameters
Returns:
a run config | [
"Create",
"a",
"run",
"config",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/bin/t2t_attack.py#L83-L131 | train |
tensorflow/tensor2tensor | tensor2tensor/bin/t2t_attack.py | prepare_data | def prepare_data(problem, hparams, params, config):
"""Construct input pipeline."""
input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams, force_repeat=True)
dataset = input_fn(params, config)
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["... | python | def prepare_data(problem, hparams, params, config):
"""Construct input pipeline."""
input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams, force_repeat=True)
dataset = input_fn(params, config)
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["... | [
"def",
"prepare_data",
"(",
"problem",
",",
"hparams",
",",
"params",
",",
"config",
")",
":",
"input_fn",
"=",
"problem",
".",
"make_estimator_input_fn",
"(",
"tf",
".",
"estimator",
".",
"ModeKeys",
".",
"EVAL",
",",
"hparams",
",",
"force_repeat",
"=",
... | Construct input pipeline. | [
"Construct",
"input",
"pipeline",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/bin/t2t_attack.py#L134-L145 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/audio_encoder.py | AudioEncoder.encode | def encode(self, s):
"""Transform a string with a filename into a list of float32.
Args:
s: path to the file with a waveform.
Returns:
samples: list of int16s
"""
# Make sure that the data is a single channel, 16bit, 16kHz wave.
# TODO(chorowski): the directory may not be writable,... | python | def encode(self, s):
"""Transform a string with a filename into a list of float32.
Args:
s: path to the file with a waveform.
Returns:
samples: list of int16s
"""
# Make sure that the data is a single channel, 16bit, 16kHz wave.
# TODO(chorowski): the directory may not be writable,... | [
"def",
"encode",
"(",
"self",
",",
"s",
")",
":",
"# Make sure that the data is a single channel, 16bit, 16kHz wave.",
"# TODO(chorowski): the directory may not be writable, this should fallback",
"# to a temp path, and provide instructions for installing sox.",
"if",
"s",
".",
"endswith... | Transform a string with a filename into a list of float32.
Args:
s: path to the file with a waveform.
Returns:
samples: list of int16s | [
"Transform",
"a",
"string",
"with",
"a",
"filename",
"into",
"a",
"list",
"of",
"float32",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/audio_encoder.py#L36-L65 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/audio_encoder.py | AudioEncoder.decode | def decode(self, ids):
"""Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the waveform was saved.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
_, tmp_file_path = te... | python | def decode(self, ids):
"""Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the waveform was saved.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
_, tmp_file_path = te... | [
"def",
"decode",
"(",
"self",
",",
"ids",
")",
":",
"_",
",",
"tmp_file_path",
"=",
"tempfile",
".",
"mkstemp",
"(",
")",
"wavfile",
".",
"write",
"(",
"tmp_file_path",
",",
"self",
".",
"_sample_rate",
",",
"np",
".",
"asarray",
"(",
"ids",
")",
")"... | Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the waveform was saved.
Raises:
ValueError: if the ids are not of the appropriate size. | [
"Transform",
"a",
"sequence",
"of",
"float32",
"into",
"a",
"waveform",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/audio_encoder.py#L67-L81 | train |
tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.new_vertex | def new_vertex(self):
"""Creates and returns a new vertex.
Returns:
A new Vertex instance with a unique index.
"""
vertex = Vertex(len(self.vertices))
self.vertices.append(vertex)
return vertex | python | def new_vertex(self):
"""Creates and returns a new vertex.
Returns:
A new Vertex instance with a unique index.
"""
vertex = Vertex(len(self.vertices))
self.vertices.append(vertex)
return vertex | [
"def",
"new_vertex",
"(",
"self",
")",
":",
"vertex",
"=",
"Vertex",
"(",
"len",
"(",
"self",
".",
"vertices",
")",
")",
"self",
".",
"vertices",
".",
"append",
"(",
"vertex",
")",
"return",
"vertex"
] | Creates and returns a new vertex.
Returns:
A new Vertex instance with a unique index. | [
"Creates",
"and",
"returns",
"a",
"new",
"vertex",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/insights/graph.py#L102-L110 | train |
tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.get_vertex | def get_vertex(self, key):
"""Returns or Creates a Vertex mapped by key.
Args:
key: A string reference for a vertex. May refer to a new Vertex in which
case it will be created.
Returns:
A the Vertex mapped to by key.
"""
if key in self.vertex_map:
return self.vertex_map[ke... | python | def get_vertex(self, key):
"""Returns or Creates a Vertex mapped by key.
Args:
key: A string reference for a vertex. May refer to a new Vertex in which
case it will be created.
Returns:
A the Vertex mapped to by key.
"""
if key in self.vertex_map:
return self.vertex_map[ke... | [
"def",
"get_vertex",
"(",
"self",
",",
"key",
")",
":",
"if",
"key",
"in",
"self",
".",
"vertex_map",
":",
"return",
"self",
".",
"vertex_map",
"[",
"key",
"]",
"vertex",
"=",
"self",
".",
"new_vertex",
"(",
")",
"self",
".",
"vertex_map",
"[",
"key"... | Returns or Creates a Vertex mapped by key.
Args:
key: A string reference for a vertex. May refer to a new Vertex in which
case it will be created.
Returns:
A the Vertex mapped to by key. | [
"Returns",
"or",
"Creates",
"a",
"Vertex",
"mapped",
"by",
"key",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/insights/graph.py#L112-L126 | train |
tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.add_edge | def add_edge(self, source, target):
"""Returns a new edge connecting source and target vertices.
Args:
source: The source Vertex.
target: The target Vertex.
Returns:
A new Edge linking source to target.
"""
edge = Edge(len(self.edges))
self.edges.append(edge)
source.out_e... | python | def add_edge(self, source, target):
"""Returns a new edge connecting source and target vertices.
Args:
source: The source Vertex.
target: The target Vertex.
Returns:
A new Edge linking source to target.
"""
edge = Edge(len(self.edges))
self.edges.append(edge)
source.out_e... | [
"def",
"add_edge",
"(",
"self",
",",
"source",
",",
"target",
")",
":",
"edge",
"=",
"Edge",
"(",
"len",
"(",
"self",
".",
"edges",
")",
")",
"self",
".",
"edges",
".",
"append",
"(",
"edge",
")",
"source",
".",
"out_edges",
".",
"append",
"(",
"... | Returns a new edge connecting source and target vertices.
Args:
source: The source Vertex.
target: The target Vertex.
Returns:
A new Edge linking source to target. | [
"Returns",
"a",
"new",
"edge",
"connecting",
"source",
"and",
"target",
"vertices",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/insights/graph.py#L128-L144 | train |
tensorflow/tensor2tensor | tensor2tensor/insights/graph.py | Graph.to_dict | def to_dict(self):
"""Returns a simplified dictionary representing the Graph.
Returns:
A dictionary that can easily be serialized to JSON.
"""
return {
"node": [v.to_dict() for v in self.vertices],
"edge": [e.to_dict() for e in self.edges]
} | python | def to_dict(self):
"""Returns a simplified dictionary representing the Graph.
Returns:
A dictionary that can easily be serialized to JSON.
"""
return {
"node": [v.to_dict() for v in self.vertices],
"edge": [e.to_dict() for e in self.edges]
} | [
"def",
"to_dict",
"(",
"self",
")",
":",
"return",
"{",
"\"node\"",
":",
"[",
"v",
".",
"to_dict",
"(",
")",
"for",
"v",
"in",
"self",
".",
"vertices",
"]",
",",
"\"edge\"",
":",
"[",
"e",
".",
"to_dict",
"(",
")",
"for",
"e",
"in",
"self",
"."... | Returns a simplified dictionary representing the Graph.
Returns:
A dictionary that can easily be serialized to JSON. | [
"Returns",
"a",
"simplified",
"dictionary",
"representing",
"the",
"Graph",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/insights/graph.py#L146-L155 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | attend | def attend(x, source, hparams, name):
"""Self-attention layer with source as memory antecedent."""
with tf.variable_scope(name):
x = tf.squeeze(x, axis=2)
if len(source.get_shape()) > 3:
source = tf.squeeze(source, axis=2)
source = common_attention.add_timing_signal_1d(source)
y = common_atten... | python | def attend(x, source, hparams, name):
"""Self-attention layer with source as memory antecedent."""
with tf.variable_scope(name):
x = tf.squeeze(x, axis=2)
if len(source.get_shape()) > 3:
source = tf.squeeze(source, axis=2)
source = common_attention.add_timing_signal_1d(source)
y = common_atten... | [
"def",
"attend",
"(",
"x",
",",
"source",
",",
"hparams",
",",
"name",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"x",
"=",
"tf",
".",
"squeeze",
"(",
"x",
",",
"axis",
"=",
"2",
")",
"if",
"len",
"(",
"source",
".",
... | Self-attention layer with source as memory antecedent. | [
"Self",
"-",
"attention",
"layer",
"with",
"source",
"as",
"memory",
"antecedent",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L62-L76 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | top_k_softmax | def top_k_softmax(x, k):
"""Calculate softmax(x), select top-k and rescale to sum to 1."""
x = tf.nn.softmax(x)
top_x, _ = tf.nn.top_k(x, k=k+1)
min_top = tf.reduce_min(top_x, axis=-1, keepdims=True)
x = tf.nn.relu((x - min_top) + 1e-12)
x /= tf.reduce_sum(x, axis=-1, keepdims=True)
return x, tf.reduce_ma... | python | def top_k_softmax(x, k):
"""Calculate softmax(x), select top-k and rescale to sum to 1."""
x = tf.nn.softmax(x)
top_x, _ = tf.nn.top_k(x, k=k+1)
min_top = tf.reduce_min(top_x, axis=-1, keepdims=True)
x = tf.nn.relu((x - min_top) + 1e-12)
x /= tf.reduce_sum(x, axis=-1, keepdims=True)
return x, tf.reduce_ma... | [
"def",
"top_k_softmax",
"(",
"x",
",",
"k",
")",
":",
"x",
"=",
"tf",
".",
"nn",
".",
"softmax",
"(",
"x",
")",
"top_x",
",",
"_",
"=",
"tf",
".",
"nn",
".",
"top_k",
"(",
"x",
",",
"k",
"=",
"k",
"+",
"1",
")",
"min_top",
"=",
"tf",
".",... | Calculate softmax(x), select top-k and rescale to sum to 1. | [
"Calculate",
"softmax",
"(",
"x",
")",
"select",
"top",
"-",
"k",
"and",
"rescale",
"to",
"sum",
"to",
"1",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L93-L100 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | compress | def compress(x, c, is_2d, hparams, name):
"""Compress."""
with tf.variable_scope(name):
# Run compression by strided convs.
cur = x
k1 = (3, 3) if is_2d else (3, 1)
k2 = (2, 2) if is_2d else (2, 1)
cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams, "rc")
if c is not None and h... | python | def compress(x, c, is_2d, hparams, name):
"""Compress."""
with tf.variable_scope(name):
# Run compression by strided convs.
cur = x
k1 = (3, 3) if is_2d else (3, 1)
k2 = (2, 2) if is_2d else (2, 1)
cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams, "rc")
if c is not None and h... | [
"def",
"compress",
"(",
"x",
",",
"c",
",",
"is_2d",
",",
"hparams",
",",
"name",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"# Run compression by strided convs.",
"cur",
"=",
"x",
"k1",
"=",
"(",
"3",
",",
"3",
")",
"if",
... | Compress. | [
"Compress",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L115-L132 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | decode_transformer | def decode_transformer(encoder_output,
encoder_decoder_attention_bias,
targets,
hparams,
name,
task=None,
causal=True):
"""Original Transformer decoder."""
orig_hparams = hparams... | python | def decode_transformer(encoder_output,
encoder_decoder_attention_bias,
targets,
hparams,
name,
task=None,
causal=True):
"""Original Transformer decoder."""
orig_hparams = hparams... | [
"def",
"decode_transformer",
"(",
"encoder_output",
",",
"encoder_decoder_attention_bias",
",",
"targets",
",",
"hparams",
",",
"name",
",",
"task",
"=",
"None",
",",
"causal",
"=",
"True",
")",
":",
"orig_hparams",
"=",
"hparams",
"with",
"tf",
".",
"variable... | Original Transformer decoder. | [
"Original",
"Transformer",
"decoder",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L145-L208 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | ae_latent_softmax | def ae_latent_softmax(latents_pred, latents_discrete, hparams):
"""Latent prediction and loss."""
vocab_size = 2 ** hparams.z_size
if hparams.num_decode_blocks < 2:
latents_logits = tf.layers.dense(latents_pred, vocab_size,
name="extra_logits")
if hparams.logit_normali... | python | def ae_latent_softmax(latents_pred, latents_discrete, hparams):
"""Latent prediction and loss."""
vocab_size = 2 ** hparams.z_size
if hparams.num_decode_blocks < 2:
latents_logits = tf.layers.dense(latents_pred, vocab_size,
name="extra_logits")
if hparams.logit_normali... | [
"def",
"ae_latent_softmax",
"(",
"latents_pred",
",",
"latents_discrete",
",",
"hparams",
")",
":",
"vocab_size",
"=",
"2",
"**",
"hparams",
".",
"z_size",
"if",
"hparams",
".",
"num_decode_blocks",
"<",
"2",
":",
"latents_logits",
"=",
"tf",
".",
"layers",
... | Latent prediction and loss. | [
"Latent",
"prediction",
"and",
"loss",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L221-L267 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | ae_latent_sample | def ae_latent_sample(latents_dense, inputs, ed, embed, iters, hparams):
"""Sample from the latent space in the autoencoder."""
if hparams.num_decode_blocks < 2 and hparams.sampling_temp == 0.0:
# TODO(lukaszkaiser): beam-search only works in non-blocked mode for now.
tf.logging.info("Running beam-search for... | python | def ae_latent_sample(latents_dense, inputs, ed, embed, iters, hparams):
"""Sample from the latent space in the autoencoder."""
if hparams.num_decode_blocks < 2 and hparams.sampling_temp == 0.0:
# TODO(lukaszkaiser): beam-search only works in non-blocked mode for now.
tf.logging.info("Running beam-search for... | [
"def",
"ae_latent_sample",
"(",
"latents_dense",
",",
"inputs",
",",
"ed",
",",
"embed",
",",
"iters",
",",
"hparams",
")",
":",
"if",
"hparams",
".",
"num_decode_blocks",
"<",
"2",
"and",
"hparams",
".",
"sampling_temp",
"==",
"0.0",
":",
"# TODO(lukaszkais... | Sample from the latent space in the autoencoder. | [
"Sample",
"from",
"the",
"latent",
"space",
"in",
"the",
"autoencoder",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L301-L322 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | ae_transformer_internal | def ae_transformer_internal(inputs,
targets,
target_space,
hparams,
cache=None,
predict_mask=1.0):
"""AE Transformer, main step used for training."""
# Summaries break with the... | python | def ae_transformer_internal(inputs,
targets,
target_space,
hparams,
cache=None,
predict_mask=1.0):
"""AE Transformer, main step used for training."""
# Summaries break with the... | [
"def",
"ae_transformer_internal",
"(",
"inputs",
",",
"targets",
",",
"target_space",
",",
"hparams",
",",
"cache",
"=",
"None",
",",
"predict_mask",
"=",
"1.0",
")",
":",
"# Summaries break with the do_refine cond, turn them off in that case.",
"global",
"_DO_SUMMARIES",... | AE Transformer, main step used for training. | [
"AE",
"Transformer",
"main",
"step",
"used",
"for",
"training",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L325-L536 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | transformer_ae_small | def transformer_ae_small():
"""Set of hyperparameters."""
hparams = transformer.transformer_small()
hparams.batch_size = 2048
hparams.learning_rate = 0.2
hparams.learning_rate_warmup_steps = 4000
hparams.num_hidden_layers = 3
hparams.hidden_size = 384
hparams.filter_size = 2048
hparams.add_hparam("com... | python | def transformer_ae_small():
"""Set of hyperparameters."""
hparams = transformer.transformer_small()
hparams.batch_size = 2048
hparams.learning_rate = 0.2
hparams.learning_rate_warmup_steps = 4000
hparams.num_hidden_layers = 3
hparams.hidden_size = 384
hparams.filter_size = 2048
hparams.add_hparam("com... | [
"def",
"transformer_ae_small",
"(",
")",
":",
"hparams",
"=",
"transformer",
".",
"transformer_small",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"2048",
"hparams",
".",
"learning_rate",
"=",
"0.2",
"hparams",
".",
"learning_rate_warmup_steps",
"=",
"4000",
"h... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L760-L830 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | imagetransformer_ae_cifar | def imagetransformer_ae_cifar():
"""Hyperparameters for CIFAR-10 experiments."""
hparams = transformer_ae_small()
hparams.filter_size = 512
hparams.num_compress_steps = 3
hparams.startup_steps = 10000
hparams.is_2d = 0
hparams.learning_rate_warmup_steps = 8000
hparams.learning_rate = 0.2
hparams.hidde... | python | def imagetransformer_ae_cifar():
"""Hyperparameters for CIFAR-10 experiments."""
hparams = transformer_ae_small()
hparams.filter_size = 512
hparams.num_compress_steps = 3
hparams.startup_steps = 10000
hparams.is_2d = 0
hparams.learning_rate_warmup_steps = 8000
hparams.learning_rate = 0.2
hparams.hidde... | [
"def",
"imagetransformer_ae_cifar",
"(",
")",
":",
"hparams",
"=",
"transformer_ae_small",
"(",
")",
"hparams",
".",
"filter_size",
"=",
"512",
"hparams",
".",
"num_compress_steps",
"=",
"3",
"hparams",
".",
"startup_steps",
"=",
"10000",
"hparams",
".",
"is_2d"... | Hyperparameters for CIFAR-10 experiments. | [
"Hyperparameters",
"for",
"CIFAR",
"-",
"10",
"experiments",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L834-L904 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | imagetransformer_ae_imagenet | def imagetransformer_ae_imagenet():
"""For 64x64 ImageNet. ~56M trainable variables."""
hparams = imagetransformer_ae_cifar()
hparams.max_length = int(64 * 64 * 3)
hparams.img_len = 64
hparams.num_heads = 4 # Heads are expensive on TPUs.
# Reduce architecture from 32x32 CIFAR-10 in order to fit in memory.
... | python | def imagetransformer_ae_imagenet():
"""For 64x64 ImageNet. ~56M trainable variables."""
hparams = imagetransformer_ae_cifar()
hparams.max_length = int(64 * 64 * 3)
hparams.img_len = 64
hparams.num_heads = 4 # Heads are expensive on TPUs.
# Reduce architecture from 32x32 CIFAR-10 in order to fit in memory.
... | [
"def",
"imagetransformer_ae_imagenet",
"(",
")",
":",
"hparams",
"=",
"imagetransformer_ae_cifar",
"(",
")",
"hparams",
".",
"max_length",
"=",
"int",
"(",
"64",
"*",
"64",
"*",
"3",
")",
"hparams",
".",
"img_len",
"=",
"64",
"hparams",
".",
"num_heads",
"... | For 64x64 ImageNet. ~56M trainable variables. | [
"For",
"64x64",
"ImageNet",
".",
"~56M",
"trainable",
"variables",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L907-L916 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | transformer_ae_base | def transformer_ae_base():
"""Set of hyperparameters."""
hparams = transformer_ae_small()
hparams.batch_size = 2048
hparams.hidden_size = 512
hparams.filter_size = 4096
hparams.num_hidden_layers = 6
return hparams | python | def transformer_ae_base():
"""Set of hyperparameters."""
hparams = transformer_ae_small()
hparams.batch_size = 2048
hparams.hidden_size = 512
hparams.filter_size = 4096
hparams.num_hidden_layers = 6
return hparams | [
"def",
"transformer_ae_base",
"(",
")",
":",
"hparams",
"=",
"transformer_ae_small",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"2048",
"hparams",
".",
"hidden_size",
"=",
"512",
"hparams",
".",
"filter_size",
"=",
"4096",
"hparams",
".",
"num_hidden_layers",
... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L920-L927 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | transformer_ae_a3 | def transformer_ae_a3():
"""Set of hyperparameters."""
hparams = transformer_ae_base()
hparams.batch_size = 4096
hparams.layer_prepostprocess_dropout = 0.3
hparams.optimizer = "Adafactor"
hparams.learning_rate = 0.25
hparams.learning_rate_warmup_steps = 10000
return hparams | python | def transformer_ae_a3():
"""Set of hyperparameters."""
hparams = transformer_ae_base()
hparams.batch_size = 4096
hparams.layer_prepostprocess_dropout = 0.3
hparams.optimizer = "Adafactor"
hparams.learning_rate = 0.25
hparams.learning_rate_warmup_steps = 10000
return hparams | [
"def",
"transformer_ae_a3",
"(",
")",
":",
"hparams",
"=",
"transformer_ae_base",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"4096",
"hparams",
".",
"layer_prepostprocess_dropout",
"=",
"0.3",
"hparams",
".",
"optimizer",
"=",
"\"Adafactor\"",
"hparams",
".",
... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L931-L939 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | transformer_ae_base_noatt | def transformer_ae_base_noatt():
"""Set of hyperparameters."""
hparams = transformer_ae_base()
hparams.reshape_method = "slice"
hparams.bottleneck_kind = "dvq"
hparams.hidden_size = 512
hparams.num_blocks = 1
hparams.num_decode_blocks = 1
hparams.z_size = 12
hparams.do_attend_decompress = False
retu... | python | def transformer_ae_base_noatt():
"""Set of hyperparameters."""
hparams = transformer_ae_base()
hparams.reshape_method = "slice"
hparams.bottleneck_kind = "dvq"
hparams.hidden_size = 512
hparams.num_blocks = 1
hparams.num_decode_blocks = 1
hparams.z_size = 12
hparams.do_attend_decompress = False
retu... | [
"def",
"transformer_ae_base_noatt",
"(",
")",
":",
"hparams",
"=",
"transformer_ae_base",
"(",
")",
"hparams",
".",
"reshape_method",
"=",
"\"slice\"",
"hparams",
".",
"bottleneck_kind",
"=",
"\"dvq\"",
"hparams",
".",
"hidden_size",
"=",
"512",
"hparams",
".",
... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L970-L980 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_vae.py | transformer_ae_small_noatt | def transformer_ae_small_noatt():
"""Set of hyperparameters."""
hparams = transformer_ae_small()
hparams.reshape_method = "slice"
hparams.bottleneck_kind = "dvq"
hparams.hidden_size = 512
hparams.num_blocks = 1
hparams.num_decode_blocks = 1
hparams.z_size = 12
hparams.do_attend_decompress = False
re... | python | def transformer_ae_small_noatt():
"""Set of hyperparameters."""
hparams = transformer_ae_small()
hparams.reshape_method = "slice"
hparams.bottleneck_kind = "dvq"
hparams.hidden_size = 512
hparams.num_blocks = 1
hparams.num_decode_blocks = 1
hparams.z_size = 12
hparams.do_attend_decompress = False
re... | [
"def",
"transformer_ae_small_noatt",
"(",
")",
":",
"hparams",
"=",
"transformer_ae_small",
"(",
")",
"hparams",
".",
"reshape_method",
"=",
"\"slice\"",
"hparams",
".",
"bottleneck_kind",
"=",
"\"dvq\"",
"hparams",
".",
"hidden_size",
"=",
"512",
"hparams",
".",
... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_vae.py#L984-L994 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_sketch.py | transformer_sketch | def transformer_sketch():
"""Basic transformer_sketch hparams."""
hparams = transformer.transformer_small()
hparams.num_compress_steps = 4
hparams.batch_size = 32
hparams.clip_grad_norm = 2.
hparams.sampling_method = "random"
return hparams | python | def transformer_sketch():
"""Basic transformer_sketch hparams."""
hparams = transformer.transformer_small()
hparams.num_compress_steps = 4
hparams.batch_size = 32
hparams.clip_grad_norm = 2.
hparams.sampling_method = "random"
return hparams | [
"def",
"transformer_sketch",
"(",
")",
":",
"hparams",
"=",
"transformer",
".",
"transformer_small",
"(",
")",
"hparams",
".",
"num_compress_steps",
"=",
"4",
"hparams",
".",
"batch_size",
"=",
"32",
"hparams",
".",
"clip_grad_norm",
"=",
"2.",
"hparams",
".",... | Basic transformer_sketch hparams. | [
"Basic",
"transformer_sketch",
"hparams",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/transformer_sketch.py#L55-L62 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | layers | def layers():
"""Get the layers module good for TF 1 and TF 2 work for now."""
global _cached_layers
if _cached_layers is not None:
return _cached_layers
layers_module = tf.layers
try:
from tensorflow.python import tf2 # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
if tf2.enable... | python | def layers():
"""Get the layers module good for TF 1 and TF 2 work for now."""
global _cached_layers
if _cached_layers is not None:
return _cached_layers
layers_module = tf.layers
try:
from tensorflow.python import tf2 # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
if tf2.enable... | [
"def",
"layers",
"(",
")",
":",
"global",
"_cached_layers",
"if",
"_cached_layers",
"is",
"not",
"None",
":",
"return",
"_cached_layers",
"layers_module",
"=",
"tf",
".",
"layers",
"try",
":",
"from",
"tensorflow",
".",
"python",
"import",
"tf2",
"# pylint: di... | Get the layers module good for TF 1 and TF 2 work for now. | [
"Get",
"the",
"layers",
"module",
"good",
"for",
"TF",
"1",
"and",
"TF",
"2",
"work",
"for",
"now",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_layers.py#L42-L56 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | dropout_with_broadcast_dims | def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
"""Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
a list of dimension numbers in which noise_shape should be 1. The random
keep/drop tensor... | python | def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
"""Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
a list of dimension numbers in which noise_shape should be 1. The random
keep/drop tensor... | [
"def",
"dropout_with_broadcast_dims",
"(",
"x",
",",
"keep_prob",
",",
"broadcast_dims",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"assert",
"\"noise_shape\"",
"not",
"in",
"kwargs",
"if",
"broadcast_dims",
":",
"shape",
"=",
"tf",
".",
"shape",
"(",
... | Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
a list of dimension numbers in which noise_shape should be 1. The random
keep/drop tensor has dimensionality 1 along these dimensions.
Args:
x: a floating point tens... | [
"Like",
"tf",
".",
"nn",
".",
"dropout",
"but",
"takes",
"broadcast_dims",
"instead",
"of",
"noise_shape",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_layers.py#L103-L130 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/common_layers.py | saturating_sigmoid | def saturating_sigmoid(x):
"""Saturating sigmoid: 1.2 * sigmoid(x) - 0.1 cut to [0, 1]."""
with tf.name_scope("saturating_sigmoid", values=[x]):
y = tf.sigmoid(x)
return tf.minimum(1.0, tf.maximum(0.0, 1.2 * y - 0.1)) | python | def saturating_sigmoid(x):
"""Saturating sigmoid: 1.2 * sigmoid(x) - 0.1 cut to [0, 1]."""
with tf.name_scope("saturating_sigmoid", values=[x]):
y = tf.sigmoid(x)
return tf.minimum(1.0, tf.maximum(0.0, 1.2 * y - 0.1)) | [
"def",
"saturating_sigmoid",
"(",
"x",
")",
":",
"with",
"tf",
".",
"name_scope",
"(",
"\"saturating_sigmoid\"",
",",
"values",
"=",
"[",
"x",
"]",
")",
":",
"y",
"=",
"tf",
".",
"sigmoid",
"(",
"x",
")",
"return",
"tf",
".",
"minimum",
"(",
"1.0",
... | Saturating sigmoid: 1.2 * sigmoid(x) - 0.1 cut to [0, 1]. | [
"Saturating",
"sigmoid",
":",
"1",
".",
"2",
"*",
"sigmoid",
"(",
"x",
")",
"-",
"0",
".",
"1",
"cut",
"to",
"[",
"0",
"1",
"]",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_layers.py#L137-L141 | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.