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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | _remove_double_brackets | Remove double brackets, but leave the viewable text.
Args:
text: a string
Returns:
a string | tensor2tensor/data_generators/wiki_revision_utils.py | def _remove_double_brackets(text):
"""Remove double brackets, but leave the viewable text.
Args:
text: a string
Returns:
a string
"""
def replacement_fn(s):
if ":" in s:
# this is probably a category or something like that.
return ""
# keep the part after the bar.
bar_pos = s... | def _remove_double_brackets(text):
"""Remove double brackets, but leave the viewable text.
Args:
text: a string
Returns:
a string
"""
def replacement_fn(s):
if ":" in s:
# this is probably a category or something like that.
return ""
# keep the part after the bar.
bar_pos = s... | [
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train | _remove_boring_lines | Remove lines that do not start with a letter or a quote.
From inspecting the data, this seems to leave in most prose and remove
most weird stuff.
Args:
text: a string
Returns:
a string | tensor2tensor/data_generators/wiki_revision_utils.py | def _remove_boring_lines(text):
"""Remove lines that do not start with a letter or a quote.
From inspecting the data, this seems to leave in most prose and remove
most weird stuff.
Args:
text: a string
Returns:
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"""
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From inspecting the data, this seems to leave in most prose and remove
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text: a string
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train | get_or_generate_vocabulary | Get or generate the vocabulary.
Args:
data_dir: a string
tmp_dir: a string
data_prefix: a string
max_page_size_exp: an integer
approx_vocab_size: an integer
strip: a boolean
Returns:
a TextEncoder | tensor2tensor/data_generators/wiki_revision_utils.py | def get_or_generate_vocabulary(data_dir,
tmp_dir,
data_prefix,
max_page_size_exp,
approx_vocab_size=32768,
strip=True):
"""Get or generate the vocabulary.
Args:... | def get_or_generate_vocabulary(data_dir,
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max_page_size_exp,
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strip=True):
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train | get_encoder_from_vocab | Get encoder from vocab file.
If vocab is not found in output dir, it will be copied there by
copy_vocab_to_output_dir to clarify the vocab used to generate the data.
Args:
vocab_filepath: path to vocab, either local or cns
Returns:
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"""Get encoder from vocab file.
If vocab is not found in output dir, it will be copied there by
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Args:
vocab_filepath: path to vocab, either local or cns
Returns:
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train | edit_distance_filter | Filter out examples that exceed max_edit_ratio between source and target.
Args:
source_target_input: a list of [source, target] pairs
max_equal_to_diff_ratio: cutoff for ratio of equal chars / diff chars
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Returns:
source_target_output: filtered subset of [source, ... | tensor2tensor/data_generators/wiki_revision_utils.py | def edit_distance_filter(source_target_input, max_equal_to_diff_ratio=0):
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Args:
source_target_input: a list of [source, target] pairs
max_equal_to_diff_ratio: cutoff for ratio of equal chars / diff chars
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"""Filter out examples that exceed max_edit_ratio between source and target.
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source_target_input: a list of [source, target] pairs
max_equal_to_diff_ratio: cutoff for ratio of equal chars / diff chars
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train | introduce_errors | Artificially add spelling errors and infill markers.
This function should be applied to the inputs of a correction model.
The artificial errors are particularly useful to train a network to
correct spelling when the training data does not contain many
natural errors.
Also replaces some substrings with an "... | tensor2tensor/data_generators/wiki_revision_utils.py | def introduce_errors(s,
corruption_rate=3e-3,
infill_marker="|?|",
max_infill_len=8):
"""Artificially add spelling errors and infill markers.
This function should be applied to the inputs of a correction model.
The artificial errors are particularly... | def introduce_errors(s,
corruption_rate=3e-3,
infill_marker="|?|",
max_infill_len=8):
"""Artificially add spelling errors and infill markers.
This function should be applied to the inputs of a correction model.
The artificial errors are particularly... | [
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train | fast_match_sequences | Compute diffs between two sequences.
This function is similar in functionality and spirit to
difflib.SequenceMatcher.get_opcodes, but it seems to run faster.
if a_start, a_end, b_start, b_end are specified, then we compute diffs of
the segments a[a_start:a_end] and b[b_start:b_end]. Returned indices
are re... | tensor2tensor/data_generators/wiki_revision_utils.py | def fast_match_sequences(a,
b,
a_start=0,
a_end=None,
b_start=0,
b_end=None,
min_match_length=3,
max_recursion_depth=128):
"""Compute diffs bet... | def fast_match_sequences(a,
b,
a_start=0,
a_end=None,
b_start=0,
b_end=None,
min_match_length=3,
max_recursion_depth=128):
"""Compute diffs bet... | [
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train | RestoreHook.begin | Load variables from checkpoint.
New model variables have the following name foramt:
new_model_scope/old_model_scope/xxx/xxx:0 To find the map of
name to variable, need to strip the new_model_scope and then
match the old_model_scope and remove the suffix :0. | tensor2tensor/utils/restore_hook.py | def begin(self):
"""Load variables from checkpoint.
New model variables have the following name foramt:
new_model_scope/old_model_scope/xxx/xxx:0 To find the map of
name to variable, need to strip the new_model_scope and then
match the old_model_scope and remove the suffix :0.
"""
variable... | def begin(self):
"""Load variables from checkpoint.
New model variables have the following name foramt:
new_model_scope/old_model_scope/xxx/xxx:0 To find the map of
name to variable, need to strip the new_model_scope and then
match the old_model_scope and remove the suffix :0.
"""
variable... | [
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train | TimeStep.create_time_step | Creates a TimeStep with both rewards and actions as optional. | tensor2tensor/envs/time_step.py | def create_time_step(cls,
observation=None,
done=False,
raw_reward=None,
processed_reward=None,
action=None):
"""Creates a TimeStep with both rewards and actions as optional."""
return cls(observa... | def create_time_step(cls,
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action=None):
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train | attention | Complete attention layer with preprocessing. | tensor2tensor/models/slicenet.py | def attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=None):
"""Complete attention layer with preprocessing."""
separabilities = [hparams.separability, hparams.separability]
if hparams.separability < 0:
separabilities = [hparams.separability - 1, hparams.separability]
targets_timed = common_... | def attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=None):
"""Complete attention layer with preprocessing."""
separabilities = [hparams.separability, hparams.separability]
if hparams.separability < 0:
separabilities = [hparams.separability - 1, hparams.separability]
targets_timed = common_... | [
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train | multi_conv_res | A stack of separable convolution blocks with residual connections. | tensor2tensor/models/slicenet.py | def multi_conv_res(x, padding, name, layers, hparams, mask=None, source=None):
"""A stack of separable convolution blocks with residual connections."""
with tf.variable_scope(name):
padding_bias = None
if mask is not None:
padding_bias = (1.0 - mask) * -1e9 # Bias to not attend to padding.
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"""A stack of separable convolution blocks with residual connections."""
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train | rank_loss | Experimental rank loss, thanks to kkurach@ for the code. | tensor2tensor/models/slicenet.py | def rank_loss(sentence_emb, image_emb, margin=0.2):
"""Experimental rank loss, thanks to kkurach@ for the code."""
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# Normalize first as this is assumed in cosine similarity later.
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train | similarity_cost | Loss telling to be more similar to your own targets than to others. | tensor2tensor/models/slicenet.py | def similarity_cost(inputs_encoded, targets_encoded):
"""Loss telling to be more similar to your own targets than to others."""
# This is a first very simple version: handle variable-length by padding
# to same length and putting everything into batch. In need of a better way.
x, y = common_layers.pad_to_same_l... | def similarity_cost(inputs_encoded, targets_encoded):
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train | slicenet_middle | Middle part of slicenet, connecting encoder and decoder. | tensor2tensor/models/slicenet.py | def slicenet_middle(inputs_encoded, targets, target_space_emb, mask, hparams):
"""Middle part of slicenet, connecting encoder and decoder."""
def norm_fn(x, name):
with tf.variable_scope(name, default_name="norm"):
return common_layers.apply_norm(x, hparams.norm_type, hparams.hidden_size,
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"""Middle part of slicenet, connecting encoder and decoder."""
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with tf.variable_scope(name, default_name="norm"):
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train | embedding_to_padding | Input embeddings -> is_padding. | tensor2tensor/models/slicenet.py | def embedding_to_padding(emb):
"""Input embeddings -> is_padding."""
emb_sum = tf.reduce_sum(tf.abs(emb), axis=-1, keep_dims=True)
return tf.to_float(tf.equal(emb_sum, 0.0)) | def embedding_to_padding(emb):
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emb_sum = tf.reduce_sum(tf.abs(emb), axis=-1, keep_dims=True)
return tf.to_float(tf.equal(emb_sum, 0.0)) | [
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train | slicenet_internal | The slicenet model, main step used for training. | tensor2tensor/models/slicenet.py | def slicenet_internal(inputs, targets, target_space, hparams, run_decoder=True):
"""The slicenet model, main step used for training."""
with tf.variable_scope("slicenet"):
# Project to hidden size if necessary
if inputs.get_shape().as_list()[-1] != hparams.hidden_size:
inputs = common_layers.conv_bloc... | def slicenet_internal(inputs, targets, target_space, hparams, run_decoder=True):
"""The slicenet model, main step used for training."""
with tf.variable_scope("slicenet"):
# Project to hidden size if necessary
if inputs.get_shape().as_list()[-1] != hparams.hidden_size:
inputs = common_layers.conv_bloc... | [
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train | slicenet_params1 | Set of hyperparameters. | tensor2tensor/models/slicenet.py | def slicenet_params1():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 1024
hparams.hidden_size = 768
hparams.dropout = 0.5
hparams.symbol_dropout = 0.2
hparams.label_smoothing = 0.1
hparams.clip_grad_norm = 2.0
hparams.num_hidden_layers = 4
hparams.kerne... | def slicenet_params1():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 1024
hparams.hidden_size = 768
hparams.dropout = 0.5
hparams.symbol_dropout = 0.2
hparams.label_smoothing = 0.1
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hparams.num_hidden_layers = 4
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train | slicenet_params1_noam | Version with Noam's decay scheme. | tensor2tensor/models/slicenet.py | def slicenet_params1_noam():
"""Version with Noam's decay scheme."""
hparams = slicenet_params1()
hparams.learning_rate_decay_scheme = "noam"
hparams.learning_rate = 1.0
hparams.learning_rate_warmup_steps = 4000
hparams.initializer = "uniform_unit_scaling"
hparams.optimizer_adam_epsilon = 1e-9
hparams.o... | def slicenet_params1_noam():
"""Version with Noam's decay scheme."""
hparams = slicenet_params1()
hparams.learning_rate_decay_scheme = "noam"
hparams.learning_rate = 1.0
hparams.learning_rate_warmup_steps = 4000
hparams.initializer = "uniform_unit_scaling"
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train | slicenet_params1_tiny | Version for fast local runs. | tensor2tensor/models/slicenet.py | def slicenet_params1_tiny():
"""Version for fast local runs."""
hparams = slicenet_params1()
hparams.attention_type = "simple"
hparams.separability = 0
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.batch_size = 512
hparams.learning_rate_warmup_steps = 200
return hparams | def slicenet_params1_tiny():
"""Version for fast local runs."""
hparams = slicenet_params1()
hparams.attention_type = "simple"
hparams.separability = 0
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.batch_size = 512
hparams.learning_rate_warmup_steps = 200
return hparams | [
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train | slicenet_range1 | Small range of hyperparameters. | tensor2tensor/models/slicenet.py | def slicenet_range1(ranged_hparams):
"""Small range of hyperparameters."""
rhp = ranged_hparams
rhp.set_float("clip_grad_norm", 1.0, 10.0, scale=rhp.LOG_SCALE)
rhp.set_float("learning_rate", 0.02, 1.0, scale=rhp.LOG_SCALE)
rhp.set_float("optimizer_adam_beta2", 0.995, 0.998)
rhp.set_float("weight_decay", 1.0... | def slicenet_range1(ranged_hparams):
"""Small range of hyperparameters."""
rhp = ranged_hparams
rhp.set_float("clip_grad_norm", 1.0, 10.0, scale=rhp.LOG_SCALE)
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train | TokenTextEncoderOov.encode | Converts a space-separated string of tokens to lists of ids.
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A... | tensor2tensor/data_generators/pointer_generator_word.py | def encode(self, s):
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Also store temporary vocabulary IDs for source OOV tokens. OOVs are
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is 50k and the source has 3 OOVs, then these temporary OOV numbers will
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Also store temporary vocabulary IDs for source OOV tokens. OOVs are
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train | TokenTextEncoderOov.encode_target | Converts a space-separated string of tokens to lists of ids.
Also store a version of extened vocabulary IDs.
For target OOVs that are in the source, encode them using the temporary
vocab IDs.
For target OOVs not in the source, encode them as <UNK>
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target: target string
source_oov... | tensor2tensor/data_generators/pointer_generator_word.py | def encode_target(self, target, source_oovs):
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Also store a version of extened vocabulary IDs.
For target OOVs that are in the source, encode them using the temporary
vocab IDs.
For target OOVs not in the source, encode them as <UNK>
... | def encode_target(self, target, source_oovs):
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Also store a version of extened vocabulary IDs.
For target OOVs that are in the source, encode them using the temporary
vocab IDs.
For target OOVs not in the source, encode them as <UNK>
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train | TokenTextEncoderOov.decode_list_oov | decode ids back to tokens, considering OOVs temporary IDs.
Args:
ids: vocab ids. Could possibly include source temporary OOV ID starting
from vocab_size.
source_oov_id_to_token: a list of source OOV tokens, with the order the
same as they appear in the source.
Returns:
decoded to... | tensor2tensor/data_generators/pointer_generator_word.py | def decode_list_oov(self, ids, source_oov_id_to_token):
"""decode ids back to tokens, considering OOVs temporary IDs.
Args:
ids: vocab ids. Could possibly include source temporary OOV ID starting
from vocab_size.
source_oov_id_to_token: a list of source OOV tokens, with the order the
sa... | def decode_list_oov(self, ids, source_oov_id_to_token):
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Args:
ids: vocab ids. Could possibly include source temporary OOV ID starting
from vocab_size.
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train | _smallest_size_at_least | Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
width: an int32 scalar tensor indicating the current ... | tensor2tensor/data_generators/vqa_utils.py | def _smallest_size_at_least(height, width, smallest_side):
"""Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current h... | def _smallest_size_at_least(height, width, smallest_side):
"""Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
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train | _aspect_preserving_resize | Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
smallest_side: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing the resized image. | tensor2tensor/data_generators/vqa_utils.py | def _aspect_preserving_resize(image, smallest_side):
"""Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
smallest_side: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing... | def _aspect_preserving_resize(image, smallest_side):
"""Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
smallest_side: A python integer or scalar `Tensor` indicating the size of
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train | _distort_color | Distort the color of a Tensor image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a distinct ordering
of color ops for each preprocessing thread.
... | tensor2tensor/data_generators/vqa_utils.py | def _distort_color(image, color_ordering=0, scope=None):
"""Distort the color of a Tensor image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a disti... | def _distort_color(image, color_ordering=0, scope=None):
"""Distort the color of a Tensor image.
Each color distortion is non-commutative and thus ordering of the color ops
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train | _apply_with_random_selector | Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is... | tensor2tensor/data_generators/vqa_utils.py | def _apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the... | def _apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
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train | _mean_image_subtraction | Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
Args:
image: a tensor of size [height, width, C].
means: a C-vector of values to subtract from each chann... | tensor2tensor/data_generators/vqa_utils.py | def _mean_image_subtraction(image, means):
"""Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
Args:
image: a tensor of size [height, width, C].
means: ... | def _mean_image_subtraction(image, means):
"""Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
Args:
image: a tensor of size [height, width, C].
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train | vqa_v2_preprocess_image | vqa v2 preprocess image. | tensor2tensor/data_generators/vqa_utils.py | def vqa_v2_preprocess_image(
image,
height,
width,
mode,
resize_side=512,
distort=True,
image_model_fn="resnet_v1_152",
):
"""vqa v2 preprocess image."""
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
assert resize_side > 0
if resize_side:
image = _aspect_preservi... | def vqa_v2_preprocess_image(
image,
height,
width,
mode,
resize_side=512,
distort=True,
image_model_fn="resnet_v1_152",
):
"""vqa v2 preprocess image."""
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
assert resize_side > 0
if resize_side:
image = _aspect_preservi... | [
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hparams: run hyperparameters
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hparams: run hyperparameters
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train | transformer_encoder | A stack of transformer layers.
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train | transformer_ffn_layer | Feed-forward layer in the transformer.
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hparams: hyperparameters for model
pad_remover: an expert_utils.PadRemover object tracking the padding
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cache=None,
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train | lmx_base | Transformer on languagemodel_lm1b32k_packed. 50M Params. | tensor2tensor/models/research/lm_experiments.py | def lmx_base():
"""Transformer on languagemodel_lm1b32k_packed. 50M Params."""
hparams = transformer.transformer_tpu()
# sharing is counterproductive when underparameterized
hparams.shared_embedding_and_softmax_weights = False
# we judge by log-ppl, so label smoothing hurts.
hparams.label_smoothing = 0.0
... | def lmx_base():
"""Transformer on languagemodel_lm1b32k_packed. 50M Params."""
hparams = transformer.transformer_tpu()
# sharing is counterproductive when underparameterized
hparams.shared_embedding_and_softmax_weights = False
# we judge by log-ppl, so label smoothing hurts.
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train | lmx_h3k_f12k | HParams for training languagemodel_lm1b32k_packed. 880M Params. | tensor2tensor/models/research/lm_experiments.py | def lmx_h3k_f12k():
"""HParams for training languagemodel_lm1b32k_packed. 880M Params."""
hparams = lmx_base()
hparams.hidden_size = 3072
hparams.filter_size = 12288
hparams.batch_size = 2048
hparams.weight_dtype = "bfloat16"
return hparams | def lmx_h3k_f12k():
"""HParams for training languagemodel_lm1b32k_packed. 880M Params."""
hparams = lmx_base()
hparams.hidden_size = 3072
hparams.filter_size = 12288
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train | lmx_h4k_f16k | HParams for training languagemodel_lm1b32k_packed. 1470M Params. | tensor2tensor/models/research/lm_experiments.py | def lmx_h4k_f16k():
"""HParams for training languagemodel_lm1b32k_packed. 1470M Params."""
hparams = lmx_base()
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hparams.filter_size = 16384
hparams.batch_size = 1024
hparams.weight_dtype = "bfloat16"
return hparams | def lmx_h4k_f16k():
"""HParams for training languagemodel_lm1b32k_packed. 1470M Params."""
hparams = lmx_base()
hparams.hidden_size = 4096
hparams.filter_size = 16384
hparams.batch_size = 1024
hparams.weight_dtype = "bfloat16"
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train | lmx_relative | Language model using relative attention. | tensor2tensor/models/research/lm_experiments.py | def lmx_relative():
"""Language model using relative attention."""
hparams = lmx_base()
hparams.self_attention_type = "dot_product_relative_v2"
hparams.activation_dtype = "float32"
hparams.weight_dtype = "float32"
return hparams | def lmx_relative():
"""Language model using relative attention."""
hparams = lmx_base()
hparams.self_attention_type = "dot_product_relative_v2"
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hparams.weight_dtype = "float32"
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train | lmx_moe_h1k_f4k_x32 | Transformer with mixture of experts. 890M Params. | tensor2tensor/models/research/lm_experiments.py | def lmx_moe_h1k_f4k_x32():
"""Transformer with mixture of experts. 890M Params."""
hparams = lmx_h1k_f4k()
hparams.ffn_layer = "local_moe_tpu"
hparams.moe_num_experts = 32
hparams.weight_dtype = "bfloat16"
hparams.batch_size = 8192
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"""Transformer with mixture of experts. 890M Params."""
hparams = lmx_h1k_f4k()
hparams.ffn_layer = "local_moe_tpu"
hparams.moe_num_experts = 32
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train | lmx_moe_h1k_f8k_x16 | Transformer with mixture of experts. 890M Params. | tensor2tensor/models/research/lm_experiments.py | def lmx_moe_h1k_f8k_x16():
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"""Transformer with mixture of experts. 890M Params."""
hparams = lmx_h1k_f4k()
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hparams.ffn_layer = "local_moe_tpu"
hparams.moe_num_experts = 16
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train | lmx_h1k_f64k | HParams for training languagemodel_lm1b32k_packed. 880M Params. | tensor2tensor/models/research/lm_experiments.py | def lmx_h1k_f64k():
"""HParams for training languagemodel_lm1b32k_packed. 880M Params."""
hparams = lmx_base()
hparams.hidden_size = 1024
hparams.filter_size = 65536
hparams.batch_size = 2048
return hparams | def lmx_h1k_f64k():
"""HParams for training languagemodel_lm1b32k_packed. 880M Params."""
hparams = lmx_base()
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train | compute_uncertainty_reward | Uncertainty reward based on logits. | tensor2tensor/rl/envs/simulated_batch_env.py | def compute_uncertainty_reward(logits, predictions):
"""Uncertainty reward based on logits."""
# TODO(rsepassi): Add support for L1/L2 loss models. Current code only
# works for softmax models.
vocab_size = logits.shape[-1]
assert vocab_size > 1
log_probs = common_layers.log_prob_from_logits(logits)
max_l... | def compute_uncertainty_reward(logits, predictions):
"""Uncertainty reward based on logits."""
# TODO(rsepassi): Add support for L1/L2 loss models. Current code only
# works for softmax models.
vocab_size = logits.shape[-1]
assert vocab_size > 1
log_probs = common_layers.log_prob_from_logits(logits)
max_l... | [
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train | SimulatedBatchEnv._reset_non_empty | Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations. | tensor2tensor/rl/envs/simulated_batch_env.py | def _reset_non_empty(self, indices):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations.
"""
reset_video_op = tf.cond(
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lambda: tf.py_fun... | def _reset_non_empty(self, indices):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations.
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reset_video_op = tf.cond(
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train | set_random_seed | Set the random seed from flag everywhere. | tensor2tensor/bin/t2t_datagen.py | def set_random_seed():
"""Set the random seed from flag everywhere."""
tf.set_random_seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed) | def set_random_seed():
"""Set the random seed from flag everywhere."""
tf.set_random_seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)
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train | generate_data_for_problem | Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS. | tensor2tensor/bin/t2t_datagen.py | def generate_data_for_problem(problem):
"""Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS."""
training_gen, dev_gen, test_gen = _SUPPORTED_PROBLEM_GENERATORS[problem]
num_train_shards = FLAGS.num_shards or 10
tf.logging.info("Generating training data for %s.", problem)
train_output_files = gene... | def generate_data_for_problem(problem):
"""Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS."""
training_gen, dev_gen, test_gen = _SUPPORTED_PROBLEM_GENERATORS[problem]
num_train_shards = FLAGS.num_shards or 10
tf.logging.info("Generating training data for %s.", problem)
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train | generate_data_for_env_problem | Generate data for `EnvProblem`s. | tensor2tensor/bin/t2t_datagen.py | def generate_data_for_env_problem(problem_name):
"""Generate data for `EnvProblem`s."""
assert FLAGS.env_problem_max_env_steps > 0, ("--env_problem_max_env_steps "
"should be greater than zero")
assert FLAGS.env_problem_batch_size > 0, ("--env_problem_batch_size shou... | def generate_data_for_env_problem(problem_name):
"""Generate data for `EnvProblem`s."""
assert FLAGS.env_problem_max_env_steps > 0, ("--env_problem_max_env_steps "
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train | generate_data_for_registered_problem | Generate data for a registered problem. | tensor2tensor/bin/t2t_datagen.py | def generate_data_for_registered_problem(problem_name):
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train | _collect_data | Traverses directory collecting input and target files.
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directory: base path to extracted audio and transcripts.
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"""Traverses directory collecting input and target files.
Args:
directory: base path to extracted audio and transcripts.
Returns:
list of (media_base, media_filepath, label) tuples
"""
# Returns:
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train | _file_exists | Checks if the filename exists under the path. | tensor2tensor/data_generators/common_voice.py | def _file_exists(path, filename):
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train | define_ppo_step | Define ppo step. | tensor2tensor/rl/ppo.py | def define_ppo_step(data_points, hparams, action_space, lr):
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observation, action, discounted_reward, norm_advantage, old_pdf = data_points
obs_shape = common_layers.shape_list(observation)
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train | define_ppo_epoch | PPO epoch. | tensor2tensor/rl/ppo.py | def define_ppo_epoch(memory, hparams, action_space, batch_size):
"""PPO epoch."""
observation, reward, done, action, old_pdf, value = memory
# This is to avoid propagating gradients through simulated environment.
observation = tf.stop_gradient(observation)
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train | calculate_generalized_advantage_estimator | Generalized advantage estimator.
Returns:
GAE estimator. It will be one element shorter than the input; this is
because to compute GAE for [0, ..., N-1] one needs V for [1, ..., N]. | tensor2tensor/rl/ppo.py | def calculate_generalized_advantage_estimator(
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# pylint: disable=g-doc-args
"""Generalized advantage estimator.
Returns:
GAE estimator. It will be one element shorter than the input; this is
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GAE estimator. It will be one element shorter than the input; this is
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train | gym_space_spec | Returns a reading spec of a gym space.
NOTE: Only implemented currently for Box and Discrete.
Args:
gym_space: instance of gym.spaces whose spec we want.
Returns:
Reading spec for that space.
Raises:
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"""Returns a reading spec of a gym space.
NOTE: Only implemented currently for Box and Discrete.
Args:
gym_space: instance of gym.spaces whose spec we want.
Returns:
Reading spec for that space.
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NOTE: Only implemented currently for Box and Discrete.
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gym_space: instance of gym.spaces whose spec we want.
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Args:
gym_space: The gym space.
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train | image_rmse | RMSE but will argmax if last dim is not 1. | tensor2tensor/utils/metrics.py | def image_rmse(predictions, labels, weights_fn=common_layers.weights_all):
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train | abs_error | Computes mean(abs(preds-target)). | tensor2tensor/utils/metrics.py | def abs_error(predictions, labels, weights_fn=None):
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del weights_fn # Unused
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train | padded_variance_explained | Explained variance, also known as R^2. | tensor2tensor/utils/metrics.py | def padded_variance_explained(predictions,
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"""Explained variance, also known as R^2."""
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train | padded_accuracy_topk | Percentage of times that top-k predictions matches labels on non-0s. | tensor2tensor/utils/metrics.py | def padded_accuracy_topk(predictions,
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"""Percentage of times that top-k predictions matches labels on non-0s."""
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train | rounding_sequence_accuracy | Sequence accuracy for L1/L2 losses: round down the predictions to ints. | tensor2tensor/utils/metrics.py | def rounding_sequence_accuracy(predictions,
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"""Sequence accuracy for L1/L2 losses: round down the predictions to ints."""
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train | padded_sequence_accuracy | Percentage of times that predictions matches labels everywhere (non-0). | tensor2tensor/utils/metrics.py | def padded_sequence_accuracy(predictions,
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train | sequence_edit_distance | Average edit distance, ignoring padding 0s.
The score returned is the edit distance divided by the total length of
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Args:
predictions: Tensor of shape [`batch_size`, `length`, 1, `num_classes`] and
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"""Average edit distance, ignoring padding 0s.
The score returned is the edit distance divided by the total length of
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"""Average edit distance, ignoring padding 0s.
The score returned is the edit distance divided by the total length of
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train | padded_neg_log_perplexity | Average log-perplexity exluding padding 0s. No smoothing. | tensor2tensor/utils/metrics.py | def padded_neg_log_perplexity(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Average log-perplexity exluding padding 0s. No smoothing."""
num, den = common_layers.padded_cross_entropy(
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"""Average log-perplexity exluding padding 0s. No smoothing."""
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train | padded_neg_log_perplexity_with_masking | Average log-perplexity with custom targets_mask. | tensor2tensor/utils/metrics.py | def padded_neg_log_perplexity_with_masking(
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"""Average log-perplexity with custom targets_mask."""
del weights_fn
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"""Average log-perplexity with custom targets_mask."""
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train | dmol_neg_log_perplexity | Average log-perplexity excluding padding 0s. No smoothing. | tensor2tensor/utils/metrics.py | def dmol_neg_log_perplexity(predictions,
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del weights_fn # Unused
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"""Average log-perplexity excluding padding 0s. No smoothing."""
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train | rounding_accuracy | Rounding accuracy for L1/L2 losses: round down the predictions to ints. | tensor2tensor/utils/metrics.py | def rounding_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Rounding accuracy for L1/L2 losses: round down the predictions to ints."""
outputs = tf.squeeze(tf.to_int32(predictions))
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... | def rounding_accuracy(predictions,
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"""Rounding accuracy for L1/L2 losses: round down the predictions to ints."""
outputs = tf.squeeze(tf.to_int32(predictions))
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train | padded_accuracy | Percentage of times that predictions matches labels on non-0s. | tensor2tensor/utils/metrics.py | def padded_accuracy(predictions,
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"""Percentage of times that predictions matches labels on non-0s."""
# If the last dimension is 1 then we're using L1/L2 loss.
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"""Percentage of times that predictions matches labels on non-0s."""
# If the last dimension is 1 then we're using L1/L2 loss.
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train | multilabel_accuracy_matchk | Used to evaluate the VQA accuracy.
Let n be the times that predictions appear in labels, then final score
is min(n/k, 1).
Refer to https://arxiv.org/pdf/1505.00468.pdf.
Args:
predictions: A tensor with shape [batch_size, 1, 1, 1, vocab_size].
labels: A tensor with shape [batch_size, length, 1, 1].
... | tensor2tensor/utils/metrics.py | def multilabel_accuracy_matchk(predictions,
labels,
k,
weights_fn=common_layers.weights_nonzero):
"""Used to evaluate the VQA accuracy.
Let n be the times that predictions appear in labels, then final score
is min(n/k, 1... | def multilabel_accuracy_matchk(predictions,
labels,
k,
weights_fn=common_layers.weights_nonzero):
"""Used to evaluate the VQA accuracy.
Let n be the times that predictions appear in labels, then final score
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train | set_precision | Precision of set predictions.
Args:
predictions : A Tensor of scores of shape [batch, nlabels].
labels: A Tensor of int32s giving true set elements,
of shape [batch, seq_length].
weights_fn: A function to weight the elements.
Returns:
hits: A Tensor of shape [batch, nlabels].
weights: A ... | tensor2tensor/utils/metrics.py | def set_precision(predictions, labels,
weights_fn=common_layers.weights_nonzero):
"""Precision of set predictions.
Args:
predictions : A Tensor of scores of shape [batch, nlabels].
labels: A Tensor of int32s giving true set elements,
of shape [batch, seq_length].
weights_fn: A f... | def set_precision(predictions, labels,
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"""Precision of set predictions.
Args:
predictions : A Tensor of scores of shape [batch, nlabels].
labels: A Tensor of int32s giving true set elements,
of shape [batch, seq_length].
weights_fn: A f... | [
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train | image_summary | Reshapes predictions and passes it to tensorboard.
Args:
predictions : The predicted image (logits).
targets : The ground truth.
hparams: model hparams.
Returns:
summary_proto: containing the summary images.
weights: A Tensor of zeros of the same shape as predictions. | tensor2tensor/utils/metrics.py | def image_summary(predictions, targets, hparams):
"""Reshapes predictions and passes it to tensorboard.
Args:
predictions : The predicted image (logits).
targets : The ground truth.
hparams: model hparams.
Returns:
summary_proto: containing the summary images.
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Args:
predictions : The predicted image (logits).
targets : The ground truth.
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train | softmax_cross_entropy_one_hot | Calculate softmax cross entropy given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels and weighs examples (unused)
Returns:
cross-entropy (scalar), weight... | tensor2tensor/utils/metrics.py | def softmax_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate softmax cross entropy given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels a... | def softmax_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate softmax cross entropy given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels a... | [
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train | sigmoid_accuracy_one_hot | Calculate accuracy for a set, given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels and weighs examples (unused)
Returns:
accuracy (scalar), weights | tensor2tensor/utils/metrics.py | def sigmoid_accuracy_one_hot(logits, labels, weights_fn=None):
"""Calculate accuracy for a set, given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels and weig... | def sigmoid_accuracy_one_hot(logits, labels, weights_fn=None):
"""Calculate accuracy for a set, given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
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train | sigmoid_recall_one_hot | Calculate recall for a set, given one-hot labels and logits.
Predictions are converted to one-hot,
as predictions[example][arg-max(example)] = 1
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes... | tensor2tensor/utils/metrics.py | def sigmoid_recall_one_hot(logits, labels, weights_fn=None):
"""Calculate recall for a set, given one-hot labels and logits.
Predictions are converted to one-hot,
as predictions[example][arg-max(example)] = 1
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batc... | def sigmoid_recall_one_hot(logits, labels, weights_fn=None):
"""Calculate recall for a set, given one-hot labels and logits.
Predictions are converted to one-hot,
as predictions[example][arg-max(example)] = 1
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logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batc... | [
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train | sigmoid_cross_entropy_one_hot | Calculate sigmoid cross entropy for one-hot lanels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels and weighs examples (unused)
Returns:
cross_entropy (scalar), weights | tensor2tensor/utils/metrics.py | def sigmoid_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate sigmoid cross entropy for one-hot lanels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels and... | def sigmoid_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate sigmoid cross entropy for one-hot lanels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
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train | roc_auc | Calculate ROC AUC.
Requires binary classes.
Args:
logits: Tensor of size [batch_size, 1, 1, num_classes]
labels: Tensor of size [batch_size, 1, 1, num_classes]
weights_fn: Function that takes in labels and weighs examples (unused)
Returns:
ROC AUC (scalar), weights | tensor2tensor/utils/metrics.py | def roc_auc(logits, labels, weights_fn=None):
"""Calculate ROC AUC.
Requires binary classes.
Args:
logits: Tensor of size [batch_size, 1, 1, num_classes]
labels: Tensor of size [batch_size, 1, 1, num_classes]
weights_fn: Function that takes in labels and weighs examples (unused)
Returns:
ROC A... | def roc_auc(logits, labels, weights_fn=None):
"""Calculate ROC AUC.
Requires binary classes.
Args:
logits: Tensor of size [batch_size, 1, 1, num_classes]
labels: Tensor of size [batch_size, 1, 1, num_classes]
weights_fn: Function that takes in labels and weighs examples (unused)
Returns:
ROC A... | [
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train | create_evaluation_metrics | Creates the evaluation metrics for the model.
Args:
problems: List of Problem instances.
model_hparams: a set of hparams.
Returns:
dict<metric name, metric function>. The metric functions have signature
(Tensor predictions, features) -> (metric Tensor, update op), where features
is a dict with... | tensor2tensor/utils/metrics.py | def create_evaluation_metrics(problems, model_hparams):
"""Creates the evaluation metrics for the model.
Args:
problems: List of Problem instances.
model_hparams: a set of hparams.
Returns:
dict<metric name, metric function>. The metric functions have signature
(Tensor predictions, features) -> ... | def create_evaluation_metrics(problems, model_hparams):
"""Creates the evaluation metrics for the model.
Args:
problems: List of Problem instances.
model_hparams: a set of hparams.
Returns:
dict<metric name, metric function>. The metric functions have signature
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train | create_eager_metrics_for_problem | See create_eager_metrics. | tensor2tensor/utils/metrics.py | def create_eager_metrics_for_problem(problem, model_hparams):
"""See create_eager_metrics."""
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train | create_eager_metrics | Create metrics accumulators and averager for Eager mode.
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weights_fn: function that takes labels and returns a weights mask. Defaults
to weights of all 1, i.e. common_layers.weights_all. Use
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... | tensor2tensor/utils/metrics.py | def create_eager_metrics(metric_names, weights_fn=common_layers.weights_all):
"""Create metrics accumulators and averager for Eager mode.
Args:
metric_names: list<str> from Metrics enum
weights_fn: function that takes labels and returns a weights mask. Defaults
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"""Create metrics accumulators and averager for Eager mode.
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train | create_eager_metrics_internal | Create metrics accumulators and averager for Eager mode.
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Args:
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train | word_error_rate | Calculate word error rate.
Args:
raw_predictions: The raw predictions.
labels: The actual labels.
lookup: A tf.constant mapping indices to output tokens.
weights_fn: Weighting function.
Returns:
The word error rate. | tensor2tensor/utils/metrics.py | def word_error_rate(raw_predictions,
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lookup=None,
weights_fn=common_layers.weights_nonzero):
"""Calculate word error rate.
Args:
raw_predictions: The raw predictions.
labels: The actual labels.
lookup: A tf.constant mapping indices to ... | def word_error_rate(raw_predictions,
labels,
lookup=None,
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labels: The actual labels.
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train | pearson_correlation_coefficient | Calculate pearson correlation coefficient.
Args:
predictions: The raw predictions.
labels: The actual labels.
weights_fn: Weighting function.
Returns:
The pearson correlation coefficient. | tensor2tensor/utils/metrics.py | def pearson_correlation_coefficient(predictions, labels, weights_fn=None):
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Args:
predictions: The raw predictions.
labels: The actual labels.
weights_fn: Weighting function.
Returns:
The pearson correlation coefficient.
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"""Calculate pearson correlation coefficient.
Args:
predictions: The raw predictions.
labels: The actual labels.
weights_fn: Weighting function.
Returns:
The pearson correlation coefficient.
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train | attention_lm_prepare_decoder | Prepare one shard of the model for the decoder.
Args:
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
decoder_self_attention_bias: a Tensor, containing large negative values
to implement masked attention and possibly biases for diagonal... | tensor2tensor/models/research/attention_lm.py | def attention_lm_prepare_decoder(targets, hparams):
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Args:
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
decoder_self_attention_bias: a Tensor, containing large negative values
to im... | def attention_lm_prepare_decoder(targets, hparams):
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Args:
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
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train | attention_lm_decoder | A stack of attention_lm layers.
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decoder_self_attention_bias: bias Tensor for self-attention
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hparams: hyperparameters for model
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decoder_input: a Tensor
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train | attention_lm_base | Set of hyperparameters. | tensor2tensor/models/research/attention_lm.py | def attention_lm_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.hidden_size = 1024
hparams.batch_size = 8192
hparams.max_length = 256
hparams.dropout = 0.0
hparams.clip_grad_norm = 0. # i.e. no gradient clipping
hparams.optimizer_adam_epsilon = 1e-9
hparams.lea... | def attention_lm_base():
"""Set of hyperparameters."""
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train | attention_lm_small | Cheap model.
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45M params
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Returns:
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45M params
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Returns:
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"""
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hparams.hidden_size = 512
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on lm1b_32k:
45M params
2 steps/sec on [GeForce GTX TITAN X]
Returns:
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"""
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train | attention_lm_translation | Version to use for seq2seq. | tensor2tensor/models/research/attention_lm.py | def attention_lm_translation():
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train | _get_ngrams | Extracts all n-grams up to a given maximum order from an input segment.
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segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
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Returns:
The Counter containing all n-grams up to max_order in segment
with... | tensor2tensor/utils/bleu_hook.py | def _get_ngrams(segment, max_order):
"""Extracts all n-grams up to a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
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Returns:
The Counter containing all n... | def _get_ngrams(segment, max_order):
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train | bleu_score | BLEU score computation between labels and predictions.
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train | bleu_tokenize | r"""Tokenize a string following the official BLEU implementation.
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See https://github.com/moses-smt/mosesdecoder/"
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train | bleu_wrapper | Compute BLEU for two files (reference and hypothesis translation). | tensor2tensor/utils/bleu_hook.py | def bleu_wrapper(ref_filename, hyp_filename, case_sensitive=False):
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train | _try_twice_tf_glob | Glob twice, first time possibly catching `NotFoundError`.
tf.gfile.Glob may crash with
```
tensorflow.python.framework.errors_impl.NotFoundError:
xy/model.ckpt-1130761_temp_9cb4cb0b0f5f4382b5ea947aadfb7a40;
No such file or directory
```
Standard glob.glob does not have this bug, but does not handle mul... | tensor2tensor/utils/bleu_hook.py | def _try_twice_tf_glob(pattern):
"""Glob twice, first time possibly catching `NotFoundError`.
tf.gfile.Glob may crash with
```
tensorflow.python.framework.errors_impl.NotFoundError:
xy/model.ckpt-1130761_temp_9cb4cb0b0f5f4382b5ea947aadfb7a40;
No such file or directory
```
Standard glob.glob does not ... | def _try_twice_tf_glob(pattern):
"""Glob twice, first time possibly catching `NotFoundError`.
tf.gfile.Glob may crash with
```
tensorflow.python.framework.errors_impl.NotFoundError:
xy/model.ckpt-1130761_temp_9cb4cb0b0f5f4382b5ea947aadfb7a40;
No such file or directory
```
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train | _read_stepfiles_list | Return list of StepFiles sorted by step from files at path_prefix. | tensor2tensor/utils/bleu_hook.py | def _read_stepfiles_list(path_prefix, path_suffix=".index", min_steps=0):
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stepfiles = []
for filename in _try_twice_tf_glob(path_prefix + "*-[0-9]*" + path_suffix):
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stepfiles = []
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train | stepfiles_iterator | Continuously yield new files with steps in filename as they appear.
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an integer marking the number of steps and match the wildcard path_prefix +
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Unlike `tf.contrib.training.checkpoints_iterator`, this implem... | tensor2tensor/utils/bleu_hook.py | def stepfiles_iterator(path_prefix, wait_minutes=0, min_steps=0,
path_suffix=".index", sleep_sec=10):
"""Continuously yield new files with steps in filename as they appear.
This is useful for checkpoint files or other files whose names differ just in
an integer marking the number of steps ... | def stepfiles_iterator(path_prefix, wait_minutes=0, min_steps=0,
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"""Continuously yield new files with steps in filename as they appear.
This is useful for checkpoint files or other files whose names differ just in
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train | _get_vqa_v2_annotations | Extract the VQA V2 annotation files to directory unless it's there. | tensor2tensor/data_generators/vqa.py | def _get_vqa_v2_annotations(directory,
annotation_url,
annotation_filename="vqa_v2.tar.gz"):
"""Extract the VQA V2 annotation files to directory unless it's there."""
annotation_file = generator_utils.maybe_download_from_drive(
directory, annotation_file... | def _get_vqa_v2_annotations(directory,
annotation_url,
annotation_filename="vqa_v2.tar.gz"):
"""Extract the VQA V2 annotation files to directory unless it's there."""
annotation_file = generator_utils.maybe_download_from_drive(
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train | _get_vqa_v2_image_raw_dataset | Extract the VQA V2 image data set to directory unless it's there. | tensor2tensor/data_generators/vqa.py | def _get_vqa_v2_image_raw_dataset(directory, image_root_url, image_urls):
"""Extract the VQA V2 image data set to directory unless it's there."""
for url in image_urls:
filename = os.path.basename(url)
download_url = os.path.join(image_root_url, url)
path = generator_utils.maybe_download(directory, file... | def _get_vqa_v2_image_raw_dataset(directory, image_root_url, image_urls):
"""Extract the VQA V2 image data set to directory unless it's there."""
for url in image_urls:
filename = os.path.basename(url)
download_url = os.path.join(image_root_url, url)
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train | _get_vqa_v2_image_feature_dataset | Extract the VQA V2 feature data set to directory unless it's there. | tensor2tensor/data_generators/vqa.py | def _get_vqa_v2_image_feature_dataset(
directory, feature_url, feature_filename="mscoco_feat.tar.gz"):
"""Extract the VQA V2 feature data set to directory unless it's there."""
feature_file = generator_utils.maybe_download_from_drive(
directory, feature_filename, feature_url)
with tarfile.open(feature_f... | def _get_vqa_v2_image_feature_dataset(
directory, feature_url, feature_filename="mscoco_feat.tar.gz"):
"""Extract the VQA V2 feature data set to directory unless it's there."""
feature_file = generator_utils.maybe_download_from_drive(
directory, feature_filename, feature_url)
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train | _parse_fail | Helper function for raising a value error for bad assignment. | tensor2tensor/utils/hparam.py | def _parse_fail(name, var_type, value, values):
"""Helper function for raising a value error for bad assignment."""
raise ValueError(
'Could not parse hparam \'%s\' of type \'%s\' with value \'%s\' in %s' %
(name, var_type.__name__, value, values)) | def _parse_fail(name, var_type, value, values):
"""Helper function for raising a value error for bad assignment."""
raise ValueError(
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train | _process_scalar_value | Update results_dictionary with a scalar value.
Used to update the results_dictionary to be returned by parse_values when
encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".)
Mutates results_dictionary.
Args:
name: Name of variable in assignment ("s" or "arr").
parse_fn: Function for p... | tensor2tensor/utils/hparam.py | def _process_scalar_value(name, parse_fn, var_type, m_dict, values,
results_dictionary):
"""Update results_dictionary with a scalar value.
Used to update the results_dictionary to be returned by parse_values when
encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".)
Mu... | def _process_scalar_value(name, parse_fn, var_type, m_dict, values,
results_dictionary):
"""Update results_dictionary with a scalar value.
Used to update the results_dictionary to be returned by parse_values when
encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".)
Mu... | [
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train | _process_list_value | Update results_dictionary from a list of values.
Used to update results_dictionary to be returned by parse_values when
encountering a clause with a list RHS (e.g. "arr=[1,2,3]".)
Mutates results_dictionary.
Args:
name: Name of variable in assignment ("arr").
parse_fn: Function for parsing individual... | tensor2tensor/utils/hparam.py | def _process_list_value(name, parse_fn, var_type, m_dict, values,
results_dictionary):
"""Update results_dictionary from a list of values.
Used to update results_dictionary to be returned by parse_values when
encountering a clause with a list RHS (e.g. "arr=[1,2,3]".)
Mutates results_... | def _process_list_value(name, parse_fn, var_type, m_dict, values,
results_dictionary):
"""Update results_dictionary from a list of values.
Used to update results_dictionary to be returned by parse_values when
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Mutates results_... | [
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train | _cast_to_type_if_compatible | Cast hparam to the provided type, if compatible.
Args:
name: Name of the hparam to be cast.
param_type: The type of the hparam.
value: The value to be cast, if compatible.
Returns:
The result of casting `value` to `param_type`.
Raises:
ValueError: If the type of `value` is not compatible wi... | tensor2tensor/utils/hparam.py | def _cast_to_type_if_compatible(name, param_type, value):
"""Cast hparam to the provided type, if compatible.
Args:
name: Name of the hparam to be cast.
param_type: The type of the hparam.
value: The value to be cast, if compatible.
Returns:
The result of casting `value` to `param_type`.
Rais... | def _cast_to_type_if_compatible(name, param_type, value):
"""Cast hparam to the provided type, if compatible.
Args:
name: Name of the hparam to be cast.
param_type: The type of the hparam.
value: The value to be cast, if compatible.
Returns:
The result of casting `value` to `param_type`.
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train | parse_values | Parses hyperparameter values from a string into a python map.
`values` is a string containing comma-separated `name=value` pairs.
For each pair, the value of the hyperparameter named `name` is set to
`value`.
If a hyperparameter name appears multiple times in `values`, a ValueError
is raised (e.g. 'a=1,a=2'... | tensor2tensor/utils/hparam.py | def parse_values(values, type_map, ignore_unknown=False):
"""Parses hyperparameter values from a string into a python map.
`values` is a string containing comma-separated `name=value` pairs.
For each pair, the value of the hyperparameter named `name` is set to
`value`.
If a hyperparameter name appears multi... | def parse_values(values, type_map, ignore_unknown=False):
"""Parses hyperparameter values from a string into a python map.
`values` is a string containing comma-separated `name=value` pairs.
For each pair, the value of the hyperparameter named `name` is set to
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L186-L298 | [
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train | HParams.add_hparam | Adds {name, value} pair to hyperparameters.
Args:
name: Name of the hyperparameter.
value: Value of the hyperparameter. Can be one of the following types:
int, float, string, int list, float list, or string list.
Raises:
ValueError: if one of the arguments is invalid. | tensor2tensor/utils/hparam.py | def add_hparam(self, name, value):
"""Adds {name, value} pair to hyperparameters.
Args:
name: Name of the hyperparameter.
value: Value of the hyperparameter. Can be one of the following types:
int, float, string, int list, float list, or string list.
Raises:
ValueError: if one of... | def add_hparam(self, name, value):
"""Adds {name, value} pair to hyperparameters.
Args:
name: Name of the hyperparameter.
value: Value of the hyperparameter. Can be one of the following types:
int, float, string, int list, float list, or string list.
Raises:
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