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/utils/metrics.py | create_eager_metrics_internal | def create_eager_metrics_internal(metric_fns,
weights_fn=common_layers.weights_all):
"""Create metrics accumulators and averager for Eager mode.
Args:
metric_fns: dict<metric name, metric function>
weights_fn: function that takes labels and returns a weights mask. Defaults... | python | def create_eager_metrics_internal(metric_fns,
weights_fn=common_layers.weights_all):
"""Create metrics accumulators and averager for Eager mode.
Args:
metric_fns: dict<metric name, metric function>
weights_fn: function that takes labels and returns a weights mask. Defaults... | [
"def",
"create_eager_metrics_internal",
"(",
"metric_fns",
",",
"weights_fn",
"=",
"common_layers",
".",
"weights_all",
")",
":",
"tfe_metrics",
"=",
"{",
"}",
"for",
"name",
"in",
"metric_fns",
":",
"tfe_metrics",
"[",
"name",
"]",
"=",
"tfe",
".",
"metrics",... | Create metrics accumulators and averager for Eager mode.
Args:
metric_fns: dict<metric name, metric function>
weights_fn: function that takes labels and returns a weights mask. Defaults
to weights of all 1, i.e. common_layers.weights_all. Use
common_layers.weights_nonzero if labels have 0-padding... | [
"Create",
"metrics",
"accumulators",
"and",
"averager",
"for",
"Eager",
"mode",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/metrics.py#L670-L701 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/metrics.py | word_error_rate | def word_error_rate(raw_predictions,
labels,
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 ... | python | def word_error_rate(raw_predictions,
labels,
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",
",",
"weights_fn",
"=",
"common_layers",
".",
"weights_nonzero",
")",
":",
"def",
"from_tokens",
"(",
"raw",
",",
"lookup_",
")",
":",
"gathered",
"=",
"tf",
".",
... | 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. | [
"Calculate",
"word",
"error",
"rate",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/metrics.py#L704-L761 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/metrics.py | pearson_correlation_coefficient | def pearson_correlation_coefficient(predictions, labels, weights_fn=None):
"""Calculate pearson correlation coefficient.
Args:
predictions: The raw predictions.
labels: The actual labels.
weights_fn: Weighting function.
Returns:
The pearson correlation coefficient.
"""
del weights_fn
_, pe... | python | def pearson_correlation_coefficient(predictions, labels, weights_fn=None):
"""Calculate pearson correlation coefficient.
Args:
predictions: The raw predictions.
labels: The actual labels.
weights_fn: Weighting function.
Returns:
The pearson correlation coefficient.
"""
del weights_fn
_, pe... | [
"def",
"pearson_correlation_coefficient",
"(",
"predictions",
",",
"labels",
",",
"weights_fn",
"=",
"None",
")",
":",
"del",
"weights_fn",
"_",
",",
"pearson",
"=",
"tf",
".",
"contrib",
".",
"metrics",
".",
"streaming_pearson_correlation",
"(",
"predictions",
... | Calculate pearson correlation coefficient.
Args:
predictions: The raw predictions.
labels: The actual labels.
weights_fn: Weighting function.
Returns:
The pearson correlation coefficient. | [
"Calculate",
"pearson",
"correlation",
"coefficient",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/metrics.py#L764-L778 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/attention_lm.py | attention_lm_prepare_decoder | def attention_lm_prepare_decoder(targets, hparams):
"""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 im... | python | def attention_lm_prepare_decoder(targets, hparams):
"""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 im... | [
"def",
"attention_lm_prepare_decoder",
"(",
"targets",
",",
"hparams",
")",
":",
"if",
"hparams",
".",
"prepend_mode",
"==",
"\"prepend_inputs_full_attention\"",
":",
"decoder_self_attention_bias",
"=",
"(",
"common_attention",
".",
"attention_bias_prepend_inputs_full_attenti... | 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... | [
"Prepare",
"one",
"shard",
"of",
"the",
"model",
"for",
"the",
"decoder",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/attention_lm.py#L66-L89 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/attention_lm.py | attention_lm_decoder | def attention_lm_decoder(decoder_input,
decoder_self_attention_bias,
hparams,
name="decoder"):
"""A stack of attention_lm layers.
Args:
decoder_input: a Tensor
decoder_self_attention_bias: bias Tensor for self-attention
(see c... | python | def attention_lm_decoder(decoder_input,
decoder_self_attention_bias,
hparams,
name="decoder"):
"""A stack of attention_lm layers.
Args:
decoder_input: a Tensor
decoder_self_attention_bias: bias Tensor for self-attention
(see c... | [
"def",
"attention_lm_decoder",
"(",
"decoder_input",
",",
"decoder_self_attention_bias",
",",
"hparams",
",",
"name",
"=",
"\"decoder\"",
")",
":",
"x",
"=",
"decoder_input",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
")",
":",
"for",
"layer",
"in",
"ra... | A stack of attention_lm layers.
Args:
decoder_input: a Tensor
decoder_self_attention_bias: bias Tensor for self-attention
(see common_attention.attention_bias())
hparams: hyperparameters for model
name: a string
Returns:
y: a Tensors | [
"A",
"stack",
"of",
"attention_lm",
"layers",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/attention_lm.py#L92-L127 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/attention_lm.py | attention_lm_base | 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... | python | 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",
"(",
")",
":",
"hparams",
"=",
"common_hparams",
".",
"basic_params1",
"(",
")",
"hparams",
".",
"hidden_size",
"=",
"1024",
"hparams",
".",
"batch_size",
"=",
"8192",
"hparams",
".",
"max_length",
"=",
"256",
"hparams",
".",
"dro... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/attention_lm.py#L131-L163 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/attention_lm.py | attention_lm_small | def attention_lm_small():
"""Cheap model.
on lm1b_32k:
45M params
2 steps/sec on [GeForce GTX TITAN X]
Returns:
an hparams object.
"""
hparams = attention_lm_base()
hparams.num_hidden_layers = 4
hparams.hidden_size = 512
hparams.filter_size = 2048
hparams.layer_prepostprocess_dropout ... | python | def attention_lm_small():
"""Cheap model.
on lm1b_32k:
45M params
2 steps/sec on [GeForce GTX TITAN X]
Returns:
an hparams object.
"""
hparams = attention_lm_base()
hparams.num_hidden_layers = 4
hparams.hidden_size = 512
hparams.filter_size = 2048
hparams.layer_prepostprocess_dropout ... | [
"def",
"attention_lm_small",
"(",
")",
":",
"hparams",
"=",
"attention_lm_base",
"(",
")",
"hparams",
".",
"num_hidden_layers",
"=",
"4",
"hparams",
".",
"hidden_size",
"=",
"512",
"hparams",
".",
"filter_size",
"=",
"2048",
"hparams",
".",
"layer_prepostprocess... | Cheap model.
on lm1b_32k:
45M params
2 steps/sec on [GeForce GTX TITAN X]
Returns:
an hparams object. | [
"Cheap",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/attention_lm.py#L167-L182 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/attention_lm.py | attention_lm_translation | def attention_lm_translation():
"""Version to use for seq2seq."""
hparams = attention_lm_base()
hparams.layer_preprocess_sequence = "n"
hparams.layer_postprocess_sequence = "da"
hparams.learning_rate = 0.4
hparams.prepend_mode = "prepend_inputs_masked_attention"
hparams.max_length = 512
hparams.label_sm... | python | def attention_lm_translation():
"""Version to use for seq2seq."""
hparams = attention_lm_base()
hparams.layer_preprocess_sequence = "n"
hparams.layer_postprocess_sequence = "da"
hparams.learning_rate = 0.4
hparams.prepend_mode = "prepend_inputs_masked_attention"
hparams.max_length = 512
hparams.label_sm... | [
"def",
"attention_lm_translation",
"(",
")",
":",
"hparams",
"=",
"attention_lm_base",
"(",
")",
"hparams",
".",
"layer_preprocess_sequence",
"=",
"\"n\"",
"hparams",
".",
"layer_postprocess_sequence",
"=",
"\"da\"",
"hparams",
".",
"learning_rate",
"=",
"0.4",
"hpa... | Version to use for seq2seq. | [
"Version",
"to",
"use",
"for",
"seq2seq",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/attention_lm.py#L186-L196 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/bleu_hook.py | _get_ngrams | 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
methods.
Returns:
The Counter containing all n... | python | 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
methods.
Returns:
The Counter containing all n... | [
"def",
"_get_ngrams",
"(",
"segment",
",",
"max_order",
")",
":",
"ngram_counts",
"=",
"collections",
".",
"Counter",
"(",
")",
"for",
"order",
"in",
"range",
"(",
"1",
",",
"max_order",
"+",
"1",
")",
":",
"for",
"i",
"in",
"range",
"(",
"0",
",",
... | 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
methods.
Returns:
The Counter containing all n-grams up to max_order in segment
with... | [
"Extracts",
"all",
"n",
"-",
"grams",
"up",
"to",
"a",
"given",
"maximum",
"order",
"from",
"an",
"input",
"segment",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/bleu_hook.py#L40-L57 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/bleu_hook.py | bleu_score | def bleu_score(predictions, labels, **unused_kwargs):
"""BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have be... | python | def bleu_score(predictions, labels, **unused_kwargs):
"""BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have be... | [
"def",
"bleu_score",
"(",
"predictions",
",",
"labels",
",",
"*",
"*",
"unused_kwargs",
")",
":",
"outputs",
"=",
"tf",
".",
"to_int32",
"(",
"tf",
".",
"argmax",
"(",
"predictions",
",",
"axis",
"=",
"-",
"1",
")",
")",
"# Convert the outputs and labels t... | BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have beam search.
Args:
predictions: tensor, model predicti... | [
"BLEU",
"score",
"computation",
"between",
"labels",
"and",
"predictions",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/bleu_hook.py#L132-L152 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/bleu_hook.py | bleu_tokenize | def bleu_tokenize(string):
r"""Tokenize a string following the official BLEU implementation.
See https://github.com/moses-smt/mosesdecoder/"
"blob/master/scripts/generic/mteval-v14.pl#L954-L983
In our case, the input string is expected to be just one line
and no HTML entities de-escaping is needed.
... | python | def bleu_tokenize(string):
r"""Tokenize a string following the official BLEU implementation.
See https://github.com/moses-smt/mosesdecoder/"
"blob/master/scripts/generic/mteval-v14.pl#L954-L983
In our case, the input string is expected to be just one line
and no HTML entities de-escaping is needed.
... | [
"def",
"bleu_tokenize",
"(",
"string",
")",
":",
"string",
"=",
"uregex",
".",
"nondigit_punct_re",
".",
"sub",
"(",
"r\"\\1 \\2 \"",
",",
"string",
")",
"string",
"=",
"uregex",
".",
"punct_nondigit_re",
".",
"sub",
"(",
"r\" \\1 \\2\"",
",",
"string",
")",... | r"""Tokenize a string following the official BLEU implementation.
See https://github.com/moses-smt/mosesdecoder/"
"blob/master/scripts/generic/mteval-v14.pl#L954-L983
In our case, the input string is expected to be just one line
and no HTML entities de-escaping is needed.
So we just tokenize on punc... | [
"r",
"Tokenize",
"a",
"string",
"following",
"the",
"official",
"BLEU",
"implementation",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/bleu_hook.py#L172-L199 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/bleu_hook.py | bleu_wrapper | def bleu_wrapper(ref_filename, hyp_filename, case_sensitive=False):
"""Compute BLEU for two files (reference and hypothesis translation)."""
ref_lines = text_encoder.native_to_unicode(
tf.gfile.Open(ref_filename, "r").read()).split("\n")
hyp_lines = text_encoder.native_to_unicode(
tf.gfile.Open(hyp_fi... | python | def bleu_wrapper(ref_filename, hyp_filename, case_sensitive=False):
"""Compute BLEU for two files (reference and hypothesis translation)."""
ref_lines = text_encoder.native_to_unicode(
tf.gfile.Open(ref_filename, "r").read()).split("\n")
hyp_lines = text_encoder.native_to_unicode(
tf.gfile.Open(hyp_fi... | [
"def",
"bleu_wrapper",
"(",
"ref_filename",
",",
"hyp_filename",
",",
"case_sensitive",
"=",
"False",
")",
":",
"ref_lines",
"=",
"text_encoder",
".",
"native_to_unicode",
"(",
"tf",
".",
"gfile",
".",
"Open",
"(",
"ref_filename",
",",
"\"r\"",
")",
".",
"re... | Compute BLEU for two files (reference and hypothesis translation). | [
"Compute",
"BLEU",
"for",
"two",
"files",
"(",
"reference",
"and",
"hypothesis",
"translation",
")",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/bleu_hook.py#L202-L215 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/bleu_hook.py | _try_twice_tf_glob | 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 ... | python | 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",
")",
":",
"try",
":",
"return",
"tf",
".",
"gfile",
".",
"Glob",
"(",
"pattern",
")",
"except",
"tf",
".",
"errors",
".",
"NotFoundError",
":",
"return",
"tf",
".",
"gfile",
".",
"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 have this bug, but does not handle mul... | [
"Glob",
"twice",
"first",
"time",
"possibly",
"catching",
"NotFoundError",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/bleu_hook.py#L221-L245 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/bleu_hook.py | _read_stepfiles_list | def _read_stepfiles_list(path_prefix, path_suffix=".index", min_steps=0):
"""Return list of StepFiles sorted by step from files at path_prefix."""
stepfiles = []
for filename in _try_twice_tf_glob(path_prefix + "*-[0-9]*" + path_suffix):
basename = filename[:-len(path_suffix)] if path_suffix else filename
... | python | def _read_stepfiles_list(path_prefix, path_suffix=".index", min_steps=0):
"""Return list of StepFiles sorted by step from files at path_prefix."""
stepfiles = []
for filename in _try_twice_tf_glob(path_prefix + "*-[0-9]*" + path_suffix):
basename = filename[:-len(path_suffix)] if path_suffix else filename
... | [
"def",
"_read_stepfiles_list",
"(",
"path_prefix",
",",
"path_suffix",
"=",
"\".index\"",
",",
"min_steps",
"=",
"0",
")",
":",
"stepfiles",
"=",
"[",
"]",
"for",
"filename",
"in",
"_try_twice_tf_glob",
"(",
"path_prefix",
"+",
"\"*-[0-9]*\"",
"+",
"path_suffix"... | Return list of StepFiles sorted by step from files at path_prefix. | [
"Return",
"list",
"of",
"StepFiles",
"sorted",
"by",
"step",
"from",
"files",
"at",
"path_prefix",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/bleu_hook.py#L248-L264 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/bleu_hook.py | stepfiles_iterator | 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 ... | python | 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",
",",
"path_suffix",
"=",
"\".index\"",
",",
"sleep_sec",
"=",
"10",
")",
":",
"# Wildcard D*-[0-9]* does not match D/x-1, so if D is a directory let",
"# path_pre... | 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 and match the wildcard path_prefix +
"*-[0-9]*" + path_suffix.
Unlike `tf.contrib.training.checkpoints_iterator`, this implem... | [
"Continuously",
"yield",
"new",
"files",
"with",
"steps",
"in",
"filename",
"as",
"they",
"appear",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/bleu_hook.py#L267-L317 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/vqa.py | _get_vqa_v2_annotations | 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... | python | 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\"",
")",
":",
"annotation_file",
"=",
"generator_utils",
".",
"maybe_download_from_drive",
"(",
"directory",
",",
"annotation_filename",
",",
"anno... | Extract the VQA V2 annotation files to directory unless it's there. | [
"Extract",
"the",
"VQA",
"V2",
"annotation",
"files",
"to",
"directory",
"unless",
"it",
"s",
"there",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/vqa.py#L44-L51 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/vqa.py | _get_vqa_v2_image_raw_dataset | 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... | python | 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",
")",
":",
"for",
"url",
"in",
"image_urls",
":",
"filename",
"=",
"os",
".",
"path",
".",
"basename",
"(",
"url",
")",
"download_url",
"=",
"os",
".",
"path",... | Extract the VQA V2 image data set to directory unless it's there. | [
"Extract",
"the",
"VQA",
"V2",
"image",
"data",
"set",
"to",
"directory",
"unless",
"it",
"s",
"there",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/vqa.py#L54-L62 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/vqa.py | _get_vqa_v2_image_feature_dataset | 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... | python | 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\"",
")",
":",
"feature_file",
"=",
"generator_utils",
".",
"maybe_download_from_drive",
"(",
"directory",
",",
"feature_filename",
",",
"f... | Extract the VQA V2 feature data set to directory unless it's there. | [
"Extract",
"the",
"VQA",
"V2",
"feature",
"data",
"set",
"to",
"directory",
"unless",
"it",
"s",
"there",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/vqa.py#L65-L71 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | _parse_fail | 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)) | python | 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",
")",
":",
"raise",
"ValueError",
"(",
"'Could not parse hparam \\'%s\\' of type \\'%s\\' with value \\'%s\\' in %s'",
"%",
"(",
"name",
",",
"var_type",
".",
"__name__",
",",
"value",
"... | Helper function for raising a value error for bad assignment. | [
"Helper",
"function",
"for",
"raising",
"a",
"value",
"error",
"for",
"bad",
"assignment",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L42-L46 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | _process_scalar_value | 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... | python | 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",
")",
":",
"try",
":",
"parsed_value",
"=",
"parse_fn",
"(",
"m_dict",
"[",
"'val'",
"]",
")",
"except",
"ValueError",
":",... | 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... | [
"Update",
"results_dictionary",
"with",
"a",
"scalar",
"value",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L55-L101 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | _process_list_value | 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_... | python | 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",
")",
":",
"if",
"m_dict",
"[",
"'index'",
"]",
"is",
"not",
"None",
":",
"raise",
"ValueError",
"(",
"'Assignment of a list to... | 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... | [
"Update",
"results_dictionary",
"from",
"a",
"list",
"of",
"values",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L104-L135 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | _cast_to_type_if_compatible | 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... | python | 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",
")",
":",
"fail_msg",
"=",
"(",
"\"Could not cast hparam '%s' of type '%s' from value %r\"",
"%",
"(",
"name",
",",
"param_type",
",",
"value",
")",
")",
"# Some callers use None, for wh... | 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... | [
"Cast",
"hparam",
"to",
"the",
"provided",
"type",
"if",
"compatible",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L138-L183 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | parse_values | 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... | python | 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",
")",
":",
"results_dictionary",
"=",
"{",
"}",
"pos",
"=",
"0",
"while",
"pos",
"<",
"len",
"(",
"values",
")",
":",
"m",
"=",
"PARAM_RE",
".",
"match",
"(",
... | 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'... | [
"Parses",
"hyperparameter",
"values",
"from",
"a",
"string",
"into",
"a",
"python",
"map",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L186-L298 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.add_hparam | 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... | python | 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",
")",
":",
"# Keys in kwargs are unique, but 'name' could the name of a pre-existing",
"# attribute of this object. In that case we refuse to use it as a",
"# hyperparameter name.",
"if",
"getattr",
"(",
"self",
",",
"n... | 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. | [
"Adds",
"{",
"name",
"value",
"}",
"pair",
"to",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L418-L441 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.set_hparam | def set_hparam(self, name, value):
"""Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
Raises:
KeyError:... | python | def set_hparam(self, name, value):
"""Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
Raises:
KeyError:... | [
"def",
"set_hparam",
"(",
"self",
",",
"name",
",",
"value",
")",
":",
"param_type",
",",
"is_list",
"=",
"self",
".",
"_hparam_types",
"[",
"name",
"]",
"if",
"isinstance",
"(",
"value",
",",
"list",
")",
":",
"if",
"not",
"is_list",
":",
"raise",
"... | Set the value of an existing hyperparameter.
This function verifies that the type of the value matches the type of the
existing hyperparameter.
Args:
name: Name of the hyperparameter.
value: New value of the hyperparameter.
Raises:
KeyError: If the hyperparameter doesn't exist.
... | [
"Set",
"the",
"value",
"of",
"an",
"existing",
"hyperparameter",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L443-L468 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.del_hparam | def del_hparam(self, name):
"""Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter.
"""
if hasattr(self, name):
delattr(self, name)
del self._hparam_types[name] | python | def del_hparam(self, name):
"""Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter.
"""
if hasattr(self, name):
delattr(self, name)
del self._hparam_types[name] | [
"def",
"del_hparam",
"(",
"self",
",",
"name",
")",
":",
"if",
"hasattr",
"(",
"self",
",",
"name",
")",
":",
"delattr",
"(",
"self",
",",
"name",
")",
"del",
"self",
".",
"_hparam_types",
"[",
"name",
"]"
] | Removes the hyperparameter with key 'name'.
Does nothing if it isn't present.
Args:
name: Name of the hyperparameter. | [
"Removes",
"the",
"hyperparameter",
"with",
"key",
"name",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L470-L480 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.parse | def parse(self, values):
"""Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
must follow the syntax described above.
... | python | def parse(self, values):
"""Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
must follow the syntax described above.
... | [
"def",
"parse",
"(",
"self",
",",
"values",
")",
":",
"type_map",
"=",
"{",
"}",
"for",
"name",
",",
"t",
"in",
"self",
".",
"_hparam_types",
".",
"items",
"(",
")",
":",
"param_type",
",",
"_",
"=",
"t",
"type_map",
"[",
"name",
"]",
"=",
"param... | Override existing hyperparameter values, parsing new values from a string.
See parse_values for more detail on the allowed format for values.
Args:
values: String. Comma separated list of `name=value` pairs where 'value'
must follow the syntax described above.
Returns:
The `HParams` ... | [
"Override",
"existing",
"hyperparameter",
"values",
"parsing",
"new",
"values",
"from",
"a",
"string",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L482-L504 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.override_from_dict | def override_from_dict(self, values_dict):
"""Override existing hyperparameter values, parsing new values from a dictionary.
Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
... | python | def override_from_dict(self, values_dict):
"""Override existing hyperparameter values, parsing new values from a dictionary.
Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
... | [
"def",
"override_from_dict",
"(",
"self",
",",
"values_dict",
")",
":",
"for",
"name",
",",
"value",
"in",
"values_dict",
".",
"items",
"(",
")",
":",
"self",
".",
"set_hparam",
"(",
"name",
",",
"value",
")",
"return",
"self"
] | Override existing hyperparameter values, parsing new values from a dictionary.
Args:
values_dict: Dictionary of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_dict` doesn't exist.
ValueError: If `values_dict` cannot be parsed. | [
"Override",
"existing",
"hyperparameter",
"values",
"parsing",
"new",
"values",
"from",
"a",
"dictionary",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L506-L521 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.to_json | def to_json(self, indent=None, separators=None, sort_keys=False):
"""Serializes the hyperparameters into JSON.
Args:
indent: If a non-negative integer, JSON array elements and object members
will be pretty-printed with that indent level. An indent level of 0, or
negative, will only insert... | python | def to_json(self, indent=None, separators=None, sort_keys=False):
"""Serializes the hyperparameters into JSON.
Args:
indent: If a non-negative integer, JSON array elements and object members
will be pretty-printed with that indent level. An indent level of 0, or
negative, will only insert... | [
"def",
"to_json",
"(",
"self",
",",
"indent",
"=",
"None",
",",
"separators",
"=",
"None",
",",
"sort_keys",
"=",
"False",
")",
":",
"def",
"remove_callables",
"(",
"x",
")",
":",
"\"\"\"Omit callable elements from input with arbitrary nesting.\"\"\"",
"if",
"isin... | Serializes the hyperparameters into JSON.
Args:
indent: If a non-negative integer, JSON array elements and object members
will be pretty-printed with that indent level. An indent level of 0, or
negative, will only insert newlines. `None` (the default) selects the
most compact represen... | [
"Serializes",
"the",
"hyperparameters",
"into",
"JSON",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L529-L556 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.parse_json | def parse_json(self, values_json):
"""Override existing hyperparameter values, parsing new values from a json object.
Args:
values_json: String containing a json object of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_json` doesn... | python | def parse_json(self, values_json):
"""Override existing hyperparameter values, parsing new values from a json object.
Args:
values_json: String containing a json object of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_json` doesn... | [
"def",
"parse_json",
"(",
"self",
",",
"values_json",
")",
":",
"values_map",
"=",
"json",
".",
"loads",
"(",
"values_json",
")",
"return",
"self",
".",
"override_from_dict",
"(",
"values_map",
")"
] | Override existing hyperparameter values, parsing new values from a json object.
Args:
values_json: String containing a json object of name:value pairs.
Returns:
The `HParams` instance.
Raises:
KeyError: If a hyperparameter in `values_json` doesn't exist.
ValueError: If `values_jso... | [
"Override",
"existing",
"hyperparameter",
"values",
"parsing",
"new",
"values",
"from",
"a",
"json",
"object",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L558-L572 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.values | def values(self):
"""Return the hyperparameter values as a Python dictionary.
Returns:
A dictionary with hyperparameter names as keys. The values are the
hyperparameter values.
"""
return {n: getattr(self, n) for n in self._hparam_types.keys()} | python | def values(self):
"""Return the hyperparameter values as a Python dictionary.
Returns:
A dictionary with hyperparameter names as keys. The values are the
hyperparameter values.
"""
return {n: getattr(self, n) for n in self._hparam_types.keys()} | [
"def",
"values",
"(",
"self",
")",
":",
"return",
"{",
"n",
":",
"getattr",
"(",
"self",
",",
"n",
")",
"for",
"n",
"in",
"self",
".",
"_hparam_types",
".",
"keys",
"(",
")",
"}"
] | Return the hyperparameter values as a Python dictionary.
Returns:
A dictionary with hyperparameter names as keys. The values are the
hyperparameter values. | [
"Return",
"the",
"hyperparameter",
"values",
"as",
"a",
"Python",
"dictionary",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L574-L581 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams.get | def get(self, key, default=None):
"""Returns the value of `key` if it exists, else `default`."""
if key in self._hparam_types:
# Ensure that default is compatible with the parameter type.
if default is not None:
param_type, is_param_list = self._hparam_types[key]
type_str = 'list<%s>... | python | def get(self, key, default=None):
"""Returns the value of `key` if it exists, else `default`."""
if key in self._hparam_types:
# Ensure that default is compatible with the parameter type.
if default is not None:
param_type, is_param_list = self._hparam_types[key]
type_str = 'list<%s>... | [
"def",
"get",
"(",
"self",
",",
"key",
",",
"default",
"=",
"None",
")",
":",
"if",
"key",
"in",
"self",
".",
"_hparam_types",
":",
"# Ensure that default is compatible with the parameter type.",
"if",
"default",
"is",
"not",
"None",
":",
"param_type",
",",
"i... | Returns the value of `key` if it exists, else `default`. | [
"Returns",
"the",
"value",
"of",
"key",
"if",
"it",
"exists",
"else",
"default",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L583-L608 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/hparam.py | HParams._get_kind_name | def _get_kind_name(param_type, is_list):
"""Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
Raises:
ValueError: If parameter type is not rec... | python | def _get_kind_name(param_type, is_list):
"""Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
Raises:
ValueError: If parameter type is not rec... | [
"def",
"_get_kind_name",
"(",
"param_type",
",",
"is_list",
")",
":",
"if",
"issubclass",
"(",
"param_type",
",",
"bool",
")",
":",
"# This check must happen before issubclass(param_type, six.integer_types),",
"# since Python considers bool to be a subclass of int.",
"typename",
... | Returns the field name given parameter type and is_list.
Args:
param_type: Data type of the hparam.
is_list: Whether this is a list.
Returns:
A string representation of the field name.
Raises:
ValueError: If parameter type is not recognized. | [
"Returns",
"the",
"field",
"name",
"given",
"parameter",
"type",
"and",
"is_list",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/hparam.py#L620-L651 | train |
tensorflow/tensor2tensor | tensor2tensor/insights/transformer_model.py | TransformerModel.process | def process(self, query):
"""Returns the visualizations for query.
Args:
query: The query to process.
Returns:
A dictionary of results with processing and graph visualizations.
"""
tf.logging.info("Processing new query [%s]" %query)
# Create the new TFDBG hook directory.
hook_... | python | def process(self, query):
"""Returns the visualizations for query.
Args:
query: The query to process.
Returns:
A dictionary of results with processing and graph visualizations.
"""
tf.logging.info("Processing new query [%s]" %query)
# Create the new TFDBG hook directory.
hook_... | [
"def",
"process",
"(",
"self",
",",
"query",
")",
":",
"tf",
".",
"logging",
".",
"info",
"(",
"\"Processing new query [%s]\"",
"%",
"query",
")",
"# Create the new TFDBG hook directory.",
"hook_dir",
"=",
"\"/tmp/t2t_server_dump/request_%d\"",
"%",
"int",
"(",
"tim... | Returns the visualizations for query.
Args:
query: The query to process.
Returns:
A dictionary of results with processing and graph visualizations. | [
"Returns",
"the",
"visualizations",
"for",
"query",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/insights/transformer_model.py#L141-L301 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/trainer.py | _default_output_dir | def _default_output_dir():
"""Default output directory."""
try:
dataset_name = gin.query_parameter("inputs.dataset_name")
except ValueError:
dataset_name = "random"
dir_name = "{model_name}_{dataset_name}_{timestamp}".format(
model_name=gin.query_parameter("train.model").configurable.name,
d... | python | def _default_output_dir():
"""Default output directory."""
try:
dataset_name = gin.query_parameter("inputs.dataset_name")
except ValueError:
dataset_name = "random"
dir_name = "{model_name}_{dataset_name}_{timestamp}".format(
model_name=gin.query_parameter("train.model").configurable.name,
d... | [
"def",
"_default_output_dir",
"(",
")",
":",
"try",
":",
"dataset_name",
"=",
"gin",
".",
"query_parameter",
"(",
"\"inputs.dataset_name\"",
")",
"except",
"ValueError",
":",
"dataset_name",
"=",
"\"random\"",
"dir_name",
"=",
"\"{model_name}_{dataset_name}_{timestamp}\... | Default output directory. | [
"Default",
"output",
"directory",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/trainer.py#L48-L62 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/trainer.py | _setup_gin | def _setup_gin():
"""Setup gin configuration."""
# Imports for configurables
# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order,reimported,unused-variable
from tensor2tensor.trax import models as _trax_models
from tensor2tensor.trax import optimizers as _trax_opt
# pylint: disable=g-impo... | python | def _setup_gin():
"""Setup gin configuration."""
# Imports for configurables
# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order,reimported,unused-variable
from tensor2tensor.trax import models as _trax_models
from tensor2tensor.trax import optimizers as _trax_opt
# pylint: disable=g-impo... | [
"def",
"_setup_gin",
"(",
")",
":",
"# Imports for configurables",
"# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order,reimported,unused-variable",
"from",
"tensor2tensor",
".",
"trax",
"import",
"models",
"as",
"_trax_models",
"from",
"tensor2tensor",
".",
"t... | Setup gin configuration. | [
"Setup",
"gin",
"configuration",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/trainer.py#L65-L81 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | train_and_eval_dataset | def train_and_eval_dataset(dataset_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys.
Args:
dataset_name: a string, the name of the dataset; if it starts with "v1_"
then we'll search T2T Problem registry for it, otherwise we assume it
is a dataset from TFDS ... | python | def train_and_eval_dataset(dataset_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys.
Args:
dataset_name: a string, the name of the dataset; if it starts with "v1_"
then we'll search T2T Problem registry for it, otherwise we assume it
is a dataset from TFDS ... | [
"def",
"train_and_eval_dataset",
"(",
"dataset_name",
",",
"data_dir",
")",
":",
"if",
"dataset_name",
".",
"startswith",
"(",
"\"v1_\"",
")",
":",
"return",
"_train_and_eval_dataset_v1",
"(",
"dataset_name",
"[",
"3",
":",
"]",
",",
"data_dir",
")",
"dataset_bu... | Return train and evaluation datasets, feature info and supervised keys.
Args:
dataset_name: a string, the name of the dataset; if it starts with "v1_"
then we'll search T2T Problem registry for it, otherwise we assume it
is a dataset from TFDS and load it from there.
data_dir: directory where the... | [
"Return",
"train",
"and",
"evaluation",
"datasets",
"feature",
"info",
"and",
"supervised",
"keys",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L48-L83 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | _make_info | def _make_info(shape_list, num_classes):
"""Create an info-like tuple for feature given some shapes and vocab size."""
feature_info = collections.namedtuple("FeatureInfo", ["shape", "num_classes"])
cur_shape = list(shape_list[0])
# We need to merge the provided shapes, put None where they disagree.
for shape ... | python | def _make_info(shape_list, num_classes):
"""Create an info-like tuple for feature given some shapes and vocab size."""
feature_info = collections.namedtuple("FeatureInfo", ["shape", "num_classes"])
cur_shape = list(shape_list[0])
# We need to merge the provided shapes, put None where they disagree.
for shape ... | [
"def",
"_make_info",
"(",
"shape_list",
",",
"num_classes",
")",
":",
"feature_info",
"=",
"collections",
".",
"namedtuple",
"(",
"\"FeatureInfo\"",
",",
"[",
"\"shape\"",
",",
"\"num_classes\"",
"]",
")",
"cur_shape",
"=",
"list",
"(",
"shape_list",
"[",
"0",... | Create an info-like tuple for feature given some shapes and vocab size. | [
"Create",
"an",
"info",
"-",
"like",
"tuple",
"for",
"feature",
"given",
"some",
"shapes",
"and",
"vocab",
"size",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L86-L98 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | _select_features | def _select_features(example, feature_list=None):
"""Select a subset of features from the example dict."""
feature_list = feature_list or ["inputs", "targets"]
return {f: example[f] for f in feature_list} | python | def _select_features(example, feature_list=None):
"""Select a subset of features from the example dict."""
feature_list = feature_list or ["inputs", "targets"]
return {f: example[f] for f in feature_list} | [
"def",
"_select_features",
"(",
"example",
",",
"feature_list",
"=",
"None",
")",
":",
"feature_list",
"=",
"feature_list",
"or",
"[",
"\"inputs\"",
",",
"\"targets\"",
"]",
"return",
"{",
"f",
":",
"example",
"[",
"f",
"]",
"for",
"f",
"in",
"feature_list... | Select a subset of features from the example dict. | [
"Select",
"a",
"subset",
"of",
"features",
"from",
"the",
"example",
"dict",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L101-L104 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | _train_and_eval_dataset_v1 | def _train_and_eval_dataset_v1(problem_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys."""
problem = problems.problem(problem_name)
train_dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, data_dir)
train_dataset = train_dataset.map(_select_features)
eval_dataset... | python | def _train_and_eval_dataset_v1(problem_name, data_dir):
"""Return train and evaluation datasets, feature info and supervised keys."""
problem = problems.problem(problem_name)
train_dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, data_dir)
train_dataset = train_dataset.map(_select_features)
eval_dataset... | [
"def",
"_train_and_eval_dataset_v1",
"(",
"problem_name",
",",
"data_dir",
")",
":",
"problem",
"=",
"problems",
".",
"problem",
"(",
"problem_name",
")",
"train_dataset",
"=",
"problem",
".",
"dataset",
"(",
"tf",
".",
"estimator",
".",
"ModeKeys",
".",
"TRAI... | Return train and evaluation datasets, feature info and supervised keys. | [
"Return",
"train",
"and",
"evaluation",
"datasets",
"feature",
"info",
"and",
"supervised",
"keys",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L107-L126 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | batch_fn | def batch_fn(dataset, training, shapes, target_names,
batch_size=32, eval_batch_size=32, bucket_batch_length=32,
bucket_max_length=256, bucket_min_length=8,
bucket_length_step=1.1, buckets=None):
"""Batching function."""
del target_names
# If bucketing is not specified, chec... | python | def batch_fn(dataset, training, shapes, target_names,
batch_size=32, eval_batch_size=32, bucket_batch_length=32,
bucket_max_length=256, bucket_min_length=8,
bucket_length_step=1.1, buckets=None):
"""Batching function."""
del target_names
# If bucketing is not specified, chec... | [
"def",
"batch_fn",
"(",
"dataset",
",",
"training",
",",
"shapes",
",",
"target_names",
",",
"batch_size",
"=",
"32",
",",
"eval_batch_size",
"=",
"32",
",",
"bucket_batch_length",
"=",
"32",
",",
"bucket_max_length",
"=",
"256",
",",
"bucket_min_length",
"=",... | Batching function. | [
"Batching",
"function",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L140-L174 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | shuffle_and_batch_data | def shuffle_and_batch_data(dataset, target_names, features_info, training):
"""Shuffle and batch the given dataset."""
def append_targets(example):
"""Append targets to the example dictionary. Needed for Keras."""
if len(target_names) == 1:
return (example, example[target_names[0]])
targets = {}
... | python | def shuffle_and_batch_data(dataset, target_names, features_info, training):
"""Shuffle and batch the given dataset."""
def append_targets(example):
"""Append targets to the example dictionary. Needed for Keras."""
if len(target_names) == 1:
return (example, example[target_names[0]])
targets = {}
... | [
"def",
"shuffle_and_batch_data",
"(",
"dataset",
",",
"target_names",
",",
"features_info",
",",
"training",
")",
":",
"def",
"append_targets",
"(",
"example",
")",
":",
"\"\"\"Append targets to the example dictionary. Needed for Keras.\"\"\"",
"if",
"len",
"(",
"target_n... | Shuffle and batch the given dataset. | [
"Shuffle",
"and",
"batch",
"the",
"given",
"dataset",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L177-L195 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | optimize_fn | def optimize_fn(model,
optimizer=None,
learning_rate_schedule=None,
loss=None,
metrics=None):
"""Compile the model in Keras."""
learning_rate_schedule = learning_rate_schedule or T2TLearningRateSchedule()
if optimizer:
optimizer = optimizer(learn... | python | def optimize_fn(model,
optimizer=None,
learning_rate_schedule=None,
loss=None,
metrics=None):
"""Compile the model in Keras."""
learning_rate_schedule = learning_rate_schedule or T2TLearningRateSchedule()
if optimizer:
optimizer = optimizer(learn... | [
"def",
"optimize_fn",
"(",
"model",
",",
"optimizer",
"=",
"None",
",",
"learning_rate_schedule",
"=",
"None",
",",
"loss",
"=",
"None",
",",
"metrics",
"=",
"None",
")",
":",
"learning_rate_schedule",
"=",
"learning_rate_schedule",
"or",
"T2TLearningRateSchedule"... | Compile the model in Keras. | [
"Compile",
"the",
"model",
"in",
"Keras",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L233-L253 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | train_fn | def train_fn(data_dir=None, output_dir=None,
model_class=gin.REQUIRED, dataset=gin.REQUIRED,
input_names=None, target_names=None,
train_steps=1000, eval_steps=1, eval_frequency=100):
"""Train the given model on the given dataset.
Args:
data_dir: Directory where the data i... | python | def train_fn(data_dir=None, output_dir=None,
model_class=gin.REQUIRED, dataset=gin.REQUIRED,
input_names=None, target_names=None,
train_steps=1000, eval_steps=1, eval_frequency=100):
"""Train the given model on the given dataset.
Args:
data_dir: Directory where the data i... | [
"def",
"train_fn",
"(",
"data_dir",
"=",
"None",
",",
"output_dir",
"=",
"None",
",",
"model_class",
"=",
"gin",
".",
"REQUIRED",
",",
"dataset",
"=",
"gin",
".",
"REQUIRED",
",",
"input_names",
"=",
"None",
",",
"target_names",
"=",
"None",
",",
"train_... | Train the given model on the given dataset.
Args:
data_dir: Directory where the data is located.
output_dir: Directory where to put the logs and checkpoints.
model_class: The model class to train.
dataset: The name of the dataset to train on.
input_names: List of strings with the names of the fea... | [
"Train",
"the",
"given",
"model",
"on",
"the",
"given",
"dataset",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L259-L324 | train |
tensorflow/tensor2tensor | tensor2tensor/v2/t2t.py | t2t_train | def t2t_train(model_name, dataset_name,
data_dir=None, output_dir=None, config_file=None, config=None):
"""Main function to train the given model on the given dataset.
Args:
model_name: The name of the model to train.
dataset_name: The name of the dataset to train on.
data_dir: Directory ... | python | def t2t_train(model_name, dataset_name,
data_dir=None, output_dir=None, config_file=None, config=None):
"""Main function to train the given model on the given dataset.
Args:
model_name: The name of the model to train.
dataset_name: The name of the dataset to train on.
data_dir: Directory ... | [
"def",
"t2t_train",
"(",
"model_name",
",",
"dataset_name",
",",
"data_dir",
"=",
"None",
",",
"output_dir",
"=",
"None",
",",
"config_file",
"=",
"None",
",",
"config",
"=",
"None",
")",
":",
"if",
"model_name",
"not",
"in",
"_MODEL_REGISTRY",
":",
"raise... | Main function to train the given model on the given dataset.
Args:
model_name: The name of the model to train.
dataset_name: The name of the dataset to train on.
data_dir: Directory where the data is located.
output_dir: Directory where to put the logs and checkpoints.
config_file: the gin config... | [
"Main",
"function",
"to",
"train",
"the",
"given",
"model",
"on",
"the",
"given",
"dataset",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/v2/t2t.py#L327-L347 | train |
tensorflow/tensor2tensor | tensor2tensor/bin/t2t_decoder.py | decode | def decode(estimator, hparams, decode_hp):
"""Decode from estimator. Interactive, from file, or from dataset."""
if FLAGS.decode_interactive:
if estimator.config.use_tpu:
raise ValueError("TPU can only decode from dataset.")
decoding.decode_interactively(estimator, hparams, decode_hp,
... | python | def decode(estimator, hparams, decode_hp):
"""Decode from estimator. Interactive, from file, or from dataset."""
if FLAGS.decode_interactive:
if estimator.config.use_tpu:
raise ValueError("TPU can only decode from dataset.")
decoding.decode_interactively(estimator, hparams, decode_hp,
... | [
"def",
"decode",
"(",
"estimator",
",",
"hparams",
",",
"decode_hp",
")",
":",
"if",
"FLAGS",
".",
"decode_interactive",
":",
"if",
"estimator",
".",
"config",
".",
"use_tpu",
":",
"raise",
"ValueError",
"(",
"\"TPU can only decode from dataset.\"",
")",
"decodi... | Decode from estimator. Interactive, from file, or from dataset. | [
"Decode",
"from",
"estimator",
".",
"Interactive",
"from",
"file",
"or",
"from",
"dataset",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/bin/t2t_decoder.py#L82-L104 | train |
tensorflow/tensor2tensor | tensor2tensor/bin/t2t_decoder.py | score_file | def score_file(filename):
"""Score each line in a file and return the scores."""
# Prepare model.
hparams = create_hparams()
encoders = registry.problem(FLAGS.problem).feature_encoders(FLAGS.data_dir)
has_inputs = "inputs" in encoders
# Prepare features for feeding into the model.
if has_inputs:
inpu... | python | def score_file(filename):
"""Score each line in a file and return the scores."""
# Prepare model.
hparams = create_hparams()
encoders = registry.problem(FLAGS.problem).feature_encoders(FLAGS.data_dir)
has_inputs = "inputs" in encoders
# Prepare features for feeding into the model.
if has_inputs:
inpu... | [
"def",
"score_file",
"(",
"filename",
")",
":",
"# Prepare model.",
"hparams",
"=",
"create_hparams",
"(",
")",
"encoders",
"=",
"registry",
".",
"problem",
"(",
"FLAGS",
".",
"problem",
")",
".",
"feature_encoders",
"(",
"FLAGS",
".",
"data_dir",
")",
"has_... | Score each line in a file and return the scores. | [
"Score",
"each",
"line",
"in",
"a",
"file",
"and",
"return",
"the",
"scores",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/bin/t2t_decoder.py#L107-L164 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | time_to_channels | def time_to_channels(embedded_video):
"""Put time dimension on channels in an embedded video."""
video_shape = common_layers.shape_list(embedded_video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
"[batch, time, height, width, channels] but ... | python | def time_to_channels(embedded_video):
"""Put time dimension on channels in an embedded video."""
video_shape = common_layers.shape_list(embedded_video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
"[batch, time, height, width, channels] but ... | [
"def",
"time_to_channels",
"(",
"embedded_video",
")",
":",
"video_shape",
"=",
"common_layers",
".",
"shape_list",
"(",
"embedded_video",
")",
"if",
"len",
"(",
"video_shape",
")",
"!=",
"5",
":",
"raise",
"ValueError",
"(",
"\"Assuming videos given as tensors in t... | Put time dimension on channels in an embedded video. | [
"Put",
"time",
"dimension",
"on",
"channels",
"in",
"an",
"embedded",
"video",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L38-L49 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_basic | def autoencoder_basic():
"""Basic autoencoder model."""
hparams = common_hparams.basic_params1()
hparams.optimizer = "adam"
hparams.learning_rate_constant = 0.0002
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup"
hparams.label_smoothing = 0.0
hparams.b... | python | def autoencoder_basic():
"""Basic autoencoder model."""
hparams = common_hparams.basic_params1()
hparams.optimizer = "adam"
hparams.learning_rate_constant = 0.0002
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup"
hparams.label_smoothing = 0.0
hparams.b... | [
"def",
"autoencoder_basic",
"(",
")",
":",
"hparams",
"=",
"common_hparams",
".",
"basic_params1",
"(",
")",
"hparams",
".",
"optimizer",
"=",
"\"adam\"",
"hparams",
".",
"learning_rate_constant",
"=",
"0.0002",
"hparams",
".",
"learning_rate_warmup_steps",
"=",
"... | Basic autoencoder model. | [
"Basic",
"autoencoder",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1027-L1069 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_autoregressive | def autoencoder_autoregressive():
"""Autoregressive autoencoder model."""
hparams = autoencoder_basic()
hparams.add_hparam("autoregressive_forget_base", False)
hparams.add_hparam("autoregressive_mode", "none")
hparams.add_hparam("autoregressive_decode_steps", 0)
hparams.add_hparam("autoregressive_eval_pure_... | python | def autoencoder_autoregressive():
"""Autoregressive autoencoder model."""
hparams = autoencoder_basic()
hparams.add_hparam("autoregressive_forget_base", False)
hparams.add_hparam("autoregressive_mode", "none")
hparams.add_hparam("autoregressive_decode_steps", 0)
hparams.add_hparam("autoregressive_eval_pure_... | [
"def",
"autoencoder_autoregressive",
"(",
")",
":",
"hparams",
"=",
"autoencoder_basic",
"(",
")",
"hparams",
".",
"add_hparam",
"(",
"\"autoregressive_forget_base\"",
",",
"False",
")",
"hparams",
".",
"add_hparam",
"(",
"\"autoregressive_mode\"",
",",
"\"none\"",
... | Autoregressive autoencoder model. | [
"Autoregressive",
"autoencoder",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1073-L1081 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_residual | def autoencoder_residual():
"""Residual autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.optimizer = "Adafactor"
hparams.clip_grad_norm = 1.0
hparams.learning_rate_constant = 0.5
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup * rsqrt... | python | def autoencoder_residual():
"""Residual autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.optimizer = "Adafactor"
hparams.clip_grad_norm = 1.0
hparams.learning_rate_constant = 0.5
hparams.learning_rate_warmup_steps = 500
hparams.learning_rate_schedule = "constant * linear_warmup * rsqrt... | [
"def",
"autoencoder_residual",
"(",
")",
":",
"hparams",
"=",
"autoencoder_autoregressive",
"(",
")",
"hparams",
".",
"optimizer",
"=",
"\"Adafactor\"",
"hparams",
".",
"clip_grad_norm",
"=",
"1.0",
"hparams",
".",
"learning_rate_constant",
"=",
"0.5",
"hparams",
... | Residual autoencoder model. | [
"Residual",
"autoencoder",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1085-L1103 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_residual_text | def autoencoder_residual_text():
"""Residual autoencoder model for text."""
hparams = autoencoder_residual()
hparams.bottleneck_bits = 32
hparams.batch_size = 1024
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
hparams.bottom = {
"inputs": modalities.identity... | python | def autoencoder_residual_text():
"""Residual autoencoder model for text."""
hparams = autoencoder_residual()
hparams.bottleneck_bits = 32
hparams.batch_size = 1024
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
hparams.bottom = {
"inputs": modalities.identity... | [
"def",
"autoencoder_residual_text",
"(",
")",
":",
"hparams",
"=",
"autoencoder_residual",
"(",
")",
"hparams",
".",
"bottleneck_bits",
"=",
"32",
"hparams",
".",
"batch_size",
"=",
"1024",
"hparams",
".",
"hidden_size",
"=",
"64",
"hparams",
".",
"max_hidden_si... | Residual autoencoder model for text. | [
"Residual",
"autoencoder",
"model",
"for",
"text",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1107-L1124 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_basic_discrete | def autoencoder_basic_discrete():
"""Basic autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.num_hidden_layers = 5
hparams.hidden_size = 64
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.1
hparams.add_hparam("discretize_warmup_steps", 16000)
return hparams | python | def autoencoder_basic_discrete():
"""Basic autoencoder model."""
hparams = autoencoder_autoregressive()
hparams.num_hidden_layers = 5
hparams.hidden_size = 64
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.1
hparams.add_hparam("discretize_warmup_steps", 16000)
return hparams | [
"def",
"autoencoder_basic_discrete",
"(",
")",
":",
"hparams",
"=",
"autoencoder_autoregressive",
"(",
")",
"hparams",
".",
"num_hidden_layers",
"=",
"5",
"hparams",
".",
"hidden_size",
"=",
"64",
"hparams",
".",
"bottleneck_bits",
"=",
"1024",
"hparams",
".",
"... | Basic autoencoder model. | [
"Basic",
"autoencoder",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1128-L1136 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_residual_discrete | def autoencoder_residual_discrete():
"""Residual discrete autoencoder model."""
hparams = autoencoder_residual()
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.05
hparams.add_hparam("discretize_warmup_steps", 16000)
hparams.add_hparam("bottleneck_kind", "tanh_discrete")
hparams.add_hparam("is... | python | def autoencoder_residual_discrete():
"""Residual discrete autoencoder model."""
hparams = autoencoder_residual()
hparams.bottleneck_bits = 1024
hparams.bottleneck_noise = 0.05
hparams.add_hparam("discretize_warmup_steps", 16000)
hparams.add_hparam("bottleneck_kind", "tanh_discrete")
hparams.add_hparam("is... | [
"def",
"autoencoder_residual_discrete",
"(",
")",
":",
"hparams",
"=",
"autoencoder_residual",
"(",
")",
"hparams",
".",
"bottleneck_bits",
"=",
"1024",
"hparams",
".",
"bottleneck_noise",
"=",
"0.05",
"hparams",
".",
"add_hparam",
"(",
"\"discretize_warmup_steps\"",
... | Residual discrete autoencoder model. | [
"Residual",
"discrete",
"autoencoder",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1140-L1153 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_residual_discrete_big | def autoencoder_residual_discrete_big():
"""Residual discrete autoencoder model, big version."""
hparams = autoencoder_residual_discrete()
hparams.hidden_size = 128
hparams.max_hidden_size = 4096
hparams.bottleneck_noise = 0.1
hparams.residual_dropout = 0.4
return hparams | python | def autoencoder_residual_discrete_big():
"""Residual discrete autoencoder model, big version."""
hparams = autoencoder_residual_discrete()
hparams.hidden_size = 128
hparams.max_hidden_size = 4096
hparams.bottleneck_noise = 0.1
hparams.residual_dropout = 0.4
return hparams | [
"def",
"autoencoder_residual_discrete_big",
"(",
")",
":",
"hparams",
"=",
"autoencoder_residual_discrete",
"(",
")",
"hparams",
".",
"hidden_size",
"=",
"128",
"hparams",
".",
"max_hidden_size",
"=",
"4096",
"hparams",
".",
"bottleneck_noise",
"=",
"0.1",
"hparams"... | Residual discrete autoencoder model, big version. | [
"Residual",
"discrete",
"autoencoder",
"model",
"big",
"version",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1157-L1164 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_ordered_discrete | def autoencoder_ordered_discrete():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_residual_discrete()
hparams.bottleneck_noise = 0.05 # Use 0.8 for ordered.
hparams.gan_loss_factor = 0.05
hparams.add_hparam("unordered", True)
return hparams | python | def autoencoder_ordered_discrete():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_residual_discrete()
hparams.bottleneck_noise = 0.05 # Use 0.8 for ordered.
hparams.gan_loss_factor = 0.05
hparams.add_hparam("unordered", True)
return hparams | [
"def",
"autoencoder_ordered_discrete",
"(",
")",
":",
"hparams",
"=",
"autoencoder_residual_discrete",
"(",
")",
"hparams",
".",
"bottleneck_noise",
"=",
"0.05",
"# Use 0.8 for ordered.",
"hparams",
".",
"gan_loss_factor",
"=",
"0.05",
"hparams",
".",
"add_hparam",
"(... | Ordered discrete autoencoder model. | [
"Ordered",
"discrete",
"autoencoder",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1168-L1174 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_ordered_discrete_image64 | def autoencoder_ordered_discrete_image64():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_ordered_discrete()
hparams.batch_size = 32
hparams.num_hidden_layers = 6
hparams.bottleneck_warmup_steps *= 2
hparams.gan_codes_warmup_steps *= 2
return hparams | python | def autoencoder_ordered_discrete_image64():
"""Ordered discrete autoencoder model."""
hparams = autoencoder_ordered_discrete()
hparams.batch_size = 32
hparams.num_hidden_layers = 6
hparams.bottleneck_warmup_steps *= 2
hparams.gan_codes_warmup_steps *= 2
return hparams | [
"def",
"autoencoder_ordered_discrete_image64",
"(",
")",
":",
"hparams",
"=",
"autoencoder_ordered_discrete",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"32",
"hparams",
".",
"num_hidden_layers",
"=",
"6",
"hparams",
".",
"bottleneck_warmup_steps",
"*=",
"2",
"hpa... | Ordered discrete autoencoder model. | [
"Ordered",
"discrete",
"autoencoder",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1178-L1186 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_ordered_text | def autoencoder_ordered_text():
"""Ordered discrete autoencoder model for text."""
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_bits = 1024
hparams.bottleneck_shared_bits = 1024-64
hparams.bottleneck_shared_bits_start_warmup = 75000
hparams.bottleneck_shared_bits_stop_warmup = 275000
hparam... | python | def autoencoder_ordered_text():
"""Ordered discrete autoencoder model for text."""
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_bits = 1024
hparams.bottleneck_shared_bits = 1024-64
hparams.bottleneck_shared_bits_start_warmup = 75000
hparams.bottleneck_shared_bits_stop_warmup = 275000
hparam... | [
"def",
"autoencoder_ordered_text",
"(",
")",
":",
"hparams",
"=",
"autoencoder_ordered_discrete",
"(",
")",
"hparams",
".",
"bottleneck_bits",
"=",
"1024",
"hparams",
".",
"bottleneck_shared_bits",
"=",
"1024",
"-",
"64",
"hparams",
".",
"bottleneck_shared_bits_start_... | Ordered discrete autoencoder model for text. | [
"Ordered",
"discrete",
"autoencoder",
"model",
"for",
"text",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1214-L1234 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_ordered_text_small | def autoencoder_ordered_text_small():
"""Ordered discrete autoencoder model for text, small version."""
hparams = autoencoder_ordered_text()
hparams.bottleneck_bits = 32
hparams.num_hidden_layers = 3
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
hparams.autoregres... | python | def autoencoder_ordered_text_small():
"""Ordered discrete autoencoder model for text, small version."""
hparams = autoencoder_ordered_text()
hparams.bottleneck_bits = 32
hparams.num_hidden_layers = 3
hparams.hidden_size = 64
hparams.max_hidden_size = 512
hparams.bottleneck_noise = 0.0
hparams.autoregres... | [
"def",
"autoencoder_ordered_text_small",
"(",
")",
":",
"hparams",
"=",
"autoencoder_ordered_text",
"(",
")",
"hparams",
".",
"bottleneck_bits",
"=",
"32",
"hparams",
".",
"num_hidden_layers",
"=",
"3",
"hparams",
".",
"hidden_size",
"=",
"64",
"hparams",
".",
"... | Ordered discrete autoencoder model for text, small version. | [
"Ordered",
"discrete",
"autoencoder",
"model",
"for",
"text",
"small",
"version",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1238-L1248 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_discrete_pong | def autoencoder_discrete_pong():
"""Discrete autoencoder model for compressing pong frames."""
hparams = autoencoder_ordered_discrete()
hparams.num_hidden_layers = 3
hparams.bottleneck_bits = 24
hparams.batch_size = 2
hparams.gan_loss_factor = 0.01
hparams.bottleneck_l2_factor = 0.001
hparams.add_hparam... | python | def autoencoder_discrete_pong():
"""Discrete autoencoder model for compressing pong frames."""
hparams = autoencoder_ordered_discrete()
hparams.num_hidden_layers = 3
hparams.bottleneck_bits = 24
hparams.batch_size = 2
hparams.gan_loss_factor = 0.01
hparams.bottleneck_l2_factor = 0.001
hparams.add_hparam... | [
"def",
"autoencoder_discrete_pong",
"(",
")",
":",
"hparams",
"=",
"autoencoder_ordered_discrete",
"(",
")",
"hparams",
".",
"num_hidden_layers",
"=",
"3",
"hparams",
".",
"bottleneck_bits",
"=",
"24",
"hparams",
".",
"batch_size",
"=",
"2",
"hparams",
".",
"gan... | Discrete autoencoder model for compressing pong frames. | [
"Discrete",
"autoencoder",
"model",
"for",
"compressing",
"pong",
"frames",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1261-L1270 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_discrete_tiny | def autoencoder_discrete_tiny():
"""Discrete autoencoder model for compressing pong frames for testing."""
hparams = autoencoder_ordered_discrete()
hparams.num_hidden_layers = 2
hparams.bottleneck_bits = 24
hparams.batch_size = 2
hparams.gan_loss_factor = 0.
hparams.bottleneck_l2_factor = 0.001
hparams.... | python | def autoencoder_discrete_tiny():
"""Discrete autoencoder model for compressing pong frames for testing."""
hparams = autoencoder_ordered_discrete()
hparams.num_hidden_layers = 2
hparams.bottleneck_bits = 24
hparams.batch_size = 2
hparams.gan_loss_factor = 0.
hparams.bottleneck_l2_factor = 0.001
hparams.... | [
"def",
"autoencoder_discrete_tiny",
"(",
")",
":",
"hparams",
"=",
"autoencoder_ordered_discrete",
"(",
")",
"hparams",
".",
"num_hidden_layers",
"=",
"2",
"hparams",
".",
"bottleneck_bits",
"=",
"24",
"hparams",
".",
"batch_size",
"=",
"2",
"hparams",
".",
"gan... | Discrete autoencoder model for compressing pong frames for testing. | [
"Discrete",
"autoencoder",
"model",
"for",
"compressing",
"pong",
"frames",
"for",
"testing",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1274-L1286 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_discrete_cifar | def autoencoder_discrete_cifar():
"""Discrete autoencoder model for compressing cifar."""
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_noise = 0.0
hparams.bottleneck_bits = 90
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.num_residual_layers = 4
hparams.batch_size = 32
... | python | def autoencoder_discrete_cifar():
"""Discrete autoencoder model for compressing cifar."""
hparams = autoencoder_ordered_discrete()
hparams.bottleneck_noise = 0.0
hparams.bottleneck_bits = 90
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.num_residual_layers = 4
hparams.batch_size = 32
... | [
"def",
"autoencoder_discrete_cifar",
"(",
")",
":",
"hparams",
"=",
"autoencoder_ordered_discrete",
"(",
")",
"hparams",
".",
"bottleneck_noise",
"=",
"0.0",
"hparams",
".",
"bottleneck_bits",
"=",
"90",
"hparams",
".",
"num_hidden_layers",
"=",
"2",
"hparams",
".... | Discrete autoencoder model for compressing cifar. | [
"Discrete",
"autoencoder",
"model",
"for",
"compressing",
"cifar",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1290-L1300 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/autoencoders.py | autoencoder_range | def autoencoder_range(rhp):
"""Tuning grid of the main autoencoder params."""
rhp.set_float("dropout", 0.01, 0.3)
rhp.set_float("gan_loss_factor", 0.01, 0.1)
rhp.set_float("bottleneck_l2_factor", 0.001, 0.1, scale=rhp.LOG_SCALE)
rhp.set_discrete("bottleneck_warmup_steps", [200, 2000])
rhp.set_float("gumbel_... | python | def autoencoder_range(rhp):
"""Tuning grid of the main autoencoder params."""
rhp.set_float("dropout", 0.01, 0.3)
rhp.set_float("gan_loss_factor", 0.01, 0.1)
rhp.set_float("bottleneck_l2_factor", 0.001, 0.1, scale=rhp.LOG_SCALE)
rhp.set_discrete("bottleneck_warmup_steps", [200, 2000])
rhp.set_float("gumbel_... | [
"def",
"autoencoder_range",
"(",
"rhp",
")",
":",
"rhp",
".",
"set_float",
"(",
"\"dropout\"",
",",
"0.01",
",",
"0.3",
")",
"rhp",
".",
"set_float",
"(",
"\"gan_loss_factor\"",
",",
"0.01",
",",
"0.1",
")",
"rhp",
".",
"set_float",
"(",
"\"bottleneck_l2_f... | Tuning grid of the main autoencoder params. | [
"Tuning",
"grid",
"of",
"the",
"main",
"autoencoder",
"params",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/autoencoders.py#L1304-L1311 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_attention.py | image_encoder | def image_encoder(image_feat,
hparams,
name="image_encoder",
save_weights_to=None,
make_image_summary=True):
"""A stack of self attention layers."""
x = image_feat
with tf.variable_scope(name):
for layer in range(hparams.num_encoder_laye... | python | def image_encoder(image_feat,
hparams,
name="image_encoder",
save_weights_to=None,
make_image_summary=True):
"""A stack of self attention layers."""
x = image_feat
with tf.variable_scope(name):
for layer in range(hparams.num_encoder_laye... | [
"def",
"image_encoder",
"(",
"image_feat",
",",
"hparams",
",",
"name",
"=",
"\"image_encoder\"",
",",
"save_weights_to",
"=",
"None",
",",
"make_image_summary",
"=",
"True",
")",
":",
"x",
"=",
"image_feat",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
... | A stack of self attention layers. | [
"A",
"stack",
"of",
"self",
"attention",
"layers",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/vqa_attention.py#L182-L232 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_attention.py | question_encoder | def question_encoder(question, hparams, name="encoder"):
"""Question encoder, run LSTM encoder and get the last output as encoding."""
with tf.variable_scope(name, "encoder", values=[question]):
question = common_layers.flatten4d3d(question)
padding = common_attention.embedding_to_padding(question)
leng... | python | def question_encoder(question, hparams, name="encoder"):
"""Question encoder, run LSTM encoder and get the last output as encoding."""
with tf.variable_scope(name, "encoder", values=[question]):
question = common_layers.flatten4d3d(question)
padding = common_attention.embedding_to_padding(question)
leng... | [
"def",
"question_encoder",
"(",
"question",
",",
"hparams",
",",
"name",
"=",
"\"encoder\"",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
",",
"\"encoder\"",
",",
"values",
"=",
"[",
"question",
"]",
")",
":",
"question",
"=",
"common_layers... | Question encoder, run LSTM encoder and get the last output as encoding. | [
"Question",
"encoder",
"run",
"LSTM",
"encoder",
"and",
"get",
"the",
"last",
"output",
"as",
"encoding",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/vqa_attention.py#L245-L295 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_attention.py | attn | def attn(image_feat, query, hparams, name="attn"):
"""Attention on image feature with question as query."""
with tf.variable_scope(name, "attn", values=[image_feat, query]):
attn_dim = hparams.attn_dim
num_glimps = hparams.num_glimps
num_channels = common_layers.shape_list(image_feat)[-1]
if len(com... | python | def attn(image_feat, query, hparams, name="attn"):
"""Attention on image feature with question as query."""
with tf.variable_scope(name, "attn", values=[image_feat, query]):
attn_dim = hparams.attn_dim
num_glimps = hparams.num_glimps
num_channels = common_layers.shape_list(image_feat)[-1]
if len(com... | [
"def",
"attn",
"(",
"image_feat",
",",
"query",
",",
"hparams",
",",
"name",
"=",
"\"attn\"",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
",",
"\"attn\"",
",",
"values",
"=",
"[",
"image_feat",
",",
"query",
"]",
")",
":",
"attn_dim",
... | Attention on image feature with question as query. | [
"Attention",
"on",
"image",
"feature",
"with",
"question",
"as",
"query",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/vqa_attention.py#L298-L318 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_attention.py | mlp | def mlp(feature, hparams, name="mlp"):
"""Multi layer perceptron with dropout and relu activation."""
with tf.variable_scope(name, "mlp", values=[feature]):
num_mlp_layers = hparams.num_mlp_layers
mlp_dim = hparams.mlp_dim
for _ in range(num_mlp_layers):
feature = common_layers.dense(feature, mlp_... | python | def mlp(feature, hparams, name="mlp"):
"""Multi layer perceptron with dropout and relu activation."""
with tf.variable_scope(name, "mlp", values=[feature]):
num_mlp_layers = hparams.num_mlp_layers
mlp_dim = hparams.mlp_dim
for _ in range(num_mlp_layers):
feature = common_layers.dense(feature, mlp_... | [
"def",
"mlp",
"(",
"feature",
",",
"hparams",
",",
"name",
"=",
"\"mlp\"",
")",
":",
"with",
"tf",
".",
"variable_scope",
"(",
"name",
",",
"\"mlp\"",
",",
"values",
"=",
"[",
"feature",
"]",
")",
":",
"num_mlp_layers",
"=",
"hparams",
".",
"num_mlp_la... | Multi layer perceptron with dropout and relu activation. | [
"Multi",
"layer",
"perceptron",
"with",
"dropout",
"and",
"relu",
"activation",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/vqa_attention.py#L321-L329 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_attention.py | vqa_attention_base | def vqa_attention_base():
"""VQA attention baseline hparams."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.use_fixed_batch_size = True,
hparams.optimizer = "adam"
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.999
hparams.optimizer_adam_epsilon = 1e-8
... | python | def vqa_attention_base():
"""VQA attention baseline hparams."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.use_fixed_batch_size = True,
hparams.optimizer = "adam"
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.999
hparams.optimizer_adam_epsilon = 1e-8
... | [
"def",
"vqa_attention_base",
"(",
")",
":",
"hparams",
"=",
"common_hparams",
".",
"basic_params1",
"(",
")",
"hparams",
".",
"batch_size",
"=",
"128",
"hparams",
".",
"use_fixed_batch_size",
"=",
"True",
",",
"hparams",
".",
"optimizer",
"=",
"\"adam\"",
"hpa... | VQA attention baseline hparams. | [
"VQA",
"attention",
"baseline",
"hparams",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/vqa_attention.py#L333-L400 | train |
tensorflow/tensor2tensor | tensor2tensor/models/research/vqa_attention.py | vqa_attention_base_range | def vqa_attention_base_range(rhp):
"""Small range of hyperparameters."""
# After starting from base, set intervals for some parameters.
rhp.set_float("learning_rate", 0.1, 1.0, scale=rhp.LOG_SCALE)
rhp.set_float("clip_grad_norm", 0.1, 10, scale=rhp.LOG_SCALE)
rhp.set_discrete("batch_size", [128, 256, 512, 102... | python | def vqa_attention_base_range(rhp):
"""Small range of hyperparameters."""
# After starting from base, set intervals for some parameters.
rhp.set_float("learning_rate", 0.1, 1.0, scale=rhp.LOG_SCALE)
rhp.set_float("clip_grad_norm", 0.1, 10, scale=rhp.LOG_SCALE)
rhp.set_discrete("batch_size", [128, 256, 512, 102... | [
"def",
"vqa_attention_base_range",
"(",
"rhp",
")",
":",
"# After starting from base, set intervals for some parameters.",
"rhp",
".",
"set_float",
"(",
"\"learning_rate\"",
",",
"0.1",
",",
"1.0",
",",
"scale",
"=",
"rhp",
".",
"LOG_SCALE",
")",
"rhp",
".",
"set_fl... | Small range of hyperparameters. | [
"Small",
"range",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/research/vqa_attention.py#L580-L587 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/history.py | History.append | def append(self, mode, metric, step, value):
"""Append (step, value) pair to history for the given mode and metric."""
if mode not in self._values:
self._values[mode] = collections.defaultdict(list)
self._values[mode][metric].append((step, value)) | python | def append(self, mode, metric, step, value):
"""Append (step, value) pair to history for the given mode and metric."""
if mode not in self._values:
self._values[mode] = collections.defaultdict(list)
self._values[mode][metric].append((step, value)) | [
"def",
"append",
"(",
"self",
",",
"mode",
",",
"metric",
",",
"step",
",",
"value",
")",
":",
"if",
"mode",
"not",
"in",
"self",
".",
"_values",
":",
"self",
".",
"_values",
"[",
"mode",
"]",
"=",
"collections",
".",
"defaultdict",
"(",
"list",
")... | Append (step, value) pair to history for the given mode and metric. | [
"Append",
"(",
"step",
"value",
")",
"pair",
"to",
"history",
"for",
"the",
"given",
"mode",
"and",
"metric",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/history.py#L52-L56 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/history.py | History.get | def get(self, mode, metric):
"""Get the history for the given metric and mode."""
if mode not in self._values:
logging.info("Metric %s not found for mode %s", metric, mode)
return []
return list(self._values[mode][metric]) | python | def get(self, mode, metric):
"""Get the history for the given metric and mode."""
if mode not in self._values:
logging.info("Metric %s not found for mode %s", metric, mode)
return []
return list(self._values[mode][metric]) | [
"def",
"get",
"(",
"self",
",",
"mode",
",",
"metric",
")",
":",
"if",
"mode",
"not",
"in",
"self",
".",
"_values",
":",
"logging",
".",
"info",
"(",
"\"Metric %s not found for mode %s\"",
",",
"metric",
",",
"mode",
")",
"return",
"[",
"]",
"return",
... | Get the history for the given metric and mode. | [
"Get",
"the",
"history",
"for",
"the",
"given",
"metric",
"and",
"mode",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/history.py#L58-L63 | train |
tensorflow/tensor2tensor | tensor2tensor/trax/history.py | History.metrics_for_mode | def metrics_for_mode(self, mode):
"""Metrics available for a given mode."""
if mode not in self._values:
logging.info("Mode %s not found", mode)
return []
return sorted(list(self._values[mode].keys())) | python | def metrics_for_mode(self, mode):
"""Metrics available for a given mode."""
if mode not in self._values:
logging.info("Mode %s not found", mode)
return []
return sorted(list(self._values[mode].keys())) | [
"def",
"metrics_for_mode",
"(",
"self",
",",
"mode",
")",
":",
"if",
"mode",
"not",
"in",
"self",
".",
"_values",
":",
"logging",
".",
"info",
"(",
"\"Mode %s not found\"",
",",
"mode",
")",
"return",
"[",
"]",
"return",
"sorted",
"(",
"list",
"(",
"se... | Metrics available for a given mode. | [
"Metrics",
"available",
"for",
"a",
"given",
"mode",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/trax/history.py#L70-L75 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | batch_norm_relu | def batch_norm_relu(inputs,
is_training,
relu=True,
init_zero=False,
data_format="channels_first"):
"""Performs a batch normalization followed by a ReLU.
Args:
inputs: `Tensor` of shape `[batch, channels, ...]`.
is_training: `b... | python | def batch_norm_relu(inputs,
is_training,
relu=True,
init_zero=False,
data_format="channels_first"):
"""Performs a batch normalization followed by a ReLU.
Args:
inputs: `Tensor` of shape `[batch, channels, ...]`.
is_training: `b... | [
"def",
"batch_norm_relu",
"(",
"inputs",
",",
"is_training",
",",
"relu",
"=",
"True",
",",
"init_zero",
"=",
"False",
",",
"data_format",
"=",
"\"channels_first\"",
")",
":",
"if",
"init_zero",
":",
"gamma_initializer",
"=",
"tf",
".",
"zeros_initializer",
"(... | Performs a batch normalization followed by a ReLU.
Args:
inputs: `Tensor` of shape `[batch, channels, ...]`.
is_training: `bool` for whether the model is training.
relu: `bool` if False, omits the ReLU operation.
init_zero: `bool` if True, initializes scale parameter of batch
normalization wi... | [
"Performs",
"a",
"batch",
"normalization",
"followed",
"by",
"a",
"ReLU",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L41-L81 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | conv2d_fixed_padding | def conv2d_fixed_padding(inputs,
filters,
kernel_size,
strides,
data_format="channels_first",
use_td=False,
targeting_rate=None,
keep_prob=None,
... | python | def conv2d_fixed_padding(inputs,
filters,
kernel_size,
strides,
data_format="channels_first",
use_td=False,
targeting_rate=None,
keep_prob=None,
... | [
"def",
"conv2d_fixed_padding",
"(",
"inputs",
",",
"filters",
",",
"kernel_size",
",",
"strides",
",",
"data_format",
"=",
"\"channels_first\"",
",",
"use_td",
"=",
"False",
",",
"targeting_rate",
"=",
"None",
",",
"keep_prob",
"=",
"None",
",",
"is_training",
... | Strided 2-D convolution with explicit padding.
The padding is consistent and is based only on `kernel_size`, not on the
dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
Args:
inputs: `Tensor` of size `[batch, channels, height_in, width_in]`.
filters: `int` number of filters in the ... | [
"Strided",
"2",
"-",
"D",
"convolution",
"with",
"explicit",
"padding",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L112-L188 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | residual_block | def residual_block(inputs,
filters,
is_training,
projection_shortcut,
strides,
final_block,
data_format="channels_first",
use_td=False,
targeting_rate=None,
... | python | def residual_block(inputs,
filters,
is_training,
projection_shortcut,
strides,
final_block,
data_format="channels_first",
use_td=False,
targeting_rate=None,
... | [
"def",
"residual_block",
"(",
"inputs",
",",
"filters",
",",
"is_training",
",",
"projection_shortcut",
",",
"strides",
",",
"final_block",
",",
"data_format",
"=",
"\"channels_first\"",
",",
"use_td",
"=",
"False",
",",
"targeting_rate",
"=",
"None",
",",
"keep... | Standard building block for residual networks with BN before convolutions.
Args:
inputs: `Tensor` of size `[batch, channels, height, width]`.
filters: `int` number of filters for the first two convolutions. Note that
the third and final convolution will use 4 times as many filters.
is_training: `... | [
"Standard",
"building",
"block",
"for",
"residual",
"networks",
"with",
"BN",
"before",
"convolutions",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L191-L257 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | bottleneck_block | def bottleneck_block(inputs,
filters,
is_training,
projection_shortcut,
strides,
final_block,
data_format="channels_first",
use_td=False,
targeting_rate... | python | def bottleneck_block(inputs,
filters,
is_training,
projection_shortcut,
strides,
final_block,
data_format="channels_first",
use_td=False,
targeting_rate... | [
"def",
"bottleneck_block",
"(",
"inputs",
",",
"filters",
",",
"is_training",
",",
"projection_shortcut",
",",
"strides",
",",
"final_block",
",",
"data_format",
"=",
"\"channels_first\"",
",",
"use_td",
"=",
"False",
",",
"targeting_rate",
"=",
"None",
",",
"ke... | Bottleneck block variant for residual networks with BN after convolutions.
Args:
inputs: `Tensor` of size `[batch, channels, height, width]`.
filters: `int` number of filters for the first two convolutions. Note that
the third and final convolution will use 4 times as many filters.
is_training: `... | [
"Bottleneck",
"block",
"variant",
"for",
"residual",
"networks",
"with",
"BN",
"after",
"convolutions",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L260-L345 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | block_layer | def block_layer(inputs,
filters,
block_fn,
blocks,
strides,
is_training,
name,
data_format="channels_first",
use_td=False,
targeting_rate=None,
keep_prob=None):... | python | def block_layer(inputs,
filters,
block_fn,
blocks,
strides,
is_training,
name,
data_format="channels_first",
use_td=False,
targeting_rate=None,
keep_prob=None):... | [
"def",
"block_layer",
"(",
"inputs",
",",
"filters",
",",
"block_fn",
",",
"blocks",
",",
"strides",
",",
"is_training",
",",
"name",
",",
"data_format",
"=",
"\"channels_first\"",
",",
"use_td",
"=",
"False",
",",
"targeting_rate",
"=",
"None",
",",
"keep_p... | Creates one layer of blocks for the ResNet model.
Args:
inputs: `Tensor` of size `[batch, channels, height, width]`.
filters: `int` number of filters for the first convolution of the layer.
block_fn: `function` for the block to use within the model
blocks: `int` number of blocks contained in the laye... | [
"Creates",
"one",
"layer",
"of",
"blocks",
"for",
"the",
"ResNet",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L348-L424 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | resnet_v2 | def resnet_v2(inputs,
block_fn,
layer_blocks,
filters,
data_format="channels_first",
is_training=False,
is_cifar=False,
use_td=False,
targeting_rate=None,
keep_prob=None):
"""Resnet model.
... | python | def resnet_v2(inputs,
block_fn,
layer_blocks,
filters,
data_format="channels_first",
is_training=False,
is_cifar=False,
use_td=False,
targeting_rate=None,
keep_prob=None):
"""Resnet model.
... | [
"def",
"resnet_v2",
"(",
"inputs",
",",
"block_fn",
",",
"layer_blocks",
",",
"filters",
",",
"data_format",
"=",
"\"channels_first\"",
",",
"is_training",
"=",
"False",
",",
"is_cifar",
"=",
"False",
",",
"use_td",
"=",
"False",
",",
"targeting_rate",
"=",
... | Resnet model.
Args:
inputs: `Tensor` images.
block_fn: `function` for the block to use within the model. Either
`residual_block` or `bottleneck_block`.
layer_blocks: list of 3 or 4 `int`s denoting the number of blocks to include
in each of the 3 or 4 block groups. Each group consists of blo... | [
"Resnet",
"model",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L427-L511 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | resnet_imagenet_34_td_weight_05_05 | def resnet_imagenet_34_td_weight_05_05():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "weight"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
return hp | python | def resnet_imagenet_34_td_weight_05_05():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "weight"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
return hp | [
"def",
"resnet_imagenet_34_td_weight_05_05",
"(",
")",
":",
"hp",
"=",
"resnet_imagenet_34",
"(",
")",
"hp",
".",
"use_td",
"=",
"\"weight\"",
"hp",
".",
"targeting_rate",
"=",
"0.5",
"hp",
".",
"keep_prob",
"=",
"0.5",
"return",
"hp"
] | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L679-L686 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | resnet_imagenet_34_td_unit_05_05 | def resnet_imagenet_34_td_unit_05_05():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "unit"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
return hp | python | def resnet_imagenet_34_td_unit_05_05():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "unit"
hp.targeting_rate = 0.5
hp.keep_prob = 0.5
return hp | [
"def",
"resnet_imagenet_34_td_unit_05_05",
"(",
")",
":",
"hp",
"=",
"resnet_imagenet_34",
"(",
")",
"hp",
".",
"use_td",
"=",
"\"unit\"",
"hp",
".",
"targeting_rate",
"=",
"0.5",
"hp",
".",
"keep_prob",
"=",
"0.5",
"return",
"hp"
] | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L690-L697 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | resnet_imagenet_34_td_unit_no_drop | def resnet_imagenet_34_td_unit_no_drop():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "unit"
hp.targeting_rate = 0.0
hp.keep_prob = 1.0
return hp | python | def resnet_imagenet_34_td_unit_no_drop():
"""Set of hyperparameters."""
hp = resnet_imagenet_34()
hp.use_td = "unit"
hp.targeting_rate = 0.0
hp.keep_prob = 1.0
return hp | [
"def",
"resnet_imagenet_34_td_unit_no_drop",
"(",
")",
":",
"hp",
"=",
"resnet_imagenet_34",
"(",
")",
"hp",
".",
"use_td",
"=",
"\"unit\"",
"hp",
".",
"targeting_rate",
"=",
"0.0",
"hp",
".",
"keep_prob",
"=",
"1.0",
"return",
"hp"
] | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L701-L708 | train |
tensorflow/tensor2tensor | tensor2tensor/models/resnet.py | resnet_cifar_15 | def resnet_cifar_15():
"""Set of hyperparameters."""
hp = resnet_base()
hp.block_fn = "residual"
hp.is_cifar = True
hp.layer_sizes = [2, 2, 2]
hp.filter_sizes = [16, 32, 64, 128]
return hp | python | def resnet_cifar_15():
"""Set of hyperparameters."""
hp = resnet_base()
hp.block_fn = "residual"
hp.is_cifar = True
hp.layer_sizes = [2, 2, 2]
hp.filter_sizes = [16, 32, 64, 128]
return hp | [
"def",
"resnet_cifar_15",
"(",
")",
":",
"hp",
"=",
"resnet_base",
"(",
")",
"hp",
".",
"block_fn",
"=",
"\"residual\"",
"hp",
".",
"is_cifar",
"=",
"True",
"hp",
".",
"layer_sizes",
"=",
"[",
"2",
",",
"2",
",",
"2",
"]",
"hp",
".",
"filter_sizes",
... | Set of hyperparameters. | [
"Set",
"of",
"hyperparameters",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/models/resnet.py#L719-L727 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/rouge.py | _len_lcs | def _len_lcs(x, y):
"""Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y
"""
table = _lcs(x, y)
n, m =... | python | def _len_lcs(x, y):
"""Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y
"""
table = _lcs(x, y)
n, m =... | [
"def",
"_len_lcs",
"(",
"x",
",",
"y",
")",
":",
"table",
"=",
"_lcs",
"(",
"x",
",",
"y",
")",
"n",
",",
"m",
"=",
"len",
"(",
"x",
")",
",",
"len",
"(",
"y",
")",
"return",
"table",
"[",
"n",
",",
"m",
"]"
] | Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y | [
"Returns",
"the",
"length",
"of",
"the",
"Longest",
"Common",
"Subsequence",
"between",
"two",
"seqs",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/rouge.py#L33-L47 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/rouge.py | _lcs | def _lcs(x, y):
"""Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
in O(nm) time where n = len(x) and m = len(y).
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of ... | python | def _lcs(x, y):
"""Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
in O(nm) time where n = len(x) and m = len(y).
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of ... | [
"def",
"_lcs",
"(",
"x",
",",
"y",
")",
":",
"n",
",",
"m",
"=",
"len",
"(",
"x",
")",
",",
"len",
"(",
"y",
")",
"table",
"=",
"{",
"}",
"for",
"i",
"in",
"range",
"(",
"n",
"+",
"1",
")",
":",
"for",
"j",
"in",
"range",
"(",
"m",
"+... | Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
in O(nm) time where n = len(x) and m = len(y).
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of words
Returns:
... | [
"Computes",
"the",
"length",
"of",
"the",
"LCS",
"between",
"two",
"seqs",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/rouge.py#L50-L74 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/rouge.py | rouge_l_sentence_level | def rouge_l_sentence_level(eval_sentences, ref_sentences):
"""Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Calculated according to:
R_lcs = LCS(X,Y)/m
P_lcs = LCS(X,Y)... | python | def rouge_l_sentence_level(eval_sentences, ref_sentences):
"""Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Calculated according to:
R_lcs = LCS(X,Y)/m
P_lcs = LCS(X,Y)... | [
"def",
"rouge_l_sentence_level",
"(",
"eval_sentences",
",",
"ref_sentences",
")",
":",
"f1_scores",
"=",
"[",
"]",
"for",
"eval_sentence",
",",
"ref_sentence",
"in",
"zip",
"(",
"eval_sentences",
",",
"ref_sentences",
")",
":",
"m",
"=",
"len",
"(",
"ref_sent... | Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Calculated according to:
R_lcs = LCS(X,Y)/m
P_lcs = LCS(X,Y)/n
F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_... | [
"Computes",
"ROUGE",
"-",
"L",
"(",
"sentence",
"level",
")",
"of",
"two",
"collections",
"of",
"sentences",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/rouge.py#L100-L131 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/rouge.py | rouge_l_fscore | def rouge_l_fscore(predictions, labels, **unused_kwargs):
"""ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gol... | python | def rouge_l_fscore(predictions, labels, **unused_kwargs):
"""ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gol... | [
"def",
"rouge_l_fscore",
"(",
"predictions",
",",
"labels",
",",
"*",
"*",
"unused_kwargs",
")",
":",
"outputs",
"=",
"tf",
".",
"to_int32",
"(",
"tf",
".",
"argmax",
"(",
"predictions",
",",
"axis",
"=",
"-",
"1",
")",
")",
"# Convert the outputs and labe... | ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge_l_fscore: approx rouge-l f1 sco... | [
"ROUGE",
"scores",
"computation",
"between",
"labels",
"and",
"predictions",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/rouge.py#L134-L153 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/rouge.py | _get_ngrams | def _get_ngrams(n, text):
"""Calculates n-grams.
Args:
n: which n-grams to calculate
text: An array of tokens
Returns:
A set of n-grams
"""
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(t... | python | def _get_ngrams(n, text):
"""Calculates n-grams.
Args:
n: which n-grams to calculate
text: An array of tokens
Returns:
A set of n-grams
"""
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(t... | [
"def",
"_get_ngrams",
"(",
"n",
",",
"text",
")",
":",
"ngram_set",
"=",
"set",
"(",
")",
"text_length",
"=",
"len",
"(",
"text",
")",
"max_index_ngram_start",
"=",
"text_length",
"-",
"n",
"for",
"i",
"in",
"range",
"(",
"max_index_ngram_start",
"+",
"1... | Calculates n-grams.
Args:
n: which n-grams to calculate
text: An array of tokens
Returns:
A set of n-grams | [
"Calculates",
"n",
"-",
"grams",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/rouge.py#L156-L171 | train |
tensorflow/tensor2tensor | tensor2tensor/utils/rouge.py | rouge_2_fscore | def rouge_2_fscore(predictions, labels, **unused_kwargs):
"""ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor,... | python | def rouge_2_fscore(predictions, labels, **unused_kwargs):
"""ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor,... | [
"def",
"rouge_2_fscore",
"(",
"predictions",
",",
"labels",
",",
"*",
"*",
"unused_kwargs",
")",
":",
"outputs",
"=",
"tf",
".",
"to_int32",
"(",
"tf",
".",
"argmax",
"(",
"predictions",
",",
"axis",
"=",
"-",
"1",
")",
")",
"# Convert the outputs and labe... | ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge2_fscore: approx rouge-2 f1 ... | [
"ROUGE",
"-",
"2",
"F1",
"score",
"computation",
"between",
"labels",
"and",
"predictions",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/rouge.py#L217-L236 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem.py | normalize_example_nlp | def normalize_example_nlp(task, example, is_infer, vocab_type, vocab_offset,
max_input_length, max_target_length,
fixed_train_length):
"""Normalize the examples from different tasks so they can be merged.
This function is specific to NLP tasks and normalizes them... | python | def normalize_example_nlp(task, example, is_infer, vocab_type, vocab_offset,
max_input_length, max_target_length,
fixed_train_length):
"""Normalize the examples from different tasks so they can be merged.
This function is specific to NLP tasks and normalizes them... | [
"def",
"normalize_example_nlp",
"(",
"task",
",",
"example",
",",
"is_infer",
",",
"vocab_type",
",",
"vocab_offset",
",",
"max_input_length",
",",
"max_target_length",
",",
"fixed_train_length",
")",
":",
"if",
"task",
".",
"has_inputs",
":",
"example",
"[",
"\... | Normalize the examples from different tasks so they can be merged.
This function is specific to NLP tasks and normalizes them so that in the
end the example only has "targets" and "task_id". For tasks that originally
have inputs, this is done by appending task_id to the inputs and prepending
targets, so normal... | [
"Normalize",
"the",
"examples",
"from",
"different",
"tasks",
"so",
"they",
"can",
"be",
"merged",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem.py#L38-L108 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem.py | flatten_zip_dataset | def flatten_zip_dataset(*args):
"""A list of examples to a dataset containing mixed examples.
Given a list of `n` dataset examples, flatten them by converting
each element into a dataset and concatenating them to convert into a
single dataset.
Args:
*args: A list containing one example each from `n` dif... | python | def flatten_zip_dataset(*args):
"""A list of examples to a dataset containing mixed examples.
Given a list of `n` dataset examples, flatten them by converting
each element into a dataset and concatenating them to convert into a
single dataset.
Args:
*args: A list containing one example each from `n` dif... | [
"def",
"flatten_zip_dataset",
"(",
"*",
"args",
")",
":",
"flattened",
"=",
"tf",
".",
"data",
".",
"Dataset",
".",
"from_tensors",
"(",
"args",
"[",
"0",
"]",
")",
"for",
"ex",
"in",
"args",
"[",
"1",
":",
"]",
":",
"flattened",
"=",
"flattened",
... | A list of examples to a dataset containing mixed examples.
Given a list of `n` dataset examples, flatten them by converting
each element into a dataset and concatenating them to convert into a
single dataset.
Args:
*args: A list containing one example each from `n` different datasets.
Returns:
flat... | [
"A",
"list",
"of",
"examples",
"to",
"a",
"dataset",
"containing",
"mixed",
"examples",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem.py#L111-L128 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem.py | aggregate_task_losses | def aggregate_task_losses(hparams,
problem_hparams,
logits,
feature_name,
feature):
"""Multiproblem loss function."""
# If no reweighting, we want the default loss to mimic the LM loss.
if not hparams.multipro... | python | def aggregate_task_losses(hparams,
problem_hparams,
logits,
feature_name,
feature):
"""Multiproblem loss function."""
# If no reweighting, we want the default loss to mimic the LM loss.
if not hparams.multipro... | [
"def",
"aggregate_task_losses",
"(",
"hparams",
",",
"problem_hparams",
",",
"logits",
",",
"feature_name",
",",
"feature",
")",
":",
"# If no reweighting, we want the default loss to mimic the LM loss.",
"if",
"not",
"hparams",
".",
"multiproblem_reweight_label_loss",
":",
... | Multiproblem loss function. | [
"Multiproblem",
"loss",
"function",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem.py#L419-L522 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem.py | aggregate_task_lm_losses | def aggregate_task_lm_losses(hparams,
problem_hparams,
logits,
feature_name,
feature):
"""LM loss for multiproblems."""
summaries = []
vocab_size = problem_hparams.vocab_size[feature_name]
if voca... | python | def aggregate_task_lm_losses(hparams,
problem_hparams,
logits,
feature_name,
feature):
"""LM loss for multiproblems."""
summaries = []
vocab_size = problem_hparams.vocab_size[feature_name]
if voca... | [
"def",
"aggregate_task_lm_losses",
"(",
"hparams",
",",
"problem_hparams",
",",
"logits",
",",
"feature_name",
",",
"feature",
")",
":",
"summaries",
"=",
"[",
"]",
"vocab_size",
"=",
"problem_hparams",
".",
"vocab_size",
"[",
"feature_name",
"]",
"if",
"vocab_s... | LM loss for multiproblems. | [
"LM",
"loss",
"for",
"multiproblems",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem.py#L525-L553 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem.py | MultiProblem.normalize_example | def normalize_example(self, task, example, encoder, hparams, is_infer):
"""Normalize the examples from different tasks so they can be merged."""
# Here we use the default function for NLP tasks that makes everything
# a part of "targets" feature. Override in your subclasses for other uses.
vocab_offset ... | python | def normalize_example(self, task, example, encoder, hparams, is_infer):
"""Normalize the examples from different tasks so they can be merged."""
# Here we use the default function for NLP tasks that makes everything
# a part of "targets" feature. Override in your subclasses for other uses.
vocab_offset ... | [
"def",
"normalize_example",
"(",
"self",
",",
"task",
",",
"example",
",",
"encoder",
",",
"hparams",
",",
"is_infer",
")",
":",
"# Here we use the default function for NLP tasks that makes everything",
"# a part of \"targets\" feature. Override in your subclasses for other uses.",... | Normalize the examples from different tasks so they can be merged. | [
"Normalize",
"the",
"examples",
"from",
"different",
"tasks",
"so",
"they",
"can",
"be",
"merged",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem.py#L145-L154 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem.py | MultiProblem.update_task_ids | def update_task_ids(self, encoder_vocab_size):
"""Generate task_ids for each problem.
These ids correspond to the index of the task in the task_list.
Args:
encoder_vocab_size: the size of the vocab which is used to compute
the index offset.
"""
for idx, task in enumerate(self.task_li... | python | def update_task_ids(self, encoder_vocab_size):
"""Generate task_ids for each problem.
These ids correspond to the index of the task in the task_list.
Args:
encoder_vocab_size: the size of the vocab which is used to compute
the index offset.
"""
for idx, task in enumerate(self.task_li... | [
"def",
"update_task_ids",
"(",
"self",
",",
"encoder_vocab_size",
")",
":",
"for",
"idx",
",",
"task",
"in",
"enumerate",
"(",
"self",
".",
"task_list",
")",
":",
"task",
".",
"set_task_id",
"(",
"idx",
"+",
"encoder_vocab_size",
")",
"tf",
".",
"logging",... | Generate task_ids for each problem.
These ids correspond to the index of the task in the task_list.
Args:
encoder_vocab_size: the size of the vocab which is used to compute
the index offset. | [
"Generate",
"task_ids",
"for",
"each",
"problem",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem.py#L385-L397 | train |
tensorflow/tensor2tensor | tensor2tensor/data_generators/multi_problem.py | MultiProblem.get_max_num_classes | def get_max_num_classes(self):
"""Compute the maximum number of classes any subtask has.
This is useful for modifying the size of the softmax to include the output
labels for the classification tasks. Currently, labels from different tasks
are overloaded.
Returns:
num: Highest number of outp... | python | def get_max_num_classes(self):
"""Compute the maximum number of classes any subtask has.
This is useful for modifying the size of the softmax to include the output
labels for the classification tasks. Currently, labels from different tasks
are overloaded.
Returns:
num: Highest number of outp... | [
"def",
"get_max_num_classes",
"(",
"self",
")",
":",
"num",
"=",
"0",
"for",
"task",
"in",
"self",
".",
"task_list",
":",
"if",
"hasattr",
"(",
"task",
",",
"\"num_classes\"",
")",
":",
"if",
"num",
"<",
"task",
".",
"num_classes",
":",
"num",
"=",
"... | Compute the maximum number of classes any subtask has.
This is useful for modifying the size of the softmax to include the output
labels for the classification tasks. Currently, labels from different tasks
are overloaded.
Returns:
num: Highest number of output classes in any text classification ... | [
"Compute",
"the",
"maximum",
"number",
"of",
"classes",
"any",
"subtask",
"has",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/multi_problem.py#L399-L416 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | RecurrentMemory.pre_attention | def pre_attention(self, segment, query_antecedent, memory_antecedent, bias):
"""Called prior to self-attention, to incorporate memory items.
Args:
segment: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: must be None. A... | python | def pre_attention(self, segment, query_antecedent, memory_antecedent, bias):
"""Called prior to self-attention, to incorporate memory items.
Args:
segment: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: must be None. A... | [
"def",
"pre_attention",
"(",
"self",
",",
"segment",
",",
"query_antecedent",
",",
"memory_antecedent",
",",
"bias",
")",
":",
"del",
"segment",
"return",
"None",
",",
"query_antecedent",
",",
"memory_antecedent",
",",
"bias"
] | Called prior to self-attention, to incorporate memory items.
Args:
segment: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: must be None. Attention normally allows this to be a
Tensor with shape [batch, length_m, ch... | [
"Called",
"prior",
"to",
"self",
"-",
"attention",
"to",
"incorporate",
"memory",
"items",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/transformer_memory.py#L31-L45 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | RecentTokensMemory.pre_attention | def pre_attention(self, segment, query_antecedent, memory_antecedent, bias):
"""Called prior to self-attention, to incorporate memory items.
Args:
segment: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: must be None. A... | python | def pre_attention(self, segment, query_antecedent, memory_antecedent, bias):
"""Called prior to self-attention, to incorporate memory items.
Args:
segment: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: must be None. A... | [
"def",
"pre_attention",
"(",
"self",
",",
"segment",
",",
"query_antecedent",
",",
"memory_antecedent",
",",
"bias",
")",
":",
"assert",
"memory_antecedent",
"is",
"None",
",",
"\"We only support language modeling\"",
"# In eval mode, batch size may be variable",
"memory_ba... | Called prior to self-attention, to incorporate memory items.
Args:
segment: an integer Tensor with shape [batch]
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: must be None. Attention normally allows this to be a
Tensor with shape [batch, length_m, ch... | [
"Called",
"prior",
"to",
"self",
"-",
"attention",
"to",
"incorporate",
"memory",
"items",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/transformer_memory.py#L110-L168 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | RecentTokensMemory.post_attention | def post_attention(self, token, x):
"""Called after self-attention. The memory can be updated here.
Args:
token: Data returned by pre_attention, which can be used to carry over
state related to the current memory operation.
x: a Tensor of data after self-attention and feed-forward
Retur... | python | def post_attention(self, token, x):
"""Called after self-attention. The memory can be updated here.
Args:
token: Data returned by pre_attention, which can be used to carry over
state related to the current memory operation.
x: a Tensor of data after self-attention and feed-forward
Retur... | [
"def",
"post_attention",
"(",
"self",
",",
"token",
",",
"x",
")",
":",
"with",
"tf",
".",
"control_dependencies",
"(",
"[",
"self",
".",
"previous_segment",
".",
"assign",
"(",
"token",
"[",
"0",
"]",
")",
",",
"self",
".",
"previous_vals",
".",
"assi... | Called after self-attention. The memory can be updated here.
Args:
token: Data returned by pre_attention, which can be used to carry over
state related to the current memory operation.
x: a Tensor of data after self-attention and feed-forward
Returns:
a (possibly modified) version of ... | [
"Called",
"after",
"self",
"-",
"attention",
".",
"The",
"memory",
"can",
"be",
"updated",
"here",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/transformer_memory.py#L170-L185 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | TransformerMemory._norm | def _norm(self, x):
"""Compute the safe norm."""
return tf.sqrt(tf.reduce_sum(tf.square(x), keepdims=True, axis=-1) + 1e-7) | python | def _norm(self, x):
"""Compute the safe norm."""
return tf.sqrt(tf.reduce_sum(tf.square(x), keepdims=True, axis=-1) + 1e-7) | [
"def",
"_norm",
"(",
"self",
",",
"x",
")",
":",
"return",
"tf",
".",
"sqrt",
"(",
"tf",
".",
"reduce_sum",
"(",
"tf",
".",
"square",
"(",
"x",
")",
",",
"keepdims",
"=",
"True",
",",
"axis",
"=",
"-",
"1",
")",
"+",
"1e-7",
")"
] | Compute the safe norm. | [
"Compute",
"the",
"safe",
"norm",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/transformer_memory.py#L226-L228 | train |
tensorflow/tensor2tensor | tensor2tensor/layers/transformer_memory.py | TransformerMemory._address_content | def _address_content(self, x):
"""Address the memory based on content similarity.
Args:
x: a tensor in the shape of [batch_size, length, depth].
Returns:
the logits for each memory entry [batch_size, length, memory_size].
"""
mem_keys = tf.layers.dense(self.mem_vals, self.key_depth,
... | python | def _address_content(self, x):
"""Address the memory based on content similarity.
Args:
x: a tensor in the shape of [batch_size, length, depth].
Returns:
the logits for each memory entry [batch_size, length, memory_size].
"""
mem_keys = tf.layers.dense(self.mem_vals, self.key_depth,
... | [
"def",
"_address_content",
"(",
"self",
",",
"x",
")",
":",
"mem_keys",
"=",
"tf",
".",
"layers",
".",
"dense",
"(",
"self",
".",
"mem_vals",
",",
"self",
".",
"key_depth",
",",
"bias_initializer",
"=",
"tf",
".",
"constant_initializer",
"(",
"1.0",
")",... | Address the memory based on content similarity.
Args:
x: a tensor in the shape of [batch_size, length, depth].
Returns:
the logits for each memory entry [batch_size, length, memory_size]. | [
"Address",
"the",
"memory",
"based",
"on",
"content",
"similarity",
"."
] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/transformer_memory.py#L230-L249 | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.