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def ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode):
"""Residual feed-forward layer with normalization at start."""
return layers.Residual(
layers.LayerNorm(),
layers.Dense(feedforward_depth),
layers.Relu(... |
def EncoderLayer(feature_depth,
feedforward_depth,
num_heads,
dropout,
mode):
"""Transformer encoder layer.
The input to the encoder is a pair (embedded source, mask) where
the mask is created from the original source to prevent attending
to t... |
def TransformerEncoder(vocab_size,
num_classes=10,
feature_depth=512,
feedforward_depth=2048,
num_layers=6,
num_heads=8,
dropout=0.1,
max_len=2048,
... |
def DecoderLayer(feature_depth,
feedforward_depth,
num_heads,
dropout,
mode):
"""Transformer decoder layer.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of att... |
def TransformerLM(vocab_size,
feature_depth=512,
feedforward_depth=2048,
num_layers=6,
num_heads=8,
dropout=0.1,
max_len=2048,
mode='train'):
"""Transformer language model (only uses the decod... |
def ChunkedDecoderLayer(feature_depth,
feedforward_depth,
num_heads,
dropout,
chunk_selector,
mode):
"""Transformer decoder layer operating on chunks.
Args:
feature_depth: int: depth of embe... |
def ChunkedTransformerLM(vocab_size,
feature_depth=512,
feedforward_depth=2048,
num_layers=6,
num_heads=8,
dropout=0.1,
chunk_selector=None,
max_... |
def Transformer(source_vocab_size,
target_vocab_size,
mode='train',
num_layers=6,
feature_depth=512,
feedforward_depth=2048,
num_heads=8,
dropout=0.1,
shared_embedding=True,
ma... |
def mtf_transformer_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.no_data_parallelism = True
hparams.use_fixed_batch_size = True
hparams.add_hparam("mtf_mode", True)
hparams.batch_size = 64
hparams.max_length = 256
hparams.add_hparam("d_model", 512)
hparams.add... |
def mtf_transformer_tiny():
"""Catch bugs locally..."""
hparams = mtf_transformer_base()
hparams.d_model = 128
hparams.d_ff = 512
hparams.batch_size = 8
hparams.encoder_layers = ["att", "drd"] * 2
hparams.decoder_layers = ["att", "enc_att", "drd"] * 2
hparams.num_heads = 8
# data parallelism and model... |
def mtf_transformer_paper_lm(size):
"""Config for language-model experiments.
Train these on languagemodel_lm1b32k_packed for 136000 steps (10 epochs)
The size parameter is an integer that controls the number of heads and the
size of the size of the feedforward hidden layers. Increasing size by 1
doubles e... |
def mtf_transformer_paper_tr(size):
"""Config for translation experiments.
Train these on translate_enfr_wmt32k_packed for 154000 steps (3 epochs)
The size parameter is an integer that controls the number of heads and the
size of the size of the feedforward hidden layers. Increasing size by 1
doubles each ... |
def mtf_transformer_lm_baseline():
"""Small language model to run on 1 TPU.
Run this on 2x2 on languagemodel_lm1b32k_packed for 272000 steps (10 epochs)
Results:
params/10^9 log-ppl(per-token)
0.14 3.202
Returns:
a hparams
"""
hparams = mtf_transformer_paper_lm(-1)
hparams... |
def multihead_graph_attention(query_antecedent,
memory_antecedent,
bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
... |
def graph_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
make_image_summary=True,
save_weights_to=None,
dropout_br... |
def _compute_edge_transforms(node_states,
depth,
num_transforms,
name="transform"):
"""Helper function that computes transformation for keys and values.
Let B be the number of batches.
Let N be the number of nodes in the graph... |
def compute_mpnn_qkv(node_states,
total_key_depth,
total_value_depth,
num_transforms):
"""Computes query, key and value for edge matrices.
Let B be the number of batches.
Let N be the number of nodes in the graph.
Let D be the size of the node hidd... |
def sparse_message_pass_batched(node_states,
adjacency_matrices,
num_edge_types,
hidden_size,
use_bias=True,
average_aggregation=False,
... |
def sparse_message_pass(node_states,
adjacency_matrices,
num_edge_types,
hidden_size,
use_bias=True,
average_aggregation=False,
name="sparse_ggnn"):
"""One message-passing st... |
def multihead_mpnn_attention(node_states,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
adjacency_matrix=None,
num_edge_types=5,
... |
def dot_product_mpnn_attention(q,
k,
v,
adjacency_matrix,
num_edge_types,
num_transforms=None,
use_weighted_sum=False,
... |
def ggnn_fast_dense(node_states,
adjacency_matrix,
num_edge_types,
total_value_depth,
name=None):
"""ggnn version of the MPNN from Gilmer et al.
Let B be the number of batches.
Let D be the size of the node hidden states.
Let K be ... |
def compute_values(edge_compatibility, v):
"""Compute values. If edge compatibilities is just adjacency, we get ggnn.
Args:
edge_compatibility: A tensor of shape [batch, num_transforms, length, depth]
v: A tensor of shape [batch, num_transforms, length, depth]
Returns:
output: A [batch, length, dept... |
def precompute_edge_matrices(adjacency, hparams):
"""Precompute the a_in and a_out tensors.
(we don't want to add to the graph everytime _fprop is called)
Args:
adjacency: placeholder of real valued vectors of shape [B, L, L, E]
hparams: HParams object
Returns:
edge_matrices: [batch, L * D, L * D] ... |
def dense_message_pass(node_states, edge_matrices):
"""Computes a_t from h_{t-1}, see bottom of page 3 in the paper.
Args:
node_states: [B, L, D] tensor (h_{t-1})
edge_matrices (tf.float32): [B, L*D, L*D]
Returns:
messages (tf.float32): [B, L, D] For each pair
of nodes in the graph a message i... |
def to_example(dictionary):
"""Helper: build tf.Example from (string -> int/float/str list) dictionary."""
features = {}
for (k, v) in six.iteritems(dictionary):
if not v:
raise ValueError("Empty generated field: %s" % str((k, v)))
if isinstance(v[0], six.integer_types):
features[k] = tf.train... |
def generate_files_distributed(generator,
output_name,
output_dir,
num_shards=1,
max_cases=None,
task_id=0):
"""generate_files but with a single writer writing to ... |
def generate_files(generator, output_filenames,
max_cases=None, cycle_every_n=1):
"""Generate cases from a generator and save as TFRecord files.
Generated cases are transformed to tf.Example protos and saved as TFRecords
in sharded files named output_dir/output_name-00..N-of-00..M=num_shards.
... |
def download_report_hook(count, block_size, total_size):
"""Report hook for download progress.
Args:
count: current block number
block_size: block size
total_size: total size
"""
percent = int(count * block_size * 100 / total_size)
print("\r%d%%" % percent + " completed", end="\r") |
def maybe_download(directory, filename, uri):
"""Download filename from uri unless it's already in directory.
Copies a remote file to local if that local file does not already exist. If
the local file pre-exists this function call, it does not check that the local
file is a copy of the remote.
Remote filen... |
def maybe_download_from_drive(directory, filename, url):
"""Download filename from Google drive unless it's already in directory.
Args:
directory: path to the directory that will be used.
filename: name of the file to download to (do nothing if it already exists).
url: URL to download from.
Returns:... |
def gunzip_file(gz_path, new_path):
"""Unzips from gz_path into new_path.
Args:
gz_path: path to the zipped file.
new_path: path to where the file will be unzipped.
"""
if tf.gfile.Exists(new_path):
tf.logging.info("File %s already exists, skipping unpacking" % new_path)
return
tf.logging.inf... |
def get_or_generate_vocab_inner(data_dir, vocab_filename, vocab_size,
generator, max_subtoken_length=None,
reserved_tokens=None):
"""Inner implementation for vocab generators.
Args:
data_dir: The base directory where data and vocab files are store... |
def get_or_generate_vocab(data_dir, tmp_dir, vocab_filename, vocab_size,
sources, file_byte_budget=1e6,
max_subtoken_length=None):
"""Generate a vocabulary from the datasets in sources."""
vocab_generator = generate_lines_for_vocab(tmp_dir, sources, file_byte_bud... |
def generate_lines_for_vocab(tmp_dir, sources, file_byte_budget=1e6):
"""Generate lines for vocabulary generation."""
tf.logging.info("Generating vocab from: %s", str(sources))
for source in sources:
url = source[0]
filename = os.path.basename(url)
compressed_file = maybe_download(tmp_dir, filename, u... |
def get_or_generate_tabbed_vocab(data_dir, tmp_dir, source_filename,
index, vocab_filename, vocab_size):
r"""Generate a vocabulary from a tabbed source file.
The source is a file of source, target pairs, where each line contains
a source string and a target string, separated by a... |
def get_or_generate_txt_vocab(data_dir, vocab_filename, vocab_size,
filepatterns):
"""Generate a vocabulary from txt files with example-per-line."""
if isinstance(filepatterns, str):
filepatterns = [filepatterns]
def generate():
tf.logging.info("Generating vocab from %s", fi... |
def _shuffle_single(fname, extra_fn=None):
"""Shuffle a single file of records.
Args:
fname: a string
extra_fn: an optional function from list of TFRecords to list of TFRecords
to be called after shuffling.
"""
records = read_records(fname)
random.shuffle(records)
if extra_fn is not None:
... |
def shuffle_dataset(filenames, extra_fn=None):
"""Shuffles the dataset.
Args:
filenames: a list of strings
extra_fn: an optional function from list of records to list of records
to be called after shuffling a file.
"""
if outputs_exist(filenames):
tf.logging.info("Skipping shuffle because out... |
def pack_examples(examples,
has_inputs,
packed_length=256,
spacing=2,
queue_size=10,
chop_long_sequences=False):
"""Pack examples into longer examples.
If has_inputs=False, we are packing single-sequence examples with
targe... |
def _pack_with_custom_ops(dataset, keys, length):
"""Helper-function for packing a dataset which has already been batched.
See pack_dataset()
Relies on custom ops which require a custom compiled binary.
Faster than _pack_with_tf_ops(), and denser packing.
Args:
dataset: a dataset containing padded batc... |
def make_tmp_dir(suffix="", prefix="tmp", dir=None): # pylint: disable=redefined-builtin
"""Make a temporary directory."""
if dir is None:
return tempfile.mkdtemp(suffix, prefix, dir)
else:
while True:
rand_term = random.randint(1, 9999)
tmp_dir = os.path.join(dir, "%s%d%s" % (prefix, rand_te... |
def tfrecord_iterator_for_problem(problem, data_dir,
dataset_split=tf.estimator.ModeKeys.TRAIN):
"""Iterate over the records on disk for the Problem."""
filenames = tf.gfile.Glob(problem.filepattern(data_dir, mode=dataset_split))
example_spec = problem.example_reading_spec()[0]
... |
def tfrecord_iterator(filenames, gzipped=False, example_spec=None):
"""Yields records from TFRecord files.
Args:
filenames: list<str>, list of TFRecord filenames to read from.
gzipped: bool, whether the TFRecord files are gzip-encoded.
example_spec: dict<str feature name, tf.VarLenFeature/tf.FixedLenFe... |
def random_deinterleave(text, separator_symbol="X"):
"""Create a fill-in-the-blanks training example from text.
Split on spaces, then cut into segments at random points. Alternate segments
are assigned to the two output strings. separator_symbol separates segments
within each of the outputs.
example:
t... |
def neural_gpu_body(inputs, hparams, name=None):
"""The core Neural GPU."""
with tf.variable_scope(name, "neural_gpu"):
def step(state, inp): # pylint: disable=missing-docstring
x = tf.nn.dropout(state, 1.0 - hparams.dropout)
for layer in range(hparams.num_hidden_layers):
x = common_layers... |
def diagonal_neural_gpu(inputs, hparams, name=None):
"""Improved Neural GPU as in https://arxiv.org/abs/1702.08727."""
with tf.variable_scope(name, "diagonal_neural_gpu"):
def step(state_tup, inp):
"""Single step of the improved Neural GPU."""
state, _ = state_tup
x = state
for layer in... |
def _reorder_shape(input_shape, output=None): # pylint: disable=invalid-name
"""Helper to determine the shape of reorder output."""
if output is None:
return input_shape
return base.nested_map(output, lambda i: input_shape[i]) |
def Reorder(x, params, output=None, **kwargs):
"""Reorder a tuple into another tuple.
For example, we can re-order (x, y) into (y, x) or even (y, (x, y), y).
The output argument specifies how to re-order, using integers that refer
to indices in the input tuple. For example, if
input = (x, y, z)
then
... |
def _nested_op(inputs, op): # pylint: disable=invalid-name
"""Helper: sum a list of arrays or nested arrays."""
# First the simple non-nested case.
if not isinstance(inputs[0], (list, tuple)):
return op(inputs)
# In the nested case, sum on each axis separately.
result_list = []
for i in range(len(input... |
def GateBranches(x, **unused_kwargs):
"""Implements a gating function on a (memory, gate, candidate) tuple.
Final update is memory * gate + (1-gate) * candidate
This gating equation may also be referred to as Highway Network.
Highway Networks: https://arxiv.org/abs/1505.00387
Args:
x: A tuple of (memor... |
def _concatenate_shape(input_shape, axis=-1): # pylint: disable=invalid-name
"""Helper to determine the shape of Concatenate output."""
ax = axis % len(input_shape[0])
concat_size = sum(shape[ax] for shape in input_shape)
out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]
return out_sha... |
def Residual(*layers, **kwargs):
"""Constructs a residual version of layers, summing input to layers output."""
shortcut = kwargs.get('shortcut', Identity()) # pylint: disable=no-value-for-parameter
if len(layers) > 1:
return Serial(
Branch(), # pylint: disable=no-value-for-parameter
Paralle... |
def train(
self,
env_fn,
hparams,
simulated,
save_continuously,
epoch,
sampling_temp=1.0,
num_env_steps=None,
env_step_multiplier=1,
eval_env_fn=None,
report_fn=None
):
"""Train."""
raise NotImplementedError() |
def update_hparams_for_universal_transformer(hparams):
"""Adds default hparams for all of the variants of the Universal Transformer.
Args:
hparams: default hparams (usually one of the standard hparams from
transformer model (like "transformer_base")
Returns:
hparams with default values for Univers... |
def universal_transformer_base():
"""Base parameters for Universal Transformer."""
hparams = transformer.transformer_base()
# To have a similar capacity to the transformer_base with 6 layers,
# we need to increase the size of the UT's layer
# since, in fact, UT has a single layer repeating multiple times.
h... |
def adaptive_universal_transformer_multilayer_tpu():
"""Multi-layer config for adaptive Transformer on TPU."""
hparams = adaptive_universal_transformer_base_tpu()
hparams.num_inrecurrence_layers = 2
hparams.mix_with_transformer = "before_ut,after_ut"
hparams.num_mixedin_layers = 1
hparams.transformer_ffn_ty... |
def adaptive_universal_transformer_multilayer_hard():
"""Multi-layer config for adaptive Transformer with hard attention."""
hparams = adaptive_universal_transformer_multilayer_tpu()
hparams.batch_size = 256
hparams.hard_attention_k = 8
hparams.add_step_timing_signal = True
# hparams.add_sru = True # This ... |
def universal_transformer_base_range(rhp):
"""Range of hyperparameters."""
# After starting from base, set intervals for some parameters.
rhp.set_discrete("num_rec_steps", [6, 8, 10])
rhp.set_discrete("hidden_size", [1024, 2048, 4096])
rhp.set_discrete("filter_size", [2048, 4096, 8192])
rhp.set_discrete("nu... |
def adaptive_universal_transformer_base_range(rhp):
"""Range of hyperparameters."""
# After starting from base, set intervals for some parameters.
rhp.set_discrete("act_max_steps", [8, 16, 32])
rhp.set_float("act_loss_weight", 0.0, 0.5)
rhp.set_discrete("hidden_size", [1024, 2048, 4096])
rhp.set_discrete("f... |
def DiagonalGate(x, params, **kwargs):
"""Split channels in 3 parts. Shifts 1st and 3rd sections to left/right."""
del params
del kwargs
# x : [batch, 1, length, depth]
x = np.pad(
x, [(0, 0), (0, 0), (1, 1), (0, 0)], mode='constant', constant_values=0.0)
depth = x.shape[-1] // 3
assert 3 * depth ==... |
def ConvDiagonalGRU(units, kernel_size=(3, 3)):
"""Build convolutional GRU with diagonal gating as in ImprovedNGPU."""
def BuildConv():
return layers.Conv(filters=units, kernel_size=kernel_size, padding='SAME')
return layers.GeneralGRUCell(
candidate_transform=BuildConv,
memory_transform=Diagona... |
def NeuralGPU(feature_depth=96, steps=16, vocab_size=2):
"""Implementation of Neural GPU: https://arxiv.org/abs/1702.08727.
Args:
feature_depth: Number of memory channels
steps: Number of times depthwise recurrence steps.
vocab_size: Vocabulary size.
Returns:
A NeuralGPU Stax model.
"""
xs =... |
def strip_ids(ids, ids_to_strip):
"""Strip ids_to_strip from the end ids."""
ids = list(ids)
while ids and ids[-1] in ids_to_strip:
ids.pop()
return ids |
def _escape_token(token, alphabet):
"""Escape away underscores and OOV characters and append '_'.
This allows the token to be expressed as the concatenation of a list
of subtokens from the vocabulary. The underscore acts as a sentinel
which allows us to invertibly concatenate multiple such lists.
Args:
... |
def encode(self, s):
"""Transform a human-readable string into a sequence of int ids.
The ids should be in the range [num_reserved_ids, vocab_size). Ids [0,
num_reserved_ids) are reserved.
EOS is not appended.
Args:
s: human-readable string to be converted.
Returns:
ids: list of ... |
def decode(self, ids, strip_extraneous=False):
"""Transform a sequence of int ids into a human-readable string.
EOS is not expected in ids.
Args:
ids: list of integers to be converted.
strip_extraneous: bool, whether to strip off extraneous tokens
(EOS and PAD).
Returns:
s: ... |
def decode_list(self, ids):
"""Transform a sequence of int ids into a their string versions.
This method supports transforming individual input/output ids to their
string versions so that sequence to/from text conversions can be visualized
in a human readable format.
Args:
ids: list of integ... |
def encode(self, s):
"""Converts a space-separated string of tokens to a list of ids."""
sentence = s
tokens = sentence.strip().split()
if self._replace_oov is not None:
tokens = [t if t in self._token_to_id else self._replace_oov
for t in tokens]
ret = [self._token_to_id[tok] ... |
def _init_vocab_from_file(self, filename):
"""Load vocab from a file.
Args:
filename: The file to load vocabulary from.
"""
with tf.gfile.Open(filename) as f:
tokens = [token.strip() for token in f.readlines()]
def token_gen():
for token in tokens:
yield token
self._... |
def _init_vocab_from_list(self, vocab_list):
"""Initialize tokens from a list of tokens.
It is ok if reserved tokens appear in the vocab list. They will be
removed. The set of tokens in vocab_list should be unique.
Args:
vocab_list: A list of tokens.
"""
def token_gen():
for token ... |
def _init_vocab(self, token_generator, add_reserved_tokens=True):
"""Initialize vocabulary with tokens from token_generator."""
self._id_to_token = {}
non_reserved_start_index = 0
if add_reserved_tokens:
self._id_to_token.update(enumerate(RESERVED_TOKENS))
non_reserved_start_index = len(RE... |
def store_to_file(self, filename):
"""Write vocab file to disk.
Vocab files have one token per line. The file ends in a newline. Reserved
tokens are written to the vocab file as well.
Args:
filename: Full path of the file to store the vocab to.
"""
with tf.gfile.Open(filename, "w") as f:... |
def decode(self, ids, strip_extraneous=False):
"""Converts a sequence of subtoken ids to a native string.
Args:
ids: a list of integers in the range [0, vocab_size)
strip_extraneous: bool, whether to strip off extraneous tokens
(EOS and PAD).
Returns:
a native string
"""
... |
def _tokens_to_subtoken_ids(self, tokens):
"""Converts a list of tokens to a list of subtoken ids.
Args:
tokens: a list of strings.
Returns:
a list of integers in the range [0, vocab_size)
"""
ret = []
for token in tokens:
ret.extend(self._token_to_subtoken_ids(token))
ret... |
def _token_to_subtoken_ids(self, token):
"""Converts token to a list of subtoken ids.
Args:
token: a string.
Returns:
a list of integers in the range [0, vocab_size)
"""
cache_location = hash(token) % self._cache_size
cache_key, cache_value = self._cache[cache_location]
if cache... |
def _subtoken_ids_to_tokens(self, subtokens):
"""Converts a list of subtoken ids to a list of tokens.
Args:
subtokens: a list of integers in the range [0, vocab_size)
Returns:
a list of strings.
"""
concatenated = "".join(
[self._subtoken_id_to_subtoken_string(s) for s in subtok... |
def _subtoken_id_to_subtoken_string(self, subtoken):
"""Converts a subtoken integer ID to a subtoken string."""
if 0 <= subtoken < self.vocab_size:
return self._all_subtoken_strings[subtoken]
return u"" |
def _escaped_token_to_subtoken_strings(self, escaped_token):
"""Converts an escaped token string to a list of subtoken strings.
Args:
escaped_token: An escaped token as a unicode string.
Returns:
A list of subtokens as unicode strings.
"""
# NOTE: This algorithm is greedy; it won't nece... |
def _escaped_token_to_subtoken_ids(self, escaped_token):
"""Converts an escaped token string to a list of subtoken IDs.
Args:
escaped_token: An escaped token as a unicode string.
Returns:
A list of subtoken IDs as integers.
"""
return [
self._subtoken_string_to_id[subtoken]
... |
def build_from_generator(cls,
generator,
target_size,
max_subtoken_length=None,
reserved_tokens=None):
"""Builds a SubwordTextEncoder from the generated text.
Args:
generator: yields text.
ta... |
def build_to_target_size(cls,
target_size,
token_counts,
min_val,
max_val,
max_subtoken_length=None,
reserved_tokens=None,
num_iter... |
def build_from_token_counts(self,
token_counts,
min_count,
num_iterations=4,
reserved_tokens=None,
max_subtoken_length=None):
"""Train a SubwordTextEncoder based on a... |
def dump(self):
"""Debugging dump of the current subtoken vocabulary."""
subtoken_strings = [(i, s)
for s, i in six.iteritems(self._subtoken_string_to_id)]
print(u", ".join(u"{0} : '{1}'".format(i, s)
for i, s in sorted(subtoken_strings))) |
def _init_subtokens_from_list(self, subtoken_strings, reserved_tokens=None):
"""Initialize token information from a list of subtoken strings.
Args:
subtoken_strings: a list of subtokens
reserved_tokens: List of reserved tokens. We must have `reserved_tokens`
as None or the empty list, or el... |
def _load_from_file_object(self, f):
"""Load from a file object.
Args:
f: File object to load vocabulary from
"""
subtoken_strings = []
for line in f:
s = line.strip()
# Some vocab files wrap words in single quotes, but others don't
if ((s.startswith("'") and s.endswith("'")... |
def _load_from_file(self, filename):
"""Load from a vocab file."""
if not tf.gfile.Exists(filename):
raise ValueError("File %s not found" % filename)
with tf.gfile.Open(filename) as f:
self._load_from_file_object(f) |
def encode(self, s):
"""Transform a string with a filename into a list of RGB integers.
Args:
s: path to the file with an image.
Returns:
ids: list of integers
"""
try:
import matplotlib.image as im # pylint: disable=g-import-not-at-top
except ImportError as e:
tf.logg... |
def decode(self, ids, strip_extraneous=False):
"""Transform a sequence of int ids into an image file.
Args:
ids: list of integers to be converted.
strip_extraneous: unused
Returns:
Path to the temporary file where the image was saved.
Raises:
ValueError: if the ids are not of ... |
def decode(self, ids, strip_extraneous=False):
"""Transform sequence of float values into string (float values).
Args:
ids: array of floats to be converted.
strip_extraneous: unused
Returns:
String having space separated float values.
Raises:
ValueError: if the ids are not of ... |
def _pack_images(images, rows, cols):
"""Helper utility to make a tiled field of images from numpy arrays.
Args:
images: Image tensor in shape [N, W, H, C].
rows: Number of images per row in tiled image.
cols: Number of images per column in tiled image.
Returns:
A tiled image of shape [W * rows,... |
def markdownify_operative_config_str(string):
"""Convert an operative config string to markdown format."""
# TODO(b/37527917): Total hack below. Implement more principled formatting.
def process(line):
"""Convert a single line to markdown format."""
if not line.startswith('#'):
return ' ' + line... |
def close(self):
"""Close SummaryWriter. Final!"""
if not self._closed:
self._event_writer.close()
self._closed = True
del self._event_writer |
def scalar(self, tag, value, step=None):
"""Saves scalar value.
Args:
tag: str: label for this data
value: int/float: number to log
step: int: training step
"""
value = float(onp.array(value))
if step is None:
step = self._step
else:
self._step = step
summary =... |
def image(self, tag, image, step=None):
"""Saves RGB image summary from onp.ndarray [H,W], [H,W,1], or [H,W,3].
Args:
tag: str: label for this data
image: ndarray: [H,W], [H,W,1], [H,W,3] save image in greyscale or colors/
step: int: training step
"""
image = onp.array(image)
if s... |
def images(self, tag, images, step=None, rows=None, cols=None):
"""Saves (rows, cols) tiled images from onp.ndarray.
If either rows or cols aren't given, they are determined automatically
from the size of the image batch, if neither are given a long column
of images is produced. This truncates the imag... |
def plot(self, tag, mpl_plt, step=None, close_plot=True):
"""Saves matplotlib plot output to summary image.
Args:
tag: str: label for this data
mpl_plt: matplotlib stateful pyplot object with prepared plotting state
step: int: training step
close_plot: bool: automatically closes plot
... |
def audio(self, tag, audiodata, step=None, sample_rate=44100):
"""Saves audio.
NB: single channel only right now.
Args:
tag: str: label for this data
audiodata: ndarray [Nsamples,]: data between (-1.0,1.0) to save as wave
step: int: training step
sample_rate: sample rate of passed ... |
def histogram(self, tag, values, bins, step=None):
"""Saves histogram of values.
Args:
tag: str: label for this data
values: ndarray: will be flattened by this routine
bins: number of bins in histogram, or array of bins for onp.histogram
step: int: training step
"""
if step is N... |
def text(self, tag, textdata, step=None):
"""Saves a text summary.
Args:
tag: str: label for this data
textdata: string, or 1D/2D list/numpy array of strings
step: int: training step
Note: markdown formatting is rendered by tensorboard.
"""
if step is None:
step = self._step... |
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