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def local_global_attention(x,
self_attention_bias,
hparams,
q_padding="LEFT",
kv_padding="LEFT"):
"""Local and global 1d self attention."""
with tf.variable_scope("self_local_global_att"):
[x_global, x_lo... |
def full_self_attention(x,
self_attention_bias,
hparams,
q_padding="LEFT",
kv_padding="LEFT"):
"""Full self-attention layer."""
x, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
if self_attention_bias is not None:
self... |
def encdec_attention_1d(x,
encoder_output,
encoder_decoder_attention_bias,
hparams):
"""Local 1d self attention."""
x, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
encoder_output, _, _ = maybe_reshape_4d_to_3d(encoder_output)
with tf.variable... |
def transformer_decoder_layers(inputs,
encoder_output,
num_layers,
hparams,
self_attention_bias=None,
encoder_decoder_attention_bias=None,
... |
def transformer_encoder_layers(inputs,
num_layers,
hparams,
attention_type=AttentionType.GLOBAL,
self_attention_bias=None,
q_padding="VALID",
... |
def ffn_layer(x, hparams, losses=None):
"""ffn layer transformer."""
with tf.variable_scope("ffn"):
if hparams.ffn_layer == "none":
return x
if hparams.ffn_layer == "conv_hidden_relu":
y = common_layers.dense_relu_dense(
x,
hparams.filter_size,
hparams.hidden_size,
... |
def get_self_attention_bias(x):
"""Creates masked self attention bias.
Args:
x: A tensor of shape [batch, length, depth]
Returns:
self_attention_bias: A tensor of shape [length, length, 1]
"""
x_shape = common_layers.shape_list(x)
self_attention_bias = common_attention.attention_bias_lower_triang... |
def postprocess_image(x, rows, cols, hparams):
"""Postprocessing after decoding.
Args:
x: Tensor of shape [batch, ...], where ... can be any rank such that the
number of elements in x is batch * rows * cols * hparams.hidden_size.
rows: Integer representing number of rows in a 2-D data point.
cols... |
def prepare_encoder(inputs, hparams, attention_type="local_1d"):
"""Prepare encoder for images."""
x = prepare_image(inputs, hparams, name="enc_channels")
# Add position signals.
x = add_pos_signals(x, hparams, "enc_pos")
x_shape = common_layers.shape_list(x)
if attention_type == "local_1d":
x = tf.resh... |
def prepare_decoder(targets, hparams):
"""Prepare decoder for images."""
targets_shape = common_layers.shape_list(targets)
channels = hparams.num_channels
curr_infer_length = None
# during training, images are [batch, IMG_LEN, IMG_LEN, 3].
# At inference, they are [batch, curr_infer_length, 1, 1]
if hpar... |
def create_output(decoder_output, rows, cols, targets, hparams):
"""Creates output from decoder output and vars.
Args:
decoder_output: Tensor of shape [batch, ...], where ... can be any rank such
that the number of elements is batch * rows * cols * hparams.hidden_size.
rows: Integer representing numb... |
def get_channel_embeddings(io_depth, targets, hidden_size, name="channel"):
"""Get separate embedding for each of the channels."""
targets_split = tf.split(targets, io_depth, axis=3)
rgb_embedding_var = tf.get_variable("rgb_target_emb_%s" % name,
[256 * io_depth, hidden_size]... |
def simulate(self, action):
"""Step the batch of environments.
The results of the step can be accessed from the variables defined below.
Args:
action: Tensor holding the batch of actions to apply.
Returns:
Operation.
"""
with tf.name_scope("environment/simulate"):
if action.... |
def _reset_non_empty(self, indices):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations.
"""
observ = tf.py_func(
self._batch_env.reset, [indices], self.observ_dtype, ... |
def include_revision(revision_num, skip_factor=1.1):
"""Decide whether to include a revision.
If the number of revisions is large, we exclude some revisions to avoid
a quadratic blowup in runtime, since the article is likely also large.
We make the ratio between consecutive included revision numbers
appprox... |
def file_page_generator(my_file, max_page_size=2**28):
"""Read wikipedia pages from a history dump.
Since some pages can be terabytes in size (with all the revisions),
we limit page size to max_page_size bytes.
Args:
my_file: an open file object.
max_page_size: an integer
Yields:
strings
"""
... |
def get_title(page):
"""Extract the title from a page.
Args:
page: a string
Returns:
a string
"""
start_pos = page.find("<title>")
end_pos = page.find("</title>")
assert start_pos != -1
assert end_pos != -1
start_pos += len("<title>")
return text_encoder.to_unicode_utf8(page[start_pos:end_p... |
def get_id(page):
"""Extract the id from a page.
Args:
page: a string
Returns:
an integer
"""
start_pos = page.find("<id>")
end_pos = page.find("</id>")
assert start_pos != -1
assert end_pos != -1
start_pos += len("<id>")
return int(page[start_pos:end_pos]) |
def get_revisions(page):
"""Extract the revisions of a page.
Args:
page: a string
Returns:
a list of strings
"""
start_string = " <revision>\n"
end_string = " </revision>\n"
ret = []
current_pos = 0
while True:
start_pos = page.find(start_string, current_pos)
if start_pos == -1:... |
def parse_page(raw_page):
"""Create a dictionary with title, id, and list of revisions.
The dictionary contains:
"title": a string
"id": an integer
"revisions": a list of strings
Args:
raw_page: a string
Returns:
a dictionary, or None in the case of an error.
"""
ret = {"title": get_title(r... |
def maybe_copy_file_to_directory(source_filepath, target_directory):
"""Copy a file to a directory if it is not already there.
Returns the target filepath.
Args:
source_filepath: a string
target_directory: a string
Returns:
a string
"""
if not tf.gfile.Exists(target_directory):
tf.logging... |
def corpus_page_generator(corpus_files, tmp_dir, max_page_size_exp):
"""Generate pages from a list of .7z encoded history dumps.
Args:
corpus_files: a list of strings
tmp_dir: a string
max_page_size_exp: an integer
Yields:
strings
"""
for remote_filepath in corpus_files:
filepath = mayb... |
def get_text(revision, strip=True):
"""Extract the text from a revision.
Args:
revision: a string
strip: a boolean
Returns:
a string
"""
# text start tag looks like "<text ..otherstuff>"
start_pos = revision.find("<text")
assert start_pos != -1
end_tag_pos = revision.find(">", start_pos)
... |
def _remove_curly_braces(text):
"""Remove everything in curly braces.
Curly braces may be nested, so we keep track of depth.
Args:
text: a string
Returns:
a string
"""
current_pos = 0
depth = 0
ret = ""
for match in re.finditer("[{}]", text):
if depth == 0:
ret += text[current_pos:... |
def _remove_double_brackets(text):
"""Remove double brackets, but leave the viewable text.
Args:
text: a string
Returns:
a string
"""
def replacement_fn(s):
if ":" in s:
# this is probably a category or something like that.
return ""
# keep the part after the bar.
bar_pos = s... |
def _remove_boring_lines(text):
"""Remove lines that do not start with a letter or a quote.
From inspecting the data, this seems to leave in most prose and remove
most weird stuff.
Args:
text: a string
Returns:
a string
"""
lines = text.split("\n")
filtered = [line for line in lines if re.matc... |
def get_or_generate_vocabulary(data_dir,
tmp_dir,
data_prefix,
max_page_size_exp,
approx_vocab_size=32768,
strip=True):
"""Get or generate the vocabulary.
Args:... |
def get_encoder_from_vocab(vocab_filepath):
"""Get encoder from vocab file.
If vocab is not found in output dir, it will be copied there by
copy_vocab_to_output_dir to clarify the vocab used to generate the data.
Args:
vocab_filepath: path to vocab, either local or cns
Returns:
A SubwordTextEncoder... |
def edit_distance_filter(source_target_input, max_equal_to_diff_ratio=0):
"""Filter out examples that exceed max_edit_ratio between source and target.
Args:
source_target_input: a list of [source, target] pairs
max_equal_to_diff_ratio: cutoff for ratio of equal chars / diff chars
between source a... |
def introduce_errors(s,
corruption_rate=3e-3,
infill_marker="|?|",
max_infill_len=8):
"""Artificially add spelling errors and infill markers.
This function should be applied to the inputs of a correction model.
The artificial errors are particularly... |
def fast_match_sequences(a,
b,
a_start=0,
a_end=None,
b_start=0,
b_end=None,
min_match_length=3,
max_recursion_depth=128):
"""Compute diffs bet... |
def begin(self):
"""Load variables from checkpoint.
New model variables have the following name foramt:
new_model_scope/old_model_scope/xxx/xxx:0 To find the map of
name to variable, need to strip the new_model_scope and then
match the old_model_scope and remove the suffix :0.
"""
variable... |
def create_time_step(cls,
observation=None,
done=False,
raw_reward=None,
processed_reward=None,
action=None):
"""Creates a TimeStep with both rewards and actions as optional."""
return cls(observa... |
def attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=None):
"""Complete attention layer with preprocessing."""
separabilities = [hparams.separability, hparams.separability]
if hparams.separability < 0:
separabilities = [hparams.separability - 1, hparams.separability]
targets_timed = common_... |
def multi_conv_res(x, padding, name, layers, hparams, mask=None, source=None):
"""A stack of separable convolution blocks with residual connections."""
with tf.variable_scope(name):
padding_bias = None
if mask is not None:
padding_bias = (1.0 - mask) * -1e9 # Bias to not attend to padding.
if p... |
def rank_loss(sentence_emb, image_emb, margin=0.2):
"""Experimental rank loss, thanks to kkurach@ for the code."""
with tf.name_scope("rank_loss"):
# Normalize first as this is assumed in cosine similarity later.
sentence_emb = tf.nn.l2_normalize(sentence_emb, 1)
image_emb = tf.nn.l2_normalize(image_emb... |
def similarity_cost(inputs_encoded, targets_encoded):
"""Loss telling to be more similar to your own targets than to others."""
# This is a first very simple version: handle variable-length by padding
# to same length and putting everything into batch. In need of a better way.
x, y = common_layers.pad_to_same_l... |
def slicenet_middle(inputs_encoded, targets, target_space_emb, mask, hparams):
"""Middle part of slicenet, connecting encoder and decoder."""
def norm_fn(x, name):
with tf.variable_scope(name, default_name="norm"):
return common_layers.apply_norm(x, hparams.norm_type, hparams.hidden_size,
... |
def embedding_to_padding(emb):
"""Input embeddings -> is_padding."""
emb_sum = tf.reduce_sum(tf.abs(emb), axis=-1, keep_dims=True)
return tf.to_float(tf.equal(emb_sum, 0.0)) |
def slicenet_internal(inputs, targets, target_space, hparams, run_decoder=True):
"""The slicenet model, main step used for training."""
with tf.variable_scope("slicenet"):
# Project to hidden size if necessary
if inputs.get_shape().as_list()[-1] != hparams.hidden_size:
inputs = common_layers.conv_bloc... |
def slicenet_params1():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 1024
hparams.hidden_size = 768
hparams.dropout = 0.5
hparams.symbol_dropout = 0.2
hparams.label_smoothing = 0.1
hparams.clip_grad_norm = 2.0
hparams.num_hidden_layers = 4
hparams.kerne... |
def slicenet_params1_noam():
"""Version with Noam's decay scheme."""
hparams = slicenet_params1()
hparams.learning_rate_decay_scheme = "noam"
hparams.learning_rate = 1.0
hparams.learning_rate_warmup_steps = 4000
hparams.initializer = "uniform_unit_scaling"
hparams.optimizer_adam_epsilon = 1e-9
hparams.o... |
def slicenet_params1_tiny():
"""Version for fast local runs."""
hparams = slicenet_params1()
hparams.attention_type = "simple"
hparams.separability = 0
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.batch_size = 512
hparams.learning_rate_warmup_steps = 200
return hparams |
def slicenet_range1(ranged_hparams):
"""Small range of hyperparameters."""
rhp = ranged_hparams
rhp.set_float("clip_grad_norm", 1.0, 10.0, scale=rhp.LOG_SCALE)
rhp.set_float("learning_rate", 0.02, 1.0, scale=rhp.LOG_SCALE)
rhp.set_float("optimizer_adam_beta2", 0.995, 0.998)
rhp.set_float("weight_decay", 1.0... |
def encode(self, s):
"""Converts a space-separated string of tokens to lists of ids.
Also store temporary vocabulary IDs for source OOV tokens. OOVs are
represented by their temporary OOV number. E.g., if the vocabulary size
is 50k and the source has 3 OOVs, then these temporary OOV numbers will
be... |
def encode_target(self, target, source_oovs):
"""Converts a space-separated string of tokens to lists of ids.
Also store a version of extened vocabulary IDs.
For target OOVs that are in the source, encode them using the temporary
vocab IDs.
For target OOVs not in the source, encode them as <UNK>
... |
def decode_list_oov(self, ids, source_oov_id_to_token):
"""decode ids back to tokens, considering OOVs temporary IDs.
Args:
ids: vocab ids. Could possibly include source temporary OOV ID starting
from vocab_size.
source_oov_id_to_token: a list of source OOV tokens, with the order the
sa... |
def _smallest_size_at_least(height, width, smallest_side):
"""Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current h... |
def _aspect_preserving_resize(image, smallest_side):
"""Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
smallest_side: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing... |
def _distort_color(image, color_ordering=0, scope=None):
"""Distort the color of a Tensor image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a disti... |
def _apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the... |
def _mean_image_subtraction(image, means):
"""Subtracts the given means from each image channel.
For example:
means = [123.68, 116.779, 103.939]
image = _mean_image_subtraction(image, means)
Note that the rank of `image` must be known.
Args:
image: a tensor of size [height, width, C].
means: ... |
def vqa_v2_preprocess_image(
image,
height,
width,
mode,
resize_side=512,
distort=True,
image_model_fn="resnet_v1_152",
):
"""vqa v2 preprocess image."""
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
assert resize_side > 0
if resize_side:
image = _aspect_preservi... |
def transformer_prepare_encoder(inputs, target_space, hparams, features=None):
"""Prepare one shard of the model for the encoder.
Args:
inputs: a Tensor.
target_space: a Tensor.
hparams: run hyperparameters
features: optionally pass the entire features dictionary as well.
This is needed now f... |
def transformer_encoder(encoder_input,
encoder_self_attention_bias,
hparams,
name="encoder",
nonpadding=None,
save_weights_to=None,
make_image_summary=True,
... |
def transformer_ffn_layer(x,
hparams,
pad_remover=None,
conv_padding="LEFT",
nonpadding_mask=None,
losses=None,
cache=None,
decode_loop_st... |
def lmx_base():
"""Transformer on languagemodel_lm1b32k_packed. 50M Params."""
hparams = transformer.transformer_tpu()
# sharing is counterproductive when underparameterized
hparams.shared_embedding_and_softmax_weights = False
# we judge by log-ppl, so label smoothing hurts.
hparams.label_smoothing = 0.0
... |
def lmx_h3k_f12k():
"""HParams for training languagemodel_lm1b32k_packed. 880M Params."""
hparams = lmx_base()
hparams.hidden_size = 3072
hparams.filter_size = 12288
hparams.batch_size = 2048
hparams.weight_dtype = "bfloat16"
return hparams |
def lmx_h4k_f16k():
"""HParams for training languagemodel_lm1b32k_packed. 1470M Params."""
hparams = lmx_base()
hparams.hidden_size = 4096
hparams.filter_size = 16384
hparams.batch_size = 1024
hparams.weight_dtype = "bfloat16"
return hparams |
def lmx_relative():
"""Language model using relative attention."""
hparams = lmx_base()
hparams.self_attention_type = "dot_product_relative_v2"
hparams.activation_dtype = "float32"
hparams.weight_dtype = "float32"
return hparams |
def lmx_moe_h1k_f4k_x32():
"""Transformer with mixture of experts. 890M Params."""
hparams = lmx_h1k_f4k()
hparams.ffn_layer = "local_moe_tpu"
hparams.moe_num_experts = 32
hparams.weight_dtype = "bfloat16"
hparams.batch_size = 8192
return hparams |
def lmx_moe_h1k_f8k_x16():
"""Transformer with mixture of experts. 890M Params."""
hparams = lmx_h1k_f4k()
hparams.filter_size = 8192
hparams.ffn_layer = "local_moe_tpu"
hparams.moe_num_experts = 16
hparams.weight_dtype = "bfloat16"
hparams.batch_size = 8192
return hparams |
def lmx_h1k_f64k():
"""HParams for training languagemodel_lm1b32k_packed. 880M Params."""
hparams = lmx_base()
hparams.hidden_size = 1024
hparams.filter_size = 65536
hparams.batch_size = 2048
return hparams |
def compute_uncertainty_reward(logits, predictions):
"""Uncertainty reward based on logits."""
# TODO(rsepassi): Add support for L1/L2 loss models. Current code only
# works for softmax models.
vocab_size = logits.shape[-1]
assert vocab_size > 1
log_probs = common_layers.log_prob_from_logits(logits)
max_l... |
def _reset_non_empty(self, indices):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations.
"""
reset_video_op = tf.cond(
self._video_condition,
lambda: tf.py_fun... |
def set_random_seed():
"""Set the random seed from flag everywhere."""
tf.set_random_seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed) |
def generate_data_for_problem(problem):
"""Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS."""
training_gen, dev_gen, test_gen = _SUPPORTED_PROBLEM_GENERATORS[problem]
num_train_shards = FLAGS.num_shards or 10
tf.logging.info("Generating training data for %s.", problem)
train_output_files = gene... |
def generate_data_for_env_problem(problem_name):
"""Generate data for `EnvProblem`s."""
assert FLAGS.env_problem_max_env_steps > 0, ("--env_problem_max_env_steps "
"should be greater than zero")
assert FLAGS.env_problem_batch_size > 0, ("--env_problem_batch_size shou... |
def generate_data_for_registered_problem(problem_name):
"""Generate data for a registered problem."""
tf.logging.info("Generating data for %s.", problem_name)
if FLAGS.num_shards:
raise ValueError("--num_shards should not be set for registered Problem.")
problem = registry.problem(problem_name)
task_id = ... |
def _collect_data(directory):
"""Traverses directory collecting input and target files.
Args:
directory: base path to extracted audio and transcripts.
Returns:
list of (media_base, media_filepath, label) tuples
"""
# Returns:
data_files = []
transcripts = [
filename for filename in os.listdir... |
def _file_exists(path, filename):
"""Checks if the filename exists under the path."""
return os.path.isfile(os.path.join(path, filename)) |
def _is_relative(path, filename):
"""Checks if the filename is relative, not absolute."""
return os.path.abspath(os.path.join(path, filename)).startswith(path) |
def define_ppo_step(data_points, hparams, action_space, lr):
"""Define ppo step."""
observation, action, discounted_reward, norm_advantage, old_pdf = data_points
obs_shape = common_layers.shape_list(observation)
observation = tf.reshape(
observation, [obs_shape[0] * obs_shape[1]] + obs_shape[2:]
)
(l... |
def define_ppo_epoch(memory, hparams, action_space, batch_size):
"""PPO epoch."""
observation, reward, done, action, old_pdf, value = memory
# This is to avoid propagating gradients through simulated environment.
observation = tf.stop_gradient(observation)
action = tf.stop_gradient(action)
reward = tf.stop... |
def calculate_generalized_advantage_estimator(
reward, value, done, gae_gamma, gae_lambda):
# pylint: disable=g-doc-args
"""Generalized advantage estimator.
Returns:
GAE estimator. It will be one element shorter than the input; this is
because to compute GAE for [0, ..., N-1] one needs V for [1, ...,... |
def gym_space_spec(gym_space):
"""Returns a reading spec of a gym space.
NOTE: Only implemented currently for Box and Discrete.
Args:
gym_space: instance of gym.spaces whose spec we want.
Returns:
Reading spec for that space.
Raises:
NotImplementedError: For spaces whose reading spec we haven'... |
def cardinality(gym_space):
"""Number of elements that can be represented by the space.
Makes the most sense for Discrete or Box type with integral dtype, ex: number
of actions in an action space.
Args:
gym_space: The gym space.
Returns:
np.int64 number of observations that can be represented by th... |
def image_rmse(predictions, labels, weights_fn=common_layers.weights_all):
"""RMSE but will argmax if last dim is not 1."""
if common_layers.shape_list(predictions)[-1] == 1:
predictions = tf.squeeze(predictions, axis=[-1])
else:
predictions = tf.argmax(predictions, axis=-1)
return padded_rmse(predictio... |
def abs_error(predictions, labels, weights_fn=None):
"""Computes mean(abs(preds-target))."""
del weights_fn # Unused
targets = tf.squeeze(labels, axis=[2, 3])
batch_abs_error = tf.abs(predictions - targets)
den = tf.ones(tf.shape(batch_abs_error), dtype=tf.float32)
return (batch_abs_error, den) |
def padded_variance_explained(predictions,
labels,
weights_fn=common_layers.weights_all):
"""Explained variance, also known as R^2."""
predictions, labels = common_layers.pad_with_zeros(predictions, labels)
targets = labels
weights = weights_fn(targets... |
def padded_accuracy_topk(predictions,
labels,
k,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that top-k predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy_topk", values=[predictions, labels... |
def rounding_sequence_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Sequence accuracy for L1/L2 losses: round down the predictions to ints."""
outputs = tf.squeeze(tf.to_int32(predictions), axis=-1)
weights = weights_fn(la... |
def padded_sequence_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that predictions matches labels everywhere (non-0)."""
# If the last dimension is 1 then we're using L1/L2 loss.
if common_layers.shape_list... |
def sequence_edit_distance(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Average edit distance, ignoring padding 0s.
The score returned is the edit distance divided by the total length of
reference truth and the weight returned is the tot... |
def padded_neg_log_perplexity(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Average log-perplexity exluding padding 0s. No smoothing."""
num, den = common_layers.padded_cross_entropy(
predictions, labels, 0.0, weights_fn=weights_... |
def padded_neg_log_perplexity_with_masking(
predictions,
labels,
features,
weights_fn=None):
"""Average log-perplexity with custom targets_mask."""
del weights_fn
if "targets_mask" not in features:
raise ValueError("masked_neg_log_perplexity requires targets_mask feature")
# Features are 4 ... |
def dmol_neg_log_perplexity(predictions,
labels,
weights_fn=None):
"""Average log-perplexity excluding padding 0s. No smoothing."""
del weights_fn # Unused
num, den = common_layers.dml_loss(
predictions, labels, reduce_sum=False)
return (-num, den) |
def rounding_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Rounding accuracy for L1/L2 losses: round down the predictions to ints."""
outputs = tf.squeeze(tf.to_int32(predictions))
labels = tf.squeeze(labels)
weights = weights_fn(labels)
... |
def padded_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that predictions matches labels on non-0s."""
# If the last dimension is 1 then we're using L1/L2 loss.
if common_layers.shape_list(predictions)[-1] == 1:
return r... |
def multilabel_accuracy_matchk(predictions,
labels,
k,
weights_fn=common_layers.weights_nonzero):
"""Used to evaluate the VQA accuracy.
Let n be the times that predictions appear in labels, then final score
is min(n/k, 1... |
def set_precision(predictions, labels,
weights_fn=common_layers.weights_nonzero):
"""Precision of set predictions.
Args:
predictions : A Tensor of scores of shape [batch, nlabels].
labels: A Tensor of int32s giving true set elements,
of shape [batch, seq_length].
weights_fn: A f... |
def image_summary(predictions, targets, hparams):
"""Reshapes predictions and passes it to tensorboard.
Args:
predictions : The predicted image (logits).
targets : The ground truth.
hparams: model hparams.
Returns:
summary_proto: containing the summary images.
weights: A Tensor of zeros of t... |
def softmax_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate softmax cross entropy given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels a... |
def sigmoid_accuracy_one_hot(logits, labels, weights_fn=None):
"""Calculate accuracy for a set, given one-hot labels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels and weig... |
def sigmoid_recall_one_hot(logits, labels, weights_fn=None):
"""Calculate recall for a set, given one-hot labels and logits.
Predictions are converted to one-hot,
as predictions[example][arg-max(example)] = 1
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batc... |
def sigmoid_cross_entropy_one_hot(logits, labels, weights_fn=None):
"""Calculate sigmoid cross entropy for one-hot lanels and logits.
Args:
logits: Tensor of size [batch-size, o=1, p=1, num-classes]
labels: Tensor of size [batch-size, o=1, p=1, num-classes]
weights_fn: Function that takes in labels and... |
def roc_auc(logits, labels, weights_fn=None):
"""Calculate ROC AUC.
Requires binary classes.
Args:
logits: Tensor of size [batch_size, 1, 1, num_classes]
labels: Tensor of size [batch_size, 1, 1, num_classes]
weights_fn: Function that takes in labels and weighs examples (unused)
Returns:
ROC A... |
def create_evaluation_metrics(problems, model_hparams):
"""Creates the evaluation metrics for the model.
Args:
problems: List of Problem instances.
model_hparams: a set of hparams.
Returns:
dict<metric name, metric function>. The metric functions have signature
(Tensor predictions, features) -> ... |
def create_eager_metrics_for_problem(problem, model_hparams):
"""See create_eager_metrics."""
metric_fns = problem.eval_metric_fns(model_hparams)
problem_hparams = problem.get_hparams(model_hparams)
target_modality = problem_hparams.modality["targets"]
weights_fn = model_hparams.weights_fn.get(
"targets... |
def create_eager_metrics(metric_names, weights_fn=common_layers.weights_all):
"""Create metrics accumulators and averager for Eager mode.
Args:
metric_names: list<str> from Metrics enum
weights_fn: function that takes labels and returns a weights mask. Defaults
to weights of all 1, i.e. common_layers... |
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