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21,900 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | RandomNormalInitializer | def RandomNormalInitializer(stddev=1e-2):
"""An initializer function for random normal coefficients."""
def init(shape, rng):
return (stddev * backend.random.normal(rng, shape)).astype('float32')
return init | python | def RandomNormalInitializer(stddev=1e-2):
"""An initializer function for random normal coefficients."""
def init(shape, rng):
return (stddev * backend.random.normal(rng, shape)).astype('float32')
return init | [
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21,901 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | GlorotNormalInitializer | def GlorotNormalInitializer(out_dim=0, in_dim=1, scale=onp.sqrt(2)):
"""An initializer function for random Glorot-scaled coefficients."""
def init(shape, rng):
fan_in, fan_out = shape[in_dim], shape[out_dim]
size = onp.prod(onp.delete(shape, [in_dim, out_dim]))
std = scale / np.sqrt((fan_in + fan_out) /... | python | def GlorotNormalInitializer(out_dim=0, in_dim=1, scale=onp.sqrt(2)):
"""An initializer function for random Glorot-scaled coefficients."""
def init(shape, rng):
fan_in, fan_out = shape[in_dim], shape[out_dim]
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21,902 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | GlorotUniformInitializer | def GlorotUniformInitializer(out_dim=0, in_dim=1):
"""An initializer function for random uniform Glorot-scaled coefficients."""
def init(shape, rng):
fan_in, fan_out = shape[in_dim], shape[out_dim]
std = np.sqrt(2.0 / (fan_in + fan_out))
a = np.sqrt(3.0) * std
return backend.random.uniform(rng, shap... | python | def GlorotUniformInitializer(out_dim=0, in_dim=1):
"""An initializer function for random uniform Glorot-scaled coefficients."""
def init(shape, rng):
fan_in, fan_out = shape[in_dim], shape[out_dim]
std = np.sqrt(2.0 / (fan_in + fan_out))
a = np.sqrt(3.0) * std
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21,903 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | one_hot | def one_hot(x, size, dtype=np.float32):
"""Make a n+1 dim one-hot array from n dim int-categorical array."""
return np.array(x[..., np.newaxis] == np.arange(size), dtype) | python | def one_hot(x, size, dtype=np.float32):
"""Make a n+1 dim one-hot array from n dim int-categorical array."""
return np.array(x[..., np.newaxis] == np.arange(size), dtype) | [
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21,904 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | padtype_to_pads | def padtype_to_pads(in_shape, window_shape, window_strides, padding):
"""Convert padding string to list of pairs of pad values."""
padding = padding.upper()
if padding == 'SAME':
out_shape = onp.ceil(
onp.true_divide(in_shape, window_strides)).astype(int)
pad_sizes = [max((out_size - 1) * stride +... | python | def padtype_to_pads(in_shape, window_shape, window_strides, padding):
"""Convert padding string to list of pairs of pad values."""
padding = padding.upper()
if padding == 'SAME':
out_shape = onp.ceil(
onp.true_divide(in_shape, window_strides)).astype(int)
pad_sizes = [max((out_size - 1) * stride +... | [
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21,905 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | _flatten_output_shape | def _flatten_output_shape(input_shape, num_axis_to_keep=1):
"""Output shape of a flatten layer."""
if num_axis_to_keep >= len(input_shape):
raise ValueError(
"num_axis_to_keep[%d] should be less than input's rank[%d]" %
(num_axis_to_keep, len(input_shape)))
return tuple(input_shape[:num_axis_t... | python | def _flatten_output_shape(input_shape, num_axis_to_keep=1):
"""Output shape of a flatten layer."""
if num_axis_to_keep >= len(input_shape):
raise ValueError(
"num_axis_to_keep[%d] should be less than input's rank[%d]" %
(num_axis_to_keep, len(input_shape)))
return tuple(input_shape[:num_axis_t... | [
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21,906 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | _batch_norm_new_params | def _batch_norm_new_params(input_shape, rng, axis=(0, 1, 2),
center=True, scale=True, **kwargs):
"""Helper to initialize batch norm params."""
del rng, kwargs
axis = (axis,) if np.isscalar(axis) else axis
shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)
beta = np... | python | def _batch_norm_new_params(input_shape, rng, axis=(0, 1, 2),
center=True, scale=True, **kwargs):
"""Helper to initialize batch norm params."""
del rng, kwargs
axis = (axis,) if np.isscalar(axis) else axis
shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)
beta = np... | [
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21,907 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | BatchNorm | def BatchNorm(x, params, axis=(0, 1, 2), epsilon=1e-5,
center=True, scale=True, **unused_kwargs):
"""Layer construction function for a batch normalization layer."""
mean = np.mean(x, axis, keepdims=True)
# Fast but less numerically-stable variance calculation than np.var.
m1 = np.mean(x**2, axis, ... | python | def BatchNorm(x, params, axis=(0, 1, 2), epsilon=1e-5,
center=True, scale=True, **unused_kwargs):
"""Layer construction function for a batch normalization layer."""
mean = np.mean(x, axis, keepdims=True)
# Fast but less numerically-stable variance calculation than np.var.
m1 = np.mean(x**2, axis, ... | [
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21,908 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | Dropout | def Dropout(x, params, rate=0.0, mode='train', rng=None, **kwargs):
"""Layer construction function for a dropout layer with given rate."""
del params, kwargs
if rng is None:
msg = ('Dropout layer requires apply_fun to be called with a rng keyword '
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"""Layer construction function for a dropout layer with given rate."""
del params, kwargs
if rng is None:
msg = ('Dropout layer requires apply_fun to be called with a rng keyword '
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21,909 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | Conv._kernel_shape | def _kernel_shape(self, input_shape):
"""Helper to calculate the kernel shape."""
kernel_size_iter = iter(self._kernel_size)
return [self._filters if c == 'O' else
input_shape[self._lhs_spec.index('C')] if c == 'I' else
next(kernel_size_iter) for c in self._rhs_spec] | python | def _kernel_shape(self, input_shape):
"""Helper to calculate the kernel shape."""
kernel_size_iter = iter(self._kernel_size)
return [self._filters if c == 'O' else
input_shape[self._lhs_spec.index('C')] if c == 'I' else
next(kernel_size_iter) for c in self._rhs_spec] | [
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21,910 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | Conv._conv_shape_tuple | def _conv_shape_tuple(self, lhs_shape, rhs_shape, strides, pads):
"""Compute the shape of a conv given input shapes in canonical order."""
if isinstance(pads, str):
pads = padtype_to_pads(lhs_shape[2:], rhs_shape[2:], strides, pads)
if len(pads) != len(lhs_shape) - 2:
msg = 'Wrong number of expl... | python | def _conv_shape_tuple(self, lhs_shape, rhs_shape, strides, pads):
"""Compute the shape of a conv given input shapes in canonical order."""
if isinstance(pads, str):
pads = padtype_to_pads(lhs_shape[2:], rhs_shape[2:], strides, pads)
if len(pads) != len(lhs_shape) - 2:
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21,911 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | Conv._conv_general_permutations | def _conv_general_permutations(self, dimension_numbers):
"""Utility for convolution dimension permutations relative to Conv HLO."""
lhs_spec, rhs_spec, out_spec = dimension_numbers
lhs_char, rhs_char, out_char = ('N', 'C'), ('O', 'I'), ('N', 'C')
charpairs = (lhs_char, rhs_char, out_char)
for i, (a,... | python | def _conv_general_permutations(self, dimension_numbers):
"""Utility for convolution dimension permutations relative to Conv HLO."""
lhs_spec, rhs_spec, out_spec = dimension_numbers
lhs_char, rhs_char, out_char = ('N', 'C'), ('O', 'I'), ('N', 'C')
charpairs = (lhs_char, rhs_char, out_char)
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21,912 | tensorflow/tensor2tensor | tensor2tensor/trax/layers/core.py | Conv._conv_general_shape_tuple | def _conv_general_shape_tuple(self, lhs_shape, rhs_shape, window_strides,
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"""Generalized computation of conv shape."""
lhs_perm, rhs_perm, out_perm = self._conv_general_permutations(
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lhs_trans = onp.take(lhs_shape, lhs_p... | python | def _conv_general_shape_tuple(self, lhs_shape, rhs_shape, window_strides,
padding, dimension_numbers):
"""Generalized computation of conv shape."""
lhs_perm, rhs_perm, out_perm = self._conv_general_permutations(
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lhs_trans = onp.take(lhs_shape, lhs_p... | [
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21,913 | tensorflow/tensor2tensor | tensor2tensor/rl/dopamine_connector.py | get_create_agent | def get_create_agent(agent_kwargs):
"""Factory for dopamine agent initialization.
Args:
agent_kwargs: dict of BatchDQNAgent parameters
Returns:
Function(sess, environment, summary_writer) -> BatchDQNAgent instance.
"""
def create_agent(sess, environment, summary_writer=None):
"""Creates a DQN a... | python | def get_create_agent(agent_kwargs):
"""Factory for dopamine agent initialization.
Args:
agent_kwargs: dict of BatchDQNAgent parameters
Returns:
Function(sess, environment, summary_writer) -> BatchDQNAgent instance.
"""
def create_agent(sess, environment, summary_writer=None):
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21,914 | tensorflow/tensor2tensor | tensor2tensor/rl/dopamine_connector.py | get_create_batch_env_fun | def get_create_batch_env_fun(batch_env_fn, time_limit):
"""Factory for dopamine environment initialization function.
Args:
batch_env_fn: function(in_graph: bool) -> batch environment.
time_limit: time steps limit for environment.
Returns:
function (with optional, unused parameters) initializing envi... | python | def get_create_batch_env_fun(batch_env_fn, time_limit):
"""Factory for dopamine environment initialization function.
Args:
batch_env_fn: function(in_graph: bool) -> batch environment.
time_limit: time steps limit for environment.
Returns:
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... | Factory for dopamine environment initialization function.
Args:
batch_env_fn: function(in_graph: bool) -> batch environment.
time_limit: time steps limit for environment.
Returns:
function (with optional, unused parameters) initializing environment. | [
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21,915 | tensorflow/tensor2tensor | tensor2tensor/rl/dopamine_connector.py | _parse_hparams | def _parse_hparams(hparams):
"""Split hparams, based on key prefixes.
Args:
hparams: hyperparameters
Returns:
Tuple of hparams for respectably: agent, optimizer, runner, replay_buffer.
"""
prefixes = ["agent_", "optimizer_", "runner_", "replay_buffer_"]
ret = []
for prefix in prefixes:
ret_... | python | def _parse_hparams(hparams):
"""Split hparams, based on key prefixes.
Args:
hparams: hyperparameters
Returns:
Tuple of hparams for respectably: agent, optimizer, runner, replay_buffer.
"""
prefixes = ["agent_", "optimizer_", "runner_", "replay_buffer_"]
ret = []
for prefix in prefixes:
ret_... | [
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21,916 | tensorflow/tensor2tensor | tensor2tensor/rl/dopamine_connector.py | _DQNAgent._build_replay_buffer | def _build_replay_buffer(self, use_staging):
"""Build WrappedReplayBuffer with custom OutOfGraphReplayBuffer."""
replay_buffer_kwargs = dict(
observation_shape=dqn_agent.NATURE_DQN_OBSERVATION_SHAPE,
stack_size=dqn_agent.NATURE_DQN_STACK_SIZE,
replay_capacity=self._replay_capacity,
... | python | def _build_replay_buffer(self, use_staging):
"""Build WrappedReplayBuffer with custom OutOfGraphReplayBuffer."""
replay_buffer_kwargs = dict(
observation_shape=dqn_agent.NATURE_DQN_OBSERVATION_SHAPE,
stack_size=dqn_agent.NATURE_DQN_STACK_SIZE,
replay_capacity=self._replay_capacity,
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21,917 | tensorflow/tensor2tensor | tensor2tensor/models/video/next_frame_glow.py | next_frame_glow_hparams | def next_frame_glow_hparams():
"""Hparams for next_frame_glow."""
hparams = glow.glow_hparams()
# Possible modes are conditional and unconditional
hparams.add_hparam("gen_mode", "conditional")
hparams.add_hparam("learn_top_scale", False)
hparams.add_hparam("condition_all_levels", True)
# For each video, s... | python | def next_frame_glow_hparams():
"""Hparams for next_frame_glow."""
hparams = glow.glow_hparams()
# Possible modes are conditional and unconditional
hparams.add_hparam("gen_mode", "conditional")
hparams.add_hparam("learn_top_scale", False)
hparams.add_hparam("condition_all_levels", True)
# For each video, s... | [
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21,918 | tensorflow/tensor2tensor | tensor2tensor/models/video/next_frame_glow.py | next_frame_glow_bair_quant | def next_frame_glow_bair_quant():
"""Hparams to reproduce bits-per-pixel results on BAIR action-free dataset."""
hparams = next_frame_glow_hparams()
hparams.video_num_input_frames = 3
hparams.video_num_target_frames = 10
hparams.num_train_frames = 4
hparams.num_cond_latents = 3
hparams.depth = 24
hparam... | python | def next_frame_glow_bair_quant():
"""Hparams to reproduce bits-per-pixel results on BAIR action-free dataset."""
hparams = next_frame_glow_hparams()
hparams.video_num_input_frames = 3
hparams.video_num_target_frames = 10
hparams.num_train_frames = 4
hparams.num_cond_latents = 3
hparams.depth = 24
hparam... | [
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21,919 | tensorflow/tensor2tensor | tensor2tensor/models/video/next_frame_glow.py | next_frame_glow_bair_qual | def next_frame_glow_bair_qual():
"""Hparams for qualitative video generation results."""
hparams = next_frame_glow_bair_quant()
hparams.coupling = "additive"
hparams.temperature = 0.5
hparams.coupling_width = 392
return hparams | python | def next_frame_glow_bair_qual():
"""Hparams for qualitative video generation results."""
hparams = next_frame_glow_bair_quant()
hparams.coupling = "additive"
hparams.temperature = 0.5
hparams.coupling_width = 392
return hparams | [
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21,920 | tensorflow/tensor2tensor | tensor2tensor/models/video/next_frame_glow.py | next_frame_glow_shapes | def next_frame_glow_shapes():
"""Hparams for qualitative and quantitative results on shapes dataset."""
hparams = next_frame_glow_bair_quant()
hparams.video_num_input_frames = 1
hparams.video_num_target_frames = 2
hparams.num_train_frames = 2
hparams.num_cond_latents = 1
hparams.coupling = "additive"
hp... | python | def next_frame_glow_shapes():
"""Hparams for qualitative and quantitative results on shapes dataset."""
hparams = next_frame_glow_bair_quant()
hparams.video_num_input_frames = 1
hparams.video_num_target_frames = 2
hparams.num_train_frames = 2
hparams.num_cond_latents = 1
hparams.coupling = "additive"
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21,921 | tensorflow/tensor2tensor | tensor2tensor/models/basic.py | basic_fc_small | def basic_fc_small():
"""Small fully connected model."""
hparams = common_hparams.basic_params1()
hparams.learning_rate = 0.1
hparams.batch_size = 128
hparams.hidden_size = 256
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_deca... | python | def basic_fc_small():
"""Small fully connected model."""
hparams = common_hparams.basic_params1()
hparams.learning_rate = 0.1
hparams.batch_size = 128
hparams.hidden_size = 256
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_deca... | [
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21,922 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | imagenet_pixelrnn_generator | def imagenet_pixelrnn_generator(tmp_dir,
training,
size=_IMAGENET_SMALL_IMAGE_SIZE):
"""Image generator for Imagenet 64x64 downsampled images.
It assumes that the data has been downloaded from
http://image-net.org/small/*_32x32.tar or
http://image... | python | def imagenet_pixelrnn_generator(tmp_dir,
training,
size=_IMAGENET_SMALL_IMAGE_SIZE):
"""Image generator for Imagenet 64x64 downsampled images.
It assumes that the data has been downloaded from
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http://image... | [
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21,923 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | imagenet_preprocess_example | def imagenet_preprocess_example(example, mode, resize_size=None,
normalize=True):
"""Preprocessing used for Imagenet and similar problems."""
resize_size = resize_size or [299, 299]
assert resize_size[0] == resize_size[1]
image = example["inputs"]
if mode == tf.estimator.ModeK... | python | def imagenet_preprocess_example(example, mode, resize_size=None,
normalize=True):
"""Preprocessing used for Imagenet and similar problems."""
resize_size = resize_size or [299, 299]
assert resize_size[0] == resize_size[1]
image = example["inputs"]
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21,924 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | _crop | def _crop(image, offset_height, offset_width, crop_height, crop_width):
"""Crops the given image using the provided offsets and sizes.
Note that the method doesn't assume we know the input image size but it does
assume we know the input image rank.
Args:
image: `Tensor` image of shape [height, width, chan... | python | def _crop(image, offset_height, offset_width, crop_height, crop_width):
"""Crops the given image using the provided offsets and sizes.
Note that the method doesn't assume we know the input image size but it does
assume we know the input image rank.
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image: `Tensor` image of shape [height, width, chan... | [
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21,925 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | distorted_bounding_box_crop | def distorted_bounding_box_crop(image,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100,
... | python | def distorted_bounding_box_crop(image,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100,
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21,926 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | _at_least_x_are_true | def _at_least_x_are_true(a, b, x):
"""At least `x` of `a` and `b` `Tensors` are true."""
match = tf.equal(a, b)
match = tf.cast(match, tf.int32)
return tf.greater_equal(tf.reduce_sum(match), x) | python | def _at_least_x_are_true(a, b, x):
"""At least `x` of `a` and `b` `Tensors` are true."""
match = tf.equal(a, b)
match = tf.cast(match, tf.int32)
return tf.greater_equal(tf.reduce_sum(match), x) | [
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21,927 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | _do_scale | def _do_scale(image, size):
"""Rescale the image by scaling the smaller spatial dimension to `size`."""
shape = tf.cast(tf.shape(image), tf.float32)
w_greater = tf.greater(shape[0], shape[1])
shape = tf.cond(w_greater,
lambda: tf.cast([shape[0] / shape[1] * size, size], tf.int32),
... | python | def _do_scale(image, size):
"""Rescale the image by scaling the smaller spatial dimension to `size`."""
shape = tf.cast(tf.shape(image), tf.float32)
w_greater = tf.greater(shape[0], shape[1])
shape = tf.cond(w_greater,
lambda: tf.cast([shape[0] / shape[1] * size, size], tf.int32),
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21,928 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | _center_crop | def _center_crop(image, size):
"""Crops to center of image with specified `size`."""
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
offset_height = ((image_height - size) + 1) / 2
offset_width = ((image_width - size) + 1) / 2
image = _crop(image, offset_height, offset_width, size, size)... | python | def _center_crop(image, size):
"""Crops to center of image with specified `size`."""
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
offset_height = ((image_height - size) + 1) / 2
offset_width = ((image_width - size) + 1) / 2
image = _crop(image, offset_height, offset_width, size, size)... | [
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21,929 | tensorflow/tensor2tensor | tensor2tensor/data_generators/imagenet.py | _normalize | def _normalize(image):
"""Normalize the image to zero mean and unit variance."""
offset = tf.constant(MEAN_RGB, shape=[1, 1, 3])
image -= offset
scale = tf.constant(STDDEV_RGB, shape=[1, 1, 3])
image /= scale
return image | python | def _normalize(image):
"""Normalize the image to zero mean and unit variance."""
offset = tf.constant(MEAN_RGB, shape=[1, 1, 3])
image -= offset
scale = tf.constant(STDDEV_RGB, shape=[1, 1, 3])
image /= scale
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21,930 | tensorflow/tensor2tensor | tensor2tensor/trax/learning_rate.py | MultifactorSchedule | def MultifactorSchedule(history=None,
factors="constant * linear_warmup * rsqrt_decay",
constant=0.1,
warmup_steps=100,
decay_factor=0.5,
steps_per_decay=20000):
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21,931 | tensorflow/tensor2tensor | tensor2tensor/trax/learning_rate.py | EvalAdjustingSchedule | def EvalAdjustingSchedule(history,
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steps_to_decrease=20,
improvement_margin=0.001,
decrease_rate=1.5,
history_mode="eval",
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improvement_margin=0.001,
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21,932 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | project_hidden | def project_hidden(x, projection_tensors, hidden_size, num_blocks):
"""Project encoder hidden state under num_blocks using projection tensors.
Args:
x: Encoder hidden state of shape [batch_size, latent_dim, hidden_size].
projection_tensors: Projection tensors used to project the hidden state.
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"""Project encoder hidden state under num_blocks using projection tensors.
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"""Slice encoder hidden state under num_blocks.
Args:
x: Encoder hidden state of shape [batch_size, latent_dim, hidden_size].
hidden_size: Dimension of the latent space.
num_blocks: Number of blocks in DVQ.
Returns:
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"""Slice encoder hidden state under num_blocks.
Args:
x: Encoder hidden state of shape [batch_size, latent_dim, hidden_size].
hidden_size: Dimension of the latent space.
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21,934 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | embedding_lookup | def embedding_lookup(x,
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block_v_size,
bottleneck_kind="dvq",
random_top_k=1,
soft_em=False,
num_samples=1,
do_hard_gumbel_softmax=Fal... | python | def embedding_lookup(x,
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num_blocks,
block_v_size,
bottleneck_kind="dvq",
random_top_k=1,
soft_em=False,
num_samples=1,
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21,935 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | vae | def vae(x, z_size, name=None):
"""Simple variational autoencoder without discretization.
Args:
x: Input to the discretization bottleneck.
z_size: Number of bits, where discrete codes range from 1 to 2**z_size.
name: Name for the bottleneck scope.
Returns:
Embedding function, latent, loss, mu and... | python | def vae(x, z_size, name=None):
"""Simple variational autoencoder without discretization.
Args:
x: Input to the discretization bottleneck.
z_size: Number of bits, where discrete codes range from 1 to 2**z_size.
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21,936 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | gumbel_sample | def gumbel_sample(shape):
"""Sample from the Gumbel distribution, protect from overflows.
Args:
shape: Shape of Gumbel samples.
Returns:
Noise drawn from Gumbel distribution.
"""
uniform_samples = tf.random_uniform(shape, minval=0.00001, maxval=0.99998)
return -tf.log(-tf.log(uniform_samples)) | python | def gumbel_sample(shape):
"""Sample from the Gumbel distribution, protect from overflows.
Args:
shape: Shape of Gumbel samples.
Returns:
Noise drawn from Gumbel distribution.
"""
uniform_samples = tf.random_uniform(shape, minval=0.00001, maxval=0.99998)
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21,937 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | gumbel_softmax | def gumbel_softmax(x,
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mode,
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temperature_warmup_steps=150000,
summary=True,
name=None):
"""Gumbel softmax discretization bottleneck.
Args:
x: Input to the discretization bottlen... | python | def gumbel_softmax(x,
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21,938 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | vq_body | def vq_body(x,
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decay=0.999,
epsilon=1e-5,
soft_em=False,
num_samples=10,
temperature=None,
do_update=True):
"""Discretize each x into one of codebook_size codes."""
x_shape = common_layers.shape... | python | def vq_body(x,
codebook_size,
beta=0.25,
decay=0.999,
epsilon=1e-5,
soft_em=False,
num_samples=10,
temperature=None,
do_update=True):
"""Discretize each x into one of codebook_size codes."""
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21,939 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | vq_loss | def vq_loss(x,
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epsilon=1e-5,
soft_em=False,
num_samples=10,
temperature=None,
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"""Compute the loss of large vocab tensors using a VQAE codebook.
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epsilon=1e-5,
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temperature=None,
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21,940 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | tanh_discrete_bottleneck | def tanh_discrete_bottleneck(x, bottleneck_bits, bottleneck_noise,
discretize_warmup_steps, mode):
"""Simple discretization through tanh, flip bottleneck_noise many bits."""
x = tf.layers.dense(x, bottleneck_bits, name="tanh_discrete_bottleneck")
d0 = tf.stop_gradient(2.0 * tf.to_floa... | python | def tanh_discrete_bottleneck(x, bottleneck_bits, bottleneck_noise,
discretize_warmup_steps, mode):
"""Simple discretization through tanh, flip bottleneck_noise many bits."""
x = tf.layers.dense(x, bottleneck_bits, name="tanh_discrete_bottleneck")
d0 = tf.stop_gradient(2.0 * tf.to_floa... | [
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21,941 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | tanh_discrete_unbottleneck | def tanh_discrete_unbottleneck(x, hidden_size):
"""Simple un-discretization from tanh."""
x = tf.layers.dense(x, hidden_size, name="tanh_discrete_unbottleneck")
return x | python | def tanh_discrete_unbottleneck(x, hidden_size):
"""Simple un-discretization from tanh."""
x = tf.layers.dense(x, hidden_size, name="tanh_discrete_unbottleneck")
return x | [
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21,942 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | isemhash_bottleneck | def isemhash_bottleneck(x,
bottleneck_bits,
bottleneck_noise,
discretize_warmup_steps,
mode,
isemhash_noise_dev=0.5,
isemhash_mix_prob=0.5):
"""Improved semantic hashing bott... | python | def isemhash_bottleneck(x,
bottleneck_bits,
bottleneck_noise,
discretize_warmup_steps,
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isemhash_noise_dev=0.5,
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21,943 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | isemhash_unbottleneck | def isemhash_unbottleneck(x, hidden_size, isemhash_filter_size_multiplier=1.0):
"""Improved semantic hashing un-bottleneck."""
filter_size = int(hidden_size * isemhash_filter_size_multiplier)
x = 0.5 * (x - 1.0) # Move from [-1, 1] to [0, 1].
with tf.variable_scope("isemhash_unbottleneck"):
h1a = tf.layers... | python | def isemhash_unbottleneck(x, hidden_size, isemhash_filter_size_multiplier=1.0):
"""Improved semantic hashing un-bottleneck."""
filter_size = int(hidden_size * isemhash_filter_size_multiplier)
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21,944 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | parametrized_bottleneck | def parametrized_bottleneck(x, hparams):
"""Meta-function calling all the above bottlenecks with hparams."""
if hparams.bottleneck_kind == "tanh_discrete":
d, _ = tanh_discrete_bottleneck(
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hparams.discretize_warmup_steps, hparams.mode)
... | python | def parametrized_bottleneck(x, hparams):
"""Meta-function calling all the above bottlenecks with hparams."""
if hparams.bottleneck_kind == "tanh_discrete":
d, _ = tanh_discrete_bottleneck(
x, hparams.bottleneck_bits, hparams.bottleneck_noise * 0.5,
hparams.discretize_warmup_steps, hparams.mode)
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21,945 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | parametrized_unbottleneck | def parametrized_unbottleneck(x, hidden_size, hparams):
"""Meta-function calling all the above un-bottlenecks with hparams."""
if hparams.bottleneck_kind == "tanh_discrete":
return tanh_discrete_unbottleneck(x, hidden_size)
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"""Meta-function calling all the above un-bottlenecks with hparams."""
if hparams.bottleneck_kind == "tanh_discrete":
return tanh_discrete_unbottleneck(x, hidden_size)
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21,946 | tensorflow/tensor2tensor | tensor2tensor/layers/discretization.py | iaf_hparams | def iaf_hparams(hidden_size=512, filter_size=4096):
"""Create hyperpameters for inverse autoregressive flows.
Args:
hidden_size: Width of attention layers and neural network output layer.
filter_size: Hidden layer width for neural network.
Returns:
hparams: Hyperpameters with basic presets for inver... | python | def iaf_hparams(hidden_size=512, filter_size=4096):
"""Create hyperpameters for inverse autoregressive flows.
Args:
hidden_size: Width of attention layers and neural network output layer.
filter_size: Hidden layer width for neural network.
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21,947 | tensorflow/tensor2tensor | tensor2tensor/data_generators/lm1b.py | _original_vocab | def _original_vocab(tmp_dir):
"""Returns a set containing the original vocabulary.
This is important for comparing with published results.
Args:
tmp_dir: directory containing dataset.
Returns:
a set of strings
"""
vocab_url = ("http://download.tensorflow.org/models/LM_LSTM_CNN/"
"v... | python | def _original_vocab(tmp_dir):
"""Returns a set containing the original vocabulary.
This is important for comparing with published results.
Args:
tmp_dir: directory containing dataset.
Returns:
a set of strings
"""
vocab_url = ("http://download.tensorflow.org/models/LM_LSTM_CNN/"
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21,948 | tensorflow/tensor2tensor | tensor2tensor/data_generators/lm1b.py | _replace_oov | def _replace_oov(original_vocab, line):
"""Replace out-of-vocab words with "UNK".
This maintains compatibility with published results.
Args:
original_vocab: a set of strings (The standard vocabulary for the dataset)
line: a unicode string - a space-delimited sequence of words.
Returns:
a unicode ... | python | def _replace_oov(original_vocab, line):
"""Replace out-of-vocab words with "UNK".
This maintains compatibility with published results.
Args:
original_vocab: a set of strings (The standard vocabulary for the dataset)
line: a unicode string - a space-delimited sequence of words.
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a unicode ... | [
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21,949 | tensorflow/tensor2tensor | tensor2tensor/models/research/cycle_gan.py | lossfn | def lossfn(real_input, fake_input, compress, hparams, lsgan, name):
"""Loss function."""
eps = 1e-12
with tf.variable_scope(name):
d1 = discriminator(real_input, compress, hparams, "discriminator")
d2 = discriminator(fake_input, compress, hparams, "discriminator",
reuse=True)
if... | python | def lossfn(real_input, fake_input, compress, hparams, lsgan, name):
"""Loss function."""
eps = 1e-12
with tf.variable_scope(name):
d1 = discriminator(real_input, compress, hparams, "discriminator")
d2 = discriminator(fake_input, compress, hparams, "discriminator",
reuse=True)
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21,950 | tensorflow/tensor2tensor | tensor2tensor/models/research/cycle_gan.py | cycle_gan_internal | def cycle_gan_internal(inputs, targets, _, hparams):
"""Cycle GAN, main step used for training."""
with tf.variable_scope("cycle_gan"):
# Embed inputs and targets.
inputs_orig, targets_orig = tf.to_int32(inputs), tf.to_int32(targets)
inputs = common_layers.embedding(
inputs_orig, hparams.vocab_s... | python | def cycle_gan_internal(inputs, targets, _, hparams):
"""Cycle GAN, main step used for training."""
with tf.variable_scope("cycle_gan"):
# Embed inputs and targets.
inputs_orig, targets_orig = tf.to_int32(inputs), tf.to_int32(targets)
inputs = common_layers.embedding(
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21,951 | tensorflow/tensor2tensor | tensor2tensor/models/video/nfg_interpolate.py | decode_hparams | def decode_hparams(overrides=""):
"""Hparams for decoding."""
hparams = decoding.decode_hparams()
# Number of interpolations between [0.0, 1.0].
hparams.add_hparam("num_interp", 11)
# Which level(s) to interpolate.
hparams.add_hparam("level_interp", [0, 1, 2])
# "all" or "ranked", interpolate all channels... | python | def decode_hparams(overrides=""):
"""Hparams for decoding."""
hparams = decoding.decode_hparams()
# Number of interpolations between [0.0, 1.0].
hparams.add_hparam("num_interp", 11)
# Which level(s) to interpolate.
hparams.add_hparam("level_interp", [0, 1, 2])
# "all" or "ranked", interpolate all channels... | [
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21,952 | tensorflow/tensor2tensor | tensor2tensor/models/video/nfg_interpolate.py | preprocess_frame | def preprocess_frame(frame):
"""Preprocess frame.
1. Converts [0, 255] to [-0.5, 0.5]
2. Adds uniform noise.
Args:
frame: 3-D Tensor representing pixels.
Returns:
frame: 3-D Tensor with values in between [-0.5, 0.5]
"""
# Normalize from [0.0, 1.0] -> [-0.5, 0.5]
frame = common_layers.convert_r... | python | def preprocess_frame(frame):
"""Preprocess frame.
1. Converts [0, 255] to [-0.5, 0.5]
2. Adds uniform noise.
Args:
frame: 3-D Tensor representing pixels.
Returns:
frame: 3-D Tensor with values in between [-0.5, 0.5]
"""
# Normalize from [0.0, 1.0] -> [-0.5, 0.5]
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21,953 | tensorflow/tensor2tensor | tensor2tensor/models/video/nfg_interpolate.py | frame_to_latents | def frame_to_latents(frame, hparams):
"""Encode frames to latents."""
# Preprocess
frame = preprocess_frame(frame)
# Encode [X_t] to [z^1_t, z^2_t .. z^l_t]
glow_vals = glow_ops.encoder_decoder(
"codec", frame, hparams, eps=None, reverse=False)
z_top, _, level_eps, _, _ = glow_vals
return z_top, le... | python | def frame_to_latents(frame, hparams):
"""Encode frames to latents."""
# Preprocess
frame = preprocess_frame(frame)
# Encode [X_t] to [z^1_t, z^2_t .. z^l_t]
glow_vals = glow_ops.encoder_decoder(
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z_top, _, level_eps, _, _ = glow_vals
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21,954 | tensorflow/tensor2tensor | tensor2tensor/models/video/nfg_interpolate.py | latents_to_frames | def latents_to_frames(z_top_interp, level_eps_interp, hparams):
"""Decodes latents to frames."""
# Decode [z^1_t, z^2_t .. z^l_t] to [X_t]
images, _, _, _ = glow_ops.encoder_decoder(
"codec", z_top_interp, hparams, eps=level_eps_interp, reverse=True)
images = glow_ops.postprocess(images)
return images | python | def latents_to_frames(z_top_interp, level_eps_interp, hparams):
"""Decodes latents to frames."""
# Decode [z^1_t, z^2_t .. z^l_t] to [X_t]
images, _, _, _ = glow_ops.encoder_decoder(
"codec", z_top_interp, hparams, eps=level_eps_interp, reverse=True)
images = glow_ops.postprocess(images)
return images | [
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21,955 | tensorflow/tensor2tensor | tensor2tensor/models/video/nfg_interpolate.py | interpolate | def interpolate(features, hparams, decode_hp):
"""Interpolate between the first input frame and last target frame.
Args:
features: dict of tensors
hparams: HParams, training hparams.
decode_hp: HParams, decode hparams.
Returns:
images: interpolated images, 4-D Tensor, shape=(num_interp, H, W, C)
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"""Interpolate between the first input frame and last target frame.
Args:
features: dict of tensors
hparams: HParams, training hparams.
decode_hp: HParams, decode hparams.
Returns:
images: interpolated images, 4-D Tensor, shape=(num_interp, H, W, C)
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21,956 | tensorflow/tensor2tensor | tensor2tensor/models/video/nfg_interpolate.py | get_summaries_log_dir | def get_summaries_log_dir(decode_hp, output_dir, dataset_split):
"""Get nested summaries_log_dir based on decode_hp."""
child_dir = decode_hp.summaries_log_dir
level_dir = "".join([str(level) for level in decode_hp.level_interp])
if decode_hp.channel_interp == "all":
rank_dir = "all"
else:
rank_dir = ... | python | def get_summaries_log_dir(decode_hp, output_dir, dataset_split):
"""Get nested summaries_log_dir based on decode_hp."""
child_dir = decode_hp.summaries_log_dir
level_dir = "".join([str(level) for level in decode_hp.level_interp])
if decode_hp.channel_interp == "all":
rank_dir = "all"
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21,957 | tensorflow/tensor2tensor | tensor2tensor/models/video/nfg_interpolate.py | interpolations_to_summary | def interpolations_to_summary(sample_ind, interpolations, first_frame,
last_frame, hparams, decode_hp):
"""Converts interpolated frames into tf summaries.
The summaries consists of:
1. Image summary corresponding to the first frame.
2. Image summary corresponding to the last f... | python | def interpolations_to_summary(sample_ind, interpolations, first_frame,
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21,958 | tensorflow/tensor2tensor | tensor2tensor/models/video/epva_params.py | next_frame_epva | def next_frame_epva():
"""EPVA hparams."""
hparams = basic_deterministic_params.next_frame_basic_deterministic()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
hparams.bottom = {
"inputs": modalities.video_raw_bottom,
"targets": modalities.video_raw_targets_bottom,
}
hp... | python | def next_frame_epva():
"""EPVA hparams."""
hparams = basic_deterministic_params.next_frame_basic_deterministic()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
hparams.bottom = {
"inputs": modalities.video_raw_bottom,
"targets": modalities.video_raw_targets_bottom,
}
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21,959 | tensorflow/tensor2tensor | tensor2tensor/utils/multistep_optimizer.py | MultistepAdamOptimizer._create_slots | def _create_slots(self, var_list):
"""Create slot variables for Adam with accumulated gradients."""
super(MultistepAdamOptimizer, self)._create_slots(var_list)
first_var = min(var_list, key=lambda x: x.name)
self._create_non_slot_variable(initial_value=0 if self._n == 1 else 1,
... | python | def _create_slots(self, var_list):
"""Create slot variables for Adam with accumulated gradients."""
super(MultistepAdamOptimizer, self)._create_slots(var_list)
first_var = min(var_list, key=lambda x: x.name)
self._create_non_slot_variable(initial_value=0 if self._n == 1 else 1,
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21,960 | tensorflow/tensor2tensor | tensor2tensor/utils/multistep_optimizer.py | MultistepAdamOptimizer._apply_cond | def _apply_cond(self, apply_fn, grad, var, *args, **kwargs):
"""Apply conditionally if counter is zero."""
grad_acc = self.get_slot(var, "grad_acc")
def apply_adam(grad_acc, apply_fn, grad, var, *args, **kwargs):
total_grad = (grad_acc + grad) / tf.cast(self._n_t, grad.dtype)
adam_op = apply_fn... | python | def _apply_cond(self, apply_fn, grad, var, *args, **kwargs):
"""Apply conditionally if counter is zero."""
grad_acc = self.get_slot(var, "grad_acc")
def apply_adam(grad_acc, apply_fn, grad, var, *args, **kwargs):
total_grad = (grad_acc + grad) / tf.cast(self._n_t, grad.dtype)
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21,961 | tensorflow/tensor2tensor | tensor2tensor/utils/multistep_optimizer.py | MultistepAdamOptimizer._finish | def _finish(self, update_ops, name_scope):
"""Updates beta_power variables every n batches and incrs counter."""
iter_ = self._get_iter_variable()
beta1_power, beta2_power = self._get_beta_accumulators()
with tf.control_dependencies(update_ops):
with tf.colocate_with(iter_):
def update_be... | python | def _finish(self, update_ops, name_scope):
"""Updates beta_power variables every n batches and incrs counter."""
iter_ = self._get_iter_variable()
beta1_power, beta2_power = self._get_beta_accumulators()
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21,962 | tensorflow/tensor2tensor | tensor2tensor/utils/devices.py | data_parallelism_from_flags | def data_parallelism_from_flags(daisy_chain_variables=True, all_workers=False):
"""Over which devices do we split each training batch.
In old-fashioned async mode, we split the batch over all GPUs on the
current worker.
In sync mode, we split the batch over all the parameter server GPUs.
This function retu... | python | def data_parallelism_from_flags(daisy_chain_variables=True, all_workers=False):
"""Over which devices do we split each training batch.
In old-fashioned async mode, we split the batch over all GPUs on the
current worker.
In sync mode, we split the batch over all the parameter server GPUs.
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21,963 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_lm.py | concat_generator | def concat_generator(filename, up_threshold, low_threshold=10):
"""Generate concatenated lines from file upto up_threshold characters."""
txt = ""
for line in tf.gfile.Open(filename):
line = line.strip()
if len(txt) + len(line) + 1 >= up_threshold:
ret = txt
txt = ""
# We don't yield ver... | python | def concat_generator(filename, up_threshold, low_threshold=10):
"""Generate concatenated lines from file upto up_threshold characters."""
txt = ""
for line in tf.gfile.Open(filename):
line = line.strip()
if len(txt) + len(line) + 1 >= up_threshold:
ret = txt
txt = ""
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21,964 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_lm.py | mix_generators | def mix_generators(generator_list):
"""Given python generators, generate from one, then from another, etc."""
i = 0
l = len(generator_list)
stopiters_seen = 0
while stopiters_seen <= l:
try:
yield six.next(generator_list[i % l])
i += 1
stopiters_seen = 0
except StopIteration:
i... | python | def mix_generators(generator_list):
"""Given python generators, generate from one, then from another, etc."""
i = 0
l = len(generator_list)
stopiters_seen = 0
while stopiters_seen <= l:
try:
yield six.next(generator_list[i % l])
i += 1
stopiters_seen = 0
except StopIteration:
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21,965 | tensorflow/tensor2tensor | tensor2tensor/data_generators/translate.py | compute_bleu_summaries | def compute_bleu_summaries(hook_args):
"""Compute BLEU core summaries using the decoder output.
Args:
hook_args: DecodeHookArgs namedtuple
Returns:
A list of tf.Summary values if hook_args.hparams contains the
reference file and the translated file.
"""
decode_hparams = hook_args.decode_hparams
... | python | def compute_bleu_summaries(hook_args):
"""Compute BLEU core summaries using the decoder output.
Args:
hook_args: DecodeHookArgs namedtuple
Returns:
A list of tf.Summary values if hook_args.hparams contains the
reference file and the translated file.
"""
decode_hparams = hook_args.decode_hparams
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21,966 | tensorflow/tensor2tensor | tensor2tensor/data_generators/translate.py | _preprocess_sgm | def _preprocess_sgm(line, is_sgm):
"""Preprocessing to strip tags in SGM files."""
if not is_sgm:
return line
# In SGM files, remove <srcset ...>, <p>, <doc ...> lines.
if line.startswith("<srcset") or line.startswith("</srcset"):
return ""
if line.startswith("<doc") or line.startswith("</doc"):
r... | python | def _preprocess_sgm(line, is_sgm):
"""Preprocessing to strip tags in SGM files."""
if not is_sgm:
return line
# In SGM files, remove <srcset ...>, <p>, <doc ...> lines.
if line.startswith("<srcset") or line.startswith("</srcset"):
return ""
if line.startswith("<doc") or line.startswith("</doc"):
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21,967 | tensorflow/tensor2tensor | tensor2tensor/data_generators/translate.py | TranslateDistillProblem.get_or_create_vocab | def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
"""Get vocab for distill problems."""
# We assume that vocab file is present in data_dir directory where the
# data generated will be stored.
vocab_filepath = os.path.join(data_dir, self.vocab_filename)
encoder = text_encoder.Subword... | python | def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
"""Get vocab for distill problems."""
# We assume that vocab file is present in data_dir directory where the
# data generated will be stored.
vocab_filepath = os.path.join(data_dir, self.vocab_filename)
encoder = text_encoder.Subword... | [
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21,968 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_trainer.py | set_hparams_from_args | def set_hparams_from_args(args):
"""Set hparams overrides from unparsed args list."""
if not args:
return
hp_prefix = "--hp_"
tf.logging.info("Found unparsed command-line arguments. Checking if any "
"start with %s and interpreting those as hparams "
"settings.", hp_pref... | python | def set_hparams_from_args(args):
"""Set hparams overrides from unparsed args list."""
if not args:
return
hp_prefix = "--hp_"
tf.logging.info("Found unparsed command-line arguments. Checking if any "
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21,969 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_trainer.py | create_hparams | def create_hparams():
"""Create hparams."""
if FLAGS.use_tpu and "tpu" not in FLAGS.hparams_set:
tf.logging.warn("Not all hyperparameter sets work on TPU. "
"Prefer hparams_sets with a '_tpu' suffix, "
"e.g. transformer_tpu, if available for your model.")
hparams_path =... | python | def create_hparams():
"""Create hparams."""
if FLAGS.use_tpu and "tpu" not in FLAGS.hparams_set:
tf.logging.warn("Not all hyperparameter sets work on TPU. "
"Prefer hparams_sets with a '_tpu' suffix, "
"e.g. transformer_tpu, if available for your model.")
hparams_path =... | [
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21,970 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_trainer.py | save_metadata | def save_metadata(hparams):
"""Saves FLAGS and hparams to output_dir."""
output_dir = os.path.expanduser(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
# Save FLAGS in txt file
if hasattr(FLAGS, "flags_into_string"):
flags_str = FLAGS.flags_into_string()
t2t_f... | python | def save_metadata(hparams):
"""Saves FLAGS and hparams to output_dir."""
output_dir = os.path.expanduser(FLAGS.output_dir)
if not tf.gfile.Exists(output_dir):
tf.gfile.MakeDirs(output_dir)
# Save FLAGS in txt file
if hasattr(FLAGS, "flags_into_string"):
flags_str = FLAGS.flags_into_string()
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21,971 | tensorflow/tensor2tensor | tensor2tensor/models/xception.py | residual_block | def residual_block(x, hparams):
"""A stack of convolution blocks with residual connection."""
k = (hparams.kernel_height, hparams.kernel_width)
dilations_and_kernels = [((1, 1), k) for _ in range(3)]
y = common_layers.subseparable_conv_block(
x,
hparams.hidden_size,
dilations_and_kernels,
... | python | def residual_block(x, hparams):
"""A stack of convolution blocks with residual connection."""
k = (hparams.kernel_height, hparams.kernel_width)
dilations_and_kernels = [((1, 1), k) for _ in range(3)]
y = common_layers.subseparable_conv_block(
x,
hparams.hidden_size,
dilations_and_kernels,
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21,972 | tensorflow/tensor2tensor | tensor2tensor/models/xception.py | xception_internal | def xception_internal(inputs, hparams):
"""Xception body."""
with tf.variable_scope("xception"):
cur = inputs
if cur.get_shape().as_list()[1] > 200:
# Large image, Xception entry flow
cur = xception_entry(cur, hparams.hidden_size)
else:
# Small image, conv
cur = common_layers.co... | python | def xception_internal(inputs, hparams):
"""Xception body."""
with tf.variable_scope("xception"):
cur = inputs
if cur.get_shape().as_list()[1] > 200:
# Large image, Xception entry flow
cur = xception_entry(cur, hparams.hidden_size)
else:
# Small image, conv
cur = common_layers.co... | [
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21,973 | tensorflow/tensor2tensor | tensor2tensor/models/xception.py | xception_entry | def xception_entry(inputs, hidden_dim):
"""Xception entry flow."""
with tf.variable_scope("xception_entry"):
def xnet_resblock(x, filters, res_relu, name):
"""Resblock."""
with tf.variable_scope(name):
y = common_layers.separable_conv_block(
x,
filters, [((1, 1), (3,... | python | def xception_entry(inputs, hidden_dim):
"""Xception entry flow."""
with tf.variable_scope("xception_entry"):
def xnet_resblock(x, filters, res_relu, name):
"""Resblock."""
with tf.variable_scope(name):
y = common_layers.separable_conv_block(
x,
filters, [((1, 1), (3,... | [
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21,974 | tensorflow/tensor2tensor | tensor2tensor/models/xception.py | xception_exit | def xception_exit(inputs):
"""Xception exit flow."""
with tf.variable_scope("xception_exit"):
x = inputs
x_shape = x.get_shape().as_list()
if x_shape[1] is None or x_shape[2] is None:
length_float = tf.to_float(tf.shape(x)[1])
length_float *= tf.to_float(tf.shape(x)[2])
spatial_dim_flo... | python | def xception_exit(inputs):
"""Xception exit flow."""
with tf.variable_scope("xception_exit"):
x = inputs
x_shape = x.get_shape().as_list()
if x_shape[1] is None or x_shape[2] is None:
length_float = tf.to_float(tf.shape(x)[1])
length_float *= tf.to_float(tf.shape(x)[2])
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21,975 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/html.py | get_text_from_html | def get_text_from_html(html):
"""Returns a plaintext representation of HTML content."""
try:
soup = bs4.BeautifulSoup(html, "html.parser")
except: # pylint: disable=bare-except
# Some docs don't parse
return ""
# Remove script and style tags
for s in soup(["script", "style"]):
s.decompose()
... | python | def get_text_from_html(html):
"""Returns a plaintext representation of HTML content."""
try:
soup = bs4.BeautifulSoup(html, "html.parser")
except: # pylint: disable=bare-except
# Some docs don't parse
return ""
# Remove script and style tags
for s in soup(["script", "style"]):
s.decompose()
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21,976 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/html.py | _soup_strings | def _soup_strings(soup):
"""Return text strings in soup."""
paragraph_tags = set([
"caption", "details", "h1", "h2", "h3", "h4", "h5", "h6", "li", "p", "td",
"div", "span"
])
skip_children = None
for descendant in soup.descendants:
# If we've treated a tag as a contiguous paragraph, don't re-... | python | def _soup_strings(soup):
"""Return text strings in soup."""
paragraph_tags = set([
"caption", "details", "h1", "h2", "h3", "h4", "h5", "h6", "li", "p", "td",
"div", "span"
])
skip_children = None
for descendant in soup.descendants:
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21,977 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformerpp_base_14l_8h_big_uncond_dr03_dan_p | def imagetransformerpp_base_14l_8h_big_uncond_dr03_dan_p():
"""Gets to 2.92 in just under 4 days on 8 p100s."""
hparams = imagetransformerpp_base_12l_8h_big_uncond_dr03_dan_l()
hparams.num_decoder_layers = 14
hparams.batch_size = 8
hparams.layer_prepostprocess_dropout = 0.2
return hparams | python | def imagetransformerpp_base_14l_8h_big_uncond_dr03_dan_p():
"""Gets to 2.92 in just under 4 days on 8 p100s."""
hparams = imagetransformerpp_base_12l_8h_big_uncond_dr03_dan_l()
hparams.num_decoder_layers = 14
hparams.batch_size = 8
hparams.layer_prepostprocess_dropout = 0.2
return hparams | [
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21,978 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformerpp_base_5l_8h_big_uncond_dr00_dan_g_bs1 | def imagetransformerpp_base_5l_8h_big_uncond_dr00_dan_g_bs1():
"""For 256x256."""
hparams = imagetransformerpp_base_10l_8h_big_uncond_dr03_dan_g()
# TODO(trandustin): I forgot to set this in the runs! Maybe it's not used in
# image transformer training implementation?
# hparams.img_len = 256
hparams.max_len... | python | def imagetransformerpp_base_5l_8h_big_uncond_dr00_dan_g_bs1():
"""For 256x256."""
hparams = imagetransformerpp_base_10l_8h_big_uncond_dr03_dan_g()
# TODO(trandustin): I forgot to set this in the runs! Maybe it's not used in
# image transformer training implementation?
# hparams.img_len = 256
hparams.max_len... | [
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21,979 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_base_8l_8h_big_cond_dr03_dan_dilated | def imagetransformer_base_8l_8h_big_cond_dr03_dan_dilated():
"""Dilated hparams."""
hparams = imagetransformer_base_8l_8h_big_cond_dr03_dan()
hparams.gap_sizes = [0, 16, 64, 0, 16, 64, 128, 0]
hparams.dec_attention_type = cia.AttentionType.DILATED
hparams.block_length = 128
hparams.block_width = 128
hpara... | python | def imagetransformer_base_8l_8h_big_cond_dr03_dan_dilated():
"""Dilated hparams."""
hparams = imagetransformer_base_8l_8h_big_cond_dr03_dan()
hparams.gap_sizes = [0, 16, 64, 0, 16, 64, 128, 0]
hparams.dec_attention_type = cia.AttentionType.DILATED
hparams.block_length = 128
hparams.block_width = 128
hpara... | [
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21,980 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer1d_base_8l_64by64 | def imagetransformer1d_base_8l_64by64():
"""hparams fo 12 layer big 1d model for imagenet 64x64."""
hparams = image_transformer_base()
hparams.num_heads = 8
hparams.hidden_size = 512
hparams.filter_size = 2048
hparams.num_decoder_layers = 8
hparams.batch_size = 1
hparams.block_length = 512
hparams.blo... | python | def imagetransformer1d_base_8l_64by64():
"""hparams fo 12 layer big 1d model for imagenet 64x64."""
hparams = image_transformer_base()
hparams.num_heads = 8
hparams.hidden_size = 512
hparams.filter_size = 2048
hparams.num_decoder_layers = 8
hparams.batch_size = 1
hparams.block_length = 512
hparams.blo... | [
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21,981 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_moe_tiny | def imagetransformer_moe_tiny():
"""Set of hyperparameters for a very small imagetransformer with MoE."""
hparams = imagetransformer_tiny()
hparams.hidden_size = 64
hparams.batch_size = 1
hparams.num_hidden_layers = 3
hparams.dec_attention_type = cia.AttentionType.MOE_LOCAL_1D
hparams.add_hparam("moe_laye... | python | def imagetransformer_moe_tiny():
"""Set of hyperparameters for a very small imagetransformer with MoE."""
hparams = imagetransformer_tiny()
hparams.hidden_size = 64
hparams.batch_size = 1
hparams.num_hidden_layers = 3
hparams.dec_attention_type = cia.AttentionType.MOE_LOCAL_1D
hparams.add_hparam("moe_laye... | [
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21,982 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_sep_channels_8l_tpu | def imagetransformer_sep_channels_8l_tpu():
"""Hparams for training imagetransformer on tpu."""
hparams = imagetransformer_sep_channels_8l()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.shared_embedding_and_softmax_weights = False
retu... | python | def imagetransformer_sep_channels_8l_tpu():
"""Hparams for training imagetransformer on tpu."""
hparams = imagetransformer_sep_channels_8l()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.shared_embedding_and_softmax_weights = False
retu... | [
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21,983 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_b10l_4h_big_uncond_dr03_tpu | def imagetransformer_b10l_4h_big_uncond_dr03_tpu():
"""Small model for tpu cifar 10."""
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 10
hparams.block_len... | python | def imagetransformer_b10l_4h_big_uncond_dr03_tpu():
"""Small model for tpu cifar 10."""
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 10
hparams.block_len... | [
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21,984 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_b10l_dr03_moe_tpu | def imagetransformer_b10l_dr03_moe_tpu():
"""Moe tpu params."""
hparams = imagetransformer_b10l_4h_big_uncond_dr03_tpu()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 10
hparams.layer_preprocess_sequence = "none"
... | python | def imagetransformer_b10l_dr03_moe_tpu():
"""Moe tpu params."""
hparams = imagetransformer_b10l_4h_big_uncond_dr03_tpu()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 10
hparams.layer_preprocess_sequence = "none"
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21,985 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_cifar_tpu_range | def imagetransformer_cifar_tpu_range(rhp):
"""Range of hyperparameters for vizier."""
# After starting from base, set intervals for some parameters.
rhp.set_float("learning_rate", 0.01, 1.0, scale=rhp.LOG_SCALE)
rhp.set_discrete("num_decoder_layers", [8, 10, 12, 14, 16])
rhp.set_discrete("hidden_size", [256, ... | python | def imagetransformer_cifar_tpu_range(rhp):
"""Range of hyperparameters for vizier."""
# After starting from base, set intervals for some parameters.
rhp.set_float("learning_rate", 0.01, 1.0, scale=rhp.LOG_SCALE)
rhp.set_discrete("num_decoder_layers", [8, 10, 12, 14, 16])
rhp.set_discrete("hidden_size", [256, ... | [
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21,986 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_b12l_4h_b128_h512_uncond_dr01_im | def imagetransformer_b12l_4h_b128_h512_uncond_dr01_im():
"""TPU related imagenet model."""
hparams = imagetransformer_b12l_4h_b256_uncond_dr03_tpu()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
hparams.learning_rate_w... | python | def imagetransformer_b12l_4h_b128_h512_uncond_dr01_im():
"""TPU related imagenet model."""
hparams = imagetransformer_b12l_4h_b256_uncond_dr03_tpu()
update_hparams_for_tpu(hparams)
hparams.batch_size = 4
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
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21,987 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_b12l_4h_b128_uncond_dr03_tpu | def imagetransformer_b12l_4h_b128_uncond_dr03_tpu():
"""TPU config for cifar 10."""
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 2
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 12
hparams.block_length ... | python | def imagetransformer_b12l_4h_b128_uncond_dr03_tpu():
"""TPU config for cifar 10."""
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 2
hparams.num_heads = 4 # heads are expensive on tpu
hparams.num_decoder_layers = 12
hparams.block_length ... | [
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21,988 | tensorflow/tensor2tensor | tensor2tensor/models/image_transformer.py | imagetransformer_b12l_8h_b256_uncond_dr03_tpu | def imagetransformer_b12l_8h_b256_uncond_dr03_tpu():
"""TPU related 12 layer 8 heads model."""
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 2
hparams.num_heads = 8 # heads are expensive on tpu
hparams.num_decoder_layers = 12
hparams.bl... | python | def imagetransformer_b12l_8h_b256_uncond_dr03_tpu():
"""TPU related 12 layer 8 heads model."""
hparams = imagetransformer_bas8l_8h_big_uncond_dr03_imgnet()
update_hparams_for_tpu(hparams)
hparams.batch_size = 2
hparams.num_heads = 8 # heads are expensive on tpu
hparams.num_decoder_layers = 12
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21,989 | tensorflow/tensor2tensor | tensor2tensor/rl/restarter.py | Restarter.training_loop | def training_loop(self):
"""Context manager wrapping the training loop, updates step counters."""
if not self.restarting:
self._write_counters(self._local_step_at_start, self._global_step)
tf.logging.info(
"Training %s up to %d, %d to go", self.model_mode,
self.target_local_step, self... | python | def training_loop(self):
"""Context manager wrapping the training loop, updates step counters."""
if not self.restarting:
self._write_counters(self._local_step_at_start, self._global_step)
tf.logging.info(
"Training %s up to %d, %d to go", self.model_mode,
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21,990 | tensorflow/tensor2tensor | tensor2tensor/data_generators/ptb.py | _read_words | def _read_words(filename):
"""Reads words from a file."""
with tf.gfile.GFile(filename, "r") as f:
if sys.version_info[0] >= 3:
return f.read().replace("\n", " %s " % EOS).split()
else:
return f.read().decode("utf-8").replace("\n", " %s " % EOS).split() | python | def _read_words(filename):
"""Reads words from a file."""
with tf.gfile.GFile(filename, "r") as f:
if sys.version_info[0] >= 3:
return f.read().replace("\n", " %s " % EOS).split()
else:
return f.read().decode("utf-8").replace("\n", " %s " % EOS).split() | [
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21,991 | tensorflow/tensor2tensor | tensor2tensor/data_generators/ptb.py | _build_vocab | def _build_vocab(filename, vocab_path, vocab_size):
"""Reads a file to build a vocabulary of `vocab_size` most common words.
The vocabulary is sorted by occurrence count and has one word per line.
Originally from:
https://github.com/tensorflow/models/blob/master/tutorials/rnn/ptb/reader.py
Args:
file... | python | def _build_vocab(filename, vocab_path, vocab_size):
"""Reads a file to build a vocabulary of `vocab_size` most common words.
The vocabulary is sorted by occurrence count and has one word per line.
Originally from:
https://github.com/tensorflow/models/blob/master/tutorials/rnn/ptb/reader.py
Args:
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21,992 | tensorflow/tensor2tensor | tensor2tensor/data_generators/ptb.py | _get_token_encoder | def _get_token_encoder(vocab_dir, vocab_name, filename):
"""Reads from file and returns a `TokenTextEncoder` for the vocabulary."""
vocab_path = os.path.join(vocab_dir, vocab_name)
if not tf.gfile.Exists(vocab_path):
_build_vocab(filename, vocab_path, 10000)
return text_encoder.TokenTextEncoder(vocab_path) | python | def _get_token_encoder(vocab_dir, vocab_name, filename):
"""Reads from file and returns a `TokenTextEncoder` for the vocabulary."""
vocab_path = os.path.join(vocab_dir, vocab_name)
if not tf.gfile.Exists(vocab_path):
_build_vocab(filename, vocab_path, 10000)
return text_encoder.TokenTextEncoder(vocab_path) | [
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21,993 | tensorflow/tensor2tensor | tensor2tensor/visualization/attention.py | resize | def resize(att_mat, max_length=None):
"""Normalize attention matrices and reshape as necessary."""
for i, att in enumerate(att_mat):
# Add extra batch dim for viz code to work.
if att.ndim == 3:
att = np.expand_dims(att, axis=0)
if max_length is not None:
# Sum across different attention val... | python | def resize(att_mat, max_length=None):
"""Normalize attention matrices and reshape as necessary."""
for i, att in enumerate(att_mat):
# Add extra batch dim for viz code to work.
if att.ndim == 3:
att = np.expand_dims(att, axis=0)
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21,994 | tensorflow/tensor2tensor | tensor2tensor/visualization/attention.py | _get_attention | def _get_attention(inp_text, out_text, enc_atts, dec_atts, encdec_atts):
"""Compute representation of the attention ready for the d3 visualization.
Args:
inp_text: list of strings, words to be displayed on the left of the vis
out_text: list of strings, words to be displayed on the right of the vis
enc_... | python | def _get_attention(inp_text, out_text, enc_atts, dec_atts, encdec_atts):
"""Compute representation of the attention ready for the d3 visualization.
Args:
inp_text: list of strings, words to be displayed on the left of the vis
out_text: list of strings, words to be displayed on the right of the vis
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21,995 | tensorflow/tensor2tensor | tensor2tensor/data_generators/tokenizer.py | decode | def decode(tokens):
"""Decode a list of tokens to a unicode string.
Args:
tokens: a list of Unicode strings
Returns:
a unicode string
"""
token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
ret = []
for i, token in enumerate(tokens):
if i > 0 and token_is_alnum[i - 1] and token_... | python | def decode(tokens):
"""Decode a list of tokens to a unicode string.
Args:
tokens: a list of Unicode strings
Returns:
a unicode string
"""
token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
ret = []
for i, token in enumerate(tokens):
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21,996 | tensorflow/tensor2tensor | tensor2tensor/data_generators/tokenizer.py | _read_filepattern | def _read_filepattern(filepattern, max_lines=None, split_on_newlines=True):
"""Reads files matching a wildcard pattern, yielding the contents.
Args:
filepattern: A wildcard pattern matching one or more files.
max_lines: If set, stop reading after reading this many lines.
split_on_newlines: A boolean. I... | python | def _read_filepattern(filepattern, max_lines=None, split_on_newlines=True):
"""Reads files matching a wildcard pattern, yielding the contents.
Args:
filepattern: A wildcard pattern matching one or more files.
max_lines: If set, stop reading after reading this many lines.
split_on_newlines: A boolean. I... | [
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21,997 | tensorflow/tensor2tensor | tensor2tensor/data_generators/tokenizer.py | corpus_token_counts | def corpus_token_counts(
text_filepattern, corpus_max_lines, split_on_newlines=True):
"""Read the corpus and compute a dictionary of token counts.
Args:
text_filepattern: A pattern matching one or more files.
corpus_max_lines: An integer; maximum total lines to read.
split_on_newlines: A boolean. I... | python | def corpus_token_counts(
text_filepattern, corpus_max_lines, split_on_newlines=True):
"""Read the corpus and compute a dictionary of token counts.
Args:
text_filepattern: A pattern matching one or more files.
corpus_max_lines: An integer; maximum total lines to read.
split_on_newlines: A boolean. I... | [
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21,998 | tensorflow/tensor2tensor | tensor2tensor/data_generators/tokenizer.py | vocab_token_counts | def vocab_token_counts(text_filepattern, max_lines):
"""Read a vocab file and return a dictionary of token counts.
Reads a two-column CSV file of tokens and their frequency in a dataset. The
tokens are presumed to be generated by encode() or the equivalent.
Args:
text_filepattern: A pattern matching one o... | python | def vocab_token_counts(text_filepattern, max_lines):
"""Read a vocab file and return a dictionary of token counts.
Reads a two-column CSV file of tokens and their frequency in a dataset. The
tokens are presumed to be generated by encode() or the equivalent.
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text_filepattern: A pattern matching one o... | [
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21,999 | tensorflow/tensor2tensor | tensor2tensor/serving/serving_utils.py | _make_example | def _make_example(input_ids, problem, input_feature_name="inputs"):
"""Make a tf.train.Example for the problem.
features[input_feature_name] = input_ids
Also fills in any other required features with dummy values.
Args:
input_ids: list<int>.
problem: Problem.
input_feature_name: name of feature f... | python | def _make_example(input_ids, problem, input_feature_name="inputs"):
"""Make a tf.train.Example for the problem.
features[input_feature_name] = input_ids
Also fills in any other required features with dummy values.
Args:
input_ids: list<int>.
problem: Problem.
input_feature_name: name of feature f... | [
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