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train | layer_norm_vars | Create Variables for layer norm. | tensor2tensor/layers/common_layers.py | def layer_norm_vars(filters):
"""Create Variables for layer norm."""
scale = tf.get_variable(
"layer_norm_scale", [filters], initializer=tf.ones_initializer())
bias = tf.get_variable(
"layer_norm_bias", [filters], initializer=tf.zeros_initializer())
return scale, bias | def layer_norm_vars(filters):
"""Create Variables for layer norm."""
scale = tf.get_variable(
"layer_norm_scale", [filters], initializer=tf.ones_initializer())
bias = tf.get_variable(
"layer_norm_bias", [filters], initializer=tf.zeros_initializer())
return scale, bias | [
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train | layer_norm_compute | Layer norm raw computation. | tensor2tensor/layers/common_layers.py | def layer_norm_compute(x, epsilon, scale, bias, layer_collection=None):
"""Layer norm raw computation."""
# Save these before they get converted to tensors by the casting below
params = (scale, bias)
epsilon, scale, bias = [cast_like(t, x) for t in [epsilon, scale, bias]]
mean = tf.reduce_mean(x, axis=[-1],... | def layer_norm_compute(x, epsilon, scale, bias, layer_collection=None):
"""Layer norm raw computation."""
# Save these before they get converted to tensors by the casting below
params = (scale, bias)
epsilon, scale, bias = [cast_like(t, x) for t in [epsilon, scale, bias]]
mean = tf.reduce_mean(x, axis=[-1],... | [
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train | layer_norm | Layer normalize the tensor x, averaging over the last dimension. | tensor2tensor/layers/common_layers.py | def layer_norm(x,
filters=None,
epsilon=1e-6,
name=None,
reuse=None,
layer_collection=None):
"""Layer normalize the tensor x, averaging over the last dimension."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(... | def layer_norm(x,
filters=None,
epsilon=1e-6,
name=None,
reuse=None,
layer_collection=None):
"""Layer normalize the tensor x, averaging over the last dimension."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(... | [
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train | group_norm | Group normalization as in https://arxiv.org/abs/1803.08494. | tensor2tensor/layers/common_layers.py | def group_norm(x, filters=None, num_groups=8, epsilon=1e-5):
"""Group normalization as in https://arxiv.org/abs/1803.08494."""
x_shape = shape_list(x)
if filters is None:
filters = x_shape[-1]
assert len(x_shape) == 4
assert filters % num_groups == 0
# Prepare variables.
scale = tf.get_variable(
... | def group_norm(x, filters=None, num_groups=8, epsilon=1e-5):
"""Group normalization as in https://arxiv.org/abs/1803.08494."""
x_shape = shape_list(x)
if filters is None:
filters = x_shape[-1]
assert len(x_shape) == 4
assert filters % num_groups == 0
# Prepare variables.
scale = tf.get_variable(
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train | noam_norm | One version of layer normalization. | tensor2tensor/layers/common_layers.py | def noam_norm(x, epsilon=1.0, name=None):
"""One version of layer normalization."""
with tf.name_scope(name, default_name="noam_norm", values=[x]):
shape = x.get_shape()
ndims = len(shape)
return (tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(
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"""One version of layer normalization."""
with tf.name_scope(name, default_name="noam_norm", values=[x]):
shape = x.get_shape()
ndims = len(shape)
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train | l2_norm | Layer normalization with l2 norm. | tensor2tensor/layers/common_layers.py | def l2_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None):
"""Layer normalization with l2 norm."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(name, default_name="l2_norm", values=[x], reuse=reuse):
scale = tf.get_variable(
"l2_norm_scale", [filters], initializer... | def l2_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None):
"""Layer normalization with l2 norm."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(name, default_name="l2_norm", values=[x], reuse=reuse):
scale = tf.get_variable(
"l2_norm_scale", [filters], initializer... | [
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train | apply_spectral_norm | Normalizes x using the spectral norm.
The implementation follows Algorithm 1 of
https://arxiv.org/abs/1802.05957. If x is not a 2-D Tensor, then it is
reshaped such that the number of channels (last-dimension) is the same.
Args:
x: Tensor with the last dimension equal to the number of filters.
Returns:... | tensor2tensor/layers/common_layers.py | def apply_spectral_norm(x):
"""Normalizes x using the spectral norm.
The implementation follows Algorithm 1 of
https://arxiv.org/abs/1802.05957. If x is not a 2-D Tensor, then it is
reshaped such that the number of channels (last-dimension) is the same.
Args:
x: Tensor with the last dimension equal to t... | def apply_spectral_norm(x):
"""Normalizes x using the spectral norm.
The implementation follows Algorithm 1 of
https://arxiv.org/abs/1802.05957. If x is not a 2-D Tensor, then it is
reshaped such that the number of channels (last-dimension) is the same.
Args:
x: Tensor with the last dimension equal to t... | [
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train | apply_norm | Apply Normalization. | tensor2tensor/layers/common_layers.py | def apply_norm(x, norm_type, depth, epsilon, layer_collection=None):
"""Apply Normalization."""
if layer_collection is not None:
assert norm_type == "layer"
if norm_type == "layer":
return layer_norm(
x, filters=depth, epsilon=epsilon, layer_collection=layer_collection)
if norm_type == "group":
... | def apply_norm(x, norm_type, depth, epsilon, layer_collection=None):
"""Apply Normalization."""
if layer_collection is not None:
assert norm_type == "layer"
if norm_type == "layer":
return layer_norm(
x, filters=depth, epsilon=epsilon, layer_collection=layer_collection)
if norm_type == "group":
... | [
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train | zero_add | Resnet connection with zero initialization.
Another type of resnet connection which returns previous_value + gamma * x.
gamma is a trainable scalar and initialized with zero. It is useful when a
module is plugged into a trained model and we want to make sure it matches the
original model's performance.
Args... | tensor2tensor/layers/common_layers.py | def zero_add(previous_value, x, name=None, reuse=None):
"""Resnet connection with zero initialization.
Another type of resnet connection which returns previous_value + gamma * x.
gamma is a trainable scalar and initialized with zero. It is useful when a
module is plugged into a trained model and we want to mak... | def zero_add(previous_value, x, name=None, reuse=None):
"""Resnet connection with zero initialization.
Another type of resnet connection which returns previous_value + gamma * x.
gamma is a trainable scalar and initialized with zero. It is useful when a
module is plugged into a trained model and we want to mak... | [
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train | layer_prepostprocess | Apply a sequence of functions to the input or output of a layer.
The sequence is specified as a string which may contain the following
characters:
a: add previous_value
n: apply normalization
d: apply dropout
z: zero add
For example, if sequence=="dna", then the output is
previous_value + no... | tensor2tensor/layers/common_layers.py | def layer_prepostprocess(previous_value,
x,
sequence,
dropout_rate,
norm_type,
depth,
epsilon,
default_name,
name=None,
... | def layer_prepostprocess(previous_value,
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norm_type,
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epsilon,
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name=None,
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train | layer_preprocess | Apply layer preprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
that may be used are:
layer_preprocess_sequence
layer_prepostprocess_dropout
norm_type
hidden_size
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Args:
layer_input: a Tensor
... | tensor2tensor/layers/common_layers.py | def layer_preprocess(layer_input, hparams, layer_collection=None):
"""Apply layer preprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
that may be used are:
layer_preprocess_sequence
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norm_type
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"""Apply layer preprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
that may be used are:
layer_preprocess_sequence
layer_prepostprocess_dropout
norm_type
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train | layer_postprocess | Apply layer postprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
that may be used are:
layer_postprocess_sequence
layer_prepostprocess_dropout
norm_type
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Args:
layer_input: a Tensor
... | tensor2tensor/layers/common_layers.py | def layer_postprocess(layer_input, layer_output, hparams):
"""Apply layer postprocessing.
See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
that may be used are:
layer_postprocess_sequence
layer_prepostprocess_dropout
norm_type
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See layer_prepostprocess() for details.
A hyperparameters object is passed for convenience. The hyperparameters
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layer_postprocess_sequence
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train | conv_block_internal | A block of convolutions.
Args:
conv_fn: convolution function, e.g. conv or separable_conv.
inputs: a Tensor
filters: an Integer
dilation_rates_and_kernel_sizes: a list of tuples (dilation, (k_w, k_h))
first_relu: whether to do a relu at start (defaults to True)
use_elu: whether to use ELUs in... | tensor2tensor/layers/common_layers.py | def conv_block_internal(conv_fn,
inputs,
filters,
dilation_rates_and_kernel_sizes,
first_relu=True,
use_elu=False,
separabilities=None,
**kwargs):
"""... | def conv_block_internal(conv_fn,
inputs,
filters,
dilation_rates_and_kernel_sizes,
first_relu=True,
use_elu=False,
separabilities=None,
**kwargs):
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train | conv_block | A block of standard 2d convolutions. | tensor2tensor/layers/common_layers.py | def conv_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 2d convolutions."""
return conv_block_internal(conv, inputs, filters,
dilation_rates_and_kernel_sizes, **kwargs) | def conv_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 2d convolutions."""
return conv_block_internal(conv, inputs, filters,
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train | conv1d_block | A block of standard 1d convolutions. | tensor2tensor/layers/common_layers.py | def conv1d_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 1d convolutions."""
return conv_block_internal(conv1d, inputs, filters,
dilation_rates_and_kernel_sizes, **kwargs) | def conv1d_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 1d convolutions."""
return conv_block_internal(conv1d, inputs, filters,
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train | separable_conv_block | A block of separable convolutions. | tensor2tensor/layers/common_layers.py | def separable_conv_block(inputs, filters, dilation_rates_and_kernel_sizes,
**kwargs):
"""A block of separable convolutions."""
return conv_block_internal(separable_conv, inputs, filters,
dilation_rates_and_kernel_sizes, **kwargs) | def separable_conv_block(inputs, filters, dilation_rates_and_kernel_sizes,
**kwargs):
"""A block of separable convolutions."""
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train | subseparable_conv_block | A block of separable convolutions. | tensor2tensor/layers/common_layers.py | def subseparable_conv_block(inputs, filters, dilation_rates_and_kernel_sizes,
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"""A block of separable convolutions."""
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train | pool | Pooling (supports "LEFT"). | tensor2tensor/layers/common_layers.py | def pool(inputs, window_size, pooling_type, padding, strides=(1, 1)):
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train | conv_block_downsample | Implements a downwards-striding conv block, like Xception exit flow. | tensor2tensor/layers/common_layers.py | def conv_block_downsample(x,
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train | get_timing_signal | Create Tensor of sinusoids of different frequencies.
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length: Length of the Tensor to create, i.e. Number of steps.
min_timescale: a float
max_timescale: a float
num_timescales: an int
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max_timescale=1e4,
num_timescales=16):
"""Create Tensor of sinusoids of different frequencies.
Args:
length: Length of the Tensor to create, i.e. Number of steps.
min_timescale: a float
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min_timescale=1,
max_timescale=1e4,
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"""Create Tensor of sinusoids of different frequencies.
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length: Length of the Tensor to create, i.e. Number of steps.
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train | add_timing_signal | Adds a bunch of sinusoids of different frequencies to a Tensor.
This allows attention to learn to use absolute and relative positions.
The timing signal should be added to some precursor of both the source
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The use of relative position is possible because sin(x+y) and cos(x+y) ... | tensor2tensor/layers/common_layers.py | def add_timing_signal(x, min_timescale=1, max_timescale=1e4, num_timescales=16):
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This allows attention to learn to use absolute and relative positions.
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train | mask_from_embedding | Input embeddings -> padding mask.
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Args:
emb: a Tensor with shape [batch, width, height, depth].
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emb: a Tensor with shape [batch, width, height, depth].
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train | length_from_embedding | Compute the length of each sequence in the batch.
Args:
emb: a sequence embedding Tensor with shape [batch, max_time, 1, depth].
Returns:
a Tensor with shape [batch]. | tensor2tensor/layers/common_layers.py | def length_from_embedding(emb):
"""Compute the length of each sequence in the batch.
Args:
emb: a sequence embedding Tensor with shape [batch, max_time, 1, depth].
Returns:
a Tensor with shape [batch].
"""
return tf.cast(tf.reduce_sum(mask_from_embedding(emb), [1, 2, 3]), tf.int32) | def length_from_embedding(emb):
"""Compute the length of each sequence in the batch.
Args:
emb: a sequence embedding Tensor with shape [batch, max_time, 1, depth].
Returns:
a Tensor with shape [batch].
"""
return tf.cast(tf.reduce_sum(mask_from_embedding(emb), [1, 2, 3]), tf.int32) | [
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train | relu_density_logit | logit(density(x)).
Useful for histograms.
Args:
x: a Tensor, typically the output of tf.relu
reduce_dims: a list of dimensions
Returns:
a Tensor | tensor2tensor/layers/common_layers.py | def relu_density_logit(x, reduce_dims):
"""logit(density(x)).
Useful for histograms.
Args:
x: a Tensor, typically the output of tf.relu
reduce_dims: a list of dimensions
Returns:
a Tensor
"""
frac = tf.reduce_mean(to_float(x > 0.0), reduce_dims)
scaled = tf.log(frac + math.exp(-10)) - tf.lo... | def relu_density_logit(x, reduce_dims):
"""logit(density(x)).
Useful for histograms.
Args:
x: a Tensor, typically the output of tf.relu
reduce_dims: a list of dimensions
Returns:
a Tensor
"""
frac = tf.reduce_mean(to_float(x > 0.0), reduce_dims)
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train | maybe_zero_out_padding | If necessary, zero out inputs to a conv for padding positions.
Args:
inputs: a Tensor with shape [batch, length, ...]
kernel_size: an integer or pair of integers
nonpadding_mask: a Tensor with shape [batch, length]
Returns:
Tensor of the same shape as inputs. | tensor2tensor/layers/common_layers.py | def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask):
"""If necessary, zero out inputs to a conv for padding positions.
Args:
inputs: a Tensor with shape [batch, length, ...]
kernel_size: an integer or pair of integers
nonpadding_mask: a Tensor with shape [batch, length]
Returns:
Ten... | def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask):
"""If necessary, zero out inputs to a conv for padding positions.
Args:
inputs: a Tensor with shape [batch, length, ...]
kernel_size: an integer or pair of integers
nonpadding_mask: a Tensor with shape [batch, length]
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train | dense_relu_dense | Hidden layer with RELU activation followed by linear projection. | tensor2tensor/layers/common_layers.py | def dense_relu_dense(inputs,
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output_activation=None,
dropout=0.0,
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name=None):
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train | dense_dropconnect | Dense layer with dropconnect. | tensor2tensor/layers/common_layers.py | def dense_dropconnect(inputs,
output_size,
dropconnect_dropout=0.0,
name="dense_dropconnect",
**kwargs):
"""Dense layer with dropconnect."""
if dropconnect_dropout != 0.0:
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**kwargs):
"""Dense layer with dropconnect."""
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train | conv_relu_conv | Hidden layer with RELU activation followed by linear projection.
Args:
inputs: A tensor.
filter_size: An integer.
output_size: An integer.
first_kernel_size: An integer.
second_kernel_size: An integer.
padding: A string.
nonpadding_mask: A tensor.
dropout: A float.
name: A string.... | tensor2tensor/layers/common_layers.py | def conv_relu_conv(inputs,
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output_size,
first_kernel_size=3,
second_kernel_size=3,
padding="SAME",
nonpadding_mask=None,
dropout=0.0,
name=None,
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filter_size,
output_size,
first_kernel_size=3,
second_kernel_size=3,
padding="SAME",
nonpadding_mask=None,
dropout=0.0,
name=None,
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train | sepconv_relu_sepconv | Hidden layer with RELU activation followed by linear projection. | tensor2tensor/layers/common_layers.py | def sepconv_relu_sepconv(inputs,
filter_size,
output_size,
first_kernel_size=(1, 1),
second_kernel_size=(1, 1),
padding="LEFT",
nonpadding_mask=None,
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first_kernel_size=(1, 1),
second_kernel_size=(1, 1),
padding="LEFT",
nonpadding_mask=None,
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train | conv_hidden_relu | Hidden layer with RELU activation followed by linear projection. | tensor2tensor/layers/common_layers.py | def conv_hidden_relu(inputs,
hidden_size,
output_size,
kernel_size=(1, 1),
second_kernel_size=(1, 1),
dropout=0.0,
**kwargs):
"""Hidden layer with RELU activation followed by linear projection... | def conv_hidden_relu(inputs,
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second_kernel_size=(1, 1),
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train | conv_gru | Convolutional GRU in 1 dimension. | tensor2tensor/layers/common_layers.py | def conv_gru(x,
kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional GRU in 1 dimension."""
# Let's make a shorthand for conv call first.
def do_conv(args, name, bias_start, padding):
... | def conv_gru(x,
kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional GRU in 1 dimension."""
# Let's make a shorthand for conv call first.
def do_conv(args, name, bias_start, padding):
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train | gru_feedfwd | position-wise Feed-fwd GRU gates following the MPNN.
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a_t: Tensor of shape [batch, length, depth] of current input
h_prev: Tensor of shape [batch, length, depth] of prev input
filters: an integer specifying number of dimensions of the filters
name: A string
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Args:
a_t: Tensor of shape [batch, length, depth] of current input
h_prev: Tensor of shape [batch, length, depth] of prev input
filters: an integer specifying number of dimensions of the filters
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"""position-wise Feed-fwd GRU gates following the MPNN.
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a_t: Tensor of shape [batch, length, depth] of current input
h_prev: Tensor of shape [batch, length, depth] of prev input
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train | conv_lstm | Convolutional LSTM in 1 dimension. | tensor2tensor/layers/common_layers.py | def conv_lstm(x,
kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional LSTM in 1 dimension."""
with tf.variable_scope(
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kernel_size,
filters,
padding="SAME",
dilation_rate=(1, 1),
name=None,
reuse=None):
"""Convolutional LSTM in 1 dimension."""
with tf.variable_scope(
name, default_name="conv_lstm", values=[x], reuse=reuse):
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train | diagonal_conv_gru | Diagonal Convolutional GRU as in https://arxiv.org/abs/1702.08727. | tensor2tensor/layers/common_layers.py | def diagonal_conv_gru(x,
kernel_size,
filters,
dropout=0.0,
name=None,
reuse=None):
"""Diagonal Convolutional GRU as in https://arxiv.org/abs/1702.08727."""
# Let's make a shorthand for conv call first.
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kernel_size,
filters,
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name=None,
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"""Diagonal Convolutional GRU as in https://arxiv.org/abs/1702.08727."""
# Let's make a shorthand for conv call first.
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train | pad_to_same_length | Pad tensors x and y on axis 1 so that they have the same length. | tensor2tensor/layers/common_layers.py | def pad_to_same_length(x, y, final_length_divisible_by=1, axis=1):
"""Pad tensors x and y on axis 1 so that they have the same length."""
if axis not in [1, 2]:
raise ValueError("Only axis=1 and axis=2 supported for now.")
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"""Pad tensors x and y on axis 1 so that they have the same length."""
if axis not in [1, 2]:
raise ValueError("Only axis=1 and axis=2 supported for now.")
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train | pad_with_zeros | Pad labels on the length dimension to match logits length. | tensor2tensor/layers/common_layers.py | def pad_with_zeros(logits, labels):
"""Pad labels on the length dimension to match logits length."""
with tf.name_scope("pad_with_zeros", values=[logits, labels]):
logits, labels = pad_to_same_length(logits, labels)
if len(labels.shape) == 3: # 2-d labels.
logits, labels = pad_to_same_length(logits, ... | def pad_with_zeros(logits, labels):
"""Pad labels on the length dimension to match logits length."""
with tf.name_scope("pad_with_zeros", values=[logits, labels]):
logits, labels = pad_to_same_length(logits, labels)
if len(labels.shape) == 3: # 2-d labels.
logits, labels = pad_to_same_length(logits, ... | [
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train | weights_prepend_inputs_to_targets | Assign weight 1.0 to only the "targets" portion of the labels.
Weight 1.0 is assigned to all nonzero labels past the first zero.
See prepend_mode in common_hparams.py
Args:
labels: A Tensor of int32s.
Returns:
A Tensor of floats. | tensor2tensor/layers/common_layers.py | def weights_prepend_inputs_to_targets(labels):
"""Assign weight 1.0 to only the "targets" portion of the labels.
Weight 1.0 is assigned to all nonzero labels past the first zero.
See prepend_mode in common_hparams.py
Args:
labels: A Tensor of int32s.
Returns:
A Tensor of floats.
"""
past_first_... | def weights_prepend_inputs_to_targets(labels):
"""Assign weight 1.0 to only the "targets" portion of the labels.
Weight 1.0 is assigned to all nonzero labels past the first zero.
See prepend_mode in common_hparams.py
Args:
labels: A Tensor of int32s.
Returns:
A Tensor of floats.
"""
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train | check_nonnegative | Check that the value is nonnegative. | tensor2tensor/layers/common_layers.py | def check_nonnegative(value):
"""Check that the value is nonnegative."""
if isinstance(value, tf.Tensor):
with tf.control_dependencies([tf.assert_greater_equal(value, 0)]):
value = tf.identity(value)
elif value < 0:
raise ValueError("Value must be non-negative.")
return value | def check_nonnegative(value):
"""Check that the value is nonnegative."""
if isinstance(value, tf.Tensor):
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value = tf.identity(value)
elif value < 0:
raise ValueError("Value must be non-negative.")
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train | weights_multi_problem | Assign weight 1.0 to only the "targets" portion of the labels.
Weight 1.0 is assigned to all labels past the taskid.
Args:
labels: A Tensor of int32s.
taskid: an int32 representing the task id for a problem.
Returns:
A Tensor of floats.
Raises:
ValueError: The Task ID must be valid. | tensor2tensor/layers/common_layers.py | def weights_multi_problem(labels, taskid=-1):
"""Assign weight 1.0 to only the "targets" portion of the labels.
Weight 1.0 is assigned to all labels past the taskid.
Args:
labels: A Tensor of int32s.
taskid: an int32 representing the task id for a problem.
Returns:
A Tensor of floats.
Raises:
... | def weights_multi_problem(labels, taskid=-1):
"""Assign weight 1.0 to only the "targets" portion of the labels.
Weight 1.0 is assigned to all labels past the taskid.
Args:
labels: A Tensor of int32s.
taskid: an int32 representing the task id for a problem.
Returns:
A Tensor of floats.
Raises:
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train | weights_multi_problem_all | Assign weight 1.0 to only examples from the given task. | tensor2tensor/layers/common_layers.py | def weights_multi_problem_all(labels, taskid=-1):
"""Assign weight 1.0 to only examples from the given task."""
taskid = check_nonnegative(taskid)
weights = to_float(tf.not_equal(labels, 0))
past_taskid = tf.cumsum(to_float(tf.equal(labels, taskid)), axis=1)
# Additionally zero out the task id location
past... | def weights_multi_problem_all(labels, taskid=-1):
"""Assign weight 1.0 to only examples from the given task."""
taskid = check_nonnegative(taskid)
weights = to_float(tf.not_equal(labels, 0))
past_taskid = tf.cumsum(to_float(tf.equal(labels, taskid)), axis=1)
# Additionally zero out the task id location
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train | weights_multi_problem_input | Assign weight 1.0 to only the inputs for the given task. | tensor2tensor/layers/common_layers.py | def weights_multi_problem_input(labels, taskid=-1):
"""Assign weight 1.0 to only the inputs for the given task."""
taskid = check_nonnegative(taskid)
weights_all_tokens = weights_multi_problem_all(labels, taskid)
weights_target = weights_multi_problem(labels, taskid)
return weights_all_tokens - weights_target | def weights_multi_problem_input(labels, taskid=-1):
"""Assign weight 1.0 to only the inputs for the given task."""
taskid = check_nonnegative(taskid)
weights_all_tokens = weights_multi_problem_all(labels, taskid)
weights_target = weights_multi_problem(labels, taskid)
return weights_all_tokens - weights_target | [
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train | weights_concatenated | Assign weight 1.0 to the "target" part of the concatenated labels.
The labels look like:
source English I love you . ID1 target French Je t'aime . ID1 source
English the cat ID1 target French le chat ID1 source English ...
We want to assign weight 1.0 to all words in the target text (including the
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"""Assign weight 1.0 to the "target" part of the concatenated labels.
The labels look like:
source English I love you . ID1 target French Je t'aime . ID1 source
English the cat ID1 target French le chat ID1 source English ...
We want to assign weight 1.0 to all words ... | def weights_concatenated(labels):
"""Assign weight 1.0 to the "target" part of the concatenated labels.
The labels look like:
source English I love you . ID1 target French Je t'aime . ID1 source
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train | padded_cross_entropy | Compute cross-entropy assuming 0s are padding.
Computes a loss numerator (the sum of losses), and loss denominator
(the number of non-padding tokens).
Args:
logits: a `Tensor` with shape `[batch, timesteps, vocab_size]`.
optionally a FactoredTensor.
labels: an integer `Tensor` with shape `[batch, ... | tensor2tensor/layers/common_layers.py | def padded_cross_entropy(logits,
labels,
label_smoothing,
weights_fn=weights_nonzero,
reduce_sum=True,
cutoff=0.0,
gaussian=False):
"""Compute cross-entropy assuming 0s... | def padded_cross_entropy(logits,
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weights_fn=weights_nonzero,
reduce_sum=True,
cutoff=0.0,
gaussian=False):
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train | padded_cross_entropy_mixture | Compute cross-entropy assuming 0s are padding.
Computes a loss numerator (the sum of losses), and loss denominator
(the number of non-padding tokens).
Computes cross-entropy for each mixture, and returns the corresponding values
for the mixture with the highest probability
Args:
logits: `Tensor` with s... | tensor2tensor/layers/common_layers.py | def padded_cross_entropy_mixture(logits,
labels,
label_smoothing,
num_mixtures,
weights_fn=weights_nonzero,
reduce_sum=False,
... | def padded_cross_entropy_mixture(logits,
labels,
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reduce_sum=False,
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train | dml_loss | Discretized mixture of logistics loss.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard deviations (one per channel),
and three coefficients which linearly parameterize dependen... | tensor2tensor/layers/common_layers.py | def dml_loss(pred, labels, weights_fn=_weights_one_third, reduce_sum=True):
"""Discretized mixture of logistics loss.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard deviations (on... | def dml_loss(pred, labels, weights_fn=_weights_one_third, reduce_sum=True):
"""Discretized mixture of logistics loss.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
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train | split_to_discretized_mix_logistic_params | Splits input tensor into parameters of discretized mixture logistic.
Args:
inputs: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard deviations (one per channel),
and three coefficients whic... | tensor2tensor/layers/common_layers.py | def split_to_discretized_mix_logistic_params(inputs):
"""Splits input tensor into parameters of discretized mixture logistic.
Args:
inputs: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard devi... | def split_to_discretized_mix_logistic_params(inputs):
"""Splits input tensor into parameters of discretized mixture logistic.
Args:
inputs: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard devi... | [
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train | discretized_mix_logistic_loss | Computes negative log probability for the discretized mixture of logistics.
The distribution of a whole pixel is a mixture of 3-dimensional discretized
logistic distributions. The 3-D discretized logistic factorizes as 3 1-D
discretized logistic distributions, one for each channel. It defines
```none
P(X = ... | tensor2tensor/layers/common_layers.py | def discretized_mix_logistic_loss(pred, labels):
"""Computes negative log probability for the discretized mixture of logistics.
The distribution of a whole pixel is a mixture of 3-dimensional discretized
logistic distributions. The 3-D discretized logistic factorizes as 3 1-D
discretized logistic distributions... | def discretized_mix_logistic_loss(pred, labels):
"""Computes negative log probability for the discretized mixture of logistics.
The distribution of a whole pixel is a mixture of 3-dimensional discretized
logistic distributions. The 3-D discretized logistic factorizes as 3 1-D
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train | sample_from_discretized_mix_logistic | Sampling from a discretized mixture of logistics.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard deviations (one per channel),
and three coefficients which linearly parameteri... | tensor2tensor/layers/common_layers.py | def sample_from_discretized_mix_logistic(pred, seed=None):
"""Sampling from a discretized mixture of logistics.
Args:
pred: A [batch, height, width, num_mixtures*10] tensor of floats
comprising one unconstrained mixture probability, three means
(one per channel), three standard deviations (one per ... | def sample_from_discretized_mix_logistic(pred, seed=None):
"""Sampling from a discretized mixture of logistics.
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train | smoothing_cross_entropy | Cross entropy with label smoothing to limit over-confidence.
Args:
logits: Tensor of shape [batch_size, ?, ?, ?, vocab_size].
labels: Tensor of shape [batch_size, ?, ?, ?].
vocab_size: Tensor representing the size of the vocabulary.
confidence: Used to determine on and off values for label smoothing.... | tensor2tensor/layers/common_layers.py | def smoothing_cross_entropy(logits,
labels,
vocab_size,
confidence,
gaussian=False):
"""Cross entropy with label smoothing to limit over-confidence.
Args:
logits: Tensor of shape [batch_size, ?, ?, ?... | def smoothing_cross_entropy(logits,
labels,
vocab_size,
confidence,
gaussian=False):
"""Cross entropy with label smoothing to limit over-confidence.
Args:
logits: Tensor of shape [batch_size, ?, ?, ?... | [
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train | global_pool_1d | Pool elements across the last dimension.
Useful to convert a list of vectors into a single vector so as
to get a representation of a set.
Args:
inputs: A tensor of shape [batch_size, sequence_length, input_dims]
containing the sequences of input vectors.
pooling_type: the pooling type to use, MAX ... | tensor2tensor/layers/common_layers.py | def global_pool_1d(inputs, pooling_type="MAX", mask=None):
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Useful to convert a list of vectors into a single vector so as
to get a representation of a set.
Args:
inputs: A tensor of shape [batch_size, sequence_length, input_dims]
containing the sequences o... | def global_pool_1d(inputs, pooling_type="MAX", mask=None):
"""Pool elements across the last dimension.
Useful to convert a list of vectors into a single vector so as
to get a representation of a set.
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inputs: A tensor of shape [batch_size, sequence_length, input_dims]
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train | running_global_pool_1d | Same global pool, but only for the elements up to the current element.
Useful for outputs where the state of future elements is not known.
Takes no mask as all elements up to the current element are assumed to exist.
Currently only supports maximum. Equivalent to using a lower triangle bias.
Args:
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"""Same global pool, but only for the elements up to the current element.
Useful for outputs where the state of future elements is not known.
Takes no mask as all elements up to the current element are assumed to exist.
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Useful for outputs where the state of future elements is not known.
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train | gated_linear_unit_layer | Gated linear unit layer.
Paper: Language Modeling with Gated Convolutional Networks.
Link: https://arxiv.org/abs/1612.08083
x = Wx * sigmoid(W'x).
Args:
x: A tensor
name: A string
Returns:
A tensor of the same shape as x. | tensor2tensor/layers/common_layers.py | def gated_linear_unit_layer(x, name=None):
"""Gated linear unit layer.
Paper: Language Modeling with Gated Convolutional Networks.
Link: https://arxiv.org/abs/1612.08083
x = Wx * sigmoid(W'x).
Args:
x: A tensor
name: A string
Returns:
A tensor of the same shape as x.
"""
with tf.variable_... | def gated_linear_unit_layer(x, name=None):
"""Gated linear unit layer.
Paper: Language Modeling with Gated Convolutional Networks.
Link: https://arxiv.org/abs/1612.08083
x = Wx * sigmoid(W'x).
Args:
x: A tensor
name: A string
Returns:
A tensor of the same shape as x.
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train | sru_with_scan | SRU cell as in https://arxiv.org/abs/1709.02755.
This implementation uses tf.scan and can incur overhead, see the full SRU
function doc for details and an implementation that is sometimes faster.
Args:
x: A tensor of shape [batch, ..., channels] ; ... is treated as time.
num_layers: How many SRU layers;... | tensor2tensor/layers/common_layers.py | def sru_with_scan(x,
num_layers=2,
activation=None,
initial_state=None,
name=None,
reuse=None):
"""SRU cell as in https://arxiv.org/abs/1709.02755.
This implementation uses tf.scan and can incur overhead, see the full SRU
f... | def sru_with_scan(x,
num_layers=2,
activation=None,
initial_state=None,
name=None,
reuse=None):
"""SRU cell as in https://arxiv.org/abs/1709.02755.
This implementation uses tf.scan and can incur overhead, see the full SRU
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train | sru | SRU cell as in https://arxiv.org/abs/1709.02755.
As defined in the paper:
(1) x'_t = W x_t
(2) f_t = sigmoid(Wf x_t + bf)
(3) r_t = sigmoid(Wr x_t + br)
(4) c_t = f_t * c_{t-1} + (1 - f_t) * x'_t
(5) h_t = r_t * activation(c_t) + (1 - r_t) * x_t
This version uses functional ops to be faster on GPUs with... | tensor2tensor/layers/common_layers.py | def sru(x,
num_layers=2,
activation=None,
initial_state=None,
name=None,
reuse=None):
"""SRU cell as in https://arxiv.org/abs/1709.02755.
As defined in the paper:
(1) x'_t = W x_t
(2) f_t = sigmoid(Wf x_t + bf)
(3) r_t = sigmoid(Wr x_t + br)
(4) c_t = f_t * c_{t-1} +... | def sru(x,
num_layers=2,
activation=None,
initial_state=None,
name=None,
reuse=None):
"""SRU cell as in https://arxiv.org/abs/1709.02755.
As defined in the paper:
(1) x'_t = W x_t
(2) f_t = sigmoid(Wf x_t + bf)
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train | linear_set_layer | Basic layer type for doing funky things with sets.
Applies a linear transformation to each element in the input set.
If a context is supplied, it is concatenated with the inputs.
e.g. One can use global_pool_1d to get a representation of the set which
can then be used as the context for the next layer.
... | tensor2tensor/layers/common_layers.py | def linear_set_layer(layer_size,
inputs,
context=None,
activation_fn=tf.nn.relu,
dropout=0.0,
name=None):
"""Basic layer type for doing funky things with sets.
Applies a linear transformation to each element in... | def linear_set_layer(layer_size,
inputs,
context=None,
activation_fn=tf.nn.relu,
dropout=0.0,
name=None):
"""Basic layer type for doing funky things with sets.
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train | ravanbakhsh_set_layer | Layer from Deep Sets paper: https://arxiv.org/abs/1611.04500 .
More parameter-efficient version of a linear-set-layer with context.
Args:
layer_size: Dimension to transform the input vectors to.
inputs: A tensor of shape [batch_size, sequence_length, vector]
containing the sequences of input vectors... | tensor2tensor/layers/common_layers.py | def ravanbakhsh_set_layer(layer_size,
inputs,
mask=None,
sequential=False,
activation_fn=tf.nn.tanh,
dropout=0.0,
name=None):
"""Layer from Deep Sets paper: https... | def ravanbakhsh_set_layer(layer_size,
inputs,
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sequential=False,
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train | fn_device_dependency_dict | State container for fn_device_dependency. | tensor2tensor/layers/common_layers.py | def fn_device_dependency_dict():
"""State container for fn_device_dependency."""
default_graph = tf.get_default_graph()
if not hasattr(default_graph, "dependency_dict"):
default_graph.dependency_dict = collections.defaultdict(list)
return default_graph.dependency_dict | def fn_device_dependency_dict():
"""State container for fn_device_dependency."""
default_graph = tf.get_default_graph()
if not hasattr(default_graph, "dependency_dict"):
default_graph.dependency_dict = collections.defaultdict(list)
return default_graph.dependency_dict | [
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train | fn_device_dependency | Add control deps for name and device. | tensor2tensor/layers/common_layers.py | def fn_device_dependency(name, device=""):
"""Add control deps for name and device."""
key = name + "_" + device
outs = []
def body():
with tf.control_dependencies(fn_device_dependency_dict()[key]):
yield outs
assert outs
deps = outs
if isinstance(outs[0], (list, tuple)):
a... | def fn_device_dependency(name, device=""):
"""Add control deps for name and device."""
key = name + "_" + device
outs = []
def body():
with tf.control_dependencies(fn_device_dependency_dict()[key]):
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assert outs
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train | underlying_variable_ref | Find the underlying variable ref.
Traverses through Identity, ReadVariableOp, and Enter ops.
Stops when op type has Variable or VarHandle in name.
Args:
t: a Tensor
Returns:
a Tensor that is a variable ref, or None on error. | tensor2tensor/layers/common_layers.py | def underlying_variable_ref(t):
"""Find the underlying variable ref.
Traverses through Identity, ReadVariableOp, and Enter ops.
Stops when op type has Variable or VarHandle in name.
Args:
t: a Tensor
Returns:
a Tensor that is a variable ref, or None on error.
"""
while t.op.type in ["Identity",... | def underlying_variable_ref(t):
"""Find the underlying variable ref.
Traverses through Identity, ReadVariableOp, and Enter ops.
Stops when op type has Variable or VarHandle in name.
Args:
t: a Tensor
Returns:
a Tensor that is a variable ref, or None on error.
"""
while t.op.type in ["Identity",... | [
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train | underlying_variable | Find the underlying tf.Variable object.
Args:
t: a Tensor
Returns:
tf.Variable. | tensor2tensor/layers/common_layers.py | def underlying_variable(t):
"""Find the underlying tf.Variable object.
Args:
t: a Tensor
Returns:
tf.Variable.
"""
t = underlying_variable_ref(t)
assert t is not None
# make sure that the graph has a variable index and that it is up-to-date
if not hasattr(tf.get_default_graph(), "var_index"):
... | def underlying_variable(t):
"""Find the underlying tf.Variable object.
Args:
t: a Tensor
Returns:
tf.Variable.
"""
t = underlying_variable_ref(t)
assert t is not None
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train | approximate_split | Split approximately equally into num_splits parts.
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x: a Tensor
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axis: an integer.
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x: a Tensor
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axis: an integer.
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train | smoothing_cross_entropy_factored_grad | Gradient function for smoothing_cross_entropy_factored. | tensor2tensor/layers/common_layers.py | def smoothing_cross_entropy_factored_grad(op, dy):
"""Gradient function for smoothing_cross_entropy_factored."""
a = op.inputs[0]
b = op.inputs[1]
labels = op.inputs[2]
confidence = op.inputs[3]
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vocab_size = shape_list(b)[0]
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train | smoothing_cross_entropy_factored | Memory-efficient computation of smoothing cross-entropy.
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Args:
a: a Tensor with shape [batch, inner_dim]
b: a Tensor with shape [vocab_size, inner_dim]
labels: an integer Tensor with shape [batch]
confidence: a float
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"""Memory-efficient computation of smoothing cross-entropy.
Avoids realizing the entire logits matrix at once.
Args:
a: a Tensor with shape [batch, inner_dim]
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train | padded_cross_entropy_factored | Memory-efficient computation of smoothing cross-entropy.
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Args:
factored_logits: a `FactoredTensor` representing a Tensor
with shape `[batch, timesteps, vocab_size]`.
labels: an integer `Tensor` with shape `[batch, timesteps]`.
label_smoothing: ... | tensor2tensor/layers/common_layers.py | def padded_cross_entropy_factored(factored_logits,
labels,
label_smoothing,
weights_fn=weights_nonzero,
reduce_sum=True):
"""Memory-efficient computation of smoothing cross-entropy.
... | def padded_cross_entropy_factored(factored_logits,
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train | fn_with_custom_grad | Decorator to create a subgraph with a custom gradient function.
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The subgraph created by the decorated function is NOT put in a Defun and so
does not suffer from the limitations of the Defun (all subgraph ops on the
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Args:... | def fn_with_custom_grad(grad_fn, use_global_vars=False):
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train | _fn_with_custom_grad | Create a subgraph with a custom gradient.
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fn: function that takes inputs as arguments and produces 1 or more Tensors.
inputs: list<Tensor>, will be passed as fn(*inputs).
grad_fn: function with signature
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fn: function that takes inputs as arguments and produces 1 or more Tensors.
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train | conv_hidden_relu_memory_efficient | LayerNorm, Conv, ReLU, Conv.
All convolutions have kernel size 1.
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Args:
x: input Tensor with shape [batch, length, io_size]
filter_size: an integer - size of the hidden layer.
epsilon: a float (for layer norm)
forget: a boolean - forget forwards activatio... | tensor2tensor/layers/common_layers.py | def conv_hidden_relu_memory_efficient(x,
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"""LayerNorm, Conv,... | def conv_hidden_relu_memory_efficient(x,
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train | shape_list | Return list of dims, statically where possible. | tensor2tensor/layers/common_layers.py | def shape_list(x):
"""Return list of dims, statically where possible."""
x = tf.convert_to_tensor(x)
# If unknown rank, return dynamic shape
if x.get_shape().dims is None:
return tf.shape(x)
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shape = tf.shape(x)
ret = []
for i, dim in enumerate(static):
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"""Return list of dims, statically where possible."""
x = tf.convert_to_tensor(x)
# If unknown rank, return dynamic shape
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train | sample_with_temperature | Either argmax or random sampling.
Args:
logits: a Tensor.
temperature: a float 0.0=argmax 1.0=random
sampling_keep_top_k: If not -1, only sample from the top k logits.
Returns:
a Tensor with one fewer dimension than logits. | tensor2tensor/layers/common_layers.py | def sample_with_temperature(logits, temperature, sampling_keep_top_k=-1):
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Args:
logits: a Tensor.
temperature: a float 0.0=argmax 1.0=random
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Returns:
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logits: a Tensor.
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train | ones_matrix_band_part | Matrix band part of ones.
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rows: int determining number of rows in output
cols: int
num_lower: int, maximum distance backward. Negative values indicate
unlimited.
num_upper: int, maximum distance forward. Negative values indicate
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out_shape: shape to reshape output by.
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rows: int determining number of rows in output
cols: int
num_lower: int, maximum distance backward. Negative values indicate
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rows: int determining number of rows in output
cols: int
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train | reshape_like_all_dims | Reshapes a to match the shape of b. | tensor2tensor/layers/common_layers.py | def reshape_like_all_dims(a, b):
"""Reshapes a to match the shape of b."""
ret = tf.reshape(a, tf.shape(b))
if not tf.executing_eagerly():
ret.set_shape(b.get_shape())
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"""Reshapes a to match the shape of b."""
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train | recompute_grad | Decorator that recomputes the function on the backwards pass.
Args:
fn: a function that takes Tensors (all as positional arguments) and returns
a tuple of Tensors.
Returns:
A wrapped fn that is identical to fn when called, but its activations will
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"""Decorator that recomputes the function on the backwards pass.
Args:
fn: a function that takes Tensors (all as positional arguments) and returns
a tuple of Tensors.
Returns:
A wrapped fn that is identical to fn when called, but its activations will
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fn: a function that takes Tensors (all as positional arguments) and returns
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train | _recompute_grad | See recompute_grad. | tensor2tensor/layers/common_layers.py | def _recompute_grad(fn, args):
"""See recompute_grad."""
cached_vs = []
cached_arg_scope = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Recompute outputs for gradient computation."""
del outputs
variables = [underlying_variable_ref(v) for v in variables]
# Recompute outputs
... | def _recompute_grad(fn, args):
"""See recompute_grad."""
cached_vs = []
cached_arg_scope = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Recompute outputs for gradient computation."""
del outputs
variables = [underlying_variable_ref(v) for v in variables]
# Recompute outputs
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train | dense | Identical to layers.dense. | tensor2tensor/layers/common_layers.py | def dense(x, units, **kwargs):
"""Identical to layers.dense."""
layer_collection = kwargs.pop("layer_collection", None)
activations = layers().Dense(units, **kwargs)(x)
if layer_collection:
# We need to find the layer parameters using scope name for the layer, so
# check that the layer is named. Otherwi... | def dense(x, units, **kwargs):
"""Identical to layers.dense."""
layer_collection = kwargs.pop("layer_collection", None)
activations = layers().Dense(units, **kwargs)(x)
if layer_collection:
# We need to find the layer parameters using scope name for the layer, so
# check that the layer is named. Otherwi... | [
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train | batch_dense | Multiply a batch of input matrices by a batch of parameter matrices.
Each input matrix is multiplied by the corresponding parameter matrix.
This is useful in a mixture-of-experts where the batch represents different
experts with different inputs.
Args:
inputs: a Tensor with shape [batch, length, input_un... | tensor2tensor/layers/common_layers.py | def batch_dense(inputs,
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activation=None,
kernel_initializer=None,
reuse=None,
name=None):
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Each input matrix is multiplied by the corresponding parameter mat... | def batch_dense(inputs,
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train | mix | Mix starting with x2, mixing mixing, going towards x1. | tensor2tensor/layers/common_layers.py | def mix(x1,
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"""Mix starting with x2, mixing mixing, going towards x1."""
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train | brelu | Bipolar ReLU as in https://arxiv.org/abs/1709.04054. | tensor2tensor/layers/common_layers.py | def brelu(x):
"""Bipolar ReLU as in https://arxiv.org/abs/1709.04054."""
x_shape = shape_list(x)
x1, x2 = tf.split(tf.reshape(x, x_shape[:-1] + [-1, 2]), 2, axis=-1)
y1 = tf.nn.relu(x1)
y2 = -tf.nn.relu(-x2)
return tf.reshape(tf.concat([y1, y2], axis=-1), x_shape) | def brelu(x):
"""Bipolar ReLU as in https://arxiv.org/abs/1709.04054."""
x_shape = shape_list(x)
x1, x2 = tf.split(tf.reshape(x, x_shape[:-1] + [-1, 2]), 2, axis=-1)
y1 = tf.nn.relu(x1)
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return tf.reshape(tf.concat([y1, y2], axis=-1), x_shape) | [
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train | belu | Bipolar ELU as in https://arxiv.org/abs/1709.04054. | tensor2tensor/layers/common_layers.py | def belu(x):
"""Bipolar ELU as in https://arxiv.org/abs/1709.04054."""
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y2 = -tf.nn.elu(-x2)
return tf.reshape(tf.concat([y1, y2], axis=-1), x_shape) | def belu(x):
"""Bipolar ELU as in https://arxiv.org/abs/1709.04054."""
x_shape = shape_list(x)
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train | gelu | Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
x with the GELU activation applied. | tensor2tensor/layers/common_layers.py | def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
x with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
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"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
x with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
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train | nac | NAC as in https://arxiv.org/abs/1808.00508. | tensor2tensor/layers/common_layers.py | def nac(x, depth, name=None, reuse=None):
"""NAC as in https://arxiv.org/abs/1808.00508."""
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m = tf.get_variable("m", [x_shape[-1], depth])
w = tf.tanh(w) ... | def nac(x, depth, name=None, reuse=None):
"""NAC as in https://arxiv.org/abs/1808.00508."""
with tf.variable_scope(name, default_name="nac", values=[x], reuse=reuse):
x_shape = shape_list(x)
w = tf.get_variable("w", [x_shape[-1], depth])
m = tf.get_variable("m", [x_shape[-1], depth])
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train | nalu | NALU as in https://arxiv.org/abs/1808.00508. | tensor2tensor/layers/common_layers.py | def nalu(x, depth, epsilon=1e-30, name=None, reuse=None):
"""NALU as in https://arxiv.org/abs/1808.00508."""
with tf.variable_scope(name, default_name="nalu", values=[x], reuse=reuse):
x_shape = shape_list(x)
x_flat = tf.reshape(x, [-1, x_shape[-1]])
gw = tf.get_variable("w", [x_shape[-1], depth])
g... | def nalu(x, depth, epsilon=1e-30, name=None, reuse=None):
"""NALU as in https://arxiv.org/abs/1808.00508."""
with tf.variable_scope(name, default_name="nalu", values=[x], reuse=reuse):
x_shape = shape_list(x)
x_flat = tf.reshape(x, [-1, x_shape[-1]])
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train | argmax_with_score | Argmax along with the value. | tensor2tensor/layers/common_layers.py | def argmax_with_score(logits, axis=None):
"""Argmax along with the value."""
axis = axis or len(logits.get_shape()) - 1
predictions = tf.argmax(logits, axis=axis)
logits_shape = shape_list(logits)
prefix_shape, vocab_size = logits_shape[:-1], logits_shape[-1]
prefix_size = 1
for d in prefix_shape:
pr... | def argmax_with_score(logits, axis=None):
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axis = axis or len(logits.get_shape()) - 1
predictions = tf.argmax(logits, axis=axis)
logits_shape = shape_list(logits)
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prefix_size = 1
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train | top_kth_iterative | Compute the k-th top element of x on the last axis iteratively.
This assumes values in x are non-negative, rescale if needed.
It is often faster than tf.nn.top_k for small k, especially if k < 30.
Note: this does not support back-propagation, it stops gradients!
Args:
x: a Tensor of non-negative numbers o... | tensor2tensor/layers/common_layers.py | def top_kth_iterative(x, k):
"""Compute the k-th top element of x on the last axis iteratively.
This assumes values in x are non-negative, rescale if needed.
It is often faster than tf.nn.top_k for small k, especially if k < 30.
Note: this does not support back-propagation, it stops gradients!
Args:
x: ... | def top_kth_iterative(x, k):
"""Compute the k-th top element of x on the last axis iteratively.
This assumes values in x are non-negative, rescale if needed.
It is often faster than tf.nn.top_k for small k, especially if k < 30.
Note: this does not support back-propagation, it stops gradients!
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train | top_1_tpu | find max and argmax over the last dimension.
Works well on TPU
Args:
inputs: A tensor with shape [..., depth]
Returns:
values: a Tensor with shape [...]
indices: a Tensor with shape [...] | tensor2tensor/layers/common_layers.py | def top_1_tpu(inputs):
"""find max and argmax over the last dimension.
Works well on TPU
Args:
inputs: A tensor with shape [..., depth]
Returns:
values: a Tensor with shape [...]
indices: a Tensor with shape [...]
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inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
mask = tf.to_i... | def top_1_tpu(inputs):
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Works well on TPU
Args:
inputs: A tensor with shape [..., depth]
Returns:
values: a Tensor with shape [...]
indices: a Tensor with shape [...]
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train | index_last_dim_with_indices | Use indices to index into the last axis of x.
This can be useful for recovering the actual probabilities of a sample from a
probability distribution.
Args:
x: Tensor, n-d.
indices: Tensor, (n-1)-d, where the dimension sizes match the first (n-1)
dimensions of x. The values of indices will be used ... | tensor2tensor/layers/common_layers.py | def index_last_dim_with_indices(x, indices):
"""Use indices to index into the last axis of x.
This can be useful for recovering the actual probabilities of a sample from a
probability distribution.
Args:
x: Tensor, n-d.
indices: Tensor, (n-1)-d, where the dimension sizes match the first (n-1)
di... | def index_last_dim_with_indices(x, indices):
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This can be useful for recovering the actual probabilities of a sample from a
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Args:
x: Tensor, n-d.
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train | should_generate_summaries | Is this an appropriate context to generate summaries.
Returns:
a boolean | tensor2tensor/layers/common_layers.py | def should_generate_summaries():
"""Is this an appropriate context to generate summaries.
Returns:
a boolean
"""
name_scope = tf.contrib.framework.get_name_scope()
if name_scope and "while/" in name_scope:
# Summaries don't work well within tf.while_loop()
return False
if tf.get_variable_scope(... | def should_generate_summaries():
"""Is this an appropriate context to generate summaries.
Returns:
a boolean
"""
name_scope = tf.contrib.framework.get_name_scope()
if name_scope and "while/" in name_scope:
# Summaries don't work well within tf.while_loop()
return False
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train | reshape_like | Reshapes a to match the shape of b in all but the last dimension. | tensor2tensor/layers/common_layers.py | def reshape_like(a, b):
"""Reshapes a to match the shape of b in all but the last dimension."""
ret = tf.reshape(a, tf.concat([tf.shape(b)[:-1], tf.shape(a)[-1:]], 0))
if not tf.executing_eagerly():
ret.set_shape(b.get_shape().as_list()[:-1] + a.get_shape().as_list()[-1:])
return ret | def reshape_like(a, b):
"""Reshapes a to match the shape of b in all but the last dimension."""
ret = tf.reshape(a, tf.concat([tf.shape(b)[:-1], tf.shape(a)[-1:]], 0))
if not tf.executing_eagerly():
ret.set_shape(b.get_shape().as_list()[:-1] + a.get_shape().as_list()[-1:])
return ret | [
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train | summarize_video | Summarize the video using image summaries starting with prefix. | tensor2tensor/layers/common_layers.py | def summarize_video(video, prefix, max_outputs=1):
"""Summarize the video using image summaries starting with prefix."""
video_shape = shape_list(video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
"[batch, time, height, width, channels] but... | def summarize_video(video, prefix, max_outputs=1):
"""Summarize the video using image summaries starting with prefix."""
video_shape = shape_list(video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
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train | cast_like | Cast x to y's dtype, if necessary. | tensor2tensor/layers/common_layers.py | def cast_like(x, y):
"""Cast x to y's dtype, if necessary."""
x = tf.convert_to_tensor(x)
y = tf.convert_to_tensor(y)
if x.dtype.base_dtype == y.dtype.base_dtype:
return x
cast_x = tf.cast(x, y.dtype)
if cast_x.device != x.device:
x_name = "(eager Tensor)"
try:
x_name = x.name
except... | def cast_like(x, y):
"""Cast x to y's dtype, if necessary."""
x = tf.convert_to_tensor(x)
y = tf.convert_to_tensor(y)
if x.dtype.base_dtype == y.dtype.base_dtype:
return x
cast_x = tf.cast(x, y.dtype)
if cast_x.device != x.device:
x_name = "(eager Tensor)"
try:
x_name = x.name
except... | [
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train | make_even_size | Pad x to be even-sized on axis 1 and 2, but only if necessary. | tensor2tensor/layers/common_layers.py | def make_even_size(x):
"""Pad x to be even-sized on axis 1 and 2, but only if necessary."""
x_shape = x.get_shape().as_list()
assert len(x_shape) > 2, "Only 3+-dimensional tensors supported."
shape = [dim if dim is not None else -1 for dim in x_shape]
new_shape = x_shape # To make sure constant shapes remain... | def make_even_size(x):
"""Pad x to be even-sized on axis 1 and 2, but only if necessary."""
x_shape = x.get_shape().as_list()
assert len(x_shape) > 2, "Only 3+-dimensional tensors supported."
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train | sliced_gan_loss | Loss inspired by the sliced WGAN paper: https://arxiv.org/abs/1804.01947.
Puts input1 and input2 through the provided discriminator to get logits.
Then, computes num_vecs random projections of the logits, sorts them on
the batch dimension and returns the L2 loss between the sorted vectors.
See the above-mentio... | tensor2tensor/layers/common_layers.py | def sliced_gan_loss(input1,
input2,
discriminator,
num_vecs,
do_random_vecs=True,
do_tanh=True,
return_logits=False):
"""Loss inspired by the sliced WGAN paper: https://arxiv.org/abs/1804.01947.
... | def sliced_gan_loss(input1,
input2,
discriminator,
num_vecs,
do_random_vecs=True,
do_tanh=True,
return_logits=False):
"""Loss inspired by the sliced WGAN paper: https://arxiv.org/abs/1804.01947.
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train | deep_discriminator | Discriminator architecture based on InfoGAN. | tensor2tensor/layers/common_layers.py | def deep_discriminator(x,
batch_norm,
is_training,
filters=64,
filter_size=4,
stride=2,
output_size=1024):
"""Discriminator architecture based on InfoGAN."""
with tf.variable_sco... | def deep_discriminator(x,
batch_norm,
is_training,
filters=64,
filter_size=4,
stride=2,
output_size=1024):
"""Discriminator architecture based on InfoGAN."""
with tf.variable_sco... | [
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train | instance_norm | Instance normalization layer. | tensor2tensor/layers/common_layers.py | def instance_norm(x):
"""Instance normalization layer."""
with tf.variable_scope("instance_norm"):
epsilon = 1e-5
mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
scale = tf.get_variable(
"scale", [x.get_shape()[-1]],
initializer=tf.truncated_normal_initializer(mean=1.0, stddev=0.02)... | def instance_norm(x):
"""Instance normalization layer."""
with tf.variable_scope("instance_norm"):
epsilon = 1e-5
mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
scale = tf.get_variable(
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initializer=tf.truncated_normal_initializer(mean=1.0, stddev=0.02)... | [
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train | general_conv | Generalized convolution layer. | tensor2tensor/layers/common_layers.py | def general_conv(x,
num_filters=64,
filter_size=7,
stride=1,
stddev=0.02,
padding="VALID",
name="conv",
do_norm="instance",
do_relu=True,
relufactor=0):
"""Generaliz... | def general_conv(x,
num_filters=64,
filter_size=7,
stride=1,
stddev=0.02,
padding="VALID",
name="conv",
do_norm="instance",
do_relu=True,
relufactor=0):
"""Generaliz... | [
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train | patch_discriminator | Patch descriminator. | tensor2tensor/layers/common_layers.py | def patch_discriminator(x, filters=64, filter_size=5, n=4,
name="patch_discrim"):
"""Patch descriminator."""
with tf.variable_scope(name):
x_shape = shape_list(x)
spatial_dims = [x_shape[1] // 4, x_shape[2] // 4]
x = tf.random_crop(x, [x_shape[0]] + spatial_dims + [x_shape[3]])
... | def patch_discriminator(x, filters=64, filter_size=5, n=4,
name="patch_discrim"):
"""Patch descriminator."""
with tf.variable_scope(name):
x_shape = shape_list(x)
spatial_dims = [x_shape[1] // 4, x_shape[2] // 4]
x = tf.random_crop(x, [x_shape[0]] + spatial_dims + [x_shape[3]])
... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_layers.py#L3689-L3709 | [
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train | mean_with_attention | Mean and attention to reduce spatial dimensions. | tensor2tensor/layers/common_layers.py | def mean_with_attention(x, name, num_heads=4):
"""Mean and attention to reduce spatial dimensions."""
with tf.variable_scope(name):
shape = shape_list(x)
m = tf.reduce_mean(x, [1, 2])
a = layers().Dense(num_heads, name="mean_attn")(x)
s = tf.reshape(a, [shape[0], -1, num_heads])
s = tf.nn.softma... | def mean_with_attention(x, name, num_heads=4):
"""Mean and attention to reduce spatial dimensions."""
with tf.variable_scope(name):
shape = shape_list(x)
m = tf.reduce_mean(x, [1, 2])
a = layers().Dense(num_heads, name="mean_attn")(x)
s = tf.reshape(a, [shape[0], -1, num_heads])
s = tf.nn.softma... | [
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train | single_discriminator | A simple single-layer convolutional discriminator. | tensor2tensor/layers/common_layers.py | def single_discriminator(x, filters=128, kernel_size=8,
strides=4, pure_mean=False):
"""A simple single-layer convolutional discriminator."""
with tf.variable_scope("discriminator"):
net = layers().Conv2D(
filters, kernel_size, strides=strides, padding="SAME", name="conv1")(x)
... | def single_discriminator(x, filters=128, kernel_size=8,
strides=4, pure_mean=False):
"""A simple single-layer convolutional discriminator."""
with tf.variable_scope("discriminator"):
net = layers().Conv2D(
filters, kernel_size, strides=strides, padding="SAME", name="conv1")(x)
... | [
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train | double_discriminator | A convolutional discriminator with 2 layers and concatenated output. | tensor2tensor/layers/common_layers.py | def double_discriminator(x, filters1=128, filters2=None,
kernel_size=8, strides=4, pure_mean=False):
"""A convolutional discriminator with 2 layers and concatenated output."""
if filters2 is None:
filters2 = 4 * filters1
with tf.variable_scope("discriminator"):
batch_size = shape_... | def double_discriminator(x, filters1=128, filters2=None,
kernel_size=8, strides=4, pure_mean=False):
"""A convolutional discriminator with 2 layers and concatenated output."""
if filters2 is None:
filters2 = 4 * filters1
with tf.variable_scope("discriminator"):
batch_size = shape_... | [
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train | upscale | Upscaling the image by a factor of f. | tensor2tensor/layers/common_layers.py | def upscale(inputs, f, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR):
"""Upscaling the image by a factor of f."""
height, width = shape_list(inputs)[1:3] # pylint: disable=unbalanced-tuple-unpacking
return tf.image.resize_images(inputs, (height * f, width * f), method) | def upscale(inputs, f, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR):
"""Upscaling the image by a factor of f."""
height, width = shape_list(inputs)[1:3] # pylint: disable=unbalanced-tuple-unpacking
return tf.image.resize_images(inputs, (height * f, width * f), method) | [
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train | cyclegan_upsample | Upsamples the given inputs.
Args:
net: A Tensor of size [batch_size, height, width, filters].
num_outputs: The number of output filters.
stride: A list of 2 scalars or a 1x2 Tensor indicating the scale,
relative to the inputs, of the output dimensions. For example, if kernel
size is [2, 3], t... | tensor2tensor/layers/common_layers.py | def cyclegan_upsample(net, num_outputs, stride, method="conv2d_transpose"):
"""Upsamples the given inputs.
Args:
net: A Tensor of size [batch_size, height, width, filters].
num_outputs: The number of output filters.
stride: A list of 2 scalars or a 1x2 Tensor indicating the scale,
relative to the... | def cyclegan_upsample(net, num_outputs, stride, method="conv2d_transpose"):
"""Upsamples the given inputs.
Args:
net: A Tensor of size [batch_size, height, width, filters].
num_outputs: The number of output filters.
stride: A list of 2 scalars or a 1x2 Tensor indicating the scale,
relative to the... | [
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] | tensorflow/tensor2tensor | python | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_layers.py#L3788-L3841 | [
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