Module keras.layers.local
Locally-connected layers.
Expand source code
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=g-classes-have-attributes
"""Locally-connected layers."""
import tensorflow.compat.v2 as tf
import numpy as np
from keras import activations
from keras import backend
from keras import constraints
from keras import initializers
from keras import regularizers
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
from keras.utils import conv_utils
from keras.utils import tf_utils
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.layers.LocallyConnected1D')
class LocallyConnected1D(Layer):
"""Locally-connected layer for 1D inputs.
The `LocallyConnected1D` layer works similarly to
the `Conv1D` layer, except that weights are unshared,
that is, a different set of filters is applied at each different patch
of the input.
Note: layer attributes cannot be modified after the layer has been called
once (except the `trainable` attribute).
Example:
```python
# apply a unshared weight convolution 1d of length 3 to a sequence with
# 10 timesteps, with 64 output filters
model = Sequential()
model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
# now model.output_shape == (None, 8, 64)
# add a new conv1d on top
model.add(LocallyConnected1D(32, 3))
# now model.output_shape == (None, 6, 32)
```
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of output filters in the convolution).
kernel_size: An integer or tuple/list of a single integer, specifying the
length of the 1D convolution window.
strides: An integer or tuple/list of a single integer, specifying the
stride length of the convolution.
padding: Currently only supports `"valid"` (case-insensitive). `"same"`
may be supported in the future. `"valid"` means no padding.
data_format: A string, one of `channels_last` (default) or
`channels_first`. The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape `(batch, length,
channels)` while `channels_first` corresponds to inputs with shape
`(batch, channels, length)`. It defaults to the `image_data_format`
value found in your Keras config file at `~/.keras/keras.json`. If you
never set it, then it will be "channels_last".
activation: Activation function to use. If you don't specify anything, no
activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation")..
kernel_constraint: Constraint function applied to the kernel matrix.
bias_constraint: Constraint function applied to the bias vector.
implementation: implementation mode, either `1`, `2`, or `3`. `1` loops
over input spatial locations to perform the forward pass. It is
memory-efficient but performs a lot of (small) ops. `2` stores layer
weights in a dense but sparsely-populated 2D matrix and implements the
forward pass as a single matrix-multiply. It uses a lot of RAM but
performs few (large) ops. `3` stores layer weights in a sparse tensor
and implements the forward pass as a single sparse matrix-multiply.
How to choose:
`1`: large, dense models,
`2`: small models,
`3`: large, sparse models, where "large" stands for large
input/output activations (i.e. many `filters`, `input_filters`,
large `input_size`, `output_size`), and "sparse" stands for few
connections between inputs and outputs, i.e. small ratio `filters *
input_filters * kernel_size / (input_size * strides)`, where inputs
to and outputs of the layer are assumed to have shapes `(input_size,
input_filters)`, `(output_size, filters)` respectively. It is
recommended to benchmark each in the setting of interest to pick the
most efficient one (in terms of speed and memory usage). Correct
choice of implementation can lead to dramatic speed improvements
(e.g. 50X), potentially at the expense of RAM. Also, only
`padding="valid"` is supported by `implementation=1`.
Input shape:
3D tensor with shape: `(batch_size, steps, input_dim)`
Output shape:
3D tensor with shape: `(batch_size, new_steps, filters)` `steps` value
might have changed due to padding or strides.
"""
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
implementation=1,
**kwargs):
super(LocallyConnected1D, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
self.padding = conv_utils.normalize_padding(padding)
if self.padding != 'valid' and implementation == 1:
raise ValueError('Invalid border mode for LocallyConnected1D '
'(only "valid" is supported if implementation is 1): ' +
padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.implementation = implementation
self.input_spec = InputSpec(ndim=3)
@property
def _use_input_spec_as_call_signature(self):
return False
@tf_utils.shape_type_conversion
def build(self, input_shape):
if self.data_format == 'channels_first':
input_dim, input_length = input_shape[1], input_shape[2]
else:
input_dim, input_length = input_shape[2], input_shape[1]
if input_dim is None:
raise ValueError(
'Axis 2 of input should be fully-defined. '
'Found shape:', input_shape)
self.output_length = conv_utils.conv_output_length(input_length,
self.kernel_size[0],
self.padding,
self.strides[0])
if self.implementation == 1:
self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim,
self.filters)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
elif self.implementation == 2:
if self.data_format == 'channels_first':
self.kernel_shape = (input_dim, input_length, self.filters,
self.output_length)
else:
self.kernel_shape = (input_length, input_dim, self.output_length,
self.filters)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.kernel_mask = get_locallyconnected_mask(
input_shape=(input_length,),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
)
elif self.implementation == 3:
self.kernel_shape = (self.output_length * self.filters,
input_length * input_dim)
self.kernel_idxs = sorted(
conv_utils.conv_kernel_idxs(
input_shape=(input_length,),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
filters_in=input_dim,
filters_out=self.filters,
data_format=self.data_format))
self.kernel = self.add_weight(
shape=(len(self.kernel_idxs),),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
else:
raise ValueError('Unrecognized implementation mode: %d.' %
self.implementation)
if self.use_bias:
self.bias = self.add_weight(
shape=(self.output_length, self.filters),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
if self.data_format == 'channels_first':
self.input_spec = InputSpec(ndim=3, axes={1: input_dim})
else:
self.input_spec = InputSpec(ndim=3, axes={-1: input_dim})
self.built = True
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
input_length = input_shape[2]
else:
input_length = input_shape[1]
length = conv_utils.conv_output_length(input_length, self.kernel_size[0],
self.padding, self.strides[0])
if self.data_format == 'channels_first':
return (input_shape[0], self.filters, length)
elif self.data_format == 'channels_last':
return (input_shape[0], length, self.filters)
def call(self, inputs):
if self.implementation == 1:
output = backend.local_conv(
inputs, self.kernel, self.kernel_size, self.strides,
(self.output_length,), self.data_format)
elif self.implementation == 2:
output = local_conv_matmul(inputs, self.kernel, self.kernel_mask,
self.compute_output_shape(inputs.shape))
elif self.implementation == 3:
output = local_conv_sparse_matmul(inputs, self.kernel, self.kernel_idxs,
self.kernel_shape,
self.compute_output_shape(inputs.shape))
else:
raise ValueError('Unrecognized implementation mode: %d.' %
self.implementation)
if self.use_bias:
output = backend.bias_add(output, self.bias, data_format=self.data_format)
output = self.activation(output)
return output
def get_config(self):
config = {
'filters':
self.filters,
'kernel_size':
self.kernel_size,
'strides':
self.strides,
'padding':
self.padding,
'data_format':
self.data_format,
'activation':
activations.serialize(self.activation),
'use_bias':
self.use_bias,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'implementation':
self.implementation
}
base_config = super(LocallyConnected1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@keras_export('keras.layers.LocallyConnected2D')
class LocallyConnected2D(Layer):
"""Locally-connected layer for 2D inputs.
The `LocallyConnected2D` layer works similarly
to the `Conv2D` layer, except that weights are unshared,
that is, a different set of filters is applied at each
different patch of the input.
Note: layer attributes cannot be modified after the layer has been called
once (except the `trainable` attribute).
Examples:
```python
# apply a 3x3 unshared weights convolution with 64 output filters on a
32x32 image
# with `data_format="channels_last"`:
model = Sequential()
model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
# now model.output_shape == (None, 30, 30, 64)
# notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64
parameters
# add a 3x3 unshared weights convolution on top, with 32 output filters:
model.add(LocallyConnected2D(32, (3, 3)))
# now model.output_shape == (None, 28, 28, 32)
```
Args:
filters: Integer, the dimensionality of the output space (i.e. the number
of output filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the width
and height of the 2D convolution window. Can be a single integer to
specify the same value for all spatial dimensions.
strides: An integer or tuple/list of 2 integers, specifying the strides of
the convolution along the width and height. Can be a single integer to
specify the same value for all spatial dimensions.
padding: Currently only support `"valid"` (case-insensitive). `"same"`
will be supported in future. `"valid"` means no padding.
data_format: A string, one of `channels_last` (default) or
`channels_first`. The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape `(batch, height, width,
channels)` while `channels_first` corresponds to inputs with shape
`(batch, channels, height, width)`. It defaults to the
`image_data_format` value found in your Keras config file at
`~/.keras/keras.json`. If you never set it, then it will be
"channels_last".
activation: Activation function to use. If you don't specify anything, no
activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to the `kernel` weights
matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to the output of the
layer (its "activation").
kernel_constraint: Constraint function applied to the kernel matrix.
bias_constraint: Constraint function applied to the bias vector.
implementation: implementation mode, either `1`, `2`, or `3`. `1` loops
over input spatial locations to perform the forward pass. It is
memory-efficient but performs a lot of (small) ops. `2` stores layer
weights in a dense but sparsely-populated 2D matrix and implements the
forward pass as a single matrix-multiply. It uses a lot of RAM but
performs few (large) ops. `3` stores layer weights in a sparse tensor
and implements the forward pass as a single sparse matrix-multiply.
How to choose:
`1`: large, dense models,
`2`: small models,
`3`: large, sparse models, where "large" stands for large
input/output activations (i.e. many `filters`, `input_filters`,
large `np.prod(input_size)`, `np.prod(output_size)`), and "sparse"
stands for few connections between inputs and outputs, i.e. small
ratio `filters * input_filters * np.prod(kernel_size) /
(np.prod(input_size) * np.prod(strides))`, where inputs to and
outputs of the layer are assumed to have shapes `input_size +
(input_filters,)`, `output_size + (filters,)` respectively. It is
recommended to benchmark each in the setting of interest to pick the
most efficient one (in terms of speed and memory usage). Correct
choice of implementation can lead to dramatic speed improvements
(e.g. 50X), potentially at the expense of RAM. Also, only
`padding="valid"` is supported by `implementation=1`.
Input shape:
4D tensor with shape: `(samples, channels, rows, cols)` if
data_format='channels_first'
or 4D tensor with shape: `(samples, rows, cols, channels)` if
data_format='channels_last'.
Output shape:
4D tensor with shape: `(samples, filters, new_rows, new_cols)` if
data_format='channels_first'
or 4D tensor with shape: `(samples, new_rows, new_cols, filters)` if
data_format='channels_last'. `rows` and `cols` values might have changed
due to padding.
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
implementation=1,
**kwargs):
super(LocallyConnected2D, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
self.padding = conv_utils.normalize_padding(padding)
if self.padding != 'valid' and implementation == 1:
raise ValueError('Invalid border mode for LocallyConnected2D '
'(only "valid" is supported if implementation is 1): ' +
padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.implementation = implementation
self.input_spec = InputSpec(ndim=4)
@property
def _use_input_spec_as_call_signature(self):
return False
@tf_utils.shape_type_conversion
def build(self, input_shape):
if self.data_format == 'channels_last':
input_row, input_col = input_shape[1:-1]
input_filter = input_shape[3]
else:
input_row, input_col = input_shape[2:]
input_filter = input_shape[1]
if input_row is None or input_col is None:
raise ValueError('The spatial dimensions of the inputs to '
' a LocallyConnected2D layer '
'should be fully-defined, but layer received '
'the inputs shape ' + str(input_shape))
output_row = conv_utils.conv_output_length(input_row, self.kernel_size[0],
self.padding, self.strides[0])
output_col = conv_utils.conv_output_length(input_col, self.kernel_size[1],
self.padding, self.strides[1])
self.output_row = output_row
self.output_col = output_col
if self.implementation == 1:
self.kernel_shape = (output_row * output_col, self.kernel_size[0] *
self.kernel_size[1] * input_filter, self.filters)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
elif self.implementation == 2:
if self.data_format == 'channels_first':
self.kernel_shape = (input_filter, input_row, input_col, self.filters,
self.output_row, self.output_col)
else:
self.kernel_shape = (input_row, input_col, input_filter,
self.output_row, self.output_col, self.filters)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.kernel_mask = get_locallyconnected_mask(
input_shape=(input_row, input_col),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
)
elif self.implementation == 3:
self.kernel_shape = (self.output_row * self.output_col * self.filters,
input_row * input_col * input_filter)
self.kernel_idxs = sorted(
conv_utils.conv_kernel_idxs(
input_shape=(input_row, input_col),
kernel_shape=self.kernel_size,
strides=self.strides,
padding=self.padding,
filters_in=input_filter,
filters_out=self.filters,
data_format=self.data_format))
self.kernel = self.add_weight(
shape=(len(self.kernel_idxs),),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
else:
raise ValueError('Unrecognized implementation mode: %d.' %
self.implementation)
if self.use_bias:
self.bias = self.add_weight(
shape=(output_row, output_col, self.filters),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
if self.data_format == 'channels_first':
self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
else:
self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
self.built = True
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding, self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding, self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], self.filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, self.filters)
def call(self, inputs):
if self.implementation == 1:
output = backend.local_conv(
inputs, self.kernel, self.kernel_size, self.strides,
(self.output_row, self.output_col),
self.data_format)
elif self.implementation == 2:
output = local_conv_matmul(inputs, self.kernel, self.kernel_mask,
self.compute_output_shape(inputs.shape))
elif self.implementation == 3:
output = local_conv_sparse_matmul(inputs, self.kernel, self.kernel_idxs,
self.kernel_shape,
self.compute_output_shape(inputs.shape))
else:
raise ValueError('Unrecognized implementation mode: %d.' %
self.implementation)
if self.use_bias:
output = backend.bias_add(output, self.bias, data_format=self.data_format)
output = self.activation(output)
return output
def get_config(self):
config = {
'filters':
self.filters,
'kernel_size':
self.kernel_size,
'strides':
self.strides,
'padding':
self.padding,
'data_format':
self.data_format,
'activation':
activations.serialize(self.activation),
'use_bias':
self.use_bias,
'kernel_initializer':
initializers.serialize(self.kernel_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'kernel_regularizer':
regularizers.serialize(self.kernel_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'kernel_constraint':
constraints.serialize(self.kernel_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint),
'implementation':
self.implementation
}
base_config = super(LocallyConnected2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def get_locallyconnected_mask(input_shape, kernel_shape, strides, padding,
data_format):
"""Return a mask representing connectivity of a locally-connected operation.
This method returns a masking numpy array of 0s and 1s (of type `np.float32`)
that, when element-wise multiplied with a fully-connected weight tensor, masks
out the weights between disconnected input-output pairs and thus implements
local connectivity through a sparse fully-connected weight tensor.
Assume an unshared convolution with given parameters is applied to an input
having N spatial dimensions with `input_shape = (d_in1, ..., d_inN)`
to produce an output with spatial shape `(d_out1, ..., d_outN)` (determined
by layer parameters such as `strides`).
This method returns a mask which can be broadcast-multiplied (element-wise)
with a 2*(N+1)-D weight matrix (equivalent to a fully-connected layer between
(N+1)-D activations (N spatial + 1 channel dimensions for input and output)
to make it perform an unshared convolution with given `kernel_shape`,
`strides`, `padding` and `data_format`.
Args:
input_shape: tuple of size N: `(d_in1, ..., d_inN)` spatial shape of the
input.
kernel_shape: tuple of size N, spatial shape of the convolutional kernel /
receptive field.
strides: tuple of size N, strides along each spatial dimension.
padding: type of padding, string `"same"` or `"valid"`.
data_format: a string, `"channels_first"` or `"channels_last"`.
Returns:
a `np.float32`-type `np.ndarray` of shape
`(1, d_in1, ..., d_inN, 1, d_out1, ..., d_outN)`
if `data_format == `"channels_first"`, or
`(d_in1, ..., d_inN, 1, d_out1, ..., d_outN, 1)`
if `data_format == "channels_last"`.
Raises:
ValueError: if `data_format` is neither `"channels_first"` nor
`"channels_last"`.
"""
mask = conv_utils.conv_kernel_mask(
input_shape=input_shape,
kernel_shape=kernel_shape,
strides=strides,
padding=padding)
ndims = int(mask.ndim / 2)
if data_format == 'channels_first':
mask = np.expand_dims(mask, 0)
mask = np.expand_dims(mask, -ndims - 1)
elif data_format == 'channels_last':
mask = np.expand_dims(mask, ndims)
mask = np.expand_dims(mask, -1)
else:
raise ValueError('Unrecognized data_format: ' + str(data_format))
return mask
def local_conv_matmul(inputs, kernel, kernel_mask, output_shape):
"""Apply N-D convolution with un-shared weights using a single matmul call.
This method outputs `inputs . (kernel * kernel_mask)`
(with `.` standing for matrix-multiply and `*` for element-wise multiply)
and requires a precomputed `kernel_mask` to zero-out weights in `kernel` and
hence perform the same operation as a convolution with un-shared
(the remaining entries in `kernel`) weights. It also does the necessary
reshapes to make `inputs` and `kernel` 2-D and `output` (N+2)-D.
Args:
inputs: (N+2)-D tensor with shape `(batch_size, channels_in, d_in1, ...,
d_inN)` or `(batch_size, d_in1, ..., d_inN, channels_in)`.
kernel: the unshared weights for N-D convolution,
an (N+2)-D tensor of shape: `(d_in1, ..., d_inN, channels_in, d_out2,
..., d_outN, channels_out)` or `(channels_in, d_in1, ..., d_inN,
channels_out, d_out2, ..., d_outN)`, with the ordering of channels
and spatial dimensions matching that of the input. Each entry is the
weight between a particular input and output location, similarly to
a fully-connected weight matrix.
kernel_mask: a float 0/1 mask tensor of shape: `(d_in1, ..., d_inN, 1,
d_out2, ..., d_outN, 1)` or `(1, d_in1, ..., d_inN, 1, d_out2, ...,
d_outN)`, with the ordering of singleton and spatial dimensions matching
that of the input. Mask represents the connectivity pattern of the layer
and is
precomputed elsewhere based on layer parameters: stride, padding, and
the receptive field shape.
output_shape: a tuple of (N+2) elements representing the output shape:
`(batch_size, channels_out, d_out1, ..., d_outN)` or `(batch_size,
d_out1, ..., d_outN, channels_out)`, with the ordering of channels and
spatial dimensions matching that of the input.
Returns:
Output (N+2)-D tensor with shape `output_shape`.
"""
inputs_flat = backend.reshape(inputs, (backend.shape(inputs)[0], -1))
kernel = kernel_mask * kernel
kernel = make_2d(kernel, split_dim=backend.ndim(kernel) // 2)
output_flat = tf.matmul(inputs_flat, kernel, b_is_sparse=True)
output = backend.reshape(output_flat, [
backend.shape(output_flat)[0],
] + output_shape.as_list()[1:])
return output
def local_conv_sparse_matmul(inputs, kernel, kernel_idxs, kernel_shape,
output_shape):
"""Apply N-D convolution with un-shared weights using a single sparse matmul.
This method outputs `inputs . tf.sparse.SparseTensor(indices=kernel_idxs,
values=kernel, dense_shape=kernel_shape)`, with `.` standing for
matrix-multiply. It also reshapes `inputs` to 2-D and `output` to (N+2)-D.
Args:
inputs: (N+2)-D tensor with shape `(batch_size, channels_in, d_in1, ...,
d_inN)` or `(batch_size, d_in1, ..., d_inN, channels_in)`.
kernel: a 1-D tensor with shape `(len(kernel_idxs),)` containing all the
weights of the layer.
kernel_idxs: a list of integer tuples representing indices in a sparse
matrix performing the un-shared convolution as a matrix-multiply.
kernel_shape: a tuple `(input_size, output_size)`, where `input_size =
channels_in * d_in1 * ... * d_inN` and `output_size = channels_out *
d_out1 * ... * d_outN`.
output_shape: a tuple of (N+2) elements representing the output shape:
`(batch_size, channels_out, d_out1, ..., d_outN)` or `(batch_size,
d_out1, ..., d_outN, channels_out)`, with the ordering of channels and
spatial dimensions matching that of the input.
Returns:
Output (N+2)-D dense tensor with shape `output_shape`.
"""
inputs_flat = backend.reshape(inputs, (backend.shape(inputs)[0], -1))
output_flat = tf.raw_ops.SparseTensorDenseMatMul(
a_indices=kernel_idxs,
a_values=kernel,
a_shape=kernel_shape,
b=inputs_flat,
adjoint_b=True)
output_flat_transpose = backend.transpose(output_flat)
output_reshaped = backend.reshape(output_flat_transpose, [
backend.shape(output_flat_transpose)[0],
] + output_shape.as_list()[1:])
return output_reshaped
def make_2d(tensor, split_dim):
"""Reshapes an N-dimensional tensor into a 2D tensor.
Dimensions before (excluding) and after (including) `split_dim` are grouped
together.
Args:
tensor: a tensor of shape `(d0, ..., d(N-1))`.
split_dim: an integer from 1 to N-1, index of the dimension to group
dimensions before (excluding) and after (including).
Returns:
Tensor of shape
`(d0 * ... * d(split_dim-1), d(split_dim) * ... * d(N-1))`.
"""
shape = tf.shape(tensor)
in_dims = shape[:split_dim]
out_dims = shape[split_dim:]
in_size = tf.reduce_prod(in_dims)
out_size = tf.reduce_prod(out_dims)
return tf.reshape(tensor, (in_size, out_size))
Functions
def get_locallyconnected_mask(input_shape, kernel_shape, strides, padding, data_format)
-
Return a mask representing connectivity of a locally-connected operation.
This method returns a masking numpy array of 0s and 1s (of type
np.float32
) that, when element-wise multiplied with a fully-connected weight tensor, masks out the weights between disconnected input-output pairs and thus implements local connectivity through a sparse fully-connected weight tensor.Assume an unshared convolution with given parameters is applied to an input having N spatial dimensions with
input_shape = (d_in1, ..., d_inN)
to produce an output with spatial shape(d_out1, …, d_outN)
(determined by layer parameters such asstrides
).This method returns a mask which can be broadcast-multiplied (element-wise) with a 2*(N+1)-D weight matrix (equivalent to a fully-connected layer between (N+1)-D activations (N spatial + 1 channel dimensions for input and output) to make it perform an unshared convolution with given
kernel_shape
,strides
,padding
anddata_format
.Args
input_shape
- tuple of size N:
(d_in1, …, d_inN)
spatial shape of the input. kernel_shape
- tuple of size N, spatial shape of the convolutional kernel / receptive field.
strides
- tuple of size N, strides along each spatial dimension.
padding
- type of padding, string
"same"
or"valid"
. data_format
- a string,
"channels_first"
or"channels_last"
.
Returns
a
np.float32
-typenp.ndarray
of shape(1, d_in1, …, d_inN, 1, d_out1, …, d_outN)
ifdata_format ==
"channels_first", or <code>(d\_in1, ..., d\_inN, 1, d\_out1, ..., d\_outN, 1)</code> if
data_format == "channels_last"`.Raises
ValueError
- if
data_format
is neither"channels_first"
nor"channels_last"
.
Expand source code
def get_locallyconnected_mask(input_shape, kernel_shape, strides, padding, data_format): """Return a mask representing connectivity of a locally-connected operation. This method returns a masking numpy array of 0s and 1s (of type `np.float32`) that, when element-wise multiplied with a fully-connected weight tensor, masks out the weights between disconnected input-output pairs and thus implements local connectivity through a sparse fully-connected weight tensor. Assume an unshared convolution with given parameters is applied to an input having N spatial dimensions with `input_shape = (d_in1, ..., d_inN)` to produce an output with spatial shape `(d_out1, ..., d_outN)` (determined by layer parameters such as `strides`). This method returns a mask which can be broadcast-multiplied (element-wise) with a 2*(N+1)-D weight matrix (equivalent to a fully-connected layer between (N+1)-D activations (N spatial + 1 channel dimensions for input and output) to make it perform an unshared convolution with given `kernel_shape`, `strides`, `padding` and `data_format`. Args: input_shape: tuple of size N: `(d_in1, ..., d_inN)` spatial shape of the input. kernel_shape: tuple of size N, spatial shape of the convolutional kernel / receptive field. strides: tuple of size N, strides along each spatial dimension. padding: type of padding, string `"same"` or `"valid"`. data_format: a string, `"channels_first"` or `"channels_last"`. Returns: a `np.float32`-type `np.ndarray` of shape `(1, d_in1, ..., d_inN, 1, d_out1, ..., d_outN)` if `data_format == `"channels_first"`, or `(d_in1, ..., d_inN, 1, d_out1, ..., d_outN, 1)` if `data_format == "channels_last"`. Raises: ValueError: if `data_format` is neither `"channels_first"` nor `"channels_last"`. """ mask = conv_utils.conv_kernel_mask( input_shape=input_shape, kernel_shape=kernel_shape, strides=strides, padding=padding) ndims = int(mask.ndim / 2) if data_format == 'channels_first': mask = np.expand_dims(mask, 0) mask = np.expand_dims(mask, -ndims - 1) elif data_format == 'channels_last': mask = np.expand_dims(mask, ndims) mask = np.expand_dims(mask, -1) else: raise ValueError('Unrecognized data_format: ' + str(data_format)) return mask
def local_conv_matmul(inputs, kernel, kernel_mask, output_shape)
-
Apply N-D convolution with un-shared weights using a single matmul call.
This method outputs
inputs . (kernel * kernel_mask)
(with.
standing for matrix-multiply and*
for element-wise multiply) and requires a precomputedkernel_mask
to zero-out weights inkernel
and hence perform the same operation as a convolution with un-shared (the remaining entries inkernel
) weights. It also does the necessary reshapes to makeinputs
andkernel
2-D andoutput
(N+2)-D.Args
inputs
- (N+2)-D tensor with shape
(batch_size, channels_in, d_in1, ..., d_inN)<code> or </code>(batch_size, d_in1, ..., d_inN, channels_in)
. kernel
- the unshared weights for N-D convolution,
an (N+2)-D tensor of shape:
(d_in1, ..., d_inN, channels_in, d_out2, ..., d_outN, channels_out)<code> or </code>(channels_in, d_in1, ..., d_inN, channels_out, d_out2, ..., d_outN)
, with the ordering of channels and spatial dimensions matching that of the input. Each entry is the weight between a particular input and output location, similarly to a fully-connected weight matrix. kernel_mask
- a float 0/1 mask tensor of shape:
(d_in1, ..., d_inN, 1, d_out2, ..., d_outN, 1)<code> or </code>(1, d_in1, ..., d_inN, 1, d_out2, ..., d_outN)
, with the ordering of singleton and spatial dimensions matching that of the input. Mask represents the connectivity pattern of the layer and is precomputed elsewhere based on layer parameters: stride, padding, and the receptive field shape. output_shape
- a tuple of (N+2) elements representing the output shape:
(batch_size, channels_out, d_out1, …, d_outN)
or(batch_size, d_out1, ..., d_outN, channels_out)
, with the ordering of channels and spatial dimensions matching that of the input.
Returns
Output (N+2)-D tensor with shape
output_shape
.Expand source code
def local_conv_matmul(inputs, kernel, kernel_mask, output_shape): """Apply N-D convolution with un-shared weights using a single matmul call. This method outputs `inputs . (kernel * kernel_mask)` (with `.` standing for matrix-multiply and `*` for element-wise multiply) and requires a precomputed `kernel_mask` to zero-out weights in `kernel` and hence perform the same operation as a convolution with un-shared (the remaining entries in `kernel`) weights. It also does the necessary reshapes to make `inputs` and `kernel` 2-D and `output` (N+2)-D. Args: inputs: (N+2)-D tensor with shape `(batch_size, channels_in, d_in1, ..., d_inN)` or `(batch_size, d_in1, ..., d_inN, channels_in)`. kernel: the unshared weights for N-D convolution, an (N+2)-D tensor of shape: `(d_in1, ..., d_inN, channels_in, d_out2, ..., d_outN, channels_out)` or `(channels_in, d_in1, ..., d_inN, channels_out, d_out2, ..., d_outN)`, with the ordering of channels and spatial dimensions matching that of the input. Each entry is the weight between a particular input and output location, similarly to a fully-connected weight matrix. kernel_mask: a float 0/1 mask tensor of shape: `(d_in1, ..., d_inN, 1, d_out2, ..., d_outN, 1)` or `(1, d_in1, ..., d_inN, 1, d_out2, ..., d_outN)`, with the ordering of singleton and spatial dimensions matching that of the input. Mask represents the connectivity pattern of the layer and is precomputed elsewhere based on layer parameters: stride, padding, and the receptive field shape. output_shape: a tuple of (N+2) elements representing the output shape: `(batch_size, channels_out, d_out1, ..., d_outN)` or `(batch_size, d_out1, ..., d_outN, channels_out)`, with the ordering of channels and spatial dimensions matching that of the input. Returns: Output (N+2)-D tensor with shape `output_shape`. """ inputs_flat = backend.reshape(inputs, (backend.shape(inputs)[0], -1)) kernel = kernel_mask * kernel kernel = make_2d(kernel, split_dim=backend.ndim(kernel) // 2) output_flat = tf.matmul(inputs_flat, kernel, b_is_sparse=True) output = backend.reshape(output_flat, [ backend.shape(output_flat)[0], ] + output_shape.as_list()[1:]) return output
def local_conv_sparse_matmul(inputs, kernel, kernel_idxs, kernel_shape, output_shape)
-
Apply N-D convolution with un-shared weights using a single sparse matmul.
This method outputs
inputs . tf.sparse.SparseTensor(indices=kernel_idxs, values=kernel, dense_shape=kernel_shape)<code>, with </code>.
standing for matrix-multiply. It also reshapesinputs
to 2-D andoutput
to (N+2)-D.Args
inputs
- (N+2)-D tensor with shape
(batch_size, channels_in, d_in1, ..., d_inN)<code> or </code>(batch_size, d_in1, ..., d_inN, channels_in)
. kernel
- a 1-D tensor with shape
(len(kernel_idxs),)
containing all the weights of the layer. kernel_idxs
- a list of integer tuples representing indices in a sparse matrix performing the un-shared convolution as a matrix-multiply.
kernel_shape
- a tuple
(input_size, output_size)
, whereinput_size = channels_in * d_in1 * ... * d_inN<code> and </code>output_size = channels_out * d_out1 * ... * d_outN
. output_shape
- a tuple of (N+2) elements representing the output shape:
(batch_size, channels_out, d_out1, …, d_outN)
or(batch_size, d_out1, ..., d_outN, channels_out)
, with the ordering of channels and spatial dimensions matching that of the input.
Returns
Output (N+2)-D dense tensor with shape
output_shape
.Expand source code
def local_conv_sparse_matmul(inputs, kernel, kernel_idxs, kernel_shape, output_shape): """Apply N-D convolution with un-shared weights using a single sparse matmul. This method outputs `inputs . tf.sparse.SparseTensor(indices=kernel_idxs, values=kernel, dense_shape=kernel_shape)`, with `.` standing for matrix-multiply. It also reshapes `inputs` to 2-D and `output` to (N+2)-D. Args: inputs: (N+2)-D tensor with shape `(batch_size, channels_in, d_in1, ..., d_inN)` or `(batch_size, d_in1, ..., d_inN, channels_in)`. kernel: a 1-D tensor with shape `(len(kernel_idxs),)` containing all the weights of the layer. kernel_idxs: a list of integer tuples representing indices in a sparse matrix performing the un-shared convolution as a matrix-multiply. kernel_shape: a tuple `(input_size, output_size)`, where `input_size = channels_in * d_in1 * ... * d_inN` and `output_size = channels_out * d_out1 * ... * d_outN`. output_shape: a tuple of (N+2) elements representing the output shape: `(batch_size, channels_out, d_out1, ..., d_outN)` or `(batch_size, d_out1, ..., d_outN, channels_out)`, with the ordering of channels and spatial dimensions matching that of the input. Returns: Output (N+2)-D dense tensor with shape `output_shape`. """ inputs_flat = backend.reshape(inputs, (backend.shape(inputs)[0], -1)) output_flat = tf.raw_ops.SparseTensorDenseMatMul( a_indices=kernel_idxs, a_values=kernel, a_shape=kernel_shape, b=inputs_flat, adjoint_b=True) output_flat_transpose = backend.transpose(output_flat) output_reshaped = backend.reshape(output_flat_transpose, [ backend.shape(output_flat_transpose)[0], ] + output_shape.as_list()[1:]) return output_reshaped
def make_2d(tensor, split_dim)
-
Reshapes an N-dimensional tensor into a 2D tensor.
Dimensions before (excluding) and after (including)
split_dim
are grouped together.Args
tensor
- a tensor of shape
(d0, ..., d(N-1))
. split_dim
- an integer from 1 to N-1, index of the dimension to group dimensions before (excluding) and after (including).
Returns
Tensor of shape
(d0 * ... * d(split_dim-1), d(split_dim) * ... * d(N-1))
.Expand source code
def make_2d(tensor, split_dim): """Reshapes an N-dimensional tensor into a 2D tensor. Dimensions before (excluding) and after (including) `split_dim` are grouped together. Args: tensor: a tensor of shape `(d0, ..., d(N-1))`. split_dim: an integer from 1 to N-1, index of the dimension to group dimensions before (excluding) and after (including). Returns: Tensor of shape `(d0 * ... * d(split_dim-1), d(split_dim) * ... * d(N-1))`. """ shape = tf.shape(tensor) in_dims = shape[:split_dim] out_dims = shape[split_dim:] in_size = tf.reduce_prod(in_dims) out_size = tf.reduce_prod(out_dims) return tf.reshape(tensor, (in_size, out_size))
Classes
class LocallyConnected1D (filters, kernel_size, strides=1, padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, implementation=1, **kwargs)
-
Locally-connected layer for 1D inputs.
The
LocallyConnected1D
layer works similarly to theConv1D
layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.Note: layer attributes cannot be modified after the layer has been called once (except the
trainable
attribute).Example:
# apply a unshared weight convolution 1d of length 3 to a sequence with # 10 timesteps, with 64 output filters model = Sequential() model.add(LocallyConnected1D(64, 3, input_shape=(10, 32))) # now model.output_shape == (None, 8, 64) # add a new conv1d on top model.add(LocallyConnected1D(32, 3)) # now model.output_shape == (None, 6, 32)
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size
- An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides
- An integer or tuple/list of a single integer, specifying the stride length of the convolution.
padding
- Currently only supports
"valid"
(case-insensitive)."same"
may be supported in the future."valid"
means no padding. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, length, channels)<code> while </code>channels_first
corresponds to inputs with shape(batch, channels, length)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". activation
- Activation function to use. If you don't specify anything, no
activation is applied
(ie. "linear" activation:
a(x) = x
). use_bias
- Boolean, whether the layer uses a bias vector.
kernel_initializer
- Initializer for the
kernel
weights matrix. bias_initializer
- Initializer for the bias vector.
kernel_regularizer
- Regularizer function applied to the
kernel
weights matrix. bias_regularizer
- Regularizer function applied to the bias vector.
activity_regularizer
- Regularizer function applied to the output of the layer (its "activation")..
kernel_constraint
- Constraint function applied to the kernel matrix.
bias_constraint
- Constraint function applied to the bias vector.
implementation
- implementation mode, either
1
,2
, or3
.1
loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops.2
stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops.3
stores layer weights in a sparse tensor and implements the forward pass as a single sparse matrix-multiply. How to choose:1
: large, dense models,2
: small models,3
: large, sparse models, where "large" stands for large input/output activations (i.e. manyfilters
,input_filters
, largeinput_size
,output_size
), and "sparse" stands for few connections between inputs and outputs, i.e. small ratiofilters * input_filters * kernel_size / (input_size * strides)
, where inputs to and outputs of the layer are assumed to have shapes(input_size, input_filters)<code>, </code>(output_size, filters)
respectively. It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM. Also, onlypadding="valid"
is supported byimplementation=1
.
Input shape: 3D tensor with shape:
(batch_size, steps, input_dim)
Output shape: 3D tensor with shape:(batch_size, new_steps, filters)
steps
value might have changed due to padding or strides.Expand source code
class LocallyConnected1D(Layer): """Locally-connected layer for 1D inputs. The `LocallyConnected1D` layer works similarly to the `Conv1D` layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. Note: layer attributes cannot be modified after the layer has been called once (except the `trainable` attribute). Example: ```python # apply a unshared weight convolution 1d of length 3 to a sequence with # 10 timesteps, with 64 output filters model = Sequential() model.add(LocallyConnected1D(64, 3, input_shape=(10, 32))) # now model.output_shape == (None, 8, 64) # add a new conv1d on top model.add(LocallyConnected1D(32, 3)) # now model.output_shape == (None, 6, 32) ``` Args: filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. padding: Currently only supports `"valid"` (case-insensitive). `"same"` may be supported in the future. `"valid"` means no padding. data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, length, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, length)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation").. kernel_constraint: Constraint function applied to the kernel matrix. bias_constraint: Constraint function applied to the bias vector. implementation: implementation mode, either `1`, `2`, or `3`. `1` loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops. `2` stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops. `3` stores layer weights in a sparse tensor and implements the forward pass as a single sparse matrix-multiply. How to choose: `1`: large, dense models, `2`: small models, `3`: large, sparse models, where "large" stands for large input/output activations (i.e. many `filters`, `input_filters`, large `input_size`, `output_size`), and "sparse" stands for few connections between inputs and outputs, i.e. small ratio `filters * input_filters * kernel_size / (input_size * strides)`, where inputs to and outputs of the layer are assumed to have shapes `(input_size, input_filters)`, `(output_size, filters)` respectively. It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM. Also, only `padding="valid"` is supported by `implementation=1`. Input shape: 3D tensor with shape: `(batch_size, steps, input_dim)` Output shape: 3D tensor with shape: `(batch_size, new_steps, filters)` `steps` value might have changed due to padding or strides. """ def __init__(self, filters, kernel_size, strides=1, padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, implementation=1, **kwargs): super(LocallyConnected1D, self).__init__(**kwargs) self.filters = filters self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, 1, 'strides') self.padding = conv_utils.normalize_padding(padding) if self.padding != 'valid' and implementation == 1: raise ValueError('Invalid border mode for LocallyConnected1D ' '(only "valid" is supported if implementation is 1): ' + padding) self.data_format = conv_utils.normalize_data_format(data_format) self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.implementation = implementation self.input_spec = InputSpec(ndim=3) @property def _use_input_spec_as_call_signature(self): return False @tf_utils.shape_type_conversion def build(self, input_shape): if self.data_format == 'channels_first': input_dim, input_length = input_shape[1], input_shape[2] else: input_dim, input_length = input_shape[2], input_shape[1] if input_dim is None: raise ValueError( 'Axis 2 of input should be fully-defined. ' 'Found shape:', input_shape) self.output_length = conv_utils.conv_output_length(input_length, self.kernel_size[0], self.padding, self.strides[0]) if self.implementation == 1: self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim, self.filters) self.kernel = self.add_weight( shape=self.kernel_shape, initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) elif self.implementation == 2: if self.data_format == 'channels_first': self.kernel_shape = (input_dim, input_length, self.filters, self.output_length) else: self.kernel_shape = (input_length, input_dim, self.output_length, self.filters) self.kernel = self.add_weight( shape=self.kernel_shape, initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.kernel_mask = get_locallyconnected_mask( input_shape=(input_length,), kernel_shape=self.kernel_size, strides=self.strides, padding=self.padding, data_format=self.data_format, ) elif self.implementation == 3: self.kernel_shape = (self.output_length * self.filters, input_length * input_dim) self.kernel_idxs = sorted( conv_utils.conv_kernel_idxs( input_shape=(input_length,), kernel_shape=self.kernel_size, strides=self.strides, padding=self.padding, filters_in=input_dim, filters_out=self.filters, data_format=self.data_format)) self.kernel = self.add_weight( shape=(len(self.kernel_idxs),), initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) else: raise ValueError('Unrecognized implementation mode: %d.' % self.implementation) if self.use_bias: self.bias = self.add_weight( shape=(self.output_length, self.filters), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None if self.data_format == 'channels_first': self.input_spec = InputSpec(ndim=3, axes={1: input_dim}) else: self.input_spec = InputSpec(ndim=3, axes={-1: input_dim}) self.built = True @tf_utils.shape_type_conversion def compute_output_shape(self, input_shape): if self.data_format == 'channels_first': input_length = input_shape[2] else: input_length = input_shape[1] length = conv_utils.conv_output_length(input_length, self.kernel_size[0], self.padding, self.strides[0]) if self.data_format == 'channels_first': return (input_shape[0], self.filters, length) elif self.data_format == 'channels_last': return (input_shape[0], length, self.filters) def call(self, inputs): if self.implementation == 1: output = backend.local_conv( inputs, self.kernel, self.kernel_size, self.strides, (self.output_length,), self.data_format) elif self.implementation == 2: output = local_conv_matmul(inputs, self.kernel, self.kernel_mask, self.compute_output_shape(inputs.shape)) elif self.implementation == 3: output = local_conv_sparse_matmul(inputs, self.kernel, self.kernel_idxs, self.kernel_shape, self.compute_output_shape(inputs.shape)) else: raise ValueError('Unrecognized implementation mode: %d.' % self.implementation) if self.use_bias: output = backend.bias_add(output, self.bias, data_format=self.data_format) output = self.activation(output) return output def get_config(self): config = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'implementation': self.implementation } base_config = super(LocallyConnected1D, self).get_config() return dict(list(base_config.items()) + list(config.items()))
Ancestors
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Layer
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights
class LocallyConnected2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, implementation=1, **kwargs)
-
Locally-connected layer for 2D inputs.
The
LocallyConnected2D
layer works similarly to theConv2D
layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.Note: layer attributes cannot be modified after the layer has been called once (except the
trainable
attribute).Examples:
# apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image # with `data_format="channels_last"`: model = Sequential() model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3))) # now model.output_shape == (None, 30, 30, 64) # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters # add a 3x3 unshared weights convolution on top, with 32 output filters: model.add(LocallyConnected2D(32, (3, 3))) # now model.output_shape == (None, 28, 28, 32)
Args
filters
- Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size
- An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
strides
- An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions.
padding
- Currently only support
"valid"
(case-insensitive)."same"
will be supported in future."valid"
means no padding. data_format
- A string, one of
channels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)<code> while </code>channels_first
corresponds to inputs with shape(batch, channels, height, width)
. It defaults to theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "channels_last". activation
- Activation function to use. If you don't specify anything, no
activation is applied
(ie. "linear" activation:
a(x) = x
). use_bias
- Boolean, whether the layer uses a bias vector.
kernel_initializer
- Initializer for the
kernel
weights matrix. bias_initializer
- Initializer for the bias vector.
kernel_regularizer
- Regularizer function applied to the
kernel
weights matrix. bias_regularizer
- Regularizer function applied to the bias vector.
activity_regularizer
- Regularizer function applied to the output of the layer (its "activation").
kernel_constraint
- Constraint function applied to the kernel matrix.
bias_constraint
- Constraint function applied to the bias vector.
implementation
- implementation mode, either
1
,2
, or3
.1
loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops.2
stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops.3
stores layer weights in a sparse tensor and implements the forward pass as a single sparse matrix-multiply. How to choose:1
: large, dense models,2
: small models,3
: large, sparse models, where "large" stands for large input/output activations (i.e. manyfilters
,input_filters
, largenp.prod(input_size)
,np.prod(output_size)
), and "sparse" stands for few connections between inputs and outputs, i.e. small ratiofilters * input_filters * np.prod(kernel_size) / (np.prod(input_size) * np.prod(strides))
, where inputs to and outputs of the layer are assumed to have shapesinput_size + (input_filters,)<code>, </code>output_size + (filters,)
respectively. It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM. Also, onlypadding="valid"
is supported byimplementation=1
.
Input shape: 4D tensor with shape:
(samples, channels, rows, cols)
if data_format='channels_first' or 4D tensor with shape:(samples, rows, cols, channels)
if data_format='channels_last'. Output shape: 4D tensor with shape:(samples, filters, new_rows, new_cols)
if data_format='channels_first' or 4D tensor with shape:(samples, new_rows, new_cols, filters)
if data_format='channels_last'.rows
andcols
values might have changed due to padding.Expand source code
class LocallyConnected2D(Layer): """Locally-connected layer for 2D inputs. The `LocallyConnected2D` layer works similarly to the `Conv2D` layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. Note: layer attributes cannot be modified after the layer has been called once (except the `trainable` attribute). Examples: ```python # apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image # with `data_format="channels_last"`: model = Sequential() model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3))) # now model.output_shape == (None, 30, 30, 64) # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters # add a 3x3 unshared weights convolution on top, with 32 output filters: model.add(LocallyConnected2D(32, (3, 3))) # now model.output_shape == (None, 28, 28, 32) ``` Args: filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. padding: Currently only support `"valid"` (case-insensitive). `"same"` will be supported in future. `"valid"` means no padding. data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). kernel_constraint: Constraint function applied to the kernel matrix. bias_constraint: Constraint function applied to the bias vector. implementation: implementation mode, either `1`, `2`, or `3`. `1` loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops. `2` stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops. `3` stores layer weights in a sparse tensor and implements the forward pass as a single sparse matrix-multiply. How to choose: `1`: large, dense models, `2`: small models, `3`: large, sparse models, where "large" stands for large input/output activations (i.e. many `filters`, `input_filters`, large `np.prod(input_size)`, `np.prod(output_size)`), and "sparse" stands for few connections between inputs and outputs, i.e. small ratio `filters * input_filters * np.prod(kernel_size) / (np.prod(input_size) * np.prod(strides))`, where inputs to and outputs of the layer are assumed to have shapes `input_size + (input_filters,)`, `output_size + (filters,)` respectively. It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM. Also, only `padding="valid"` is supported by `implementation=1`. Input shape: 4D tensor with shape: `(samples, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, rows, cols, channels)` if data_format='channels_last'. Output shape: 4D tensor with shape: `(samples, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to padding. """ def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, implementation=1, **kwargs): super(LocallyConnected2D, self).__init__(**kwargs) self.filters = filters self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, 2, 'strides') self.padding = conv_utils.normalize_padding(padding) if self.padding != 'valid' and implementation == 1: raise ValueError('Invalid border mode for LocallyConnected2D ' '(only "valid" is supported if implementation is 1): ' + padding) self.data_format = conv_utils.normalize_data_format(data_format) self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.implementation = implementation self.input_spec = InputSpec(ndim=4) @property def _use_input_spec_as_call_signature(self): return False @tf_utils.shape_type_conversion def build(self, input_shape): if self.data_format == 'channels_last': input_row, input_col = input_shape[1:-1] input_filter = input_shape[3] else: input_row, input_col = input_shape[2:] input_filter = input_shape[1] if input_row is None or input_col is None: raise ValueError('The spatial dimensions of the inputs to ' ' a LocallyConnected2D layer ' 'should be fully-defined, but layer received ' 'the inputs shape ' + str(input_shape)) output_row = conv_utils.conv_output_length(input_row, self.kernel_size[0], self.padding, self.strides[0]) output_col = conv_utils.conv_output_length(input_col, self.kernel_size[1], self.padding, self.strides[1]) self.output_row = output_row self.output_col = output_col if self.implementation == 1: self.kernel_shape = (output_row * output_col, self.kernel_size[0] * self.kernel_size[1] * input_filter, self.filters) self.kernel = self.add_weight( shape=self.kernel_shape, initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) elif self.implementation == 2: if self.data_format == 'channels_first': self.kernel_shape = (input_filter, input_row, input_col, self.filters, self.output_row, self.output_col) else: self.kernel_shape = (input_row, input_col, input_filter, self.output_row, self.output_col, self.filters) self.kernel = self.add_weight( shape=self.kernel_shape, initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.kernel_mask = get_locallyconnected_mask( input_shape=(input_row, input_col), kernel_shape=self.kernel_size, strides=self.strides, padding=self.padding, data_format=self.data_format, ) elif self.implementation == 3: self.kernel_shape = (self.output_row * self.output_col * self.filters, input_row * input_col * input_filter) self.kernel_idxs = sorted( conv_utils.conv_kernel_idxs( input_shape=(input_row, input_col), kernel_shape=self.kernel_size, strides=self.strides, padding=self.padding, filters_in=input_filter, filters_out=self.filters, data_format=self.data_format)) self.kernel = self.add_weight( shape=(len(self.kernel_idxs),), initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) else: raise ValueError('Unrecognized implementation mode: %d.' % self.implementation) if self.use_bias: self.bias = self.add_weight( shape=(output_row, output_col, self.filters), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None if self.data_format == 'channels_first': self.input_spec = InputSpec(ndim=4, axes={1: input_filter}) else: self.input_spec = InputSpec(ndim=4, axes={-1: input_filter}) self.built = True @tf_utils.shape_type_conversion def compute_output_shape(self, input_shape): if self.data_format == 'channels_first': rows = input_shape[2] cols = input_shape[3] elif self.data_format == 'channels_last': rows = input_shape[1] cols = input_shape[2] rows = conv_utils.conv_output_length(rows, self.kernel_size[0], self.padding, self.strides[0]) cols = conv_utils.conv_output_length(cols, self.kernel_size[1], self.padding, self.strides[1]) if self.data_format == 'channels_first': return (input_shape[0], self.filters, rows, cols) elif self.data_format == 'channels_last': return (input_shape[0], rows, cols, self.filters) def call(self, inputs): if self.implementation == 1: output = backend.local_conv( inputs, self.kernel, self.kernel_size, self.strides, (self.output_row, self.output_col), self.data_format) elif self.implementation == 2: output = local_conv_matmul(inputs, self.kernel, self.kernel_mask, self.compute_output_shape(inputs.shape)) elif self.implementation == 3: output = local_conv_sparse_matmul(inputs, self.kernel, self.kernel_idxs, self.kernel_shape, self.compute_output_shape(inputs.shape)) else: raise ValueError('Unrecognized implementation mode: %d.' % self.implementation) if self.use_bias: output = backend.bias_add(output, self.bias, data_format=self.data_format) output = self.activation(output) return output def get_config(self): config = { 'filters': self.filters, 'kernel_size': self.kernel_size, 'strides': self.strides, 'padding': self.padding, 'data_format': self.data_format, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'implementation': self.implementation } base_config = super(LocallyConnected2D, self).get_config() return dict(list(base_config.items()) + list(config.items()))
Ancestors
- Layer
- tensorflow.python.module.module.Module
- tensorflow.python.training.tracking.tracking.AutoTrackable
- tensorflow.python.training.tracking.base.Trackable
- LayerVersionSelector
Inherited members
Layer
:activity_regularizer
add_loss
add_metric
add_update
add_variable
add_weight
apply
build
call
compute_dtype
compute_mask
compute_output_shape
compute_output_signature
count_params
dtype
dtype_policy
dynamic
finalize_state
from_config
get_config
get_input_at
get_input_mask_at
get_input_shape_at
get_losses_for
get_output_at
get_output_mask_at
get_output_shape_at
get_updates_for
get_weights
inbound_nodes
input
input_mask
input_shape
input_spec
losses
metrics
name
non_trainable_variables
non_trainable_weights
outbound_nodes
output
output_mask
output_shape
set_weights
supports_masking
trainable_variables
trainable_weights
variable_dtype
variables
weights