446 lines
18 KiB
Python
446 lines
18 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Locally-connected layers.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.keras import activations
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras import constraints
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from tensorflow.python.keras import initializers
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from tensorflow.python.keras import regularizers
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from tensorflow.python.keras.engine.base_layer import InputSpec
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from tensorflow.python.keras.engine.base_layer import Layer
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from tensorflow.python.keras.utils import conv_utils
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from tensorflow.python.keras.utils import tf_utils
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('keras.layers.LocallyConnected1D')
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class LocallyConnected1D(Layer):
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"""Locally-connected layer for 1D inputs.
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The `LocallyConnected1D` layer works similarly to
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the `Conv1D` layer, except that weights are unshared,
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that is, a different set of filters is applied at each different patch
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of the input.
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Example:
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```python
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# apply a unshared weight convolution 1d of length 3 to a sequence with
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# 10 timesteps, with 64 output filters
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model = Sequential()
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model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
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# now model.output_shape == (None, 8, 64)
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# add a new conv1d on top
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model.add(LocallyConnected1D(32, 3))
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# now model.output_shape == (None, 6, 32)
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```
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Arguments:
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filters: Integer, the dimensionality of the output space
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(i.e. the number of output filters in the convolution).
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kernel_size: An integer or tuple/list of a single integer,
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specifying the length of the 1D convolution window.
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strides: An integer or tuple/list of a single integer,
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specifying the stride length of the convolution.
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Specifying any stride value != 1 is incompatible with specifying
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any `dilation_rate` value != 1.
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padding: Currently only supports `"valid"` (case-insensitive).
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`"same"` may be supported in the future.
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape
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`(batch, length, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, length)`.
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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "channels_last".
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activation: Activation function to use.
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If you don't specify anything, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix.
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bias_initializer: Initializer for the bias vector.
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation")..
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kernel_constraint: Constraint function applied to the kernel matrix.
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bias_constraint: Constraint function applied to the bias vector.
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Input shape:
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3D tensor with shape: `(batch_size, steps, input_dim)`
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Output shape:
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3D tensor with shape: `(batch_size, new_steps, filters)`
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`steps` value might have changed due to padding or strides.
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"""
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def __init__(self,
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filters,
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kernel_size,
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strides=1,
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padding='valid',
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data_format=None,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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bias_initializer='zeros',
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kernel_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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bias_constraint=None,
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**kwargs):
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super(LocallyConnected1D, self).__init__(**kwargs)
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self.filters = filters
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self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
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self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
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self.padding = conv_utils.normalize_padding(padding)
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if self.padding != 'valid':
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raise ValueError('Invalid border mode for LocallyConnected1D '
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'(only "valid" is supported): ' + padding)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.input_spec = InputSpec(ndim=3)
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@tf_utils.shape_type_conversion
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def build(self, input_shape):
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if self.data_format == 'channels_first':
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input_dim, input_length = input_shape[1], input_shape[2]
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else:
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input_dim, input_length = input_shape[2], input_shape[1]
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if input_dim is None:
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raise ValueError('Axis 2 of input should be fully-defined. '
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'Found shape:', input_shape)
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self.output_length = conv_utils.conv_output_length(
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input_length, self.kernel_size[0], self.padding, self.strides[0])
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self.kernel_shape = (self.output_length, self.kernel_size[0] * input_dim,
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self.filters)
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self.kernel = self.add_weight(
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shape=self.kernel_shape,
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initializer=self.kernel_initializer,
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name='kernel',
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint)
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if self.use_bias:
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self.bias = self.add_weight(
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shape=(self.output_length, self.filters),
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initializer=self.bias_initializer,
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name='bias',
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint)
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else:
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self.bias = None
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if self.data_format == 'channels_first':
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self.input_spec = InputSpec(ndim=3, axes={1: input_dim})
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else:
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self.input_spec = InputSpec(ndim=3, axes={-1: input_dim})
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self.built = True
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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if self.data_format == 'channels_first':
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input_length = input_shape[2]
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else:
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input_length = input_shape[1]
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length = conv_utils.conv_output_length(input_length, self.kernel_size[0],
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self.padding, self.strides[0])
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if self.data_format == 'channels_first':
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return (input_shape[0], self.filters, length)
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elif self.data_format == 'channels_last':
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return (input_shape[0], length, self.filters)
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def call(self, inputs):
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output = K.local_conv(inputs, self.kernel, self.kernel_size, self.strides,
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(self.output_length,), self.data_format)
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if self.use_bias:
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output = K.bias_add(output, self.bias, data_format=self.data_format)
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output = self.activation(output)
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return output
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def get_config(self):
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config = {
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'filters':
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self.filters,
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'kernel_size':
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self.kernel_size,
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'strides':
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self.strides,
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'padding':
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self.padding,
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'data_format':
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self.data_format,
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'activation':
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activations.serialize(self.activation),
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'use_bias':
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self.use_bias,
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'kernel_initializer':
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initializers.serialize(self.kernel_initializer),
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'bias_initializer':
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initializers.serialize(self.bias_initializer),
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'kernel_regularizer':
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regularizers.serialize(self.kernel_regularizer),
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'bias_regularizer':
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regularizers.serialize(self.bias_regularizer),
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'activity_regularizer':
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regularizers.serialize(self.activity_regularizer),
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'kernel_constraint':
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constraints.serialize(self.kernel_constraint),
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'bias_constraint':
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constraints.serialize(self.bias_constraint)
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}
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base_config = super(LocallyConnected1D, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@tf_export('keras.layers.LocallyConnected2D')
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class LocallyConnected2D(Layer):
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"""Locally-connected layer for 2D inputs.
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The `LocallyConnected2D` layer works similarly
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to the `Conv2D` layer, except that weights are unshared,
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that is, a different set of filters is applied at each
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different patch of the input.
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Examples:
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```python
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# apply a 3x3 unshared weights convolution with 64 output filters on a
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32x32 image
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# with `data_format="channels_last"`:
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model = Sequential()
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model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
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# now model.output_shape == (None, 30, 30, 64)
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# notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64
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parameters
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# add a 3x3 unshared weights convolution on top, with 32 output filters:
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model.add(LocallyConnected2D(32, (3, 3)))
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# now model.output_shape == (None, 28, 28, 32)
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```
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Arguments:
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filters: Integer, the dimensionality of the output space
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(i.e. the number of output filters in the convolution).
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kernel_size: An integer or tuple/list of 2 integers, specifying the
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width and height of the 2D convolution window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the convolution along the width and height.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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padding: Currently only support `"valid"` (case-insensitive).
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`"same"` will be supported in future.
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape
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`(batch, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, height, width)`.
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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "channels_last".
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activation: Activation function to use.
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If you don't specify anything, no activation is applied
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(ie. "linear" activation: `a(x) = x`).
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use_bias: Boolean, whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix.
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bias_initializer: Initializer for the bias vector.
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation")..
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kernel_constraint: Constraint function applied to the kernel matrix.
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bias_constraint: Constraint function applied to the bias vector.
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Input shape:
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4D tensor with shape:
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`(samples, channels, rows, cols)` if data_format='channels_first'
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or 4D tensor with shape:
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`(samples, rows, cols, channels)` if data_format='channels_last'.
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Output shape:
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4D tensor with shape:
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`(samples, filters, new_rows, new_cols)` if data_format='channels_first'
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or 4D tensor with shape:
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`(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
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`rows` and `cols` values might have changed due to padding.
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"""
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def __init__(self,
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filters,
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kernel_size,
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strides=(1, 1),
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padding='valid',
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data_format=None,
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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bias_initializer='zeros',
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kernel_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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bias_constraint=None,
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**kwargs):
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super(LocallyConnected2D, self).__init__(**kwargs)
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self.filters = filters
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self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
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self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
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self.padding = conv_utils.normalize_padding(padding)
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if self.padding != 'valid':
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raise ValueError('Invalid border mode for LocallyConnected2D '
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'(only "valid" is supported): ' + padding)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.input_spec = InputSpec(ndim=4)
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@tf_utils.shape_type_conversion
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def build(self, input_shape):
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if self.data_format == 'channels_last':
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input_row, input_col = input_shape[1:-1]
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input_filter = input_shape[3]
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|
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
|
||
|
|
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)
|
||
|
|
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):
|
||
|
|
output = K.local_conv(inputs, self.kernel, self.kernel_size, self.strides,
|
||
|
|
(self.output_row, self.output_col), self.data_format)
|
||
|
|
|
||
|
|
if self.use_bias:
|
||
|
|
output = K.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)
|
||
|
|
}
|
||
|
|
base_config = super(LocallyConnected2D, self).get_config()
|
||
|
|
return dict(list(base_config.items()) + list(config.items()))
|