2587 lines
109 KiB
Python
2587 lines
109 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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"""Keras convolution layers and image transformation 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.eager import context
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.keras import activations
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from tensorflow.python.keras import backend
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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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# imports for backwards namespace compatibility
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# pylint: disable=unused-import
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from tensorflow.python.keras.layers.pooling import AveragePooling1D
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from tensorflow.python.keras.layers.pooling import AveragePooling2D
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from tensorflow.python.keras.layers.pooling import AveragePooling3D
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from tensorflow.python.keras.layers.pooling import MaxPooling1D
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from tensorflow.python.keras.layers.pooling import MaxPooling2D
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from tensorflow.python.keras.layers.pooling import MaxPooling3D
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# pylint: enable=unused-import
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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.ops import array_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.util.tf_export import tf_export
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class Conv(Layer):
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"""Abstract nD convolution layer (private, used as implementation base).
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This layer creates a convolution kernel that is convolved
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(actually cross-correlated) with the layer input to produce a tensor of
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outputs. If `use_bias` is True (and a `bias_initializer` is provided),
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a bias vector is created and added to the outputs. Finally, if
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`activation` is not `None`, it is applied to the outputs as well.
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Arguments:
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rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
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filters: Integer, the dimensionality of the output space (i.e. the number
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of filters in the convolution).
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kernel_size: An integer or tuple/list of n integers, specifying the
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length of the convolution window.
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strides: An integer or tuple/list of n integers,
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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: One of `"valid"` or `"same"` (case-insensitive).
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data_format: A string, 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, ..., channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, ...)`.
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dilation_rate: An integer or tuple/list of n integers, specifying
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the dilation rate to use for dilated convolution.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any `strides` value != 1.
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activation: Activation function. Set it to None to maintain a
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linear activation.
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use_bias: Boolean, whether the layer uses a bias.
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kernel_initializer: An initializer for the convolution kernel.
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bias_initializer: An initializer for the bias vector. If None, the default
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initializer will be used.
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kernel_regularizer: Optional regularizer for the convolution kernel.
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bias_regularizer: Optional regularizer for the bias vector.
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activity_regularizer: Optional regularizer function for the output.
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kernel_constraint: Optional projection function to be applied to the
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kernel after being updated by an `Optimizer` (e.g. used to implement
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norm constraints or value constraints for layer weights). The function
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must take as input the unprojected variable and must return the
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projected variable (which must have the same shape). Constraints are
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not safe to use when doing asynchronous distributed training.
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bias_constraint: Optional projection function to be applied to the
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bias after being updated by an `Optimizer`.
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trainable: Boolean, if `True` also add variables to the graph collection
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`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
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name: A string, the name of the layer.
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"""
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def __init__(self, rank,
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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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dilation_rate=1,
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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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trainable=True,
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name=None,
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**kwargs):
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super(Conv, self).__init__(
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trainable=trainable,
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name=name,
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activity_regularizer=regularizers.get(activity_regularizer),
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**kwargs)
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self.rank = rank
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self.filters = filters
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self.kernel_size = conv_utils.normalize_tuple(
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kernel_size, rank, 'kernel_size')
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self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
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self.padding = conv_utils.normalize_padding(padding)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.dilation_rate = conv_utils.normalize_tuple(
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dilation_rate, rank, 'dilation_rate')
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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.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=self.rank + 2)
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def build(self, input_shape):
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input_shape = tensor_shape.TensorShape(input_shape)
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if self.data_format == 'channels_first':
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channel_axis = 1
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else:
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channel_axis = -1
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if input_shape[channel_axis].value is None:
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raise ValueError('The channel dimension of the inputs '
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'should be defined. Found `None`.')
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input_dim = int(input_shape[channel_axis])
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kernel_shape = self.kernel_size + (input_dim, self.filters)
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self.kernel = self.add_weight(
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name='kernel',
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shape=kernel_shape,
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initializer=self.kernel_initializer,
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint,
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trainable=True,
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dtype=self.dtype)
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if self.use_bias:
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self.bias = self.add_weight(
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name='bias',
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shape=(self.filters,),
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initializer=self.bias_initializer,
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint,
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trainable=True,
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dtype=self.dtype)
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else:
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self.bias = None
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self.input_spec = InputSpec(ndim=self.rank + 2,
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axes={channel_axis: input_dim})
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self._convolution_op = nn_ops.Convolution(
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input_shape,
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filter_shape=self.kernel.get_shape(),
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dilation_rate=self.dilation_rate,
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strides=self.strides,
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padding=self.padding.upper(),
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data_format=conv_utils.convert_data_format(self.data_format,
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self.rank + 2))
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self.built = True
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def call(self, inputs):
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outputs = self._convolution_op(inputs, self.kernel)
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if self.use_bias:
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if self.data_format == 'channels_first':
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if self.rank == 1:
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# nn.bias_add does not accept a 1D input tensor.
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bias = array_ops.reshape(self.bias, (1, self.filters, 1))
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outputs += bias
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if self.rank == 2:
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outputs = nn.bias_add(outputs, self.bias, data_format='NCHW')
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if self.rank == 3:
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# As of Mar 2017, direct addition is significantly slower than
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# bias_add when computing gradients. To use bias_add, we collapse Z
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# and Y into a single dimension to obtain a 4D input tensor.
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outputs_shape = outputs.shape.as_list()
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if outputs_shape[0] is None:
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outputs_shape[0] = -1
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outputs_4d = array_ops.reshape(outputs,
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[outputs_shape[0], outputs_shape[1],
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outputs_shape[2] * outputs_shape[3],
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outputs_shape[4]])
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outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')
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outputs = array_ops.reshape(outputs_4d, outputs_shape)
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else:
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outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')
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if self.activation is not None:
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return self.activation(outputs)
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return outputs
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def compute_output_shape(self, input_shape):
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input_shape = tensor_shape.TensorShape(input_shape).as_list()
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if self.data_format == 'channels_last':
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space = input_shape[1:-1]
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new_space = []
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for i in range(len(space)):
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new_dim = conv_utils.conv_output_length(
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space[i],
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self.kernel_size[i],
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padding=self.padding,
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stride=self.strides[i],
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dilation=self.dilation_rate[i])
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new_space.append(new_dim)
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return tensor_shape.TensorShape([input_shape[0]] + new_space +
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[self.filters])
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else:
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space = input_shape[2:]
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new_space = []
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for i in range(len(space)):
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new_dim = conv_utils.conv_output_length(
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space[i],
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self.kernel_size[i],
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padding=self.padding,
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stride=self.strides[i],
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dilation=self.dilation_rate[i])
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new_space.append(new_dim)
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return tensor_shape.TensorShape([input_shape[0], self.filters] +
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new_space)
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def get_config(self):
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config = {
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'filters': self.filters,
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'kernel_size': self.kernel_size,
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'strides': self.strides,
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'padding': self.padding,
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'data_format': self.data_format,
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'dilation_rate': self.dilation_rate,
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'activation': activations.serialize(self.activation),
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'use_bias': self.use_bias,
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'kernel_initializer': initializers.serialize(self.kernel_initializer),
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'bias_initializer': initializers.serialize(self.bias_initializer),
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'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
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'bias_regularizer': 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': constraints.serialize(self.kernel_constraint),
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'bias_constraint': constraints.serialize(self.bias_constraint)
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}
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base_config = super(Conv, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@tf_export('keras.layers.Conv1D', 'keras.layers.Convolution1D')
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class Conv1D(Conv):
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"""1D convolution layer (e.g. temporal convolution).
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This layer creates a convolution kernel that is convolved
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with the layer input over a single spatial (or temporal) dimension
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to produce a tensor of outputs.
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If `use_bias` is True, a bias vector is created and added to the outputs.
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Finally, if `activation` is not `None`,
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it is applied to the outputs as well.
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When using this layer as the first layer in a model,
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provide an `input_shape` argument
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(tuple of integers or `None`, e.g.
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`(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
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or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
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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: One of `"valid"`, `"causal"` or `"same"` (case-insensitive).
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`"causal"` results in causal (dilated) convolutions, e.g. output[t]
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does not depend on input[t+1:]. Useful when modeling temporal data
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where the model should not violate the temporal order.
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See [WaveNet: A Generative Model for Raw Audio, section
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2.1](https://arxiv.org/abs/1609.03499).
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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dilation_rate: an integer or tuple/list of a single integer, specifying
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the dilation rate to use for dilated convolution.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any `strides` value != 1.
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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='channels_last',
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dilation_rate=1,
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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(Conv1D, self).__init__(
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rank=1,
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filters=filters,
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kernel_size=kernel_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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bias_initializer=initializers.get(bias_initializer),
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kernel_regularizer=regularizers.get(kernel_regularizer),
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bias_regularizer=regularizers.get(bias_regularizer),
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activity_regularizer=regularizers.get(activity_regularizer),
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kernel_constraint=constraints.get(kernel_constraint),
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bias_constraint=constraints.get(bias_constraint),
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**kwargs)
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@tf_export('keras.layers.Conv2D', 'keras.layers.Convolution2D')
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class Conv2D(Conv):
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"""2D convolution layer (e.g. spatial convolution over images).
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This layer creates a convolution kernel that is convolved
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with the layer input to produce a tensor of
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outputs. If `use_bias` is True,
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a bias vector is created and added to the outputs. Finally, if
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`activation` is not `None`, it is applied to the outputs as well.
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When using this layer as the first layer in a model,
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provide the keyword argument `input_shape`
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(tuple of integers, does not include the sample axis),
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e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
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in `data_format="channels_last"`.
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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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height and width 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 height and width.
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||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Specifying any stride value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: one of `"valid"` or `"same"` (case-insensitive).
|
||
|
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".
|
||
|
dilation_rate: an integer or tuple/list of 2 integers, specifying
|
||
|
the dilation rate to use for dilated convolution.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Currently, specifying any `dilation_rate` value != 1 is
|
||
|
incompatible with specifying any stride value != 1.
|
||
|
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.
|
||
|
|
||
|
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,
|
||
|
dilation_rate=(1, 1),
|
||
|
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,
|
||
|
**kwargs):
|
||
|
super(Conv2D, self).__init__(
|
||
|
rank=2,
|
||
|
filters=filters,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
dilation_rate=dilation_rate,
|
||
|
activation=activations.get(activation),
|
||
|
use_bias=use_bias,
|
||
|
kernel_initializer=initializers.get(kernel_initializer),
|
||
|
bias_initializer=initializers.get(bias_initializer),
|
||
|
kernel_regularizer=regularizers.get(kernel_regularizer),
|
||
|
bias_regularizer=regularizers.get(bias_regularizer),
|
||
|
activity_regularizer=regularizers.get(activity_regularizer),
|
||
|
kernel_constraint=constraints.get(kernel_constraint),
|
||
|
bias_constraint=constraints.get(bias_constraint),
|
||
|
**kwargs)
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.Conv3D', 'keras.layers.Convolution3D')
|
||
|
class Conv3D(Conv):
|
||
|
"""3D convolution layer (e.g. spatial convolution over volumes).
|
||
|
|
||
|
This layer creates a convolution kernel that is convolved
|
||
|
with the layer input to produce a tensor of
|
||
|
outputs. If `use_bias` is True,
|
||
|
a bias vector is created and added to the outputs. Finally, if
|
||
|
`activation` is not `None`, it is applied to the outputs as well.
|
||
|
|
||
|
When using this layer as the first layer in a model,
|
||
|
provide the keyword argument `input_shape`
|
||
|
(tuple of integers, does not include the sample axis),
|
||
|
e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
|
||
|
with a single channel,
|
||
|
in `data_format="channels_last"`.
|
||
|
|
||
|
Arguments:
|
||
|
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 3 integers, specifying the
|
||
|
depth, height and width of the 3D convolution window.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
strides: An integer or tuple/list of 3 integers,
|
||
|
specifying the strides of the convolution along each spatial
|
||
|
dimension.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Specifying any stride value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: one of `"valid"` or `"same"` (case-insensitive).
|
||
|
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, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
|
||
|
while `channels_first` corresponds to inputs with shape
|
||
|
`(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
|
||
|
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".
|
||
|
dilation_rate: an integer or tuple/list of 3 integers, specifying
|
||
|
the dilation rate to use for dilated convolution.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Currently, specifying any `dilation_rate` value != 1 is
|
||
|
incompatible with specifying any stride value != 1.
|
||
|
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.
|
||
|
|
||
|
Input shape:
|
||
|
5D tensor with shape:
|
||
|
`(samples, channels, conv_dim1, conv_dim2, conv_dim3)` if
|
||
|
data_format='channels_first'
|
||
|
or 5D tensor with shape:
|
||
|
`(samples, conv_dim1, conv_dim2, conv_dim3, channels)` if
|
||
|
data_format='channels_last'.
|
||
|
|
||
|
Output shape:
|
||
|
5D tensor with shape:
|
||
|
`(samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)` if
|
||
|
data_format='channels_first'
|
||
|
or 5D tensor with shape:
|
||
|
`(samples, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)` if
|
||
|
data_format='channels_last'.
|
||
|
`new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have
|
||
|
changed due to padding.
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
filters,
|
||
|
kernel_size,
|
||
|
strides=(1, 1, 1),
|
||
|
padding='valid',
|
||
|
data_format=None,
|
||
|
dilation_rate=(1, 1, 1),
|
||
|
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,
|
||
|
**kwargs):
|
||
|
super(Conv3D, self).__init__(
|
||
|
rank=3,
|
||
|
filters=filters,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
dilation_rate=dilation_rate,
|
||
|
activation=activations.get(activation),
|
||
|
use_bias=use_bias,
|
||
|
kernel_initializer=initializers.get(kernel_initializer),
|
||
|
bias_initializer=initializers.get(bias_initializer),
|
||
|
kernel_regularizer=regularizers.get(kernel_regularizer),
|
||
|
bias_regularizer=regularizers.get(bias_regularizer),
|
||
|
activity_regularizer=regularizers.get(activity_regularizer),
|
||
|
kernel_constraint=constraints.get(kernel_constraint),
|
||
|
bias_constraint=constraints.get(bias_constraint),
|
||
|
**kwargs)
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.Conv2DTranspose',
|
||
|
'keras.layers.Convolution2DTranspose')
|
||
|
class Conv2DTranspose(Conv2D):
|
||
|
"""Transposed convolution layer (sometimes called Deconvolution).
|
||
|
|
||
|
The need for transposed convolutions generally arises
|
||
|
from the desire to use a transformation going in the opposite direction
|
||
|
of a normal convolution, i.e., from something that has the shape of the
|
||
|
output of some convolution to something that has the shape of its input
|
||
|
while maintaining a connectivity pattern that is compatible with
|
||
|
said convolution.
|
||
|
|
||
|
When using this layer as the first layer in a model,
|
||
|
provide the keyword argument `input_shape`
|
||
|
(tuple of integers, does not include the sample axis),
|
||
|
e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
|
||
|
in `data_format="channels_last"`.
|
||
|
|
||
|
Arguments:
|
||
|
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
|
||
|
height and width 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 height and width.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Specifying any stride value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: one of `"valid"` or `"same"` (case-insensitive).
|
||
|
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".
|
||
|
dilation_rate: an integer or tuple/list of 2 integers, specifying
|
||
|
the dilation rate to use for dilated convolution.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Currently, specifying any `dilation_rate` value != 1 is
|
||
|
incompatible with specifying any stride value != 1.
|
||
|
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.
|
||
|
|
||
|
Input shape:
|
||
|
4D tensor with shape:
|
||
|
`(batch, channels, rows, cols)` if data_format='channels_first'
|
||
|
or 4D tensor with shape:
|
||
|
`(batch, rows, cols, channels)` if data_format='channels_last'.
|
||
|
|
||
|
Output shape:
|
||
|
4D tensor with shape:
|
||
|
`(batch, filters, new_rows, new_cols)` if data_format='channels_first'
|
||
|
or 4D tensor with shape:
|
||
|
`(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
|
||
|
`rows` and `cols` values might have changed due to padding.
|
||
|
|
||
|
References:
|
||
|
- [A guide to convolution arithmetic for deep
|
||
|
learning](https://arxiv.org/abs/1603.07285v1)
|
||
|
- [Deconvolutional
|
||
|
Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
|
||
|
"""
|
||
|
|
||
|
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,
|
||
|
**kwargs):
|
||
|
super(Conv2DTranspose, self).__init__(
|
||
|
filters=filters,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
activation=activations.get(activation),
|
||
|
use_bias=use_bias,
|
||
|
kernel_initializer=initializers.get(kernel_initializer),
|
||
|
bias_initializer=initializers.get(bias_initializer),
|
||
|
kernel_regularizer=regularizers.get(kernel_regularizer),
|
||
|
bias_regularizer=regularizers.get(bias_regularizer),
|
||
|
activity_regularizer=regularizers.get(activity_regularizer),
|
||
|
kernel_constraint=constraints.get(kernel_constraint),
|
||
|
bias_constraint=constraints.get(bias_constraint),
|
||
|
**kwargs)
|
||
|
|
||
|
def build(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape)
|
||
|
if len(input_shape) != 4:
|
||
|
raise ValueError('Inputs should have rank 4. Received input shape: ' +
|
||
|
str(input_shape))
|
||
|
if self.data_format == 'channels_first':
|
||
|
channel_axis = 1
|
||
|
else:
|
||
|
channel_axis = -1
|
||
|
if input_shape[channel_axis].value is None:
|
||
|
raise ValueError('The channel dimension of the inputs '
|
||
|
'should be defined. Found `None`.')
|
||
|
input_dim = int(input_shape[channel_axis])
|
||
|
self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
|
||
|
kernel_shape = self.kernel_size + (self.filters, input_dim)
|
||
|
|
||
|
self.kernel = self.add_weight(
|
||
|
name='kernel',
|
||
|
shape=kernel_shape,
|
||
|
initializer=self.kernel_initializer,
|
||
|
regularizer=self.kernel_regularizer,
|
||
|
constraint=self.kernel_constraint,
|
||
|
trainable=True,
|
||
|
dtype=self.dtype)
|
||
|
if self.use_bias:
|
||
|
self.bias = self.add_weight(
|
||
|
name='bias',
|
||
|
shape=(self.filters,),
|
||
|
initializer=self.bias_initializer,
|
||
|
regularizer=self.bias_regularizer,
|
||
|
constraint=self.bias_constraint,
|
||
|
trainable=True,
|
||
|
dtype=self.dtype)
|
||
|
else:
|
||
|
self.bias = None
|
||
|
self.built = True
|
||
|
|
||
|
def call(self, inputs):
|
||
|
inputs_shape = array_ops.shape(inputs)
|
||
|
batch_size = inputs_shape[0]
|
||
|
if self.data_format == 'channels_first':
|
||
|
c_axis, h_axis, w_axis = 1, 2, 3
|
||
|
else:
|
||
|
c_axis, h_axis, w_axis = 3, 1, 2
|
||
|
|
||
|
height, width = inputs_shape[h_axis], inputs_shape[w_axis]
|
||
|
kernel_h, kernel_w = self.kernel_size
|
||
|
stride_h, stride_w = self.strides
|
||
|
|
||
|
# Infer the dynamic output shape:
|
||
|
out_height = conv_utils.deconv_output_length(height,
|
||
|
kernel_h,
|
||
|
self.padding,
|
||
|
stride_h)
|
||
|
out_width = conv_utils.deconv_output_length(width,
|
||
|
kernel_w,
|
||
|
self.padding,
|
||
|
stride_w)
|
||
|
if self.data_format == 'channels_first':
|
||
|
output_shape = (batch_size, self.filters, out_height, out_width)
|
||
|
strides = (1, 1, stride_h, stride_w)
|
||
|
else:
|
||
|
output_shape = (batch_size, out_height, out_width, self.filters)
|
||
|
strides = (1, stride_h, stride_w, 1)
|
||
|
|
||
|
output_shape_tensor = array_ops.stack(output_shape)
|
||
|
outputs = nn.conv2d_transpose(
|
||
|
inputs,
|
||
|
self.kernel,
|
||
|
output_shape_tensor,
|
||
|
strides,
|
||
|
padding=self.padding.upper(),
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
|
||
|
|
||
|
if not context.executing_eagerly():
|
||
|
# Infer the static output shape:
|
||
|
out_shape = inputs.get_shape().as_list()
|
||
|
out_shape[c_axis] = self.filters
|
||
|
out_shape[h_axis] = conv_utils.deconv_output_length(out_shape[h_axis],
|
||
|
kernel_h,
|
||
|
self.padding,
|
||
|
stride_h)
|
||
|
out_shape[w_axis] = conv_utils.deconv_output_length(out_shape[w_axis],
|
||
|
kernel_w,
|
||
|
self.padding,
|
||
|
stride_w)
|
||
|
outputs.set_shape(out_shape)
|
||
|
|
||
|
if self.use_bias:
|
||
|
outputs = nn.bias_add(
|
||
|
outputs,
|
||
|
self.bias,
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
|
||
|
|
||
|
if self.activation is not None:
|
||
|
return self.activation(outputs)
|
||
|
return outputs
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
output_shape = list(input_shape)
|
||
|
if self.data_format == 'channels_first':
|
||
|
c_axis, h_axis, w_axis = 1, 2, 3
|
||
|
else:
|
||
|
c_axis, h_axis, w_axis = 3, 1, 2
|
||
|
|
||
|
kernel_h, kernel_w = self.kernel_size
|
||
|
stride_h, stride_w = self.strides
|
||
|
|
||
|
output_shape[c_axis] = self.filters
|
||
|
output_shape[h_axis] = conv_utils.deconv_output_length(
|
||
|
output_shape[h_axis], kernel_h, self.padding, stride_h)
|
||
|
output_shape[w_axis] = conv_utils.deconv_output_length(
|
||
|
output_shape[w_axis], kernel_w, self.padding, stride_w)
|
||
|
return tensor_shape.TensorShape(output_shape)
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.Conv3DTranspose',
|
||
|
'keras.layers.Convolution3DTranspose')
|
||
|
class Conv3DTranspose(Conv3D):
|
||
|
"""Transposed convolution layer (sometimes called Deconvolution).
|
||
|
|
||
|
The need for transposed convolutions generally arises
|
||
|
from the desire to use a transformation going in the opposite direction
|
||
|
of a normal convolution, i.e., from something that has the shape of the
|
||
|
output of some convolution to something that has the shape of its input
|
||
|
while maintaining a connectivity pattern that is compatible with
|
||
|
said convolution.
|
||
|
|
||
|
When using this layer as the first layer in a model,
|
||
|
provide the keyword argument `input_shape`
|
||
|
(tuple of integers, does not include the sample axis),
|
||
|
e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels
|
||
|
if `data_format="channels_last"`.
|
||
|
|
||
|
Arguments:
|
||
|
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 3 integers, specifying the
|
||
|
depth, height and width of the 3D convolution window.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
strides: An integer or tuple/list of 3 integers,
|
||
|
specifying the strides of the convolution along the depth, height
|
||
|
and width.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Specifying any stride value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: one of `"valid"` or `"same"` (case-insensitive).
|
||
|
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, depth, height, width, channels)` while `channels_first`
|
||
|
corresponds to inputs with shape
|
||
|
`(batch, channels, depth, 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".
|
||
|
dilation_rate: an integer or tuple/list of 3 integers, specifying
|
||
|
the dilation rate to use for dilated convolution.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Currently, specifying any `dilation_rate` value != 1 is
|
||
|
incompatible with specifying any stride value != 1.
|
||
|
activation: Activation function to use
|
||
|
(see [activations](../activations.md)).
|
||
|
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
|
||
|
(see [initializers](../initializers.md)).
|
||
|
bias_initializer: Initializer for the bias vector
|
||
|
(see [initializers](../initializers.md)).
|
||
|
kernel_regularizer: Regularizer function applied to
|
||
|
the `kernel` weights matrix
|
||
|
(see [regularizer](../regularizers.md)).
|
||
|
bias_regularizer: Regularizer function applied to the bias vector
|
||
|
(see [regularizer](../regularizers.md)).
|
||
|
activity_regularizer: Regularizer function applied to
|
||
|
the output of the layer (its "activation").
|
||
|
(see [regularizer](../regularizers.md)).
|
||
|
kernel_constraint: Constraint function applied to the kernel matrix
|
||
|
(see [constraints](../constraints.md)).
|
||
|
bias_constraint: Constraint function applied to the bias vector
|
||
|
(see [constraints](../constraints.md)).
|
||
|
|
||
|
Input shape:
|
||
|
5D tensor with shape:
|
||
|
`(batch, channels, depth, rows, cols)` if data_format='channels_first'
|
||
|
or 5D tensor with shape:
|
||
|
`(batch, depth, rows, cols, channels)` if data_format='channels_last'.
|
||
|
|
||
|
Output shape:
|
||
|
5D tensor with shape:
|
||
|
`(batch, filters, new_depth, new_rows, new_cols)` if
|
||
|
data_format='channels_first'
|
||
|
or 5D tensor with shape:
|
||
|
`(batch, new_depth, new_rows, new_cols, filters)` if
|
||
|
data_format='channels_last'.
|
||
|
`depth` and `rows` and `cols` values might have changed due to padding.
|
||
|
|
||
|
References:
|
||
|
- [A guide to convolution arithmetic for deep
|
||
|
learning](https://arxiv.org/abs/1603.07285v1)
|
||
|
- [Deconvolutional
|
||
|
Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
filters,
|
||
|
kernel_size,
|
||
|
strides=(1, 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,
|
||
|
**kwargs):
|
||
|
super(Conv3DTranspose, self).__init__(
|
||
|
filters=filters,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
activation=activations.get(activation),
|
||
|
use_bias=use_bias,
|
||
|
kernel_initializer=initializers.get(kernel_initializer),
|
||
|
bias_initializer=initializers.get(bias_initializer),
|
||
|
kernel_regularizer=regularizers.get(kernel_regularizer),
|
||
|
bias_regularizer=regularizers.get(bias_regularizer),
|
||
|
activity_regularizer=regularizers.get(activity_regularizer),
|
||
|
kernel_constraint=constraints.get(kernel_constraint),
|
||
|
bias_constraint=constraints.get(bias_constraint),
|
||
|
**kwargs)
|
||
|
|
||
|
def build(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape)
|
||
|
if len(input_shape) != 5:
|
||
|
raise ValueError('Inputs should have rank 5, received input shape:',
|
||
|
str(input_shape))
|
||
|
if self.data_format == 'channels_first':
|
||
|
channel_axis = 1
|
||
|
else:
|
||
|
channel_axis = -1
|
||
|
if input_shape[channel_axis].value is None:
|
||
|
raise ValueError('The channel dimension of the inputs '
|
||
|
'should be defined, found None: ' + str(input_shape))
|
||
|
input_dim = int(input_shape[channel_axis])
|
||
|
kernel_shape = self.kernel_size + (self.filters, input_dim)
|
||
|
self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})
|
||
|
|
||
|
self.kernel = self.add_weight(
|
||
|
'kernel',
|
||
|
shape=kernel_shape,
|
||
|
initializer=self.kernel_initializer,
|
||
|
regularizer=self.kernel_regularizer,
|
||
|
constraint=self.kernel_constraint,
|
||
|
trainable=True,
|
||
|
dtype=self.dtype)
|
||
|
if self.use_bias:
|
||
|
self.bias = self.add_weight(
|
||
|
'bias',
|
||
|
shape=(self.filters,),
|
||
|
initializer=self.bias_initializer,
|
||
|
regularizer=self.bias_regularizer,
|
||
|
constraint=self.bias_constraint,
|
||
|
trainable=True,
|
||
|
dtype=self.dtype)
|
||
|
else:
|
||
|
self.bias = None
|
||
|
self.built = True
|
||
|
|
||
|
def call(self, inputs):
|
||
|
inputs_shape = array_ops.shape(inputs)
|
||
|
batch_size = inputs_shape[0]
|
||
|
if self.data_format == 'channels_first':
|
||
|
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
|
||
|
else:
|
||
|
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
|
||
|
|
||
|
self.input_spec = InputSpec(ndim=5, axes={c_axis: inputs_shape[c_axis]})
|
||
|
|
||
|
depth = inputs_shape[d_axis]
|
||
|
height = inputs_shape[h_axis]
|
||
|
width = inputs_shape[w_axis]
|
||
|
|
||
|
kernel_d, kernel_h, kernel_w = self.kernel_size
|
||
|
stride_d, stride_h, stride_w = self.strides
|
||
|
|
||
|
# Infer the dynamic output shape:
|
||
|
out_depth = conv_utils.deconv_output_length(depth,
|
||
|
kernel_d,
|
||
|
self.padding,
|
||
|
stride_d)
|
||
|
out_height = conv_utils.deconv_output_length(height,
|
||
|
kernel_h,
|
||
|
self.padding,
|
||
|
stride_h)
|
||
|
out_width = conv_utils.deconv_output_length(width,
|
||
|
kernel_w,
|
||
|
self.padding,
|
||
|
stride_w)
|
||
|
if self.data_format == 'channels_first':
|
||
|
output_shape = (batch_size, self.filters, out_depth, out_height,
|
||
|
out_width)
|
||
|
strides = (1, 1, stride_d, stride_h, stride_w)
|
||
|
else:
|
||
|
output_shape = (batch_size, out_depth, out_height, out_width,
|
||
|
self.filters)
|
||
|
strides = (1, stride_d, stride_h, stride_w, 1)
|
||
|
|
||
|
output_shape_tensor = array_ops.stack(output_shape)
|
||
|
outputs = nn.conv3d_transpose(
|
||
|
inputs,
|
||
|
self.kernel,
|
||
|
output_shape_tensor,
|
||
|
strides,
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=5),
|
||
|
padding=self.padding.upper())
|
||
|
|
||
|
if not context.executing_eagerly():
|
||
|
# Infer the static output shape:
|
||
|
out_shape = inputs.get_shape().as_list()
|
||
|
out_shape[c_axis] = self.filters
|
||
|
out_shape[d_axis] = conv_utils.deconv_output_length(out_shape[d_axis],
|
||
|
kernel_d,
|
||
|
self.padding,
|
||
|
stride_d)
|
||
|
out_shape[h_axis] = conv_utils.deconv_output_length(out_shape[h_axis],
|
||
|
kernel_h,
|
||
|
self.padding,
|
||
|
stride_h)
|
||
|
out_shape[w_axis] = conv_utils.deconv_output_length(out_shape[w_axis],
|
||
|
kernel_w,
|
||
|
self.padding,
|
||
|
stride_w)
|
||
|
outputs.set_shape(out_shape)
|
||
|
|
||
|
if self.use_bias:
|
||
|
outputs_shape = outputs.shape.as_list()
|
||
|
if outputs_shape[0] is None:
|
||
|
outputs_shape[0] = -1
|
||
|
if self.data_format == 'channels_first':
|
||
|
outputs_4d = array_ops.reshape(outputs, [
|
||
|
outputs_shape[0], outputs_shape[1],
|
||
|
outputs_shape[2] * outputs_shape[3], outputs_shape[4]
|
||
|
])
|
||
|
else:
|
||
|
outputs_4d = array_ops.reshape(outputs, [
|
||
|
outputs_shape[0], outputs_shape[1] * outputs_shape[2],
|
||
|
outputs_shape[3], outputs_shape[4]
|
||
|
])
|
||
|
outputs_4d = nn.bias_add(
|
||
|
outputs_4d,
|
||
|
self.bias,
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
|
||
|
outputs = array_ops.reshape(outputs_4d, outputs_shape)
|
||
|
|
||
|
if self.activation is not None:
|
||
|
return self.activation(outputs)
|
||
|
return outputs
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
output_shape = list(input_shape)
|
||
|
if self.data_format == 'channels_first':
|
||
|
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
|
||
|
else:
|
||
|
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
|
||
|
|
||
|
kernel_d, kernel_h, kernel_w = self.kernel_size
|
||
|
stride_d, stride_h, stride_w = self.strides
|
||
|
|
||
|
output_shape[c_axis] = self.filters
|
||
|
output_shape[d_axis] = conv_utils.deconv_output_length(
|
||
|
output_shape[d_axis], kernel_d, self.padding, stride_d)
|
||
|
output_shape[h_axis] = conv_utils.deconv_output_length(
|
||
|
output_shape[h_axis], kernel_h, self.padding, stride_h)
|
||
|
output_shape[w_axis] = conv_utils.deconv_output_length(
|
||
|
output_shape[w_axis], kernel_w, self.padding, stride_w)
|
||
|
return tensor_shape.TensorShape(output_shape)
|
||
|
|
||
|
|
||
|
class SeparableConv(Conv):
|
||
|
"""Abstract base layer for separable nD convolution.
|
||
|
|
||
|
This layer performs a depthwise convolution that acts separately on
|
||
|
channels, followed by a pointwise convolution that mixes channels.
|
||
|
If `use_bias` is True and a bias initializer is provided,
|
||
|
it adds a bias vector to the output.
|
||
|
It then optionally applies an activation function to produce the final output.
|
||
|
|
||
|
Arguments:
|
||
|
rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
|
||
|
filters: Integer, the dimensionality of the output space (i.e. the number
|
||
|
of filters in the convolution).
|
||
|
kernel_size: A tuple or list of integers specifying the spatial
|
||
|
dimensions of the filters. Can be a single integer to specify the same
|
||
|
value for all spatial dimensions.
|
||
|
strides: A tuple or list of integers specifying the strides
|
||
|
of the convolution. Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Specifying any `stride` value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: One of `"valid"` or `"same"` (case-insensitive).
|
||
|
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, ..., channels)` while `channels_first` corresponds to
|
||
|
inputs with shape `(batch, channels, ...)`.
|
||
|
dilation_rate: An integer or tuple/list of 2 integers, specifying
|
||
|
the dilation rate to use for dilated convolution.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Currently, specifying any `dilation_rate` value != 1 is
|
||
|
incompatible with specifying any stride value != 1.
|
||
|
depth_multiplier: The number of depthwise convolution output channels for
|
||
|
each input channel. The total number of depthwise convolution output
|
||
|
channels will be equal to `num_filters_in * depth_multiplier`.
|
||
|
activation: Activation function. Set it to None to maintain a
|
||
|
linear activation.
|
||
|
use_bias: Boolean, whether the layer uses a bias.
|
||
|
depthwise_initializer: An initializer for the depthwise convolution kernel.
|
||
|
pointwise_initializer: An initializer for the pointwise convolution kernel.
|
||
|
bias_initializer: An initializer for the bias vector. If None, the default
|
||
|
initializer will be used.
|
||
|
depthwise_regularizer: Optional regularizer for the depthwise
|
||
|
convolution kernel.
|
||
|
pointwise_regularizer: Optional regularizer for the pointwise
|
||
|
convolution kernel.
|
||
|
bias_regularizer: Optional regularizer for the bias vector.
|
||
|
activity_regularizer: Optional regularizer function for the output.
|
||
|
depthwise_constraint: Optional projection function to be applied to the
|
||
|
depthwise kernel after being updated by an `Optimizer` (e.g. used for
|
||
|
norm constraints or value constraints for layer weights). The function
|
||
|
must take as input the unprojected variable and must return the
|
||
|
projected variable (which must have the same shape). Constraints are
|
||
|
not safe to use when doing asynchronous distributed training.
|
||
|
pointwise_constraint: Optional projection function to be applied to the
|
||
|
pointwise kernel after being updated by an `Optimizer`.
|
||
|
bias_constraint: Optional projection function to be applied to the
|
||
|
bias after being updated by an `Optimizer`.
|
||
|
trainable: Boolean, if `True` also add variables to the graph collection
|
||
|
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
|
||
|
name: A string, the name of the layer.
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
rank,
|
||
|
filters,
|
||
|
kernel_size,
|
||
|
strides=1,
|
||
|
padding='valid',
|
||
|
data_format=None,
|
||
|
dilation_rate=1,
|
||
|
depth_multiplier=1,
|
||
|
activation=None,
|
||
|
use_bias=True,
|
||
|
depthwise_initializer='glorot_uniform',
|
||
|
pointwise_initializer='glorot_uniform',
|
||
|
bias_initializer='zeros',
|
||
|
depthwise_regularizer=None,
|
||
|
pointwise_regularizer=None,
|
||
|
bias_regularizer=None,
|
||
|
activity_regularizer=None,
|
||
|
depthwise_constraint=None,
|
||
|
pointwise_constraint=None,
|
||
|
bias_constraint=None,
|
||
|
trainable=True,
|
||
|
name=None,
|
||
|
**kwargs):
|
||
|
super(SeparableConv, self).__init__(
|
||
|
rank=rank,
|
||
|
filters=filters,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
dilation_rate=dilation_rate,
|
||
|
activation=activations.get(activation),
|
||
|
use_bias=use_bias,
|
||
|
bias_initializer=initializers.get(bias_initializer),
|
||
|
bias_regularizer=regularizers.get(bias_regularizer),
|
||
|
activity_regularizer=regularizers.get(activity_regularizer),
|
||
|
bias_constraint=bias_constraint,
|
||
|
trainable=trainable,
|
||
|
name=name,
|
||
|
**kwargs)
|
||
|
self.depth_multiplier = depth_multiplier
|
||
|
self.depthwise_initializer = initializers.get(depthwise_initializer)
|
||
|
self.pointwise_initializer = initializers.get(pointwise_initializer)
|
||
|
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
|
||
|
self.pointwise_regularizer = regularizers.get(pointwise_regularizer)
|
||
|
self.depthwise_constraint = constraints.get(depthwise_constraint)
|
||
|
self.pointwise_constraint = constraints.get(pointwise_constraint)
|
||
|
|
||
|
def build(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape)
|
||
|
if self.data_format == 'channels_first':
|
||
|
channel_axis = 1
|
||
|
else:
|
||
|
channel_axis = -1
|
||
|
if input_shape[channel_axis].value is None:
|
||
|
raise ValueError('The channel dimension of the inputs '
|
||
|
'should be defined. Found `None`.')
|
||
|
input_dim = int(input_shape[channel_axis])
|
||
|
self.input_spec = InputSpec(ndim=self.rank + 2,
|
||
|
axes={channel_axis: input_dim})
|
||
|
depthwise_kernel_shape = self.kernel_size + (input_dim,
|
||
|
self.depth_multiplier)
|
||
|
pointwise_kernel_shape = (
|
||
|
1,) * self.rank + (self.depth_multiplier * input_dim, self.filters)
|
||
|
|
||
|
self.depthwise_kernel = self.add_weight(
|
||
|
name='depthwise_kernel',
|
||
|
shape=depthwise_kernel_shape,
|
||
|
initializer=self.depthwise_initializer,
|
||
|
regularizer=self.depthwise_regularizer,
|
||
|
constraint=self.depthwise_constraint,
|
||
|
trainable=True,
|
||
|
dtype=self.dtype)
|
||
|
self.pointwise_kernel = self.add_weight(
|
||
|
name='pointwise_kernel',
|
||
|
shape=pointwise_kernel_shape,
|
||
|
initializer=self.pointwise_initializer,
|
||
|
regularizer=self.pointwise_regularizer,
|
||
|
constraint=self.pointwise_constraint,
|
||
|
trainable=True,
|
||
|
dtype=self.dtype)
|
||
|
if self.use_bias:
|
||
|
self.bias = self.add_weight(
|
||
|
name='bias',
|
||
|
shape=(self.filters,),
|
||
|
initializer=self.bias_initializer,
|
||
|
regularizer=self.bias_regularizer,
|
||
|
constraint=self.bias_constraint,
|
||
|
trainable=True,
|
||
|
dtype=self.dtype)
|
||
|
else:
|
||
|
self.bias = None
|
||
|
self.built = True
|
||
|
|
||
|
def call(self, inputs):
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {
|
||
|
'filters': self.filters,
|
||
|
'kernel_size': self.kernel_size,
|
||
|
'strides': self.strides,
|
||
|
'padding': self.padding,
|
||
|
'data_format': self.data_format,
|
||
|
'dilation_rate': self.dilation_rate,
|
||
|
'activation': activations.serialize(self.activation),
|
||
|
'use_bias': self.use_bias,
|
||
|
'depthwise_initializer':
|
||
|
initializers.serialize(self.depthwise_initializer),
|
||
|
'pointwise_initializer':
|
||
|
initializers.serialize(self.pointwise_initializer),
|
||
|
'bias_initializer': initializers.serialize(self.bias_initializer),
|
||
|
'depthwise_regularizer':
|
||
|
regularizers.serialize(self.depthwise_regularizer),
|
||
|
'pointwise_regularizer':
|
||
|
regularizers.serialize(self.pointwise_regularizer),
|
||
|
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
|
||
|
'activity_regularizer':
|
||
|
regularizers.serialize(self.activity_regularizer),
|
||
|
'depthwise_constraint':
|
||
|
constraints.serialize(self.depthwise_constraint),
|
||
|
'pointwise_constraint':
|
||
|
constraints.serialize(self.pointwise_constraint),
|
||
|
'bias_constraint': constraints.serialize(self.bias_constraint)
|
||
|
}
|
||
|
base_config = super(SeparableConv, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.SeparableConv1D',
|
||
|
'keras.layers.SeparableConvolution1D')
|
||
|
class SeparableConv1D(SeparableConv):
|
||
|
"""Depthwise separable 1D convolution.
|
||
|
|
||
|
This layer performs a depthwise convolution that acts separately on
|
||
|
channels, followed by a pointwise convolution that mixes channels.
|
||
|
If `use_bias` is True and a bias initializer is provided,
|
||
|
it adds a bias vector to the output.
|
||
|
It then optionally applies an activation function to produce the final output.
|
||
|
|
||
|
Arguments:
|
||
|
filters: Integer, the dimensionality of the output space (i.e. the number
|
||
|
of filters in the convolution).
|
||
|
kernel_size: A single integer specifying the spatial
|
||
|
dimensions of the filters.
|
||
|
strides: A single integer specifying the strides
|
||
|
of the convolution.
|
||
|
Specifying any `stride` value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: One of `"valid"` or `"same"` (case-insensitive).
|
||
|
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)`.
|
||
|
dilation_rate: A single integer, specifying
|
||
|
the dilation rate to use for dilated convolution.
|
||
|
Currently, specifying any `dilation_rate` value != 1 is
|
||
|
incompatible with specifying any stride value != 1.
|
||
|
depth_multiplier: The number of depthwise convolution output channels for
|
||
|
each input channel. The total number of depthwise convolution output
|
||
|
channels will be equal to `num_filters_in * depth_multiplier`.
|
||
|
activation: Activation function. Set it to None to maintain a
|
||
|
linear activation.
|
||
|
use_bias: Boolean, whether the layer uses a bias.
|
||
|
depthwise_initializer: An initializer for the depthwise convolution kernel.
|
||
|
pointwise_initializer: An initializer for the pointwise convolution kernel.
|
||
|
bias_initializer: An initializer for the bias vector. If None, the default
|
||
|
initializer will be used.
|
||
|
depthwise_regularizer: Optional regularizer for the depthwise
|
||
|
convolution kernel.
|
||
|
pointwise_regularizer: Optional regularizer for the pointwise
|
||
|
convolution kernel.
|
||
|
bias_regularizer: Optional regularizer for the bias vector.
|
||
|
activity_regularizer: Optional regularizer function for the output.
|
||
|
depthwise_constraint: Optional projection function to be applied to the
|
||
|
depthwise kernel after being updated by an `Optimizer` (e.g. used for
|
||
|
norm constraints or value constraints for layer weights). The function
|
||
|
must take as input the unprojected variable and must return the
|
||
|
projected variable (which must have the same shape). Constraints are
|
||
|
not safe to use when doing asynchronous distributed training.
|
||
|
pointwise_constraint: Optional projection function to be applied to the
|
||
|
pointwise kernel after being updated by an `Optimizer`.
|
||
|
bias_constraint: Optional projection function to be applied to the
|
||
|
bias after being updated by an `Optimizer`.
|
||
|
trainable: Boolean, if `True` also add variables to the graph collection
|
||
|
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
|
||
|
name: A string, the name of the layer.
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
filters,
|
||
|
kernel_size,
|
||
|
strides=1,
|
||
|
padding='valid',
|
||
|
data_format=None,
|
||
|
dilation_rate=1,
|
||
|
depth_multiplier=1,
|
||
|
activation=None,
|
||
|
use_bias=True,
|
||
|
depthwise_initializer='glorot_uniform',
|
||
|
pointwise_initializer='glorot_uniform',
|
||
|
bias_initializer='zeros',
|
||
|
depthwise_regularizer=None,
|
||
|
pointwise_regularizer=None,
|
||
|
bias_regularizer=None,
|
||
|
activity_regularizer=None,
|
||
|
depthwise_constraint=None,
|
||
|
pointwise_constraint=None,
|
||
|
bias_constraint=None,
|
||
|
**kwargs):
|
||
|
super(SeparableConv1D, self).__init__(
|
||
|
rank=1,
|
||
|
filters=filters,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
dilation_rate=dilation_rate,
|
||
|
depth_multiplier=depth_multiplier,
|
||
|
activation=activations.get(activation),
|
||
|
use_bias=use_bias,
|
||
|
depthwise_initializer=initializers.get(depthwise_initializer),
|
||
|
pointwise_initializer=initializers.get(pointwise_initializer),
|
||
|
bias_initializer=initializers.get(bias_initializer),
|
||
|
depthwise_regularizer=regularizers.get(depthwise_regularizer),
|
||
|
pointwise_regularizer=regularizers.get(pointwise_regularizer),
|
||
|
bias_regularizer=regularizers.get(bias_regularizer),
|
||
|
activity_regularizer=regularizers.get(activity_regularizer),
|
||
|
depthwise_constraint=constraints.get(depthwise_constraint),
|
||
|
pointwise_constraint=constraints.get(pointwise_constraint),
|
||
|
bias_constraint=constraints.get(bias_constraint),
|
||
|
**kwargs)
|
||
|
|
||
|
def call(self, inputs):
|
||
|
if self.data_format == 'channels_last':
|
||
|
strides = (1,) + self.strides * 2 + (1,)
|
||
|
spatial_start_dim = 1
|
||
|
else:
|
||
|
strides = (1, 1) + self.strides * 2
|
||
|
spatial_start_dim = 2
|
||
|
|
||
|
# Explicitly broadcast inputs and kernels to 4D.
|
||
|
# TODO(fchollet): refactor when a native separable_conv1d op is available.
|
||
|
inputs = array_ops.expand_dims(inputs, spatial_start_dim)
|
||
|
depthwise_kernel = array_ops.expand_dims(self.depthwise_kernel, 0)
|
||
|
pointwise_kernel = array_ops.expand_dims(self.pointwise_kernel, 0)
|
||
|
dilation_rate = (1,) + self.dilation_rate
|
||
|
|
||
|
outputs = nn.separable_conv2d(
|
||
|
inputs,
|
||
|
depthwise_kernel,
|
||
|
pointwise_kernel,
|
||
|
strides=strides,
|
||
|
padding=self.padding.upper(),
|
||
|
rate=dilation_rate,
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
|
||
|
|
||
|
if self.use_bias:
|
||
|
outputs = nn.bias_add(
|
||
|
outputs,
|
||
|
self.bias,
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
|
||
|
|
||
|
outputs = array_ops.squeeze(outputs, [spatial_start_dim])
|
||
|
|
||
|
if self.activation is not None:
|
||
|
return self.activation(outputs)
|
||
|
return outputs
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.SeparableConv2D',
|
||
|
'keras.layers.SeparableConvolution2D')
|
||
|
class SeparableConv2D(SeparableConv):
|
||
|
"""Depthwise separable 2D convolution.
|
||
|
|
||
|
Separable convolutions consist in first performing
|
||
|
a depthwise spatial convolution
|
||
|
(which acts on each input channel separately)
|
||
|
followed by a pointwise convolution which mixes together the resulting
|
||
|
output channels. The `depth_multiplier` argument controls how many
|
||
|
output channels are generated per input channel in the depthwise step.
|
||
|
|
||
|
Intuitively, separable convolutions can be understood as
|
||
|
a way to factorize a convolution kernel into two smaller kernels,
|
||
|
or as an extreme version of an Inception block.
|
||
|
|
||
|
Arguments:
|
||
|
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
|
||
|
height and width 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 height and width.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Specifying any stride value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: one of `"valid"` or `"same"` (case-insensitive).
|
||
|
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".
|
||
|
dilation_rate: An integer or tuple/list of 2 integers, specifying
|
||
|
the dilation rate to use for dilated convolution.
|
||
|
Currently, specifying any `dilation_rate` value != 1 is
|
||
|
incompatible with specifying any `strides` value != 1.
|
||
|
depth_multiplier: The number of depthwise convolution output channels
|
||
|
for each input channel.
|
||
|
The total number of depthwise convolution output
|
||
|
channels will be equal to `filters_in * depth_multiplier`.
|
||
|
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.
|
||
|
depthwise_initializer: Initializer for the depthwise kernel matrix.
|
||
|
pointwise_initializer: Initializer for the pointwise kernel matrix.
|
||
|
bias_initializer: Initializer for the bias vector.
|
||
|
depthwise_regularizer: Regularizer function applied to
|
||
|
the depthwise kernel matrix.
|
||
|
pointwise_regularizer: Regularizer function applied to
|
||
|
the pointwise kernel matrix.
|
||
|
bias_regularizer: Regularizer function applied to the bias vector.
|
||
|
activity_regularizer: Regularizer function applied to
|
||
|
the output of the layer (its "activation")..
|
||
|
depthwise_constraint: Constraint function applied to
|
||
|
the depthwise kernel matrix.
|
||
|
pointwise_constraint: Constraint function applied to
|
||
|
the pointwise kernel matrix.
|
||
|
bias_constraint: Constraint function applied to the bias vector.
|
||
|
|
||
|
Input shape:
|
||
|
4D tensor with shape:
|
||
|
`(batch, channels, rows, cols)` if data_format='channels_first'
|
||
|
or 4D tensor with shape:
|
||
|
`(batch, rows, cols, channels)` if data_format='channels_last'.
|
||
|
|
||
|
Output shape:
|
||
|
4D tensor with shape:
|
||
|
`(batch, filters, new_rows, new_cols)` if data_format='channels_first'
|
||
|
or 4D tensor with shape:
|
||
|
`(batch, 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,
|
||
|
dilation_rate=(1, 1),
|
||
|
depth_multiplier=1,
|
||
|
activation=None,
|
||
|
use_bias=True,
|
||
|
depthwise_initializer='glorot_uniform',
|
||
|
pointwise_initializer='glorot_uniform',
|
||
|
bias_initializer='zeros',
|
||
|
depthwise_regularizer=None,
|
||
|
pointwise_regularizer=None,
|
||
|
bias_regularizer=None,
|
||
|
activity_regularizer=None,
|
||
|
depthwise_constraint=None,
|
||
|
pointwise_constraint=None,
|
||
|
bias_constraint=None,
|
||
|
**kwargs):
|
||
|
super(SeparableConv2D, self).__init__(
|
||
|
rank=2,
|
||
|
filters=filters,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
dilation_rate=dilation_rate,
|
||
|
depth_multiplier=depth_multiplier,
|
||
|
activation=activations.get(activation),
|
||
|
use_bias=use_bias,
|
||
|
depthwise_initializer=initializers.get(depthwise_initializer),
|
||
|
pointwise_initializer=initializers.get(pointwise_initializer),
|
||
|
bias_initializer=initializers.get(bias_initializer),
|
||
|
depthwise_regularizer=regularizers.get(depthwise_regularizer),
|
||
|
pointwise_regularizer=regularizers.get(pointwise_regularizer),
|
||
|
bias_regularizer=regularizers.get(bias_regularizer),
|
||
|
activity_regularizer=regularizers.get(activity_regularizer),
|
||
|
depthwise_constraint=constraints.get(depthwise_constraint),
|
||
|
pointwise_constraint=constraints.get(pointwise_constraint),
|
||
|
bias_constraint=constraints.get(bias_constraint),
|
||
|
**kwargs)
|
||
|
|
||
|
def call(self, inputs):
|
||
|
# Apply the actual ops.
|
||
|
if self.data_format == 'channels_last':
|
||
|
strides = (1,) + self.strides + (1,)
|
||
|
else:
|
||
|
strides = (1, 1) + self.strides
|
||
|
outputs = nn.separable_conv2d(
|
||
|
inputs,
|
||
|
self.depthwise_kernel,
|
||
|
self.pointwise_kernel,
|
||
|
strides=strides,
|
||
|
padding=self.padding.upper(),
|
||
|
rate=self.dilation_rate,
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
|
||
|
|
||
|
if self.use_bias:
|
||
|
outputs = nn.bias_add(
|
||
|
outputs,
|
||
|
self.bias,
|
||
|
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
|
||
|
|
||
|
if self.activation is not None:
|
||
|
return self.activation(outputs)
|
||
|
return outputs
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.DepthwiseConv2D')
|
||
|
class DepthwiseConv2D(Conv2D):
|
||
|
"""Depthwise separable 2D convolution.
|
||
|
|
||
|
Depthwise Separable convolutions consists in performing
|
||
|
just the first step in a depthwise spatial convolution
|
||
|
(which acts on each input channel separately).
|
||
|
The `depth_multiplier` argument controls how many
|
||
|
output channels are generated per input channel in the depthwise step.
|
||
|
|
||
|
Arguments:
|
||
|
kernel_size: An integer or tuple/list of 2 integers, specifying the
|
||
|
height and width 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 height and width.
|
||
|
Can be a single integer to specify the same value for
|
||
|
all spatial dimensions.
|
||
|
Specifying any stride value != 1 is incompatible with specifying
|
||
|
any `dilation_rate` value != 1.
|
||
|
padding: one of `'valid'` or `'same'` (case-insensitive).
|
||
|
depth_multiplier: The number of depthwise convolution output channels
|
||
|
for each input channel.
|
||
|
The total number of depthwise convolution output
|
||
|
channels will be equal to `filters_in * depth_multiplier`.
|
||
|
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.
|
||
|
depthwise_initializer: Initializer for the depthwise kernel matrix.
|
||
|
bias_initializer: Initializer for the bias vector.
|
||
|
depthwise_regularizer: Regularizer function applied to
|
||
|
the depthwise kernel matrix.
|
||
|
bias_regularizer: Regularizer function applied to the bias vector.
|
||
|
activity_regularizer: Regularizer function applied to
|
||
|
the output of the layer (its 'activation').
|
||
|
depthwise_constraint: Constraint function applied to
|
||
|
the depthwise kernel matrix.
|
||
|
bias_constraint: Constraint function applied to the bias vector.
|
||
|
|
||
|
Input shape:
|
||
|
4D tensor with shape:
|
||
|
`[batch, channels, rows, cols]` if data_format='channels_first'
|
||
|
or 4D tensor with shape:
|
||
|
`[batch, rows, cols, channels]` if data_format='channels_last'.
|
||
|
|
||
|
Output shape:
|
||
|
4D tensor with shape:
|
||
|
`[batch, filters, new_rows, new_cols]` if data_format='channels_first'
|
||
|
or 4D tensor with shape:
|
||
|
`[batch, new_rows, new_cols, filters]` if data_format='channels_last'.
|
||
|
`rows` and `cols` values might have changed due to padding.
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
kernel_size,
|
||
|
strides=(1, 1),
|
||
|
padding='valid',
|
||
|
depth_multiplier=1,
|
||
|
data_format=None,
|
||
|
activation=None,
|
||
|
use_bias=True,
|
||
|
depthwise_initializer='glorot_uniform',
|
||
|
bias_initializer='zeros',
|
||
|
depthwise_regularizer=None,
|
||
|
bias_regularizer=None,
|
||
|
activity_regularizer=None,
|
||
|
depthwise_constraint=None,
|
||
|
bias_constraint=None,
|
||
|
**kwargs):
|
||
|
super(DepthwiseConv2D, self).__init__(
|
||
|
filters=None,
|
||
|
kernel_size=kernel_size,
|
||
|
strides=strides,
|
||
|
padding=padding,
|
||
|
data_format=data_format,
|
||
|
activation=activation,
|
||
|
use_bias=use_bias,
|
||
|
bias_regularizer=bias_regularizer,
|
||
|
activity_regularizer=activity_regularizer,
|
||
|
bias_constraint=bias_constraint,
|
||
|
**kwargs)
|
||
|
self.depth_multiplier = depth_multiplier
|
||
|
self.depthwise_initializer = initializers.get(depthwise_initializer)
|
||
|
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
|
||
|
self.depthwise_constraint = constraints.get(depthwise_constraint)
|
||
|
self.bias_initializer = initializers.get(bias_initializer)
|
||
|
|
||
|
def build(self, input_shape):
|
||
|
if len(input_shape) < 4:
|
||
|
raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. '
|
||
|
'Received input shape:', str(input_shape))
|
||
|
if self.data_format == 'channels_first':
|
||
|
channel_axis = 1
|
||
|
else:
|
||
|
channel_axis = 3
|
||
|
if input_shape[channel_axis] is None:
|
||
|
raise ValueError('The channel dimension of the inputs to '
|
||
|
'`DepthwiseConv2D` '
|
||
|
'should be defined. Found `None`.')
|
||
|
input_dim = int(input_shape[channel_axis])
|
||
|
depthwise_kernel_shape = (self.kernel_size[0],
|
||
|
self.kernel_size[1],
|
||
|
input_dim,
|
||
|
self.depth_multiplier)
|
||
|
|
||
|
self.depthwise_kernel = self.add_weight(
|
||
|
shape=depthwise_kernel_shape,
|
||
|
initializer=self.depthwise_initializer,
|
||
|
name='depthwise_kernel',
|
||
|
regularizer=self.depthwise_regularizer,
|
||
|
constraint=self.depthwise_constraint)
|
||
|
|
||
|
if self.use_bias:
|
||
|
self.bias = self.add_weight(shape=(input_dim * self.depth_multiplier,),
|
||
|
initializer=self.bias_initializer,
|
||
|
name='bias',
|
||
|
regularizer=self.bias_regularizer,
|
||
|
constraint=self.bias_constraint)
|
||
|
else:
|
||
|
self.bias = None
|
||
|
# Set input spec.
|
||
|
self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
|
||
|
self.built = True
|
||
|
|
||
|
def call(self, inputs, training=None):
|
||
|
outputs = backend.depthwise_conv2d(
|
||
|
inputs,
|
||
|
self.depthwise_kernel,
|
||
|
strides=self.strides,
|
||
|
padding=self.padding,
|
||
|
dilation_rate=self.dilation_rate,
|
||
|
data_format=self.data_format)
|
||
|
|
||
|
if self.use_bias:
|
||
|
outputs = backend.bias_add(
|
||
|
outputs,
|
||
|
self.bias,
|
||
|
data_format=self.data_format)
|
||
|
|
||
|
if self.activation is not None:
|
||
|
return self.activation(outputs)
|
||
|
|
||
|
return outputs
|
||
|
|
||
|
@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]
|
||
|
out_filters = input_shape[1] * self.depth_multiplier
|
||
|
elif self.data_format == 'channels_last':
|
||
|
rows = input_shape[1]
|
||
|
cols = input_shape[2]
|
||
|
out_filters = input_shape[3] * self.depth_multiplier
|
||
|
|
||
|
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], out_filters, rows, cols)
|
||
|
elif self.data_format == 'channels_last':
|
||
|
return (input_shape[0], rows, cols, out_filters)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = super(DepthwiseConv2D, self).get_config()
|
||
|
config.pop('filters')
|
||
|
config.pop('kernel_initializer')
|
||
|
config.pop('kernel_regularizer')
|
||
|
config.pop('kernel_constraint')
|
||
|
config['depth_multiplier'] = self.depth_multiplier
|
||
|
config['depthwise_initializer'] = initializers.serialize(
|
||
|
self.depthwise_initializer)
|
||
|
config['depthwise_regularizer'] = regularizers.serialize(
|
||
|
self.depthwise_regularizer)
|
||
|
config['depthwise_constraint'] = constraints.serialize(
|
||
|
self.depthwise_constraint)
|
||
|
return config
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.UpSampling1D')
|
||
|
class UpSampling1D(Layer):
|
||
|
"""Upsampling layer for 1D inputs.
|
||
|
|
||
|
Repeats each temporal step `size` times along the time axis.
|
||
|
|
||
|
Arguments:
|
||
|
size: integer. Upsampling factor.
|
||
|
|
||
|
Input shape:
|
||
|
3D tensor with shape: `(batch, steps, features)`.
|
||
|
|
||
|
Output shape:
|
||
|
3D tensor with shape: `(batch, upsampled_steps, features)`.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, size=2, **kwargs):
|
||
|
super(UpSampling1D, self).__init__(**kwargs)
|
||
|
self.size = int(size)
|
||
|
self.input_spec = InputSpec(ndim=3)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
size = self.size * input_shape[1] if input_shape[1] is not None else None
|
||
|
return tensor_shape.TensorShape([input_shape[0], size, input_shape[2]])
|
||
|
|
||
|
def call(self, inputs):
|
||
|
output = backend.repeat_elements(inputs, self.size, axis=1)
|
||
|
return output
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'size': self.size}
|
||
|
base_config = super(UpSampling1D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.UpSampling2D')
|
||
|
class UpSampling2D(Layer):
|
||
|
"""Upsampling layer for 2D inputs.
|
||
|
|
||
|
Repeats the rows and columns of the data
|
||
|
by size[0] and size[1] respectively.
|
||
|
|
||
|
Arguments:
|
||
|
size: int, or tuple of 2 integers.
|
||
|
The upsampling factors for rows and columns.
|
||
|
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".
|
||
|
|
||
|
Input shape:
|
||
|
4D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, rows, cols, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, rows, cols)`
|
||
|
|
||
|
Output shape:
|
||
|
4D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, upsampled_rows, upsampled_cols, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, upsampled_rows, upsampled_cols)`
|
||
|
"""
|
||
|
|
||
|
def __init__(self, size=(2, 2), data_format=None, **kwargs):
|
||
|
super(UpSampling2D, self).__init__(**kwargs)
|
||
|
self.data_format = conv_utils.normalize_data_format(data_format)
|
||
|
self.size = conv_utils.normalize_tuple(size, 2, 'size')
|
||
|
self.input_spec = InputSpec(ndim=4)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
if self.data_format == 'channels_first':
|
||
|
height = self.size[0] * input_shape[
|
||
|
2] if input_shape[2] is not None else None
|
||
|
width = self.size[1] * input_shape[
|
||
|
3] if input_shape[3] is not None else None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], input_shape[1], height, width])
|
||
|
else:
|
||
|
height = self.size[0] * input_shape[
|
||
|
1] if input_shape[1] is not None else None
|
||
|
width = self.size[1] * input_shape[
|
||
|
2] if input_shape[2] is not None else None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], height, width, input_shape[3]])
|
||
|
|
||
|
def call(self, inputs):
|
||
|
return backend.resize_images(
|
||
|
inputs, self.size[0], self.size[1], self.data_format)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'size': self.size, 'data_format': self.data_format}
|
||
|
base_config = super(UpSampling2D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.UpSampling3D')
|
||
|
class UpSampling3D(Layer):
|
||
|
"""Upsampling layer for 3D inputs.
|
||
|
|
||
|
Repeats the 1st, 2nd and 3rd dimensions
|
||
|
of the data by size[0], size[1] and size[2] respectively.
|
||
|
|
||
|
Arguments:
|
||
|
size: int, or tuple of 3 integers.
|
||
|
The upsampling factors for dim1, dim2 and dim3.
|
||
|
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, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
|
||
|
while `channels_first` corresponds to inputs with shape
|
||
|
`(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
|
||
|
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".
|
||
|
|
||
|
Input shape:
|
||
|
5D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, dim1, dim2, dim3, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, dim1, dim2, dim3)`
|
||
|
|
||
|
Output shape:
|
||
|
5D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`
|
||
|
"""
|
||
|
|
||
|
def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
|
||
|
self.data_format = conv_utils.normalize_data_format(data_format)
|
||
|
self.size = conv_utils.normalize_tuple(size, 3, 'size')
|
||
|
self.input_spec = InputSpec(ndim=5)
|
||
|
super(UpSampling3D, self).__init__(**kwargs)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
if self.data_format == 'channels_first':
|
||
|
dim1 = self.size[0] * input_shape[
|
||
|
2] if input_shape[2] is not None else None
|
||
|
dim2 = self.size[1] * input_shape[
|
||
|
3] if input_shape[3] is not None else None
|
||
|
dim3 = self.size[2] * input_shape[
|
||
|
4] if input_shape[4] is not None else None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], input_shape[1], dim1, dim2, dim3])
|
||
|
else:
|
||
|
dim1 = self.size[0] * input_shape[
|
||
|
1] if input_shape[1] is not None else None
|
||
|
dim2 = self.size[1] * input_shape[
|
||
|
2] if input_shape[2] is not None else None
|
||
|
dim3 = self.size[2] * input_shape[
|
||
|
3] if input_shape[3] is not None else None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], dim1, dim2, dim3, input_shape[4]])
|
||
|
|
||
|
def call(self, inputs):
|
||
|
return backend.resize_volumes(
|
||
|
inputs, self.size[0], self.size[1], self.size[2], self.data_format)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'size': self.size, 'data_format': self.data_format}
|
||
|
base_config = super(UpSampling3D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.ZeroPadding1D')
|
||
|
class ZeroPadding1D(Layer):
|
||
|
"""Zero-padding layer for 1D input (e.g. temporal sequence).
|
||
|
|
||
|
Arguments:
|
||
|
padding: int, or tuple of int (length 2), or dictionary.
|
||
|
- If int:
|
||
|
How many zeros to add at the beginning and end of
|
||
|
the padding dimension (axis 1).
|
||
|
- If tuple of int (length 2):
|
||
|
How many zeros to add at the beginning and at the end of
|
||
|
the padding dimension (`(left_pad, right_pad)`).
|
||
|
|
||
|
Input shape:
|
||
|
3D tensor with shape `(batch, axis_to_pad, features)`
|
||
|
|
||
|
Output shape:
|
||
|
3D tensor with shape `(batch, padded_axis, features)`
|
||
|
"""
|
||
|
|
||
|
def __init__(self, padding=1, **kwargs):
|
||
|
super(ZeroPadding1D, self).__init__(**kwargs)
|
||
|
self.padding = conv_utils.normalize_tuple(padding, 2, 'padding')
|
||
|
self.input_spec = InputSpec(ndim=3)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
if input_shape[1] is not None:
|
||
|
length = input_shape[1] + self.padding[0] + self.padding[1]
|
||
|
else:
|
||
|
length = None
|
||
|
return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
|
||
|
|
||
|
def call(self, inputs):
|
||
|
return backend.temporal_padding(inputs, padding=self.padding)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'padding': self.padding}
|
||
|
base_config = super(ZeroPadding1D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.ZeroPadding2D')
|
||
|
class ZeroPadding2D(Layer):
|
||
|
"""Zero-padding layer for 2D input (e.g. picture).
|
||
|
|
||
|
This layer can add rows and columns of zeros
|
||
|
at the top, bottom, left and right side of an image tensor.
|
||
|
|
||
|
Arguments:
|
||
|
padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
|
||
|
- If int: the same symmetric padding
|
||
|
is applied to height and width.
|
||
|
- If tuple of 2 ints:
|
||
|
interpreted as two different
|
||
|
symmetric padding values for height and width:
|
||
|
`(symmetric_height_pad, symmetric_width_pad)`.
|
||
|
- If tuple of 2 tuples of 2 ints:
|
||
|
interpreted as
|
||
|
`((top_pad, bottom_pad), (left_pad, right_pad))`
|
||
|
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".
|
||
|
|
||
|
Input shape:
|
||
|
4D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, rows, cols, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, rows, cols)`
|
||
|
|
||
|
Output shape:
|
||
|
4D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, padded_rows, padded_cols, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, padded_rows, padded_cols)`
|
||
|
"""
|
||
|
|
||
|
def __init__(self, padding=(1, 1), data_format=None, **kwargs):
|
||
|
super(ZeroPadding2D, self).__init__(**kwargs)
|
||
|
self.data_format = conv_utils.normalize_data_format(data_format)
|
||
|
if isinstance(padding, int):
|
||
|
self.padding = ((padding, padding), (padding, padding))
|
||
|
elif hasattr(padding, '__len__'):
|
||
|
if len(padding) != 2:
|
||
|
raise ValueError('`padding` should have two elements. '
|
||
|
'Found: ' + str(padding))
|
||
|
height_padding = conv_utils.normalize_tuple(padding[0], 2,
|
||
|
'1st entry of padding')
|
||
|
width_padding = conv_utils.normalize_tuple(padding[1], 2,
|
||
|
'2nd entry of padding')
|
||
|
self.padding = (height_padding, width_padding)
|
||
|
else:
|
||
|
raise ValueError('`padding` should be either an int, '
|
||
|
'a tuple of 2 ints '
|
||
|
'(symmetric_height_pad, symmetric_width_pad), '
|
||
|
'or a tuple of 2 tuples of 2 ints '
|
||
|
'((top_pad, bottom_pad), (left_pad, right_pad)). '
|
||
|
'Found: ' + str(padding))
|
||
|
self.input_spec = InputSpec(ndim=4)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
if self.data_format == 'channels_first':
|
||
|
if input_shape[2] is not None:
|
||
|
rows = input_shape[2] + self.padding[0][0] + self.padding[0][1]
|
||
|
else:
|
||
|
rows = None
|
||
|
if input_shape[3] is not None:
|
||
|
cols = input_shape[3] + self.padding[1][0] + self.padding[1][1]
|
||
|
else:
|
||
|
cols = None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], input_shape[1], rows, cols])
|
||
|
elif self.data_format == 'channels_last':
|
||
|
if input_shape[1] is not None:
|
||
|
rows = input_shape[1] + self.padding[0][0] + self.padding[0][1]
|
||
|
else:
|
||
|
rows = None
|
||
|
if input_shape[2] is not None:
|
||
|
cols = input_shape[2] + self.padding[1][0] + self.padding[1][1]
|
||
|
else:
|
||
|
cols = None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], rows, cols, input_shape[3]])
|
||
|
|
||
|
def call(self, inputs):
|
||
|
return backend.spatial_2d_padding(
|
||
|
inputs, padding=self.padding, data_format=self.data_format)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'padding': self.padding, 'data_format': self.data_format}
|
||
|
base_config = super(ZeroPadding2D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.ZeroPadding3D')
|
||
|
class ZeroPadding3D(Layer):
|
||
|
"""Zero-padding layer for 3D data (spatial or spatio-temporal).
|
||
|
|
||
|
Arguments:
|
||
|
padding: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
|
||
|
- If int: the same symmetric padding
|
||
|
is applied to height and width.
|
||
|
- If tuple of 3 ints:
|
||
|
interpreted as two different
|
||
|
symmetric padding values for height and width:
|
||
|
`(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
|
||
|
- If tuple of 3 tuples of 2 ints:
|
||
|
interpreted as
|
||
|
`((left_dim1_pad, right_dim1_pad), (left_dim2_pad,
|
||
|
right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
|
||
|
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, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
|
||
|
while `channels_first` corresponds to inputs with shape
|
||
|
`(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
|
||
|
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".
|
||
|
|
||
|
Input shape:
|
||
|
5D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad,
|
||
|
depth)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, depth, first_axis_to_pad, second_axis_to_pad,
|
||
|
third_axis_to_pad)`
|
||
|
|
||
|
Output shape:
|
||
|
5D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, first_padded_axis, second_padded_axis, third_axis_to_pad,
|
||
|
depth)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, depth, first_padded_axis, second_padded_axis,
|
||
|
third_axis_to_pad)`
|
||
|
"""
|
||
|
|
||
|
def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
|
||
|
super(ZeroPadding3D, self).__init__(**kwargs)
|
||
|
self.data_format = conv_utils.normalize_data_format(data_format)
|
||
|
if isinstance(padding, int):
|
||
|
self.padding = ((padding, padding), (padding, padding), (padding,
|
||
|
padding))
|
||
|
elif hasattr(padding, '__len__'):
|
||
|
if len(padding) != 3:
|
||
|
raise ValueError('`padding` should have 3 elements. '
|
||
|
'Found: ' + str(padding))
|
||
|
dim1_padding = conv_utils.normalize_tuple(padding[0], 2,
|
||
|
'1st entry of padding')
|
||
|
dim2_padding = conv_utils.normalize_tuple(padding[1], 2,
|
||
|
'2nd entry of padding')
|
||
|
dim3_padding = conv_utils.normalize_tuple(padding[2], 2,
|
||
|
'3rd entry of padding')
|
||
|
self.padding = (dim1_padding, dim2_padding, dim3_padding)
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
'`padding` should be either an int, '
|
||
|
'a tuple of 3 ints '
|
||
|
'(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), '
|
||
|
'or a tuple of 3 tuples of 2 ints '
|
||
|
'((left_dim1_pad, right_dim1_pad),'
|
||
|
' (left_dim2_pad, right_dim2_pad),'
|
||
|
' (left_dim3_pad, right_dim2_pad)). '
|
||
|
'Found: ' + str(padding))
|
||
|
self.input_spec = InputSpec(ndim=5)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
if self.data_format == 'channels_first':
|
||
|
if input_shape[2] is not None:
|
||
|
dim1 = input_shape[2] + 2 * self.padding[0][0]
|
||
|
else:
|
||
|
dim1 = None
|
||
|
if input_shape[3] is not None:
|
||
|
dim2 = input_shape[3] + 2 * self.padding[1][0]
|
||
|
else:
|
||
|
dim2 = None
|
||
|
if input_shape[4] is not None:
|
||
|
dim3 = input_shape[4] + 2 * self.padding[2][0]
|
||
|
else:
|
||
|
dim3 = None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], input_shape[1], dim1, dim2, dim3])
|
||
|
elif self.data_format == 'channels_last':
|
||
|
if input_shape[1] is not None:
|
||
|
dim1 = input_shape[1] + 2 * self.padding[0][1]
|
||
|
else:
|
||
|
dim1 = None
|
||
|
if input_shape[2] is not None:
|
||
|
dim2 = input_shape[2] + 2 * self.padding[1][1]
|
||
|
else:
|
||
|
dim2 = None
|
||
|
if input_shape[3] is not None:
|
||
|
dim3 = input_shape[3] + 2 * self.padding[2][1]
|
||
|
else:
|
||
|
dim3 = None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], dim1, dim2, dim3, input_shape[4]])
|
||
|
|
||
|
def call(self, inputs):
|
||
|
return backend.spatial_3d_padding(
|
||
|
inputs, padding=self.padding, data_format=self.data_format)
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'padding': self.padding, 'data_format': self.data_format}
|
||
|
base_config = super(ZeroPadding3D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.Cropping1D')
|
||
|
class Cropping1D(Layer):
|
||
|
"""Cropping layer for 1D input (e.g. temporal sequence).
|
||
|
|
||
|
It crops along the time dimension (axis 1).
|
||
|
|
||
|
Arguments:
|
||
|
cropping: int or tuple of int (length 2)
|
||
|
How many units should be trimmed off at the beginning and end of
|
||
|
the cropping dimension (axis 1).
|
||
|
If a single int is provided,
|
||
|
the same value will be used for both.
|
||
|
|
||
|
Input shape:
|
||
|
3D tensor with shape `(batch, axis_to_crop, features)`
|
||
|
|
||
|
Output shape:
|
||
|
3D tensor with shape `(batch, cropped_axis, features)`
|
||
|
"""
|
||
|
|
||
|
def __init__(self, cropping=(1, 1), **kwargs):
|
||
|
super(Cropping1D, self).__init__(**kwargs)
|
||
|
self.cropping = conv_utils.normalize_tuple(cropping, 2, 'cropping')
|
||
|
self.input_spec = InputSpec(ndim=3)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
if input_shape[1] is not None:
|
||
|
length = input_shape[1] - self.cropping[0] - self.cropping[1]
|
||
|
else:
|
||
|
length = None
|
||
|
return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
|
||
|
|
||
|
def call(self, inputs):
|
||
|
if self.cropping[1] == 0:
|
||
|
return inputs[:, self.cropping[0]:, :]
|
||
|
else:
|
||
|
return inputs[:, self.cropping[0]:-self.cropping[1], :]
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'cropping': self.cropping}
|
||
|
base_config = super(Cropping1D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.Cropping2D')
|
||
|
class Cropping2D(Layer):
|
||
|
"""Cropping layer for 2D input (e.g. picture).
|
||
|
|
||
|
It crops along spatial dimensions, i.e. height and width.
|
||
|
|
||
|
Arguments:
|
||
|
cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
|
||
|
- If int: the same symmetric cropping
|
||
|
is applied to height and width.
|
||
|
- If tuple of 2 ints:
|
||
|
interpreted as two different
|
||
|
symmetric cropping values for height and width:
|
||
|
`(symmetric_height_crop, symmetric_width_crop)`.
|
||
|
- If tuple of 2 tuples of 2 ints:
|
||
|
interpreted as
|
||
|
`((top_crop, bottom_crop), (left_crop, right_crop))`
|
||
|
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".
|
||
|
|
||
|
Input shape:
|
||
|
4D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, rows, cols, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, rows, cols)`
|
||
|
|
||
|
Output shape:
|
||
|
4D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, cropped_rows, cropped_cols, channels)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, channels, cropped_rows, cropped_cols)`
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
```python
|
||
|
# Crop the input 2D images or feature maps
|
||
|
model = Sequential()
|
||
|
model.add(Cropping2D(cropping=((2, 2), (4, 4)),
|
||
|
input_shape=(28, 28, 3)))
|
||
|
# now model.output_shape == (None, 24, 20, 3)
|
||
|
model.add(Conv2D(64, (3, 3), padding='same))
|
||
|
model.add(Cropping2D(cropping=((2, 2), (2, 2))))
|
||
|
# now model.output_shape == (None, 20, 16. 64)
|
||
|
```
|
||
|
"""
|
||
|
|
||
|
def __init__(self, cropping=((0, 0), (0, 0)), data_format=None, **kwargs):
|
||
|
super(Cropping2D, self).__init__(**kwargs)
|
||
|
self.data_format = conv_utils.normalize_data_format(data_format)
|
||
|
if isinstance(cropping, int):
|
||
|
self.cropping = ((cropping, cropping), (cropping, cropping))
|
||
|
elif hasattr(cropping, '__len__'):
|
||
|
if len(cropping) != 2:
|
||
|
raise ValueError('`cropping` should have two elements. '
|
||
|
'Found: ' + str(cropping))
|
||
|
height_cropping = conv_utils.normalize_tuple(cropping[0], 2,
|
||
|
'1st entry of cropping')
|
||
|
width_cropping = conv_utils.normalize_tuple(cropping[1], 2,
|
||
|
'2nd entry of cropping')
|
||
|
self.cropping = (height_cropping, width_cropping)
|
||
|
else:
|
||
|
raise ValueError('`cropping` should be either an int, '
|
||
|
'a tuple of 2 ints '
|
||
|
'(symmetric_height_crop, symmetric_width_crop), '
|
||
|
'or a tuple of 2 tuples of 2 ints '
|
||
|
'((top_crop, bottom_crop), (left_crop, right_crop)). '
|
||
|
'Found: ' + str(cropping))
|
||
|
self.input_spec = InputSpec(ndim=4)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
# pylint: disable=invalid-unary-operand-type
|
||
|
if self.data_format == 'channels_first':
|
||
|
return tensor_shape.TensorShape([
|
||
|
input_shape[0], input_shape[1],
|
||
|
input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
|
||
|
if input_shape[2] else None,
|
||
|
input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
|
||
|
if input_shape[3] else None
|
||
|
])
|
||
|
else:
|
||
|
return tensor_shape.TensorShape([
|
||
|
input_shape[0],
|
||
|
input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
|
||
|
if input_shape[1] else None,
|
||
|
input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
|
||
|
if input_shape[2] else None, input_shape[3]
|
||
|
])
|
||
|
# pylint: enable=invalid-unary-operand-type
|
||
|
|
||
|
def call(self, inputs):
|
||
|
# pylint: disable=invalid-unary-operand-type
|
||
|
if self.data_format == 'channels_first':
|
||
|
if self.cropping[0][1] == self.cropping[1][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:]
|
||
|
elif self.cropping[0][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
|
||
|
-self.cropping[1][1]]
|
||
|
elif self.cropping[1][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
|
||
|
self.cropping[1][0]:]
|
||
|
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
|
||
|
self.cropping[1][0]:-self.cropping[1][1]]
|
||
|
else:
|
||
|
if self.cropping[0][1] == self.cropping[1][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:, :]
|
||
|
elif self.cropping[0][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
|
||
|
-self.cropping[1][1], :]
|
||
|
elif self.cropping[1][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
|
||
|
self.cropping[1][0]:, :]
|
||
|
return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
|
||
|
1][0]:-self.cropping[1][1], :] # pylint: disable=invalid-unary-operand-type
|
||
|
# pylint: enable=invalid-unary-operand-type
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'cropping': self.cropping, 'data_format': self.data_format}
|
||
|
base_config = super(Cropping2D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
@tf_export('keras.layers.Cropping3D')
|
||
|
class Cropping3D(Layer):
|
||
|
"""Cropping layer for 3D data (e.g.
|
||
|
|
||
|
spatial or spatio-temporal).
|
||
|
|
||
|
Arguments:
|
||
|
cropping: int, or tuple of 23ints, or tuple of 3 tuples of 2 ints.
|
||
|
- If int: the same symmetric cropping
|
||
|
is applied to depth, height, and width.
|
||
|
- If tuple of 3 ints:
|
||
|
interpreted as two different
|
||
|
symmetric cropping values for depth, height, and width:
|
||
|
`(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
|
||
|
- If tuple of 3 tuples of 2 ints:
|
||
|
interpreted as
|
||
|
`((left_dim1_crop, right_dim1_crop), (left_dim2_crop,
|
||
|
right_dim2_crop), (left_dim3_crop, right_dim3_crop))`
|
||
|
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, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
|
||
|
while `channels_first` corresponds to inputs with shape
|
||
|
`(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
|
||
|
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".
|
||
|
|
||
|
Input shape:
|
||
|
5D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop,
|
||
|
depth)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, depth, first_axis_to_crop, second_axis_to_crop,
|
||
|
third_axis_to_crop)`
|
||
|
|
||
|
Output shape:
|
||
|
5D tensor with shape:
|
||
|
- If `data_format` is `"channels_last"`:
|
||
|
`(batch, first_cropped_axis, second_cropped_axis, third_cropped_axis,
|
||
|
depth)`
|
||
|
- If `data_format` is `"channels_first"`:
|
||
|
`(batch, depth, first_cropped_axis, second_cropped_axis,
|
||
|
third_cropped_axis)`
|
||
|
"""
|
||
|
|
||
|
def __init__(self,
|
||
|
cropping=((1, 1), (1, 1), (1, 1)),
|
||
|
data_format=None,
|
||
|
**kwargs):
|
||
|
super(Cropping3D, self).__init__(**kwargs)
|
||
|
self.data_format = conv_utils.normalize_data_format(data_format)
|
||
|
if isinstance(cropping, int):
|
||
|
self.cropping = ((cropping, cropping), (cropping, cropping), (cropping,
|
||
|
cropping))
|
||
|
elif hasattr(cropping, '__len__'):
|
||
|
if len(cropping) != 3:
|
||
|
raise ValueError('`cropping` should have 3 elements. '
|
||
|
'Found: ' + str(cropping))
|
||
|
dim1_cropping = conv_utils.normalize_tuple(cropping[0], 2,
|
||
|
'1st entry of cropping')
|
||
|
dim2_cropping = conv_utils.normalize_tuple(cropping[1], 2,
|
||
|
'2nd entry of cropping')
|
||
|
dim3_cropping = conv_utils.normalize_tuple(cropping[2], 2,
|
||
|
'3rd entry of cropping')
|
||
|
self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping)
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
'`cropping` should be either an int, '
|
||
|
'a tuple of 3 ints '
|
||
|
'(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop), '
|
||
|
'or a tuple of 3 tuples of 2 ints '
|
||
|
'((left_dim1_crop, right_dim1_crop),'
|
||
|
' (left_dim2_crop, right_dim2_crop),'
|
||
|
' (left_dim3_crop, right_dim2_crop)). '
|
||
|
'Found: ' + str(cropping))
|
||
|
self.input_spec = InputSpec(ndim=5)
|
||
|
|
||
|
def compute_output_shape(self, input_shape):
|
||
|
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||
|
# pylint: disable=invalid-unary-operand-type
|
||
|
if self.data_format == 'channels_first':
|
||
|
if input_shape[2] is not None:
|
||
|
dim1 = input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
|
||
|
else:
|
||
|
dim1 = None
|
||
|
if input_shape[3] is not None:
|
||
|
dim2 = input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
|
||
|
else:
|
||
|
dim2 = None
|
||
|
if input_shape[4] is not None:
|
||
|
dim3 = input_shape[4] - self.cropping[2][0] - self.cropping[2][1]
|
||
|
else:
|
||
|
dim3 = None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], input_shape[1], dim1, dim2, dim3])
|
||
|
elif self.data_format == 'channels_last':
|
||
|
if input_shape[1] is not None:
|
||
|
dim1 = input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
|
||
|
else:
|
||
|
dim1 = None
|
||
|
if input_shape[2] is not None:
|
||
|
dim2 = input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
|
||
|
else:
|
||
|
dim2 = None
|
||
|
if input_shape[3] is not None:
|
||
|
dim3 = input_shape[3] - self.cropping[2][0] - self.cropping[2][1]
|
||
|
else:
|
||
|
dim3 = None
|
||
|
return tensor_shape.TensorShape(
|
||
|
[input_shape[0], dim1, dim2, dim3, input_shape[4]])
|
||
|
# pylint: enable=invalid-unary-operand-type
|
||
|
|
||
|
def call(self, inputs):
|
||
|
# pylint: disable=invalid-unary-operand-type
|
||
|
if self.data_format == 'channels_first':
|
||
|
if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
|
||
|
self.cropping[2][0]:]
|
||
|
elif self.cropping[0][1] == self.cropping[1][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
|
||
|
self.cropping[2][0]:-self.cropping[2][1]]
|
||
|
elif self.cropping[1][1] == self.cropping[2][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
|
||
|
self.cropping[1][0]:, self.cropping[2][0]:]
|
||
|
elif self.cropping[0][1] == self.cropping[2][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
|
||
|
-self.cropping[1][1], self.cropping[2][0]:]
|
||
|
elif self.cropping[0][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:, self.cropping[1][
|
||
|
0]:-self.cropping[1][1], self.cropping[2][0]:-self.cropping[2][1]]
|
||
|
elif self.cropping[1][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
|
||
|
cropping[1][0]:, self.cropping[2][0]:-self.cropping[2][1]]
|
||
|
elif self.cropping[2][1] == 0:
|
||
|
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
|
||
|
cropping[1][0]:-self.cropping[1][1], self.cropping[2][0]:]
|
||
|
return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
|
||
|
self.cropping[1][0]:-self.cropping[1][1], self.cropping[2][
|
||
|
0]:-self.cropping[2][1]]
|
||
|
else:
|
||
|
if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
|
||
|
self.cropping[2][0]:, :]
|
||
|
elif self.cropping[0][1] == self.cropping[1][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
|
||
|
self.cropping[2][0]:-self.cropping[2][1], :]
|
||
|
elif self.cropping[1][1] == self.cropping[2][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
|
||
|
self.cropping[1][0]:, self.cropping[2][0]:, :]
|
||
|
elif self.cropping[0][1] == self.cropping[2][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
|
||
|
-self.cropping[1][1], self.cropping[2][0]:, :]
|
||
|
elif self.cropping[0][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:, self.cropping[1][
|
||
|
0]:-self.cropping[1][1], self.cropping[2][0]:
|
||
|
-self.cropping[2][1], :]
|
||
|
elif self.cropping[1][1] == 0:
|
||
|
return inputs[:, self.cropping[0][
|
||
|
0]:-self.cropping[0][1], self.cropping[1][0]:, self.cropping[2][0]:
|
||
|
-self.cropping[2][1], :]
|
||
|
elif self.cropping[2][1] == 0:
|
||
|
return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
|
||
|
self.cropping[1][0]:-self.cropping[1][1], self.cropping[
|
||
|
2][0]:, :]
|
||
|
return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
|
||
|
1][0]:-self.cropping[1][1], self.cropping[2][0]: # pylint: disable=invalid-unary-operand-type
|
||
|
-self.cropping[2][1], :] # pylint: disable=invalid-unary-operand-type
|
||
|
# pylint: enable=invalid-unary-operand-type
|
||
|
|
||
|
def get_config(self):
|
||
|
config = {'cropping': self.cropping, 'data_format': self.data_format}
|
||
|
base_config = super(Cropping3D, self).get_config()
|
||
|
return dict(list(base_config.items()) + list(config.items()))
|
||
|
|
||
|
|
||
|
# Aliases
|
||
|
|
||
|
Convolution1D = Conv1D
|
||
|
Convolution2D = Conv2D
|
||
|
Convolution3D = Conv3D
|
||
|
SeparableConvolution1D = SeparableConv1D
|
||
|
SeparableConvolution2D = SeparableConv2D
|
||
|
Convolution2DTranspose = Conv2DTranspose
|
||
|
Convolution3DTranspose = Conv3DTranspose
|
||
|
Deconvolution2D = Deconv2D = Conv2DTranspose
|
||
|
Deconvolution3D = Deconv3D = Conv3DTranspose
|