# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Keras convolution layers and image transformation layers. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.eager import context from tensorflow.python.framework import tensor_shape from tensorflow.python.keras import activations from tensorflow.python.keras import backend from tensorflow.python.keras import constraints from tensorflow.python.keras import initializers from tensorflow.python.keras import regularizers from tensorflow.python.keras.engine.base_layer import InputSpec from tensorflow.python.keras.engine.base_layer import Layer # imports for backwards namespace compatibility # pylint: disable=unused-import from tensorflow.python.keras.layers.pooling import AveragePooling1D from tensorflow.python.keras.layers.pooling import AveragePooling2D from tensorflow.python.keras.layers.pooling import AveragePooling3D from tensorflow.python.keras.layers.pooling import MaxPooling1D from tensorflow.python.keras.layers.pooling import MaxPooling2D from tensorflow.python.keras.layers.pooling import MaxPooling3D # pylint: enable=unused-import from tensorflow.python.keras.utils import conv_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn from tensorflow.python.ops import nn_ops from tensorflow.python.util.tf_export import tf_export class Conv(Layer): """Abstract nD convolution layer (private, used as implementation base). This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If `use_bias` is True (and a `bias_initializer` is provided), a bias vector is created and added to the outputs. Finally, if `activation` is not `None`, it is applied to the outputs as well. 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: An integer or tuple/list of n integers, specifying the length of the convolution window. strides: An integer or tuple/list of n integers, specifying the stride length 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, ..., channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, ...)`. dilation_rate: An integer or tuple/list of n 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. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. kernel_initializer: An initializer for the convolution kernel. bias_initializer: An initializer for the bias vector. If None, the default initializer will be used. kernel_regularizer: Optional regularizer for the convolution kernel. bias_regularizer: Optional regularizer for the bias vector. activity_regularizer: Optional regularizer function for the output. kernel_constraint: Optional projection function to be applied to the kernel after being updated by an `Optimizer` (e.g. used to implement 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. 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, 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, trainable=True, name=None, **kwargs): super(Conv, self).__init__( trainable=trainable, name=name, activity_regularizer=regularizers.get(activity_regularizer), **kwargs) self.rank = rank self.filters = filters self.kernel_size = conv_utils.normalize_tuple( kernel_size, rank, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, rank, 'strides') self.padding = conv_utils.normalize_padding(padding) self.data_format = conv_utils.normalize_data_format(data_format) self.dilation_rate = conv_utils.normalize_tuple( dilation_rate, rank, 'dilation_rate') self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.input_spec = InputSpec(ndim=self.rank + 2) 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]) kernel_shape = self.kernel_size + (input_dim, self.filters) 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.input_spec = InputSpec(ndim=self.rank + 2, axes={channel_axis: input_dim}) self._convolution_op = nn_ops.Convolution( input_shape, filter_shape=self.kernel.get_shape(), dilation_rate=self.dilation_rate, strides=self.strides, padding=self.padding.upper(), data_format=conv_utils.convert_data_format(self.data_format, self.rank + 2)) self.built = True def call(self, inputs): outputs = self._convolution_op(inputs, self.kernel) if self.use_bias: if self.data_format == 'channels_first': if self.rank == 1: # nn.bias_add does not accept a 1D input tensor. bias = array_ops.reshape(self.bias, (1, self.filters, 1)) outputs += bias if self.rank == 2: outputs = nn.bias_add(outputs, self.bias, data_format='NCHW') if self.rank == 3: # As of Mar 2017, direct addition is significantly slower than # bias_add when computing gradients. To use bias_add, we collapse Z # and Y into a single dimension to obtain a 4D input tensor. outputs_shape = outputs.shape.as_list() if outputs_shape[0] is None: outputs_shape[0] = -1 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='NCHW') outputs = array_ops.reshape(outputs_4d, outputs_shape) else: outputs = nn.bias_add(outputs, self.bias, data_format='NHWC') 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() if self.data_format == 'channels_last': space = input_shape[1:-1] new_space = [] for i in range(len(space)): new_dim = conv_utils.conv_output_length( space[i], self.kernel_size[i], padding=self.padding, stride=self.strides[i], dilation=self.dilation_rate[i]) new_space.append(new_dim) return tensor_shape.TensorShape([input_shape[0]] + new_space + [self.filters]) else: space = input_shape[2:] new_space = [] for i in range(len(space)): new_dim = conv_utils.conv_output_length( space[i], self.kernel_size[i], padding=self.padding, stride=self.strides[i], dilation=self.dilation_rate[i]) new_space.append(new_dim) return tensor_shape.TensorShape([input_shape[0], self.filters] + new_space) 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, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint) } base_config = super(Conv, self).get_config() return dict(list(base_config.items()) + list(config.items())) @tf_export('keras.layers.Conv1D', 'keras.layers.Convolution1D') class Conv1D(Conv): """1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension 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 an `input_shape` argument (tuple of integers or `None`, e.g. `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors, or `(None, 128)` for variable-length sequences of 128-dimensional vectors. 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 a single integer, specifying the length of the 1D convolution window. strides: An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: One of `"valid"`, `"causal"` or `"same"` (case-insensitive). `"causal"` results in causal (dilated) convolutions, e.g. output[t] does not depend on input[t+1:]. Useful when modeling temporal data where the model should not violate the temporal order. See [WaveNet: A Generative Model for Raw Audio, section 2.1](https://arxiv.org/abs/1609.03499). data_format: A string, one of `channels_last` (default) or `channels_first`. dilation_rate: an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` 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: 3D tensor with shape: `(batch_size, steps, input_dim)` Output shape: 3D tensor with shape: `(batch_size, new_steps, filters)` `steps` value might have changed due to padding or strides. """ def __init__(self, filters, kernel_size, strides=1, padding='valid', data_format='channels_last', dilation_rate=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(Conv1D, self).__init__( rank=1, 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.Conv2D', 'keras.layers.Convolution2D') class Conv2D(Conv): """2D convolution layer (e.g. spatial convolution over images). 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, 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: `(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