1456 lines
64 KiB
Python
1456 lines
64 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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# pylint: disable=unused-import,g-bad-import-order
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"""Contains the convolutional layer classes and their functional aliases.
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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 ops
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.keras import layers as keras_layers
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from tensorflow.python.layers import base
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from tensorflow.python.layers import utils
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import init_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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@tf_export('layers.Conv1D')
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class Conv1D(keras_layers.Conv1D, base.Layer):
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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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(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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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 a single integer, specifying the
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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"` 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, length, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, length)`.
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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. 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, 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=None,
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bias_initializer=init_ops.zeros_initializer(),
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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(Conv1D, self).__init__(
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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=activation,
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use_bias=use_bias,
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kernel_initializer=kernel_initializer,
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bias_initializer=bias_initializer,
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kernel_regularizer=kernel_regularizer,
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bias_regularizer=bias_regularizer,
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activity_regularizer=activity_regularizer,
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kernel_constraint=kernel_constraint,
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bias_constraint=bias_constraint,
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trainable=trainable,
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name=name, **kwargs)
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@tf_export('layers.conv1d')
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def conv1d(inputs,
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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=None,
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bias_initializer=init_ops.zeros_initializer(),
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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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reuse=None):
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"""Functional interface for 1D convolution layer (e.g. temporal convolution).
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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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inputs: Tensor input.
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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 a single integer, specifying the
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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"` 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, length, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, length)`.
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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. 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
|
||
|
|
norm constraints or value constraints for layer weights). The function
|
||
|
|
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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reuse: Boolean, whether to reuse the weights of a previous layer
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by the same name.
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Returns:
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Output tensor.
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Raises:
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ValueError: if eager execution is enabled.
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"""
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layer = Conv1D(
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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=activation,
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use_bias=use_bias,
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kernel_initializer=kernel_initializer,
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bias_initializer=bias_initializer,
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kernel_regularizer=kernel_regularizer,
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bias_regularizer=bias_regularizer,
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activity_regularizer=activity_regularizer,
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kernel_constraint=kernel_constraint,
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bias_constraint=bias_constraint,
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trainable=trainable,
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name=name,
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_reuse=reuse,
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_scope=name)
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return layer.apply(inputs)
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@tf_export('layers.Conv2D')
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class Conv2D(keras_layers.Conv2D, base.Layer):
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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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(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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|
|
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Arguments:
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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 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
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all spatial dimensions.
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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`.
|
||
|
|
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)`.
|
||
|
|
|
||
|
|
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. 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.
|
||
|
|
"""
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||
|
|
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||
|
|
def __init__(self, filters,
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kernel_size,
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strides=(1, 1),
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|
padding='valid',
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data_format='channels_last',
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dilation_rate=(1, 1),
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activation=None,
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use_bias=True,
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kernel_initializer=None,
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bias_initializer=init_ops.zeros_initializer(),
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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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||
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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(Conv2D, self).__init__(
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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=activation,
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use_bias=use_bias,
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kernel_initializer=kernel_initializer,
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bias_initializer=bias_initializer,
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kernel_regularizer=kernel_regularizer,
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bias_regularizer=bias_regularizer,
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activity_regularizer=activity_regularizer,
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kernel_constraint=kernel_constraint,
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bias_constraint=bias_constraint,
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trainable=trainable,
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name=name, **kwargs)
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||
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@tf_export('layers.conv2d')
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||
|
|
def conv2d(inputs,
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||
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|
filters,
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||
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|
kernel_size,
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||
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|
strides=(1, 1),
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||
|
|
padding='valid',
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||
|
|
data_format='channels_last',
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||
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dilation_rate=(1, 1),
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activation=None,
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||
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use_bias=True,
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||
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|
kernel_initializer=None,
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||
|
|
bias_initializer=init_ops.zeros_initializer(),
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||
|
|
kernel_regularizer=None,
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||
|
|
bias_regularizer=None,
|
||
|
|
activity_regularizer=None,
|
||
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kernel_constraint=None,
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||
|
|
bias_constraint=None,
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||
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trainable=True,
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||
|
|
name=None,
|
||
|
|
reuse=None):
|
||
|
|
"""Functional interface for the 2D convolution layer.
|
||
|
|
|
||
|
|
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:
|
||
|
|
inputs: Tensor input.
|
||
|
|
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 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)`.
|
||
|
|
|
||
|
|
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. 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.
|
||
|
|
reuse: Boolean, whether to reuse the weights of a previous layer
|
||
|
|
by the same name.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
"""
|
||
|
|
layer = Conv2D(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
dilation_rate=dilation_rate,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
kernel_initializer=kernel_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
kernel_regularizer=kernel_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
kernel_constraint=kernel_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
_reuse=reuse,
|
||
|
|
_scope=name)
|
||
|
|
return layer.apply(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.Conv3D')
|
||
|
|
class Conv3D(keras_layers.Conv3D, base.Layer):
|
||
|
|
"""3D convolution layer (e.g. spatial convolution over volumes).
|
||
|
|
|
||
|
|
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:
|
||
|
|
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 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)`.
|
||
|
|
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. 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, filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
dilation_rate=(1, 1, 1),
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
kernel_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
kernel_regularizer=None,
|
||
|
|
bias_regularizer=None,
|
||
|
|
activity_regularizer=None,
|
||
|
|
kernel_constraint=None,
|
||
|
|
bias_constraint=None,
|
||
|
|
trainable=True,
|
||
|
|
name=None,
|
||
|
|
**kwargs):
|
||
|
|
super(Conv3D, self).__init__(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
dilation_rate=dilation_rate,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
kernel_initializer=kernel_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
kernel_regularizer=kernel_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
kernel_constraint=kernel_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.conv3d')
|
||
|
|
def conv3d(inputs,
|
||
|
|
filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
dilation_rate=(1, 1, 1),
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
kernel_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
kernel_regularizer=None,
|
||
|
|
bias_regularizer=None,
|
||
|
|
activity_regularizer=None,
|
||
|
|
kernel_constraint=None,
|
||
|
|
bias_constraint=None,
|
||
|
|
trainable=True,
|
||
|
|
name=None,
|
||
|
|
reuse=None):
|
||
|
|
"""Functional interface for the 3D convolution layer.
|
||
|
|
|
||
|
|
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:
|
||
|
|
inputs: Tensor input.
|
||
|
|
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 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)`.
|
||
|
|
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. 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.
|
||
|
|
reuse: Boolean, whether to reuse the weights of a previous layer
|
||
|
|
by the same name.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
"""
|
||
|
|
layer = Conv3D(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
dilation_rate=dilation_rate,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
kernel_initializer=kernel_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
kernel_regularizer=kernel_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
kernel_constraint=kernel_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
_reuse=reuse,
|
||
|
|
_scope=name)
|
||
|
|
return layer.apply(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.SeparableConv1D')
|
||
|
|
class SeparableConv1D(keras_layers.SeparableConv1D, base.Layer):
|
||
|
|
"""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='channels_last',
|
||
|
|
dilation_rate=1,
|
||
|
|
depth_multiplier=1,
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
depthwise_initializer=None,
|
||
|
|
pointwise_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
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(SeparableConv1D, self).__init__(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
dilation_rate=dilation_rate,
|
||
|
|
depth_multiplier=depth_multiplier,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
depthwise_initializer=depthwise_initializer,
|
||
|
|
pointwise_initializer=pointwise_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
depthwise_regularizer=depthwise_regularizer,
|
||
|
|
pointwise_regularizer=pointwise_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
depthwise_constraint=depthwise_constraint,
|
||
|
|
pointwise_constraint=pointwise_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.SeparableConv2D')
|
||
|
|
class SeparableConv2D(keras_layers.SeparableConv2D, base.Layer):
|
||
|
|
"""Depthwise separable 2D 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 tuple or list of 2 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 2 positive 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, height, width, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, height, width)`.
|
||
|
|
|
||
|
|
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, filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
dilation_rate=(1, 1),
|
||
|
|
depth_multiplier=1,
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
depthwise_initializer=None,
|
||
|
|
pointwise_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
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(SeparableConv2D, self).__init__(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
dilation_rate=dilation_rate,
|
||
|
|
depth_multiplier=depth_multiplier,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
depthwise_initializer=depthwise_initializer,
|
||
|
|
pointwise_initializer=pointwise_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
depthwise_regularizer=depthwise_regularizer,
|
||
|
|
pointwise_regularizer=pointwise_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
depthwise_constraint=depthwise_constraint,
|
||
|
|
pointwise_constraint=pointwise_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.separable_conv1d')
|
||
|
|
def separable_conv1d(inputs,
|
||
|
|
filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=1,
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
dilation_rate=1,
|
||
|
|
depth_multiplier=1,
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
depthwise_initializer=None,
|
||
|
|
pointwise_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
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,
|
||
|
|
reuse=None):
|
||
|
|
"""Functional interface for the depthwise separable 1D convolution layer.
|
||
|
|
|
||
|
|
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:
|
||
|
|
inputs: Input tensor.
|
||
|
|
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.
|
||
|
|
reuse: Boolean, whether to reuse the weights of a previous layer
|
||
|
|
by the same name.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
"""
|
||
|
|
layer = SeparableConv1D(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
dilation_rate=dilation_rate,
|
||
|
|
depth_multiplier=depth_multiplier,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
depthwise_initializer=depthwise_initializer,
|
||
|
|
pointwise_initializer=pointwise_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
depthwise_regularizer=depthwise_regularizer,
|
||
|
|
pointwise_regularizer=pointwise_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
depthwise_constraint=depthwise_constraint,
|
||
|
|
pointwise_constraint=pointwise_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
_reuse=reuse,
|
||
|
|
_scope=name)
|
||
|
|
return layer.apply(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.separable_conv2d')
|
||
|
|
def separable_conv2d(inputs,
|
||
|
|
filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
dilation_rate=(1, 1),
|
||
|
|
depth_multiplier=1,
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
depthwise_initializer=None,
|
||
|
|
pointwise_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
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,
|
||
|
|
reuse=None):
|
||
|
|
"""Functional interface for the depthwise separable 2D convolution layer.
|
||
|
|
|
||
|
|
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:
|
||
|
|
inputs: Input tensor.
|
||
|
|
filters: Integer, the dimensionality of the output space (i.e. the number
|
||
|
|
of filters in the convolution).
|
||
|
|
kernel_size: A tuple or list of 2 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 2 positive 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, height, width, channels)` while `channels_first` corresponds to
|
||
|
|
inputs with shape `(batch, channels, height, width)`.
|
||
|
|
|
||
|
|
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.
|
||
|
|
reuse: Boolean, whether to reuse the weights of a previous layer
|
||
|
|
by the same name.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
"""
|
||
|
|
layer = SeparableConv2D(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
dilation_rate=dilation_rate,
|
||
|
|
depth_multiplier=depth_multiplier,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
depthwise_initializer=depthwise_initializer,
|
||
|
|
pointwise_initializer=pointwise_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
depthwise_regularizer=depthwise_regularizer,
|
||
|
|
pointwise_regularizer=pointwise_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
depthwise_constraint=depthwise_constraint,
|
||
|
|
pointwise_constraint=pointwise_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
_reuse=reuse,
|
||
|
|
_scope=name)
|
||
|
|
return layer.apply(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.Conv2DTranspose')
|
||
|
|
class Conv2DTranspose(keras_layers.Conv2DTranspose, base.Layer):
|
||
|
|
"""Transposed 2D convolution layer (sometimes called 2D 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.
|
||
|
|
|
||
|
|
Arguments:
|
||
|
|
filters: Integer, the dimensionality of the output space (i.e. the number
|
||
|
|
of filters in the convolution).
|
||
|
|
kernel_size: A tuple or list of 2 positive 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 2 positive integers specifying the strides
|
||
|
|
of the convolution. Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
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)`.
|
||
|
|
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, filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
kernel_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
kernel_regularizer=None,
|
||
|
|
bias_regularizer=None,
|
||
|
|
activity_regularizer=None,
|
||
|
|
kernel_constraint=None,
|
||
|
|
bias_constraint=None,
|
||
|
|
trainable=True,
|
||
|
|
name=None,
|
||
|
|
**kwargs):
|
||
|
|
super(Conv2DTranspose, self).__init__(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
kernel_initializer=kernel_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
kernel_regularizer=kernel_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
kernel_constraint=kernel_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.conv2d_transpose')
|
||
|
|
def conv2d_transpose(inputs,
|
||
|
|
filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
kernel_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
kernel_regularizer=None,
|
||
|
|
bias_regularizer=None,
|
||
|
|
activity_regularizer=None,
|
||
|
|
kernel_constraint=None,
|
||
|
|
bias_constraint=None,
|
||
|
|
trainable=True,
|
||
|
|
name=None,
|
||
|
|
reuse=None):
|
||
|
|
"""Functional interface for transposed 2D convolution layer.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
Arguments:
|
||
|
|
inputs: Input tensor.
|
||
|
|
filters: Integer, the dimensionality of the output space (i.e. the number
|
||
|
|
of filters in the convolution).
|
||
|
|
kernel_size: A tuple or list of 2 positive 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 2 positive integers specifying the strides
|
||
|
|
of the convolution. Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
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)`.
|
||
|
|
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.
|
||
|
|
reuse: Boolean, whether to reuse the weights of a previous layer
|
||
|
|
by the same name.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
"""
|
||
|
|
layer = Conv2DTranspose(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
kernel_initializer=kernel_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
kernel_regularizer=kernel_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
kernel_constraint=kernel_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
_reuse=reuse,
|
||
|
|
_scope=name)
|
||
|
|
return layer.apply(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.Conv3DTranspose')
|
||
|
|
class Conv3DTranspose(keras_layers.Conv3DTranspose, base.Layer):
|
||
|
|
"""Transposed 3D convolution layer (sometimes called 3D Deconvolution).
|
||
|
|
|
||
|
|
Arguments:
|
||
|
|
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 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.
|
||
|
|
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)`.
|
||
|
|
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,
|
||
|
|
filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
kernel_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
kernel_regularizer=None,
|
||
|
|
bias_regularizer=None,
|
||
|
|
activity_regularizer=None,
|
||
|
|
kernel_constraint=None,
|
||
|
|
bias_constraint=None,
|
||
|
|
trainable=True,
|
||
|
|
name=None,
|
||
|
|
**kwargs):
|
||
|
|
super(Conv3DTranspose, self).__init__(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
kernel_initializer=kernel_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
kernel_regularizer=kernel_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
kernel_constraint=kernel_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export('layers.conv3d_transpose')
|
||
|
|
def conv3d_transpose(inputs,
|
||
|
|
filters,
|
||
|
|
kernel_size,
|
||
|
|
strides=(1, 1, 1),
|
||
|
|
padding='valid',
|
||
|
|
data_format='channels_last',
|
||
|
|
activation=None,
|
||
|
|
use_bias=True,
|
||
|
|
kernel_initializer=None,
|
||
|
|
bias_initializer=init_ops.zeros_initializer(),
|
||
|
|
kernel_regularizer=None,
|
||
|
|
bias_regularizer=None,
|
||
|
|
activity_regularizer=None,
|
||
|
|
kernel_constraint=None,
|
||
|
|
bias_constraint=None,
|
||
|
|
trainable=True,
|
||
|
|
name=None,
|
||
|
|
reuse=None):
|
||
|
|
"""Functional interface for transposed 3D convolution layer.
|
||
|
|
|
||
|
|
Arguments:
|
||
|
|
inputs: Input tensor.
|
||
|
|
filters: Integer, the dimensionality of the output space (i.e. the number
|
||
|
|
of filters in the convolution).
|
||
|
|
kernel_size: A tuple or list of 3 positive 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 3 positive integers specifying the strides
|
||
|
|
of the convolution. Can be a single integer to specify the same value for
|
||
|
|
all spatial dimensions.
|
||
|
|
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)`.
|
||
|
|
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.
|
||
|
|
reuse: Boolean, whether to reuse the weights of a previous layer
|
||
|
|
by the same name.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Output tensor.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if eager execution is enabled.
|
||
|
|
"""
|
||
|
|
layer = Conv3DTranspose(
|
||
|
|
filters=filters,
|
||
|
|
kernel_size=kernel_size,
|
||
|
|
strides=strides,
|
||
|
|
padding=padding,
|
||
|
|
data_format=data_format,
|
||
|
|
activation=activation,
|
||
|
|
use_bias=use_bias,
|
||
|
|
kernel_initializer=kernel_initializer,
|
||
|
|
bias_initializer=bias_initializer,
|
||
|
|
kernel_regularizer=kernel_regularizer,
|
||
|
|
bias_regularizer=bias_regularizer,
|
||
|
|
activity_regularizer=activity_regularizer,
|
||
|
|
kernel_constraint=kernel_constraint,
|
||
|
|
bias_constraint=bias_constraint,
|
||
|
|
trainable=trainable,
|
||
|
|
name=name,
|
||
|
|
_reuse=reuse,
|
||
|
|
_scope=name)
|
||
|
|
return layer.apply(inputs)
|
||
|
|
|
||
|
|
|
||
|
|
# Aliases
|
||
|
|
|
||
|
|
Convolution1D = Conv1D
|
||
|
|
Convolution2D = Conv2D
|
||
|
|
Convolution3D = Conv3D
|
||
|
|
SeparableConvolution2D = SeparableConv2D
|
||
|
|
Convolution2DTranspose = Deconvolution2D = Deconv2D = Conv2DTranspose
|
||
|
|
Convolution3DTranspose = Deconvolution3D = Deconv3D = Conv3DTranspose
|
||
|
|
convolution1d = conv1d
|
||
|
|
convolution2d = conv2d
|
||
|
|
convolution3d = conv3d
|
||
|
|
separable_convolution2d = separable_conv2d
|
||
|
|
convolution2d_transpose = deconvolution2d = deconv2d = conv2d_transpose
|
||
|
|
convolution3d_transpose = deconvolution3d = deconv3d = conv3d_transpose
|