204 lines
8.3 KiB
Python
204 lines
8.3 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras 2D convolution layer."""
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from keras import activations
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from keras import constraints
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from keras import initializers
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from keras import regularizers
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from keras.dtensor import utils
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from keras.layers.convolutional.base_conv import Conv
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.Conv2D", "keras.layers.Convolution2D")
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class Conv2D(Conv):
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"""2D convolution layer (e.g. spatial convolution over images).
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This layer creates a convolution kernel that is convolved
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with the layer input to produce a tensor of
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outputs. If `use_bias` is True,
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a bias vector is created and added to the outputs. Finally, if
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`activation` is not `None`, it is applied to the outputs as well.
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When using this layer as the first layer in a model,
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provide the keyword argument `input_shape`
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(tuple of integers or `None`, does not include the sample axis),
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e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
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in `data_format="channels_last"`. You can use `None` when
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a dimension has variable size.
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Examples:
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>>> # The inputs are 28x28 RGB images with `channels_last` and the batch
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>>> # size is 4.
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>>> input_shape = (4, 28, 28, 3)
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>>> x = tf.random.normal(input_shape)
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>>> y = tf.keras.layers.Conv2D(
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... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
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>>> print(y.shape)
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(4, 26, 26, 2)
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>>> # With `dilation_rate` as 2.
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>>> input_shape = (4, 28, 28, 3)
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>>> x = tf.random.normal(input_shape)
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>>> y = tf.keras.layers.Conv2D(
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... 2, 3,
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... activation='relu',
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... dilation_rate=2,
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... input_shape=input_shape[1:])(x)
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>>> print(y.shape)
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(4, 24, 24, 2)
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>>> # With `padding` as "same".
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>>> input_shape = (4, 28, 28, 3)
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>>> x = tf.random.normal(input_shape)
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>>> y = tf.keras.layers.Conv2D(
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... 2, 3, activation='relu', padding="same", input_shape=input_shape[1:])(x)
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>>> print(y.shape)
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(4, 28, 28, 2)
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>>> # With extended batch shape [4, 7]:
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>>> input_shape = (4, 7, 28, 28, 3)
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>>> x = tf.random.normal(input_shape)
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>>> y = tf.keras.layers.Conv2D(
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... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
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>>> print(y.shape)
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(4, 7, 26, 26, 2)
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Args:
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filters: Integer, the dimensionality of the output space (i.e. the number
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of output filters in the convolution).
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kernel_size: An integer or tuple/list of 2 integers, specifying the height
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and width of the 2D convolution window. Can be a single integer to
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specify the same value for all spatial dimensions.
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strides: An integer or tuple/list of 2 integers, specifying the strides of
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the convolution along the height and width. Can be a single integer to
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specify the same value for all spatial dimensions. Specifying any stride
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value != 1 is incompatible with specifying any `dilation_rate` value !=
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1.
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padding: one of `"valid"` or `"same"` (case-insensitive).
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`"valid"` means no padding. `"same"` results in padding with zeros
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evenly to the left/right or up/down of the input. When `padding="same"`
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and `strides=1`, the output has the same size as the input.
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data_format: A string, one of `channels_last` (default) or
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`channels_first`. The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape `(batch_size, height,
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width, channels)` while `channels_first` corresponds to inputs with
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shape `(batch_size, channels, height, width)`. It defaults to the
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`image_data_format` value found in your Keras config file at
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`~/.keras/keras.json`. If you never set it, then it will be
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`channels_last`. Note that the `channels_first` format is currently not
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supported by TensorFlow on CPU.
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dilation_rate: an integer or tuple/list of 2 integers, specifying the
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dilation rate to use for dilated convolution. Can be a single integer to
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specify the same value for all spatial dimensions. Currently, specifying
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any `dilation_rate` value != 1 is incompatible with specifying any
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stride value != 1.
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groups: A positive integer specifying the number of groups in which the
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input is split along the channel axis. Each group is convolved
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separately with `filters / groups` filters. The output is the
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concatenation of all the `groups` results along the channel axis. Input
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channels and `filters` must both be divisible by `groups`.
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activation: Activation function to use. If you don't specify anything, no
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activation is applied (see `keras.activations`).
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use_bias: Boolean, whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix (see
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`keras.initializers`). Defaults to 'glorot_uniform'.
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bias_initializer: Initializer for the bias vector (see
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`keras.initializers`). Defaults to 'zeros'.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix (see `keras.regularizers`).
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bias_regularizer: Regularizer function applied to the bias vector (see
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`keras.regularizers`).
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activity_regularizer: Regularizer function applied to the output of the
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layer (its "activation") (see `keras.regularizers`).
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kernel_constraint: Constraint function applied to the kernel matrix (see
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`keras.constraints`).
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bias_constraint: Constraint function applied to the bias vector (see
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`keras.constraints`).
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Input shape:
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4+D tensor with shape: `batch_shape + (channels, rows, cols)` if
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`data_format='channels_first'`
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or 4+D tensor with shape: `batch_shape + (rows, cols, channels)` if
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`data_format='channels_last'`.
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Output shape:
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4+D tensor with shape: `batch_shape + (filters, new_rows, new_cols)` if
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`data_format='channels_first'` or 4+D tensor with shape: `batch_shape +
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(new_rows, new_cols, filters)` if `data_format='channels_last'`. `rows`
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and `cols` values might have changed due to padding.
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Returns:
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A tensor of rank 4+ representing
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`activation(conv2d(inputs, kernel) + bias)`.
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Raises:
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ValueError: if `padding` is `"causal"`.
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ValueError: when both `strides > 1` and `dilation_rate > 1`.
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"""
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@utils.allow_initializer_layout
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def __init__(
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self,
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filters,
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kernel_size,
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strides=(1, 1),
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padding="valid",
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data_format=None,
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dilation_rate=(1, 1),
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groups=1,
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activation=None,
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use_bias=True,
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kernel_initializer="glorot_uniform",
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bias_initializer="zeros",
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kernel_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_constraint=None,
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bias_constraint=None,
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**kwargs
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):
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super().__init__(
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rank=2,
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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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groups=groups,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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bias_initializer=initializers.get(bias_initializer),
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kernel_regularizer=regularizers.get(kernel_regularizer),
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bias_regularizer=regularizers.get(bias_regularizer),
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activity_regularizer=regularizers.get(activity_regularizer),
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kernel_constraint=constraints.get(kernel_constraint),
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bias_constraint=constraints.get(bias_constraint),
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**kwargs
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)
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# Alias
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Convolution2D = Conv2D
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