tf.keras.layers.DepthwiseConv2D

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Depthwise separable 2D convolution.

Inherits From: Conv2D

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.DepthwiseConv2D, `tf.compat.v2.keras.layers.DepthwiseConv2D`

tf.keras.layers.DepthwiseConv2D(
    kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1,
    data_format=None, activation=None, use_bias=True,
    depthwise_initializer='glorot_uniform', bias_initializer='zeros',
    depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None,
    depthwise_constraint=None, bias_constraint=None, **kwargs
)

Depthwise Separable convolutions consists in performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step.

Arguments
kernel_size	An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
strides	An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding	one of 'valid' or 'same' (case-insensitive).
depth_multiplier	The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to filters_in * depth_multiplier.
data_format	A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be 'channels_last'.
activation	Activation function to use. If you don't specify anything, no activation is applied (ie. 'linear' activation: a(x) = x).
use_bias	Boolean, whether the layer uses a bias vector.
depthwise_initializer	Initializer for the depthwise kernel matrix.
bias_initializer	Initializer for the bias vector.
depthwise_regularizer	Regularizer function applied to the depthwise kernel matrix.
bias_regularizer	Regularizer function applied to the bias vector.
activity_regularizer	Regularizer function applied to the output of the layer (its 'activation').
depthwise_constraint	Constraint function applied to the depthwise kernel matrix.
bias_constraint	Constraint function applied to the bias vector.

Input shape:

4D tensor with shape: [batch, channels, rows, cols] if data_format='channels_first' or 4D tensor with shape: [batch, rows, cols, channels] if data_format='channels_last'.

Output shape:

4D tensor with shape: [batch, filters, new_rows, new_cols] if data_format='channels_first' or 4D tensor with shape: [batch, new_rows, new_cols, filters] if data_format='channels_last'. rows and cols values might have changed due to padding.