tf.keras.layers.SeparableConv2D

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Depthwise separable 2D convolution.

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Main aliases

tf.keras.layers.SeparableConvolution2D

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.SeparableConv2D, tf.compat.v1.keras.layers.SeparableConvolution2D, `tf.compat.v2.keras.layers.SeparableConv2D`, `tf.compat.v2.keras.layers.SeparableConvolution2D`

tf.keras.layers.SeparableConv2D(
    filters, kernel_size, strides=(1, 1), padding='valid', data_format=None,
    dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True,
    depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform',
    bias_initializer='zeros', depthwise_regularizer=None,
    pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None,
    depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None,
    **kwargs
)

Separable convolutions consist in first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step.

Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.

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

Input shape:

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

Output shape:

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