tf.keras.layers.SeparableConv2D

View source on GitHub

Depthwise separable 2D convolution.

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.