tf.keras.ops.separable_conv

General N-D separable convolution.

tf.keras.ops.separable_conv(
    inputs,
    depthwise_kernel,
    pointwise_kernel,
    strides=1,
    padding='valid',
    data_format=None,
    dilation_rate=1
)

This ops supports 1D and 2D separable convolution. separable_conv is a depthwise conv followed by a pointwise conv.

inputsTensor of rank N+2. inputs has shape (batch_size,) + inputs_spatial_shape + (num_channels,) if data_format="channels_last", or (batch_size, num_channels) + inputs_spatial_shape if data_format="channels_first".
depthwise_kernelTensor of rank N+2. depthwise_kernel has shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], num_input_channels should match the number of channels in inputs.
pointwise_kernelTensor of rank N+2. pointwise_kernel has shape (*ones_like(kernel_spatial_shape), num_input_channels * num_channels_multiplier, num_output_channels).
stridesint or int tuple/list of len(inputs_spatial_shape), specifying the strides of the convolution along each spatial dimension. If strides is int, then every spatial dimension shares the same strides.
paddingstring, either "valid" or "same". "valid" means no padding is applied, and "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input when strides=1.
data_formatA string, either "channels_last" or "channels_first". data_format determines the ordering of the dimensions in the inputs. If data_format="channels_last", inputs is of shape (batch_size, ..., channels) while if data_format="channels_first", inputs is of shape (batch_size, channels, ...).
dilation_rateint or int tuple/list of len(inputs_spatial_shape), specifying the dilation rate to use for dilated convolution. If dilation_rate is int, then every spatial dimension shares the same dilation_rate.

A tensor of rank N+2, the result of the depthwise conv operation.