tf.keras.ops.depthwise_conv

General N-D depthwise convolution.

Main aliases

tf.keras.ops.nn.depthwise_conv

tf.keras.ops.depthwise_conv(
    inputs,
    kernel,
    strides=1,
    padding='valid',
    data_format=None,
    dilation_rate=1
)

This ops supports 1D and 2D depthwise convolution.

Args
`inputs`	Tensor 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"`.
`kernel`	Tensor of rank N+2. `kernel` has shape [kernel_spatial_shape, num_input_channels, num_channels_multiplier], `num_input_channels` should match the number of channels in `inputs`.
`strides`	int 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`.
`padding`	string, 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_format`	A 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_rate`	int 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`.

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

Args