Computes the gradients of depthwise convolution with respect to the filter.
tf.nn.depthwise_conv2d_backprop_filter(
input, filter_sizes, out_backprop, strides, padding, data_format='NHWC',
dilations=[1, 1, 1, 1], name=None
)
input: A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
4-D with shape based on data_format. For example, if
data_format is 'NHWC' then input is a 4-D [batch, in_height,
in_width, in_channels] tensor.filter_sizes: A Tensor of type int32.
An integer vector representing the tensor shape of filter,
where filter is a 4-D
[filter_height, filter_width, in_channels, depthwise_multiplier] tensor.out_backprop: A Tensor. Must have the same type as input.
4-D with shape based on data_format.
For example, if data_format is 'NHWC' then
out_backprop shape is [batch, out_height, out_width, out_channels].
Gradients w.r.t. the output of the convolution.strides: A list of ints.
The stride of the sliding window for each dimension of the input
of the convolution.padding: A string from: "SAME", "VALID".
The type of padding algorithm to use.data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC".
Specify the data format of the input and output data. With the
default format "NHWC", the data is stored in the order of:
[batch, height, width, channels].
Alternatively, the format could be "NCHW", the data storage order of:
[batch, channels, height, width].dilations: An optional list of ints. Defaults to [1, 1, 1, 1].
1-D tensor of length 4. The dilation factor for each dimension of
input. If set to k > 1, there will be k-1 skipped cells between each filter
element on that dimension. The dimension order is determined by the value of
data_format, see above for details. Dilations in the batch and depth
dimensions must be 1.name: A name for the operation (optional).A Tensor. Has the same type as input.