tf.raw_ops.DepthwiseConv2dNativeBackpropFilter

Computes the gradients of depthwise convolution with respect to the filter.

tf.raw_ops.DepthwiseConv2dNativeBackpropFilter(
    input,
    filter_sizes,
    out_backprop,
    strides,
    padding,
    explicit_paddings=[],
    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", "EXPLICIT". The type of padding algorithm to use.
explicit_paddings An optional list of ints. Defaults to [].
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.