Computes the gradients of 3-D convolution with respect to the filter.
tf.compat.v1.nn.conv3d_backprop_filter(
input, filter_sizes, out_backprop, strides, padding, data_format='NDHWC',
dilations=[1, 1, 1, 1, 1], name=None
)
input: A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
Shape [batch, depth, rows, cols, in_channels].filter_sizes: A Tensor of type int32.
An integer vector representing the tensor shape of filter,
where filter is a 5-D
[filter_depth, filter_height, filter_width, in_channels, out_channels]
tensor.out_backprop: A Tensor. Must have the same type as input.
Backprop signal of shape [batch, out_depth, out_rows, out_cols,
out_channels].strides: A list of ints that has length >= 5.
1-D tensor of length 5. The stride of the sliding window for each
dimension of input. Must have strides[0] = strides[4] = 1.padding: A string from: "SAME", "VALID".
The type of padding algorithm to use.data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
The data format of the input and output data. With the
default format "NDHWC", the data is stored in the order of:
[batch, in_depth, in_height, in_width, in_channels].
Alternatively, the format could be "NCDHW", the data storage order is:
[batch, in_channels, in_depth, in_height, in_width].dilations: An optional list of ints. Defaults to [1, 1, 1, 1, 1].
1-D tensor of length 5. 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.