dilations=[1, 1, 1, 1, 1],

Defined in tensorflow/python/ops/

See the guide: Neural Network > Convolution

Computes the gradients of 3-D convolution with respect to the filter.


  • 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.