tf.raw_ops.Conv3DBackpropInputV2

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

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.Conv3DBackpropInputV2

tf.raw_ops.Conv3DBackpropInputV2(
    input_sizes, filter, out_backprop, strides, padding,
    data_format='NDHWC', dilations=[1, 1, 1, 1, 1], name=None
)

Args
input_sizes	A Tensor. Must be one of the following types: int32, int64. An integer vector representing the tensor shape of input, where input is a 5-D [batch, depth, rows, cols, in_channels] tensor.
filter	A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [depth, rows, cols, in_channels, out_channels]. in_channels must match between input and filter.
out_backprop	A Tensor. Must have the same type as filter. 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).

Returns
A Tensor. Has the same type as filter.