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The transpose of conv3d.

This operation is sometimes called "deconvolution" after (Zeiler et al., 2010), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.

value A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
filter A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
output_shape A 1-D Tensor representing the output shape of the deconvolution op.
strides A list of ints. The stride of the sliding window for each dimension of the input tensor.
padding A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
data_format A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
name Optional name for the returned tensor.
input Alias of value.
filters Alias of filter.
dilations An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. 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 if a 5-d tensor must be 1.

A Tensor with the same type as value.

ValueError If input/output depth does not match filter's shape, or if padding is other than 'VALID' or 'SAME'.


Deconvolutional Networks: Zeiler et al., 2010 (pdf)