tf.nn.conv3d_transpose

The transpose of conv3d.

tf.nn.conv3d_transpose(
    input, filters, output_shape, strides, padding='SAME',
    data_format='NDHWC', dilations=None, name=None
)

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

input A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels] for NDHWC data format or [batch, in_channels, depth, height, width] for NCDHW data format.
filters A 5-D Tensor with the same type as input and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of input.
output_shape A 1-D Tensor representing the output shape of the deconvolution op.
strides An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of input. 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 0. The dimension order is determined by the value of data_format, see below for details.
padding A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
data_format A string. 'NDHWC' and 'NCDHW' are supported.
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.
name Optional name for the returned tensor.

A Tensor with the same type as input.

References:

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