This operation is sometimes called "deconvolution" after Deconvolutional
Networks, but is
actually the transpose (gradient) of conv2d rather than an actual
deconvolution.
Args
input
A 4-D Tensor of type float and shape [batch, height, width,
in_channels] for NHWC data format or [batch, in_channels, height,
width] for NCHW data format.
filters
A 4-D Tensor with the same type as input and shape [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, 2 or 4. The
stride of the sliding window for each dimension of input. If a single
value is given it is replicated in the 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. 'NHWC' and 'NCHW' are supported.
dilations
An int or list of ints that has length 1, 2 or 4,
defaults to 1. The dilation factor for each dimension ofinput. If a
single value is given it is replicated in the 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 4-d tensor
must be 1.
name
Optional name for the returned tensor.
Returns
A Tensor with the same type as input.
Raises
ValueError
If input/output depth does not match filter's shape, or if
padding is other than 'VALID' or 'SAME'.