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.
Args |
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
here
for more information.
|
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.
|
Returns |
A Tensor with the same type as input .
|
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Last updated 2023-03-17 UTC.
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