tf.compat.v1.nn.conv3d_transpose
The transpose of conv3d
.
tf.compat.v1.nn.conv3d_transpose(
value, filter=None, output_shape=None, strides=None, padding='SAME',
data_format='NDHWC', name=None, input=None, filters=None, dilations=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 |
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.
|
Returns |
A Tensor with the same type as value .
|
Raises |
ValueError
|
If input/output depth does not match filter 's shape, or if
padding is other than 'VALID' or 'SAME' .
|
References:
Deconvolutional Networks:
Zeiler et al., 2010
(pdf)
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Last updated 2020-10-01 UTC.
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