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TensorFlow 1 version
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View source on GitHub
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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.
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output_shape
|
A 1-D Tensor representing the output shape of the
deconvolution op.
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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
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A string, either 'VALID' or 'SAME'. The padding algorithm. See
the "returns" section of tf.nn.convolution for details.
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data_format
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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.
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name
|
Optional name for the returned tensor. |
Returns | |
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A Tensor with the same type as input.
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References:
Deconvolutional Networks: Zeiler et al., 2010 (pdf)