TensorFlow 1 version | View source on GitHub |
The transpose of convolution
.
tf.nn.conv_transpose(
input, filters, output_shape, strides, padding='SAME',
data_format=None, 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
|
An N+2 dimensional Tensor of shape
[batch_size] + input_spatial_shape + [in_channels] if data_format does
not start with "NC" (default), or
[batch_size, in_channels] + input_spatial_shape if data_format starts
with "NC". It must be one of the following types:
half , bfloat16 , float32 , float64 .
|
filters
|
An N+2 dimensional Tensor with the same type as input and
shape spatial_filter_shape + [in_channels, out_channels] .
|
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 , N or N+2 . The
stride of the sliding window for each dimension of input . If a single
value is given it is replicated in the spatial dimensions. 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 or None. Specifies whether the channel dimension of
the input and output is the last dimension (default, or if data_format
does not start with "NC"), or the second dimension (if data_format
starts with "NC"). For N=1, the valid values are "NWC" (default) and
"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW".
For N=3, the valid values are "NDHWC" (default) and "NCDHW".
|
dilations
|
An int or list of ints that has length 1 , N or N+2 ,
defaults to 1. The dilation factor for each dimension ofinput . If a
single value is given it is replicated in the spatial dimensions. 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.
|
name
|
A name for the operation (optional). If not specified "conv_transpose" is used. |
Returns | |
---|---|
A Tensor with the same type as value .
|
References:
Deconvolutional Networks: Zeiler et al., 2010 (pdf)