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The transpose of conv2d
.
tf.compat.v1.nn.conv2d_transpose(
value=None, filter=None, output_shape=None, strides=None, padding='SAME',
data_format='NHWC', name=None, input=None, filters=None, dilations=None
)
This operation is sometimes called "deconvolution" after Deconvolutional
Networks,
but is really the transpose (gradient) of conv2d
rather than an actual
deconvolution.
value
: 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.filter
: A 4-D Tensor
with the same type as value
and shape
[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
: 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.name
: Optional name for the returned tensor.input
: Alias for value.filters
: Alias for filter.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.A Tensor
with the same type as value
.
ValueError
: If input/output depth does not match filter
's shape, or if
padding is other than 'VALID'
or 'SAME'
.