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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'.