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tf.nn.conv2d_transpose

TensorFlow 2 version View source on GitHub

The transpose of conv2d.

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'.