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TensorFlow 2 version View source on GitHub

The transpose of conv1d.

This operation is sometimes called "deconvolution" after Deconvolutional Networks, but is really the transpose (gradient) of conv1d rather than an actual deconvolution.

input A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
filters A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
output_shape A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
strides An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
padding A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
data_format A string. 'NWC' and 'NCW' are supported.
dilations An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
name Optional name for the returned tensor.

A Tensor with the same type as value.

ValueError If input/output depth does not match filter's shape, if output_shape is not at 3-element vector, if padding is other than 'VALID' or 'SAME', or if data_format is invalid.