tf.nn.conv1d_transpose

The transpose of conv1d.

Aliases:

tf.nn.conv1d_transpose(
    input,
    filters,
    output_shape,
    strides,
    padding='SAME',
    data_format='NWC',
    dilations=None,
    name=None
)

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

Args:

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.

Returns:

A Tensor with the same type as value.

Raises:

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.