tf.nn.conv1d_transpose

View source on GitHub

The transpose of conv1d.

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.