tf.nn.conv_transpose

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The transpose of convolution.

Aliases:

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

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

Args:

  • input: An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
  • filters: An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
  • 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, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. 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 or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
  • dilations: An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. 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.
  • name: A name for the operation (optional). If not specified "conv_transpose" is used.

Returns:

A Tensor with the same type as value.