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tf.nn.conv3d_transpose

TensorFlow 2.0 version View source on GitHub

The transpose of conv3d.

Aliases:

  • tf.compat.v1.nn.conv3d_transpose
tf.nn.conv3d_transpose(
    value,
    filter=None,
    output_shape=None,
    strides=None,
    padding='SAME',
    data_format='NDHWC',
    name=None,
    input=None,
    filters=None,
    dilations=None
)

This operation is sometimes called "deconvolution" after Deconvolutional Networks, but is really the transpose (gradient) of conv3d rather than an actual deconvolution.

Args:

  • value: A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
  • filter: A 5-D Tensor with the same type as value and shape [depth, 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: A list of ints. The stride of the sliding window for each dimension of the input tensor.
  • padding: A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
  • data_format: A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
  • name: Optional name for the returned tensor.
  • input: Alias of value.
  • filters: Alias of filter.
  • dilations: An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, 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 5-d tensor must be 1.

Returns:

A Tensor with the same type as value.

Raises:

  • ValueError: If input/output depth does not match filter's shape, or if padding is other than 'VALID' or 'SAME'.