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tf.nn.conv3d_transpose

TensorFlow 1 version View source on GitHub

The transpose of conv3d.

Aliases:

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

This operation is sometimes called "deconvolution" after Deconvolutional Networks, but is actually the transpose (gradient) of conv2d rather than an actual deconvolution.

Args:

  • input: A 5-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
  • filters: A 5-D Tensor with the same type as value and shape [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: An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of input. 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 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. 'NDHWC' and 'NCDHW' are supported.
  • 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.
  • name: Optional name for the returned tensor.

Returns:

A Tensor with the same type as value.