tf.nn.atrous_conv2d_transpose(value, filters, output_shape, rate, padding, name=None)

tf.nn.atrous_conv2d_transpose(value, filters, output_shape, rate, padding, name=None)

See the guide: Neural Network > Convolution

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after Deconvolutional Networks, but is actually the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.

Args:

  • value: A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
  • filters: A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
  • output_shape: A 1-D Tensor of shape representing the output shape of the deconvolution op.
  • rate: A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
  • padding: A string, either 'VALID' or 'SAME'. The padding algorithm.
  • 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 filters' shape, or if padding is other than 'VALID' or 'SAME', or if the rate is less than one, or if the output_shape is not a tensor with 4 elements.

Defined in tensorflow/python/ops/nn_ops.py.