Keras Transposed Convolution 2D Layer (deprecated)

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Corresponds to the Keras Transposed Convolution 2D Layer .

Input ports

1. Type: Keras Deep Learning Network
   Keras Network
   The Keras deep learning network to which to add a Transposed Convolution 2D layer.

Output ports

1. Type: Keras Deep Learning Network
   Keras Network
   The Keras deep learning network with an added Transposed Convolution 2D layer.

External resources

• KNIME Deep Learning Keras Integration