JETNET 3.0 offers the possibility to use shared weights for exploiting translational symmetries. An example is when the input units consist of a matrix of cells, e.g. cells in a calorimeter, where it is known a priori that identical features can occur anywhere in the matrix with translational symmetry. It is then profitable to configure the network so that the hidden (feature) nodes cover several overlapping smaller portions (large enough to cover the size of a subfeature) of the input matrix. Weights connecting to corresponding parts of the different receptive fields are then shared, i.e. assumed to be identical.
Such configurations can be achieved in JETNET 3.0 using the switches MSTJN(23-27). The geometry of the input matrix is specified with MSTJN(23) and MSTJN(24). Periodic boundary conditions are assumed if these values are negative. The geometry of the receptive fields is specified with MSTJN(25) and MSTJN(26), where . The number of hidden units used for each receptive field is specified by MSTJN(27). At initialization, an array of receptive fields is generated with maximum overlap, i.e. the fields are only shifted one (x or y) unit at a time, and new hidden units are generated if the specified number of hidden units is less than necessary. Any remaining hidden units are assumed to have full connectivity from the inputs.
However, it is faster to train small network modules and later combining them into a larger network. The above solution is inefficient for large input matrices, since all weights are nevertheless updated.