def get_model(inp, patch_op):
icnn1 = batch_norm(utils_lasagne.GCNNLayer([inp, patch_op], 16, nrings=5, nrays=16))
ffn1 = LL.DenseLayer(icnn1, 512)
icnn2 = batch_norm(utils_lasagne.GCNNLayer([icnn1, patch_op], 32, nrings=5, nrays=16))
ffn2 = LL.DenseLayer(icnn2, 512)
icnn3 = batch_norm(utils_lasagne.GCNNLayer([icnn2, patch_op], 64, nrings=5, nrays=16))
ffn3 = LL.DenseLayer(icnn3, 512)
ffn4 = LL.ConcatLayer([inp,ffn1,ffn2,ffn3],axis=1, cropping=None);
ffn = LL.DenseLayer(ffn4, nclasses, nonlinearity=utils_lasagne.log_softmax)
return ffn
def get_model(inp, patch_op):
icnn = LL.DenseLayer(inp, 16)
icnn = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 16, nrings=4, nrays=8))
icnn = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 32, nrings=4, nrays=8))
icnn = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 64, nrings=4, nrays=8))
ffn = batch_norm(LL.DenseLayer(icnn, 512))
ffn = LL.DenseLayer(icnn, nclasses, nonlinearity=utils_lasagne.log_softmax)
return ffn