def test_softmax_weight_init():
 nvis = 5
 num_classes = 10
 MLP(layers=[Softmax(num_classes, 's', irange=0.1)], nvis=nvis)
 MLP(layers=[Softmax(num_classes, 's', istdev=0.1)], nvis=nvis)
 MLP(layers=[Softmax(num_classes, 's', sparse_init=2)], nvis=nvis)

def test_softmax_weight_init ( ) : nvis = 5 num_classes = 10 MLP ( layers = [ Softmax ( num_classes , 's' , irange = 0.1 ) ] , nvis = nvis ) MLP ( layers = [ Softmax ( num_classes , 's' , istdev = 0.1 ) ] , nvis = nvis ) MLP ( layers = [ Softmax ( num_classes , 's' , sparse_init = 2 ) ] , nvis = nvis )
constructs softmax layers with different weight initialization parameters .