def mlp3layer( param ):
factory = NNFactory( param )
# setup network for 2 layer perceptron
i_node = np.zeros( (param.batch_size, param.input_size), 'float32' )
o_node = np.zeros( (param.batch_size, param.num_class), 'float32' )
h1_node = np.zeros( (param.batch_size, param.num_hidden), 'float32' )
h2_node = np.zeros_like( h1_node )
h1_node2 = np.zeros( (param.batch_size, param.num_hidden2), 'float32' )
h2_node2 = np.zeros_like( h1_node2 )
o_label = factory.create_olabel()
nodes = [ i_node, h1_node, h2_node, h1_node2, h2_node2, o_node ]
layers = [ nnet.FullLayer( i_node, h1_node, param.init_sigma, param.rec_gsqr() ) ]
layers+= [ nnet.ActiveLayer( h1_node, h2_node, param.node_type ) ]
layers+= [ nnet.FullLayer( h2_node, h1_node2, param.init_sigma, param.rec_gsqr() ) ]
layers+= [ nnet.ActiveLayer( h1_node2, h2_node2, param.node_type ) ]
layers+= [ nnet.FullLayer( h2_node2, o_node, param.init_sigma, param.rec_gsqr() ) ]
layers+= [ factory.create_outlayer( o_node, o_label ) ]
return net
def mlp2layer(param):
factory = NNFactory(param)
# setup network for 2 layer perceptron
i_node = np.zeros((param.batch_size, param.input_size), 'float32')
o_node = np.zeros((param.batch_size, param.num_class), 'float32')
h1_node = np.zeros((param.batch_size, param.num_hidden), 'float32')
h2_node = np.zeros_like(h1_node)
o_label = np.zeros((param.batch_size), 'int8')
nodes = [i_node, h1_node, h2_node, o_node]
layers = [
nnet.FullLayer(i_node, h1_node, param.init_sigma, param.rec_gsqr())
]
layers += [nnet.ActiveLayer(h1_node, h2_node, param.node_type)]
layers += [
nnet.FullLayer(h2_node, o_node, param.init_sigma, param.rec_gsqr())
]
layers += [nnet.SoftmaxLayer(o_node, o_label)]
net = nnet.NNetwork(layers, nodes, o_label, factory)
return net