def example2():
 l_in = lasagne.layers.InputLayer((100, 1, 20, 20))
 l_conv1 = lasagne.layers.Conv2DLayer(l_in, num_filters=32, filter_size=(5, 5))
 l_pool1 = lasagne.layers.MaxPool2DLayer(l_conv1, pool_size=(2, 2))
 l_dense1 = lasagne.layers.DenseLayer(l_pool1, num_units=20)
 l_conv2 = lasagne.layers.Conv2DLayer(l_in, num_filters=32, filter_size=(5, 5))
 l_pool2 = lasagne.layers.MaxPool2DLayer(l_conv2, pool_size=(2, 2))
 l_dense2 = lasagne.layers.DenseLayer(l_pool2, num_units=20)
 l_concat = lasagne.layers.ConcatLayer((l_dense1, l_dense2))
 l_out = lasagne.layers.DenseLayer(l_concat, num_units=10)
 layers = get_all_layers(l_out)
 print get_network_str(layers, get_network=False, incomings=True, outgoings=True)
 return None

def example2 ( ) : l_in = lasagne . layers . InputLayer ( ( 100 , 1 , 20 , 20 ) ) l_conv1 = lasagne . layers . Conv2DLayer ( l_in , num_filters = 32 , filter_size = ( 5 , 5 ) ) l_pool1 = lasagne . layers . MaxPool2DLayer ( l_conv1 , pool_size = ( 2 , 2 ) ) l_dense1 = lasagne . layers . DenseLayer ( l_pool1 , num_units = 20 ) l_conv2 = lasagne . layers . Conv2DLayer ( l_in , num_filters = 32 , filter_size = ( 5 , 5 ) ) l_pool2 = lasagne . layers . MaxPool2DLayer ( l_conv2 , pool_size = ( 2 , 2 ) ) l_dense2 = lasagne . layers . DenseLayer ( l_pool2 , num_units = 20 ) l_concat = lasagne . layers . ConcatLayer ( ( l_dense1 , l_dense2 ) ) l_out = lasagne . layers . DenseLayer ( l_concat , num_units = 10 ) layers = get_all_layers ( l_out ) print get_network_str ( layers , get_network = False , incomings = True , outgoings = True ) return None
two branches .