filter_size=(5, 5), nonlinearity=lasagne.nonlinearities.leaky_rectify) network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2)) network = lasagne.layers.Conv2DLayer( network, num_filters=8, filter_size=(5, 5), nonlinearity=lasagne.nonlinearities.leaky_rectify) network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2)) network = lasagne.layers.DenseLayer( lasagne.layers.dropout(network, p=.5), num_units=2, nonlinearity=lasagne.nonlinearities.softmax) return network if __name__ == '__main__': if len(sys.argv) < 4: print('Usage: python3 path/to/data_dir path/to/output_dir n_epochs') exit() exp = Experiment(data=sys.argv[1], directory=sys.argv[2], network=network) n = 10 if len(sys.argv) == 3 else int(sys.argv[3]) exp.train(epochs=n) exp.test() exp.save()