dense_net = Network( name='1_dense', dimensions=(None, int(train_images.shape[1])), input_var=input_var, y=y, config=network_dense_config, input_network={'network': conv_net, 'layer': 4, 'get_params': True}, num_classes=10, activation='rectify', pred_activation='softmax' ) train_images = np.expand_dims(train_images, axis=1) test_images = np.expand_dims(test_images, axis=1) # # Use to load model from disk # # dense_net = Network.load_model('models/20170704194033_3_dense_test.network') dense_net.train( epochs=200, train_x=train_images[:50000], train_y=train_labels[:50000], val_x=train_images[50000:60000], val_y=train_labels[50000:60000], batch_ratio=0.05, plot=False ) dense_net.save_record() run_test(dense_net, test_x=test_images, test_y=test_labels) dense_net.save_model()
'dropouts': [0.2], } dense_net = Network(name='3_dense_test', dimensions=[None] + list(train_images.shape[1:]), input_var=input_var, y=y, config=network_dense_config, input_network=None, num_classes=10, activation='rectify', pred_activation='softmax', optimizer='adam') # # Use to load model from disk # # dense_net = Network.load_model('models/20170704194033_3_dense_test.network') dense_net.train(epochs=2, train_x=train_images[:50000], train_y=train_labels[:50000], val_x=train_images[50000:60000], val_y=train_labels[50000:60000], batch_ratio=0.05, plot=True) dense_net.save_record() run_test(dense_net, test_x=train_images[50000:60000], test_y=train_labels[50000:60000]) dense_net.save_model()
y=y, config=network_dense_config, input_network={ 'network': conv_net, 'layer': 4, 'get_params': True }, num_classes=10, activation='rectify', pred_activation='softmax') ensemble_dense = Snapshot(name='snap_test', template_network=dense_net, n_snapshots=5) train_images = np.expand_dims(train_images, axis=1) test_images = np.expand_dims(test_images, axis=1) ensemble_dense.train(epochs=500, train_x=train_images[:50000], train_y=train_labels[:50000], val_x=train_images[50000:60000], val_y=train_labels[50000:60000], batch_ratio=0.05, plot=False) ensemble_dense.save_record() # ensemble_dense = Snapshot.load_ensemble('models/20170713183810_snap1') run_test(ensemble_dense, test_x=test_images, test_y=test_labels) ensemble_dense.save_model()