input=layers[-1].output, input_shape=layers[-1].output_shape, output_shape=11, dropout_input=layers[-1].dropout_output, active_func=actfuncs.sigmoid )) model = NeuralNet(layers, [X, S], layers[-1].output) model.target = A model.cost = costfuncs.binxent(layers[-1].dropout_output, A) + \ 1e-3 * model.get_norm(2) model.error = costfuncs.binerr(layers[-1].output, A) sgd.train(model, dataset, lr=1e-2, momentum=0.9, batch_size=100, n_epochs=300, epoch_waiting=10) return model if __name__ == '__main__': dataset_file = 'data_{0}.pkl'.format(args.dataset[0]) out_file = 'model_attr.pkl' if args.output is None else \ 'model_attr_{0}.pkl'.format(args.output) dataset = load_data(dataset_file) model = train_model(dataset, not args.no_scpool) save_data(model, out_file)
)) model = NeuralNet(layers, X, layers[-1].output) model.target = S ''' model.cost = costfuncs.binxent(layers[-1].dropout_output, S.flatten(2)) + \ 1e-3 * model.get_norm(2) model.error = costfuncs.binerr(layers[-1].output, S.flatten(2)) ''' model.cost = costfuncs.weighted_norm2( layers[-1].dropout_output, S.flatten(2), 1.0) + \ 1e-3 * model.get_norm(2) model.error = costfuncs.weighted_norm2( layers[-1].output, S.flatten(2), 1.0) sgd.train(model, dataset, lr=1e-2, momentum=0.9, batch_size=100, n_epochs=300, epoch_waiting=10, never_stop=True) return model if __name__ == '__main__': dataset = load_dataset() model = train_model(dataset) save_data(model, 'model_seg_handcrafted.pkl')
t = target[:, j].ravel() fpr, tpr, thresh = stats.roc(o, t) auc = stats.auc(fpr, tpr) ret[j] = (auc, fpr, tpr, thresh) return ret def show_stats(ret): import matplotlib.pyplot as plt n_cols = 4 n_rows = len(ret) // n_cols + 1 for j, (auc, fpr, tpr, thresh) in enumerate(ret): # Plot stats plt.subplot(n_rows, n_cols, j + 1) plt.plot(fpr, tpr) plt.title('AUC = {:.2f}%'.format(auc * 100)) plt.show() matdata = loadmat('svm_result_mix.mat') target = matdata['targets'] output = matdata['outputs'] ret = compute_stats(output, target) save_data(ret, 'stats_attr_svm_mix.pkl') show_stats(ret)
n_cols = 4 n_rows = len(ret) // n_cols + 1 for j, (auc, fpr, tpr, thresh) in enumerate(ret): # Plot stats plt.subplot(n_rows, n_cols, j) plt.plot(fpr, tpr) plt.title('AUC = {:.2f}%'.format(auc * 100)) plt.show() if __name__ == '__main__': dataset_file = 'data_{0}.pkl'.format(args.dataset[0]) model_file = 'model_{0}.pkl'.format(args.model[0]) out_file = 'stats.pkl' if args.output is None else \ 'stats_{0}.pkl'.format(args.output) if not args.display_only: dataset = load_data(dataset_file) model = load_data(model_file) output = compute_output(model, dataset.test) ret = compute_stats(output, dataset.test.target.cpu_data) save_data(ret, out_file) ret = load_data(out_file) show_stats(ret)