diff options
Diffstat (limited to 'train.py')
| -rwxr-xr-x | train.py | 10 |
1 files changed, 5 insertions, 5 deletions
@@ -251,17 +251,17 @@ def main(): target_pred, distances[i] = test_model(args.eigen, faces_train[i], faces_test, target_train[i], target_test, args) target_pred = np.argmin(distances, axis=0) elif args.reigen: - target_pred = np.zeros((args.reigen-args.eigen, 2*n_faces)) - accuracy = np.zeros((args.reigen-args.eigen, 2*n_faces)) - rec_error = np.zeros((args.reigen-args.eigen, 2*n_faces)) + target_pred = np.zeros((args.reigen-args.eigen, target_test.shape[0])) + accuracy = np.zeros(args.reigen-args.eigen) + rec_error = np.zeros((args.reigen-args.eigen, target_test.shape[0])) for M in range(args.eigen, args.reigen): start = timer() - target_pred[i], rec_error[M - args.eigen] = test_model(M, faces_train, faces_test, target_train, target_test, args) + target_pred[M - args.eigen], rec_error[M - args.eigen] = test_model(M, faces_train, faces_test, target_train, target_test, args) end = timer() print("Run with", M, "eigenvalues completed in ", end-start, "seconds") print("Memory Used:", psutil.Process(os.getpid()).memory_info().rss) - accuracy[i] = accuracy_score(target_test, target_pred[i]) + accuracy[M - args.eigen] = accuracy_score(target_test, target_pred[M-args.eigen]) # Plot print('Max efficiency of ', max(accuracy), '% for M =', np.argmax(accuracy)) plt.plot(range(args.eigen, args.reigen), 100*accuracy) |