diff options
| -rwxr-xr-x | train.py | 17 |
1 files changed, 11 insertions, 6 deletions
@@ -9,6 +9,8 @@ import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import sys import random +import os +import psutil from random import randint from sklearn.neighbors import KNeighborsClassifier @@ -103,7 +105,10 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args): e_vecs = np.fliplr(e_vecs).T[:M] deviations_tr = np.flip(deviations_tr) deviations_tst = np.flip(deviations_tst) - + if (args.classifyalt): + faces_train = np.mean(faces_train.reshape([n_faces,8,2576]), axis=1) + target_train = range(n_faces) + raw_faces_train = np.mean(raw_faces_train.reshape([n_faces,8,2576]), axis=1) faces_train = np.dot(faces_train, e_vecs.T) faces_test = np.dot(faces_test, e_vecs.T) @@ -172,11 +177,11 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args): for i in range (104): j = int(np.floor(i/2)) targer_prob_vec [i] = targer_prob[i][j] - avg_targer_prob = np.zeros(52) - for i in range (52): + avg_targer_prob = np.zeros(n_faces) + for i in range (n_faces): avg_targer_prob[i] = (targer_prob_vec[2*i] + targer_prob_vec[2*i + 1])/2 #WE CAN FIX THIS BY RESHAPING TARGER_PROB_VEC AND TAKING THE MEAN ON THE RIGHT AXIS - plt.bar(range(52), avg_targer_prob) + plt.bar(range(n_faces), avg_targer_prob) plt.show() #Better n_neighbors = 2 @@ -200,10 +205,9 @@ def main(): parser.add_argument("-l", "--lda", help="Use LDA", action='store_true') parser.add_argument("-r", "--reconstruct", help="Use PCA reconstruction, specify face NR", type=int, default=0) parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true') - parser.add_argument("-q", "--pca_r", help="Use Reduced PCA", action='store_true') parser.add_argument("-pr", "--prob", help="Certainty on each guess", action='store_true') - + parser.add_argument("-alt", "--classifyalt", help="Alternative method ON", action='store_true') args = parser.parse_args() raw_faces = genfromtxt(args.data, delimiter=',') @@ -218,6 +222,7 @@ def main(): accuracy[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) #plot plt.plot(range(args.eigen, args.reigen), 100*accuracy) plt.xlabel('Number of Eigenvectors used (M)') |