from YouTubeFacesDB import YouTubeFacesDB from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.optimizers import SGD, Adam, RMSprop, Adagrad, Adadelta from keras.regularizers import l2, activity_l2 from keras.utils import np_utils ############################################################################### # Load the data from disk ############################################################################### tstart = time() db = YouTubeFacesDB('ytfdb.h5', mean_removal=True, output_type='vector') N = db.nb_samples d = db.input_dim C = db.nb_classes mean_face = db.mean print(N, 'images of size', d, 'loaded in', time() - tstart) ############################################################################### # Split into a training set and a test set ############################################################################### db.split_dataset(validation_size=0.25) ############################################################################### # Train a not very deep network ###############################################################################
from YouTubeFacesDB.vgg import vgg16 import numpy as np import torch.cuda import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variable from pathlib2 import Path import os # Read the script directory, will come in handy later! script_dir = os.path.dirname(__file__) # Read the database file # db = YouTubeFacesDB('/home/ritvik/dl4cv/ytfdb.h5') db = YouTubeFacesDB(os.path.join(script_dir, "ytfdb.h5")) # db = YouTubeFacesDB('/home/ritvik/testdata.h5') db.split_dataset(validation_size=0.2, test_size=0.1) testAcc = [] testLogs = open("testRecognizerLogs.txt", "a") # Check if a previously saved model exists myNetwork = Path("recognizer.pt") if myNetwork.is_file(): print("Saved model exists...!!!") network = torch.load("recognizer.pt") if torch.cuda.is_available(): network.cuda() ######################################### ############TEST THE MODEL###############