#source_train_images = "D:/project2 AI/facedata/facedatatrain"
#source_train_labels = "D:/project2 AI/facedata/facedatatrainlabels"
#source_test_images = "D:/project2 AI/facedata/facedatatest"
#source_test_labels = "D:/project2 AI/facedata/facedatatestlabels"
source_train_images = "/Users/jainipatel/Downloads/data/facedata/facedatatrain"
source_train_labels = "/Users/jainipatel/Downloads/data/facedata/facedatatrainlabels"
source_test_images = "/Users/jainipatel/Downloads/data/facedata/facedatatest"
source_test_labels = "/Users/jainipatel/Downloads/data/facedata/facedatatestlabels"
fetch_data_train = rd.load_data(source_train_images, 451, 70, 60)
fetch_data_test = rd.load_data(source_test_images, 150, 70, 60)
Y_train_labels = labels = rd.load_label(source_train_labels)
X_train = rd.matrix_transformation(fetch_data_train, 70, 60)
X_test = rd.matrix_transformation(fetch_data_test, 70, 60)
Y_test_labels = rd.load_label(source_test_labels)
tem = 0.99
accuracy_array = []
percent_training = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
total_training_time = 0
start1 = time.time()
for i in range(0, 10):
start = time.time()
tem -= 0.10
if tem < 0:
tem = 0.001
x_train, x_test, y_train, y_test = train_test_split(X_train,
source_train_images = "/Users/jainipatel/Downloads/data/digitdata/trainingimages"
source_train_labels = "/Users/jainipatel/Downloads/data/digitdata/traininglabels"
source_test_images = "/Users/jainipatel/Downloads/data/digitdata/testimages"
source_test_labels = "/Users/jainipatel/Downloads/data/digitdata/testlabels"
# source_train_images = "D:/project2 AI/digitdata/trainingimages"
# source_train_labels = "D:/project2 AI/digitdata/traininglabels"
# source_test_images = "D:/project2 AI/digitdata/testimages"
# source_test_labels = "D:/project2 AI/digitdata/testlabels"
fetch_data_train = rd.load_data(source_train_images, 5000, 28, 28)
fetch_data_test = rd.load_data(source_test_images, 1000, 28, 28)
Y_train_labels = labels = rd.load_label(source_train_labels)
X_train = rd.matrix_transformation(fetch_data_train, 28, 28)
X_test = rd.matrix_transformation(fetch_data_test, 28, 28)
Y_test_labels = rd.load_label(source_test_labels)
# print(len(Y1))
tem = 0.99
accuracy_array = []
percent_training = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
total_training_time = 0
start1 = time.time()
for i in range(0, 10):
start = time.time()
tem -= 0.10
if tem < 0:
tem = 0.001