def load2d(test=False, cols=None):
X, y = load(test=test)
X = X.reshape(-1, 1, 96, 96)
return X, y
def vectorized_result(j):
e = np.zeros(10)
e[j] = 1.0
return e
def normalize_input(X):
return np.array([x / 255 for x in X])
images, labels = loadlocal_mnist(
images_path='../Datasets/MNIST_digits/train-images-idx3-ubyte',
labels_path='../Datasets/MNIST_digits/train-labels-idx1-ubyte')
#net = network.Network([784, 64, 40, 10])
net = network.load('MNIST_digits', 20)
#images = images[:10000]
#labels = labels[:10000]
for ep in range(10, 100):
example = 0
ep_loss = 0
start = time.time()
for X, y in zip(images, labels):
ep_loss += net.train(normalize_input(X), vectorized_result(y), ep)
#ep_loss += network.mse_loss(vectorized_result(y), net.feedforward(X))
example += 1
if example % 10000 == 0:
end = time.time()
if example > 0:
def load2d(test=False, cols=None):
X, y = load(test=test)
X = X.reshape(-1, 1, 96, 96)
return X, y
img = cv2.imread("/home/simon/Downloads/george-clooney.jpg")
img = cv2.imread("/home/simon/Pictures/smoss.jpg")
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = detect(grey)
print rects, len(rects)
face = rects[0]
grey = grey[face[1]:face[3],face[0]:face[2]]/255.0
grey = cv2.resize(grey, (96,96))
draw_rects(img, rects, (0,255,0))
cv2.imshow("X", grey)
net1 = pickle.load(file('net1.pickle'))
net2 = pickle.load(file('net2.pickle'))
sample1 = load(test=True)[0][6:7]
print sample1
sample2 = load2d(test=True)[0][6:7]
print grey.shape
sample2 = grey.reshape(-1,1,96,96)
sample2 = np.array(sample2, np.float32)
print sample2
y_pred1 = net1.predict(sample1)[0]
import time
for i in range(100):
y_pred2 = net2.predict(sample2)[0]
print i
img = cv2.imread("/home/simon/Downloads/george-clooney.jpg")
img = cv2.imread("/home/simon/Pictures/smoss.jpg")
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = detect(grey)
print rects, len(rects)
face = rects[0]
grey = grey[face[1]:face[3], face[0]:face[2]] / 255.0
grey = cv2.resize(grey, (96, 96))
draw_rects(img, rects, (0, 255, 0))
cv2.imshow("X", grey)
net1 = pickle.load(file('net1.pickle'))
net2 = pickle.load(file('net2.pickle'))
sample1 = load(test=True)[0][6:7]
print sample1
sample2 = load2d(test=True)[0][6:7]
print grey.shape
sample2 = grey.reshape(-1, 1, 96, 96)
sample2 = np.array(sample2, np.float32)
print sample2
y_pred1 = net1.predict(sample1)[0]
import time
for i in range(100):
y_pred2 = net2.predict(sample2)[0]
print i
