same_face = []
diff_face = []
for i in range(9999):
# 如果两张图片标签相同，则将两个图片作为相同组样本
if labels[i] == labels[i + 1]:
same_face.append([images[i], images[i + 1]])
# 如果两张图片标签不同，则作为差异组样本
else:
diff_face.append([images[i], images[i + 1]])
# 转化为numpy.ndarray，便于传入keras构造的神经网络进行计算
x_train1 = np.array([f[0] for f in same_face + diff_face])
x_train2 = np.array([f[1] for f in same_face + diff_face])
y_train = np.array([1 for i in same_face] + [0 for j in diff_face])
# 如果有模型文件存在，则导入之前的模型参数继续计算
if os.path.exists("H:/face_detection/LeNet.h5"):
LeNet = load_model("H:/face_detection/LeNet.h5")
print("model loaded")
# 使用重构后的generator进行训练
LeNet.fit_generator(generator=get_train_batch([x_train1, x_train2],
y_train,
batch_size=100),
steps_per_epoch=100,
epochs=5)
# 保存模型
LeNet.save("H:/face_detection/face_detection/LeNet.h5")
