def checkpoint_save(epoch: int, nn_model: model, nn_optimizer: torch.optim, training_loss: list, validation_loss: list, model_name: str, locations: dict, args):
"""
Save model checkpoints
"""
checkpoint_name = model_name.replace('.tar','_chkepo_{0}.tar'.format(str(epoch).zfill(3)))
torch.save({'epoch':epoch,
'model_state_dict':nn_model.state_dict(),
'optimizer_state_dict':nn_optimizer.state_dict(),
'training_loss':training_loss,
'validation_loss':validation_loss,
'arguments':args},
locations['model_loc']+'/'+checkpoint_name)
# one_hot_labels[i][train_label[i]] = 1
# train_labels_1hot = one_hot_labels
# train_labels_1hot.shape
# load data
print("Loading data .................")
# train_image = load_data(train_image_path)
test_image = load_data(test_image_path)
label_name = ['city', 'forest', 'sea']
print("Starting test image .......................................")
# Lấy dữ liệu đã train lên để test
VGG13 = Model()
VGG13.load_weights('trained_model_1v.hdf5')
def test(index):
# predict() sử dụng mô hình để dự đoán ảnh đầu vào.
# predict() hoạt động ntn ???????????????????======================================================
predict = VGG13.predict(test_image[index].reshape((1,HEIGHT,WIDTH,DEEP)))
plt.imshow(test_image[index])
print("Du doan nhan cua anh:")
print(predict)
if np.argmax(predict) == 0:
print('I am sure this is city')
plt.show()
elif np.argmax(predict) == 1:
print('I am sure this is forest')