[PYTHON/KERAS] 학습 모델 로드하기

import keras.datasets.mnist as mnist
import keras.models as models
import keras.utils as utils
import numpy as np

# 데이터를 로드한다.
(trainInputNDArray, trainCorrectOutputNDArray), (testInputNDArray, testCorrectOutputNDArray) = mnist.load_data()

testInputNDArray         = testInputNDArray.reshape(10000, 784).astype("float32") / 255.0
testCorrectOutputNDArray = utils.np_utils.to_categorical(testCorrectOutputNDArray)

testRandomIndexNDArray = np.random.choice(testInputNDArray.shape[0], 5)
testInputNDArray       = testInputNDArray[testRandomIndexNDArray]

# 모델 데이터를 로드한다.
model = models.load_model("model.data")

# 모델을 사용한다.
testOutputNDArray = model.predict_classes(testInputNDArray)

for i in range(5):
    print("정답 : " + str(np.argmax(testCorrectOutputNDArray[testRandomIndexNDArray[i]])) + ", 예측 : " + str(testOutputNDArray[i]))