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- import tensorflow
- from tensorflow.keras.datasets import mnist
- from tensorflow.keras.models import load_model
- from tensorflow.keras.layers import Dropout, Flatten
- import matplotlib.pyplot as plt
- (trainX, trainY), (testX, testY) = mnist.load_data()
- img_rows, img_cols = 28, 28
- trainX = trainX.reshape(trainX.shape[0], img_rows, img_cols, 1)
- testX = testX.reshape(testX.shape[0], img_rows, img_cols, 1)
- input_shape = (img_rows, img_cols, 1) # 1 karena grayscale, 3 untuk berwarna
- trainX = trainX.astype('float32')
- testX = testX.astype('float32')
- trainX /= 255
- testX /= 255
- testY = tensorflow.keras.utils.to_categorical(testY, 10)
- model = load_model("mnist.h5")
- score = model.evaluate(testX, testY)
- print(score)
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