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- import data
- from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
- from keras.models import Sequential, load_model
- model = Sequential()
- model.add(Conv2D(16, (3, 3)))
- model.add(MaxPooling2D(pool_size=(2, 2)))
- model.add(Conv2D(16, (3, 3)))
- model.add(Flatten())
- model.add(Dense(128, activation='relu'))
- model.add(Dense(data.n_classes, activation='softmax'))
- model.compile(loss='categorical_crossentropy', optimizer='adam')
- model.fit(data.x_train, data.y_train)
- model.save('model.h5')
- model = load_model('model.h5')
- y_predicted = model.predict(data.x_test)
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