ex2_1_ann_mnist_cl.py

# 1.분류 ANN을 위한 인공지능 모델 구현
from keras import layers, models

# 2. 분산 방식 모델링을 포함하는 함수형 구현 
def ANN_models_func(Nin, Nh, Nout):
    x = layers.Input(shape=(Nin,))
    h = layers.Activation('relu')(layers.Dense(Nh)(x))
    y = layers.Activation('softmax')(layers.Dense(Nout)(h))
    model = models.Model(x, y)
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 3. # 연쇄 방식 모델링을 포함하는 함수형 구현
def ANN_seq_func(Nin, Nh, Nout):
    model = models.Sequential()
    model.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,)))
    model.add(layers.Dense(Nout, activation='softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam', metrics=['accuracy'])
    return model

# 4. 분산 방식 모델링을 포함하는 객체지향형 구현
class ANN_models_class(models.Model):
    def __init__(self, Nin, Nh, Nout):
        # Prepare network layers and activate functions
        hidden = layers.Dense(Nh)
        output = layers.Dense(Nout)
        relu = layers.Activation('relu')
        softmax = layers.Activation('softmax')

        # Connect network elements
        x = layers.Input(shape=(Nin,))
        h = relu(hidden(x))
        y = softmax(output(h))

        super().__init__(x, y)
        self.compile(loss='categorical_crossentropy',
                     optimizer='adam', metrics=['accuracy'])

# 5. 연쇄 방식 모델링을 포함하는 객체지향형 구현
class ANN_seq_class(models.Sequential):
    def __init__(self, Nin, Nh, Nout):
        super().__init__()
        self.add(layers.Dense(Nh, activation='relu', input_shape=(Nin,)))
        self.add(layers.Dense(Nout, activation='softmax'))
        self.compile(loss='categorical_crossentropy',
                     optimizer='adam', metrics=['accuracy'])

# 6. 분류 ANN에 사용할 데이터 불러오기
import numpy as np
from keras import datasets  # mnist
from keras.utils import np_utils  # to_categorical


def Data_func():
    (X_train, y_train), (X_test, y_test) = datasets.mnist.load_data()

    Y_train = np_utils.to_categorical(y_train)
    Y_test = np_utils.to_categorical(y_test)

    L, W, H = X_train.shape
    X_train = X_train.reshape(-1, W * H)
    X_test = X_test.reshape(-1, W * H)

    X_train = X_train / 255.0
    X_test = X_test / 255.0

    return (X_train, Y_train), (X_test, Y_test)


# 7. 분류 ANN 학습 결과 그래프 구현
import matplotlib.pyplot as plt
from keraspp.skeras import plot_loss, plot_acc

# 8. 분류 ANN 학습 및 성능 분석
def main():
    Nin = 784
    Nh = 100
    number_of_class = 10
    Nout = number_of_class

    # model = ANN_models_func(Nin, Nh, Nout)
    # model = ANN_seq_func(Nin, Nh, Nout)
    # model = ANN_models_class(Nin, Nh, Nout)
    model = ANN_seq_class(Nin, Nh, Nout)
    (X_train, Y_train), (X_test, Y_test) = Data_func()

    history = model.fit(X_train, Y_train, epochs=5, batch_size=100, validation_split=0.2)
    performace_test = model.evaluate(X_test, Y_test, batch_size=100)
    print('Test Loss and Accuracy ->', performace_test)

    plot_loss(history)
    plt.show()
    plot_acc(history)
    plt.show()
    
# Run code
if __name__ == '__main__':
    main()