ex3_2_dnn_cifar10_cl.py

# 분류 DNN 모델 구현 ########################
from keras import layers, models


class DNN(models.Sequential):
    def __init__(self, Nin, Nh_l, Pd_l, Nout):
        super().__init__()

        self.add(layers.Dense(Nh_l[0], activation='relu',
                              input_shape=(Nin,), name='Hidden-1'))
        self.add(layers.Dropout(Pd_l[0]))

        self.add(layers.Dense(Nh_l[1], activation='relu',
                              name='Hidden-2'))
        self.add(layers.Dropout(Pd_l[1]))

        self.add(layers.Dense(Nout, activation='softmax'))

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


# 데이터 준비 ##############################
import numpy as np
from keras import datasets
from keras.utils import np_utils


def Data_func():
    (X_train, y_train), (X_test, y_test) = datasets.cifar10.load_data()

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

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

    X_train = X_train / 255.0
    X_test = X_test / 255.0

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


# 학습 효과 분석 ##############################
from keraspp.skeras import plot_loss, plot_acc
import matplotlib.pyplot as plt


# 분류 DNN 학습 및 테스팅 ####################
def main(Pd_l=[0.0, 0.0]):
    Nh_l = [100, 50]
    number_of_class = 10
    Nout = number_of_class

    (X_train, Y_train), (X_test, Y_test) = Data_func()
    model = DNN(X_train.shape[1], Nh_l, Pd_l, Nout)
    history = model.fit(X_train, Y_train, epochs=100, 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_acc(history, '(a) 학습을 통한 정확도의 변화')
    plt.show()
    plot_loss(history, '(b) 학습을 통한 손실의 변화')
    plt.show()


if __name__ == '__main__':
    main(Pd_l=[0.0, 0.0])