영상입력 이진분류모델 레시피(깊은 컨볼루션 신경망 모델)

# 1. 사용할 패키지 불러오기

import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import Conv2D, MaxPooling2D, Flatten
from keras.layers import Dropout
%matplotlib inline

# 2. 데이터 생성하기
width = 28
height = 28

# 훈련셋과 시험셋 불러오기
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, width, height, 1).astype('float32') / 255.0
x_test = x_test.reshape(10000, width, height, 1).astype('float32') / 255.0

# 훈련셋과 검증셋 분리
x_val = x_train[50000:]
y_val = y_train[50000:]
x_train = x_train[:50000]
y_train = y_train[:50000]

# 데이터셋 전처리 : 홀수는 1, 짝수는 0으로 변환
y_train = y_train % 2
y_val = y_val % 2
y_test = y_test % 2

# 3. 모델 구성하기
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(width, height, 1)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# 4. 모델 학습과정 설정하기
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])

# 5. 모델 학습시키기
hist = model.fit(x_train, y_train, epochs=30, batch_size=32, validation_data=(x_val, y_val))

# 6. 학습과정 살펴보기
fig, loss_ax = plt.subplots()

acc_ax = loss_ax.twinx()

loss_ax.plot(hist.history['loss'], 'blue', label='train loss')
loss_ax.plot(hist.history['val_loss'], 'red', label='val loss')
loss_ax.set_ylim([0.0, 0.5])

acc_ax.plot(hist.history['acc'], 'purple', label='train acc')
acc_ax.plot(hist.history['val_acc'], 'green', label='val acc')
acc_ax.set_ylim([0.8, 1.0])

loss_ax.set_xlabel('epoch')
loss_ax.set_ylabel('loss')
acc_ax.set_ylabel('accuray')

loss_ax.legend(loc='upper left')
acc_ax.legend(loc='lower left')

plt.show()

# 7. 모델 평가하기
loss_and_metrics = model.evaluate(x_test, y_test, batch_size=32)
print(loss_and_metrics)

# 8. 모델 사용하기
yhat_test = model.predict(x_test, batch_size=32)

plt_row = 5
plt_col = 5

plt.rcParams["figure.figsize"] = (10,10)

f, axarr = plt.subplots(plt_row, plt_col)

for i in range(plt_row*plt_col):
       sub_plt = axarr[int(i/plt_row), int(i%plt_col)]
       sub_plt.axis('off')
       sub_plt.imshow(x_test[i].reshape(width, height))

sub_plt_title = "R: "
if y_test[i]:
        sub_plt_title = sub_plt_title + "odd "
else:
        sub_plt_title = sub_plt_title + "even "

sub_plt_title = sub_plt_title + "P: "
if yhat_test[i] >= 0.5:
        sub_plt_title = sub_plt_title + "odd "
else:
        sub_plt_title = sub_plt_title + "even "

sub_plt.set_title(sub_plt_title)

plt.show()

[출처] 블록과 함께하는 파이썬 딥러닝 케라스, 김태영 지음, DigitalBooks, p233~237