TensorFlow
TensorFlow 기초 25 - MNIST로 CNN 처리
코딩탕탕
2022. 12. 7. 12:03
# MNIST로 CNN 처리
# 1) Conv(이미지 특징 추출) + Pooling(Conv 결과를 샘플링 - Conv 결과인 Feature map의 크기를 다시 줄임)
# 2) 원래의 이미지 크기를 줄여 최종적으로 FCLayer를 진행(Conv + Pooling 결과 다차원 배열 자료를 1차원으로 만들어 한 줄로 세우기)
# 3) Dense 층으로 넘겨 분류 작업 수행
import tensorflow as tf
from keras import datasets, layers, models
(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape) # (60000, 28, 28) (60000,) (10000, 28, 28) (10000,)
# CNN은 채널을 사용하기 때문에 3차원 데이터를 4차원으로 변경
x_train = x_train.reshape((60000, 28, 28, 1)) # 흑백은 channel이 1개
x_test = x_test.reshape((10000, 28, 28, 1)) # 예) x_test[3, 12, 13, 1]
# print(x_train.ndim)
# print(x_train[:1])
x_train = x_train / 255.0
x_test = x_test / 255.0
# print(x_train[:1])
# label의 원핫 처리는 model에게 위임
# model
input_shape = (28, 28, 1)
# Sequential api 사용
model = models.Sequential()
model.add(layers.Conv2D(filters=16, kernel_size=(3, 3), strides=(1, 1), padding='valid',
activation='relu', input_shape=input_shape))
model.add(layers.MaxPool2D(pool_size=(2, 2)))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='valid', activation='relu'))
model.add(layers.MaxPool2D(pool_size=(2, 2))) # filters는 Dense의 unit과 비슷한 개념이다.
model.add(layers.Dropout(rate=0.2))
model.add(layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='valid', activation='relu'))
model.add(layers.MaxPool2D(pool_size=(2, 2))) # kernel_size 명은 생략 가능
model.add(layers.Dropout(rate=0.2))
model.add(layers.Flatten()) # FCLayer(Fully Connected Layer) : 2차원 1차원으로 변경(모든 배열자료를 한 줄로 세움)
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Dense(10, activation='softmax'))
print(model.summary())
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\
from keras.callbacks import EarlyStopping
es = EarlyStopping(monitor='val_loss', patience=3) # patience는 많이 줘야됨
history = model.fit(x_train, y_train, batch_size=128, epochs=1000, verbose=0, validation_split=0.2,
callbacks=[es])
# history 저장
import pickle
history = history.history
with open('cnn2_history.pickle', 'wb') as f:
pickle.dump(history, f)
# 모델 평가
train_loss, train_acc = model.evaluate(x_train, y_train)
test_loss, test_acc = model.evaluate(x_test, y_test)
print('train_loss : {}, train_acc : {}'.format(train_loss, train_acc))
print('test_loss : {}, test_acc : {}'.format(test_loss, test_acc))
# 모델 저장
model.save('cnn2_model.h5')
print()
# --- 학습된 모델로 작업 ---
mymodel = tf.keras.models.load_model('cnn2_model.h5')
# predict
import numpy as np
print('예측값 :', np.argmax(mymodel.predict(x_test[:1])))
print('예측값 :', np.argmax(mymodel.predict(x_test[[0]]))) # 위랑 같은 의미
print('실제값 :', y_test[0])
# 시각화
import matplotlib.pyplot as plt
with open('cnn2_history.pickle', 'rb') as f:
history = pickle.load(f)
def plot_acc_func(title=None):
plt.plot(history['accuracy'], label='accuracy')
plt.plot(history['val_accuracy'], label='val_accuracy')
plt.title(title)
plt.xlabel('epochs')
plt.ylabel(title)
plt.legend()
plot_acc_func('accuracy')
plt.show()
def plot_loss_func(title=None):
plt.plot(history['loss'], label='loss')
plt.plot(history['val_loss'], label='val_loss')
plt.title(title)
plt.xlabel('epochs')
plt.ylabel(title)
plt.legend()
plot_acc_func('loss')
plt.show()
<console>
(60000, 28, 28) (60000,) (10000, 28, 28) (10000,)
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 16) 160
max_pooling2d (MaxPooling2D (None, 13, 13, 16) 0
)
dropout (Dropout) (None, 13, 13, 16) 0
conv2d_1 (Conv2D) (None, 11, 11, 32) 4640
max_pooling2d_1 (MaxPooling (None, 5, 5, 32) 0
2D)
dropout_1 (Dropout) (None, 5, 5, 32) 0
conv2d_2 (Conv2D) (None, 3, 3, 64) 18496
max_pooling2d_2 (MaxPooling (None, 1, 1, 64) 0
2D)
dropout_2 (Dropout) (None, 1, 1, 64) 0
flatten (Flatten) (None, 64) 0
dense (Dense) (None, 64) 4160
dropout_3 (Dropout) (None, 64) 0
dense_1 (Dense) (None, 32) 2080
dropout_4 (Dropout) (None, 32) 0
dense_2 (Dense) (None, 10) 330
=================================================================
Total params: 29,866
Trainable params: 29,866
Non-trainable params: 0
_________________________________________________________________
None
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313/313 [==============================] - 1s 3ms/step - loss: 0.0486 - accuracy: 0.9870
train_loss : 0.030139632523059845, train_acc : 0.9913166761398315
test_loss : 0.04862738028168678, test_acc : 0.9869999885559082
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 92ms/step
예측값 : 7
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 13ms/step
예측값 : 7
실제값 : 7
CNN은 채널을 사용하기 때문에 3차원 그 전에 데이터를 4차원으로 변경해야 된다.(예) x_test[3, 12, 13, 1])
label의 원핫 처리는 model에게 위임하였다.
