MNIST

이전 포스팅에서 다룬 MNIST 손글씨 인식 결과를 이용해서 그래프로 확인하는 예제입니다.

예제 코드

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint, EarlyStopping

import matplotlib.pyplot as plt
import os
import numpy

MODEL_SAVE_FOLDER_PATH = './model/'

if not os.path.exists(MODEL_SAVE_FOLDER_PATH):
  os.mkdir(MODEL_SAVE_FOLDER_PATH)

model_path = MODEL_SAVE_FOLDER_PATH + 'mnist-' + '{epoch:02d}-{val_loss:.4f}.hdf5'

cb_checkpoint = ModelCheckpoint(filepath=model_path, monitor='val_loss',
                                verbose=1, save_best_only=True)

cb_early_stopping = EarlyStopping(monitor='val_loss', patience=10)

(X_train, Y_train), (X_validation, Y_validation) = mnist.load_data()

X_train = X_train.reshape(X_train.shape[0], 784).astype('float64') / 255
X_validation = X_validation.reshape(X_validation.shape[0], 784).astype('float64') / 255

Y_train = np_utils.to_categorical(Y_train, 10)
Y_validation = np_utils.to_categorical(Y_validation, 10)

model = Sequential()
model.add(Dense(512, input_dim=784, activation='relu'))
model.add(Dense(10, activation='softmax'))

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

history = model.fit(X_train, Y_train, validation_data=(X_validation, Y_validation),
          epochs=30, batch_size=200, verbose=0,
          callbacks=[cb_checkpoint, cb_early_stopping])

print('\nAccuracy: {:.4f}'.format(model.evaluate(X_validation, Y_validation)[1]))

y_vloss = history.history['val_loss']
y_loss = history.history['loss']

x_len = numpy.arange(len(y_loss))
plt.plot(x_len, y_vloss, marker='.', c='red', label="Validation-set Loss")
plt.plot(x_len, y_loss, marker='.', c='blue', label="Train-set Loss")

plt.legend(loc='upper right')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()

실행 결과

그래프를 보면 학습 데이터에 대한 오차는 학습을 계속할수록 줄어들지만, 실제 검증용 데이터에 대한 오차는 일정 수준 이상부터는 큰 차이가 없는 것을 알 수 있습니다.