Chapter16_MNISTDeep.py

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.callbacks import ModelCheckpoint, EarlyStopping

import matplotlib.pyplot as plt
import numpy
import os
import tensorflow as tf

seed=0
numpy.random.seed(seed)
tf.set_random_seed(seed)

(X_train, Y_train), (X_test, Y_test) = mnist.load_data()

X_train = X_train.reshape(X_train.shape[0],28 , 28, 1).astype('float32') / 255
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255
Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(28,28,1), activation='relu'))
model.add(Conv2D(64, (3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

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

MODEL_DIR = './model/'
if not os.path.exists(MODEL_DIR):
    os.mkdir(MODEL_DIR)
modelpath="./model/{epoch:02d}-{val_loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath=modelpath, monitor='val_loss', verbose=1, save_best_only=True)
early_stopping_callback = EarlyStopping(monitor='val_loss', patience= 10)

history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=30, batch_size=200, verbose=0, callbacks=[early_stopping_callback, checkpoint])
print("\n Test Accuracy: %.4f" % (model.evaluate(X_test, Y_test)[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='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c="blue", label='Trainset_loss')

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