【keras】LeNet实现

#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Time: 2018/8/15
# @Author: xfLi

import numpy as np

from keras.models import Sequential
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.core import Dense, Flatten, Activation
from keras.datasets import mnist
from keras.utils import np_utils
from keras.optimizers import SGD, Adam, RMSprop

NB_EPOCH = 20
BATCH_SIZE = 128
VERBOSE = 1
OPTIMIZER = Adam()
VALIDATION_SPLIT=0.2
IMG_ROWS, IMG_COLS = 28, 28 # input image dimensions
NB_CLASSES = 10  # number of outputs = number of digits
INPUT_SHAPE = (1, IMG_ROWS, IMG_COLS)
np.random.seed(1671)

#定义网络
class LeNet(object):
    @staticmethod
    def build(input_shape, classes):
        model = Sequential()
        model.add(Conv2D(20, kernel_size=5, padding='same',
                         input_shape=input_shape))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        model.add(Conv2D(50, kernel_size=5, padding='same'))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        model.add(Flatten())
        model.add(Dense(500))
        model.add(Activation('relu'))
        model.add(Dense(classes))
        model.add(Activation('softmax'))

        return model

#加载数据
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

x_train = x_train[:, np.newaxis, :, :]
x_test = x_test[:, np.newaxis, :, :]
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

y_train = np_utils.to_categorical(y_train, NB_CLASSES)
y_test = np_utils.to_categorical(y_test, NB_CLASSES)

model = LeNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(optimizer=OPTIMIZER,
              loss='categorical_crossentropy',
              metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=BATCH_SIZE,
                    epochs=NB_EPOCH, verbose=VERBOSE,
                    validation_split=VALIDATION_SPLIT)
score = model.evaluate(x_test, y_test, verbose=VERBOSE)
print('\nTest score:', score[0])
print('test accuracy:', score[1])