Keras 深度学习框架Python Example:CNN/mnist

原文：http://blog.csdn.net/zhoubl668/article/details/45559955

mnist data: http://pan.baidu.com/s/1qCdS6

data.py:

#coding:utf-8"""Author:weponSource:https://github.com/wepe"""import osfrom PIL import Imageimport numpy as np#读取文件夹mnist下的42000张图片，图片为灰度图，所以为1通道，图像大小28*28#如果是将彩色图作为输入,则将1替换为3，并且data[i,:,:,:] = arr改为data[i,:,:,:] = [arr[:,:,0],arr[:,:,1],arr[:,:,2]]def load_data():data = np.empty((42000,1,28,28),dtype="float32")label = np.empty((42000,),dtype="uint8")imgs = os.listdir("./mnist")num = len(imgs)for i in range(num):img = Image.open("./mnist/"+imgs[i])arr = np.asarray(img,dtype="float32")data[i,:,:,:] = arrlabel[i] = int(imgs[i].split('.')[0])return data,label

由于Keras系统升级，cnn.py代码调整如下：

#coding:utf-8'''    GPU run command:        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cnn.py    CPU run command:        python cnn.py'''#导入各种用到的模块组件from __future__ import absolute_importfrom __future__ import print_functionfrom keras.preprocessing.image import ImageDataGeneratorfrom keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Activation, Flattenfrom keras.layers.advanced_activations import PReLUfrom keras.layers.convolutional import Convolution2D, MaxPooling2Dfrom keras.optimizers import SGD, Adadelta, Adagradfrom keras.utils import np_utils, generic_utilsfrom six.moves import rangefrom data import load_dataimport random#加载数据data, label = load_data()#打乱数据index = [i for i in range(len(data))]random.shuffle(index)data = data[index]label = label[index]print(data.shape[0], ' samples')#label为0~9共10个类别，keras要求格式为binary class matrices,转化一下，直接调用keras提供的这个函数label = np_utils.to_categorical(label, 10)################开始建立CNN模型################生成一个modelmodel = Sequential()#第一个卷积层，4个卷积核，每个卷积核大小5*5。1表示输入的图片的通道,灰度图为1通道。#border_mode可以是valid或者full，具体看这里说明：http://deeplearning.net/software/theano/library/tensor/nnet/conv.html#theano.tensor.nnet.conv.conv2d#激活函数用tanh#你还可以在model.add(Activation('tanh'))后加上dropout的技巧: model.add(Dropout(0.5))model.add(Convolution2D(4, 5, 5, border_mode='valid', input_shape=(1,28,28)))model.add(Activation('tanh'))#第二个卷积层，8个卷积核，每个卷积核大小3*3。4表示输入的特征图个数，等于上一层的卷积核个数#激活函数用tanh#采用maxpooling，poolsize为(2,2)model.add(Convolution2D(8, 3, 3, border_mode='valid'))model.add(Activation('tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))#第三个卷积层，16个卷积核，每个卷积核大小3*3#激活函数用tanh#采用maxpooling，poolsize为(2,2)model.add(Convolution2D(16,  3, 3, border_mode='valid'))model.add(Activation('tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))#全连接层，先将前一层输出的二维特征图flatten为一维的。#Dense就是隐藏层。16就是上一层输出的特征图个数。4是根据每个卷积层计算出来的：(28-5+1)得到24,(24-3+1)/2得到11，(11-3+1)/2得到4#全连接有128个神经元节点,初始化方式为normalmodel.add(Flatten())model.add(Dense(128, init='normal'))model.add(Activation('tanh'))#Softmax分类，输出是10类别model.add(Dense(10, init='normal'))model.add(Activation('softmax'))##############开始训练模型###############使用SGD + momentum#model.compile里的参数loss就是损失函数(目标函数)sgd = SGD(l2=0.0,lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)model.compile(loss='categorical_crossentropy', optimizer=sgd,class_mode="categorical")#调用fit方法，就是一个训练过程. 训练的epoch数设为10，batch_size为100．#数据经过随机打乱shuffle=True。verbose=1，训练过程中输出的信息，0、1、2三种方式都可以，无关紧要。show_accuracy=True，训练时每一个epoch都输出accuracy。#validation_split=0.2，将20%的数据作为验证集。model.fit(data, label, batch_size=100, nb_epoch=10,shuffle=True,verbose=1,show_accuracy=True,validation_split=0.2)"""#使用data augmentation的方法#一些参数和调用的方法，请看文档datagen = ImageDataGenerator(        featurewise_center=True, # set input mean to 0 over the dataset        samplewise_center=False, # set each sample mean to 0        featurewise_std_normalization=True, # divide inputs by std of the dataset        samplewise_std_normalization=False, # divide each input by its std        zca_whitening=False, # apply ZCA whitening        rotation_range=20, # randomly rotate images in the range (degrees, 0 to 180)        width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)        height_shift_range=0.2, # randomly shift images vertically (fraction of total height)        horizontal_flip=True, # randomly flip images        vertical_flip=False) # randomly flip images# compute quantities required for featurewise normalization # (std, mean, and principal components if ZCA whitening is applied)datagen.fit(data)for e in range(nb_epoch):    print('-'*40)    print('Epoch', e)    print('-'*40)    print("Training...")    # batch train with realtime data augmentation    progbar = generic_utils.Progbar(data.shape[0])    for X_batch, Y_batch in datagen.flow(data, label):        loss,accuracy = model.train(X_batch, Y_batch,accuracy=True)        progbar.add(X_batch.shape[0], values=[("train loss", loss),("accuracy:", accuracy)] )"""