MNIST手写数字的识别——CNN篇

这里贴一个用nolearn，lasagne训练CNN的例子，数据集嘛，当然是MNIST咯，keras暂时还没研究过，但nolearn训练CNN真的炒鸡炒鸡方便啊

这里简单说下CNN的结构，首先是输入层，是一个1*28*28的图像矩阵，用32个5*5*1的滤波器去虑，得到32*24*24的hidden layer，然后对这个东西进行（2,2）的maxpool，结果是32*12*12的hidden layer，然后在用17个32*5*5的滤波器去过滤，得到17*8*8的hidden layer，然后在进行（2,2）的maxpool，得到17*4*4的hidden layer。先把这个hidde layer投射到56个神经元的hidden layer（这里就像普通的神经网络了，所以叫dense layer），最后是输出层，输出有10个，用softmax进行判定。这里和Coursera上的AndrewNg老师的作业不太一样，因为作业中是用10个2分的logistic regression classifier进行分类的，所以对于单个training example，其造成的cost 是10部分的相加，但对于softmax，单个training example的cost直接就是一个东西。

# coding=utf-8# 按别人的改的# 版权未知，盗版不究# typhoonbxq# the University of Hong Kongfrom urllib import urlretrieveimport cPickle as pickleimport osimport gzipimport numpy as np# import theanoimport lasagneimport csvfrom lasagne import layersfrom lasagne.updates import nesterov_momentumfrom nolearn.lasagne import NeuralNetdef load_dataset():    url = 'http://deeplearning.net/data/mnist/mnist.pkl.gz'    filename = 'mnist.pkl.gz'    if not os.path.exists(filename):        print("Downloading MNIST dataset...")        urlretrieve(url, filename)    with gzip.open(filename, 'rb') as f:        data = pickle.load(f)    X_train, y_train = data[0]    X_val, y_val = data[1]    X_test, y_test = data[2]    X_train = X_train.reshape((-1, 1, 28, 28))    X_val = X_val.reshape((-1, 1, 28, 28))    X_test = X_test.reshape((-1, 1, 28, 28))    y_train = y_train.astype(np.uint8)    y_val = y_val.astype(np.uint8)    y_test = y_test.astype(np.uint8)    return X_train, y_train, X_val, y_val, X_test, y_testX_train, y_train, X_val, y_val, X_test, y_test = load_dataset()# Set the parameters for the CNNnet1 = NeuralNet(    layers=[('input', layers.InputLayer),            ('conv2d1', layers.Conv2DLayer),            ('maxpool1', layers.MaxPool2DLayer),            ('conv2d2', layers.Conv2DLayer),            ('maxpool2', layers.MaxPool2DLayer),           # ('dropout1', layers.DropoutLayer),            ('dense', layers.DenseLayer),            #('dropout2', layers.DropoutLayer),            ('output', layers.DenseLayer),            ],    # input layer    input_shape=(None, 1, 28, 28),    # layer conv2d1    conv2d1_num_filters=32,    conv2d1_filter_size=(5, 5),    conv2d1_nonlinearity=lasagne.nonlinearities.rectify,    conv2d1_W=lasagne.init.GlorotUniform(),    # layer maxpool1    maxpool1_pool_size=(2, 2),    # layer conv2d2    conv2d2_num_filters=17,    conv2d2_filter_size=(5, 5),    conv2d2_nonlinearity=lasagne.nonlinearities.rectify,    # layer maxpool2    maxpool2_pool_size=(2, 2),    # dropout1    #dropout1_p=0.5,    # dense    dense_num_units=56,    dense_nonlinearity=lasagne.nonlinearities.rectify,    # dropout2    #dropout2_p=0.5,    # output    output_nonlinearity=lasagne.nonlinearities.softmax,    output_num_units=10,    # optimization method params    update=nesterov_momentum,    update_learning_rate=0.01,    update_momentum=0.9,# Below is a very important parameter, increasing max_epochs will increase the prediction accuracy# I suggest this is the maximum of the turn for which we update the parameters# I remember when training a CNN, we limit the traing time    max_epochs=5,    verbose=1,    )# Train the networknn = net1.fit(X_train, y_train)preds = net1.predict(X_test)l = len(preds)count = 0f0 = open('F:\\result.csv','wb')f1 = csv.writer(f0)Y = y_test.tolist()for i in range(0,l):    f1.writerow([Y[i],preds[i]])    if(preds[i] == y_test[i]):        count = count + 1acc = count * 100.0 / lprint "The accuracy is %.2f%%"%(acc)f0.close()

然后贴个运行的结果，

可以看出来，正确率是非常高的，这说明……（进入实验报告模式）

最后我想贴一张纸证明这个30307是怎么计算的，也算是一个基本功吧。