本文共 5662 字，大约阅读时间需要 18 分钟。

0：环境

本教程的环境是Python2.7、Keras+Tensorflow、sklearn、matplotlib、numpy

Ubuntu18.

1：原理

LeNet结构是LeCun在1998年提出了神经网络结构。本结构在OCR文字识别方面有较大优势，识别精度较高。

网络的结构是：INPUT => CONV => ACT => POOL => CONV => ACT => POOL => FC => ACT => FC => SOFTMAX

见下表：

输入层是28 x 28 x 1的黑白图片。

第二层是 28 x 28 x 20的大小，生成方式是使用20个5 x 5的filter分别对输入图片进行卷积。与神经网络类似，每个点都有自身的激活函数。同时带有POOLING（池化层），本层的Filter矩阵是2 x 2。每2 x 2之间只留下一个最大值自上到下从左到右的处理，POOL的输出尺寸只有输入的1/4大小。

第三层也是CONV + ACT + POOL的形式。

第四层是全连接的500个神经元。全连接的意思是，第三层的全部输入都分别和第四层的每个神经元相连。假如不说明FC，则默认两层之间的连接线，有一定概率会失去连接。

第五层是输出层。

2：代码

有两个文件，分别是lenet.py和lenet_mnist.py。放在同一个目录即可。

2.1 lenet.py

# import the necessary packagesfrom keras.models import Sequentialfrom keras.layers.convolutional import Conv2Dfrom keras.layers.convolutional import MaxPooling2Dfrom keras.layers.core import Activationfrom keras.layers.core import Flattenfrom keras.layers.core import Densefrom keras import backend as Kclass LeNet:	@staticmethod	def build(width, height, depth, classes):		# initialize the model		model = Sequential()		inputShape = (height, width, depth)					# if we are using "channels first", update the input shape		if K.image_data_format() == "channels_first":			inputShape = (depth, height, width)		# first set of CONV => RELU => POOL layers		model.add(Conv2D(20, (5, 5), padding="same",			input_shape=inputShape))		model.add(Activation("relu"))		model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))		# second set of CONV => RELU => POOL layers		model.add(Conv2D(50, (5, 5), padding="same"))		model.add(Activation("relu"))		model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))		# first (and only) set of FC => RELU layers		model.add(Flatten())		model.add(Dense(500))		model.add(Activation("relu"))		# softmax classifier		model.add(Dense(classes))		model.add(Activation("softmax"))				# return the constructed network architecture		return model

2.2 lenet_mnist.py

# import the necessary packagesfrom lenet import LeNetfrom keras.optimizers import SGDfrom sklearn.preprocessing import LabelBinarizerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classification_reportfrom sklearn import datasetsfrom keras import backend as Kimport matplotlib.pyplot as pltimport numpy as npimport os# grab the MNIST dataset (if this is your first time using this# dataset then the 55MB download may take a minute)print("[INFO] accessing MNIST...")#dataset = datasets.fetch_mldata("MNIST Original")path1 = os.path.dirname(os.path.abspath(__file__))dataset = datasets.fetch_mldata("MNIST Original", data_home=path1)data = dataset.data# if we are using "channels first" ordering, then reshape the# design matrix such that the matrix is:# num_samples x depth x rows x columnsif K.image_data_format() == "channels_first":	data = data.reshape(data.shape[0], 1, 28, 28)	# otherwise, we are using "channels last" ordering, so the design# matrix shape should be: num_samples x rows x columns x depthelse:	data = data.reshape(data.shape[0], 28, 28, 1)# scale the input data to the range [0, 1] and perform a train/test# split(trainX, testX, trainY, testY) = train_test_split(data / 255.0,	dataset.target.astype("int"), test_size=0.25, random_state=42)	# convert the labels from integers to vectorsle = LabelBinarizer()trainY = le.fit_transform(trainY)testY = le.transform(testY)# initialize the optimizer and modelprint("[INFO] compiling model...")opt = SGD(lr=0.01)model = LeNet.build(width=28, height=28, depth=1, classes=10)model.compile(loss="categorical_crossentropy", optimizer=opt,	metrics=["accuracy"])# train the networkprint("[INFO] training network...")H = model.fit(trainX, trainY, validation_data=(testX, testY),	batch_size=128, epochs=20, verbose=1)# evaluate the networkprint("[INFO] evaluating network...")predictions = model.predict(testX, batch_size=128)print(classification_report(testY.argmax(axis=1),	predictions.argmax(axis=1),	target_names=[str(x) for x in le.classes_]))# plot the training loss and accuracyplt.style.use("ggplot")plt.figure()plt.plot(np.arange(0, 20), H.history["loss"], label="train_loss")plt.plot(np.arange(0, 20), H.history["val_loss"], label="val_loss")plt.plot(np.arange(0, 20), H.history["acc"], label="train_acc")plt.plot(np.arange(0, 20), H.history["val_acc"], label="val_acc")plt.title("Training Loss and Accuracy")plt.xlabel("Epoch #")plt.ylabel("Loss/Accuracy")plt.legend()plt.show()

初次运行以下代码，初次调用sklearn的fetch_mldata会在本文件的目录内生成mldata文件夹，并把MNIST的dataset下载到该目录内。只有50多MB，但是速度和机器性能、网络有关。

path1 = os.path.dirname(os.path.abspath(__file__))dataset = datasets.fetch_mldata("MNIST Original", data_home=path1)

3：运行结果

[INFO] evaluating network...             precision    recall  f1-score   support          0       0.99      0.99      0.99      1677          1       0.99      0.99      0.99      1935          2       0.98      0.99      0.98      1767          3       0.99      0.97      0.98      1766          4       0.98      0.99      0.99      1691          5       0.99      0.97      0.98      1653          6       0.99      0.99      0.99      1754          7       0.99      0.98      0.98      1846          8       0.94      0.98      0.96      1702          9       0.98      0.97      0.98      1709avg / total       0.98      0.98      0.98     17500

可以达到98%的精度，数据十分可观。

我的电脑是i3-6100的CPU，需要90秒才能完成一次迭代，没有GPU。书本的作者的CPU要30秒，GPU只要3秒。

所以，TODO：在GPU上跑

参考资料（也是代码来源）：Deep.Learning.for.Computer.Vision.with.Python.Starter.Bundle.pdf