LeNet

# 包含了两个卷积层以及一个全连接层，最后通过softmax激活函数输出分类结果。其中，第一层包含20个 5x5 的卷积核，第二层包含50个 5x5 的卷积核。每个卷积层在卷积之后都会接一个ReLU激活函数，并且紧随其后进行最大池化操作。全连接层包含500个神经元，使用ReLU作为激活函数。最后输出层的神经元数量与分类类别相同，使用 softmax 作为激活函数。
#import the necessary packages
from tensorflow import keras
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dense
from keras import backend as K


class LeNet:
    @staticmethod
    def build(width, height, depth, classes):
        # initialize the model
        model = Sequential()
        inputShape = (height, width, depth)
        # if we are using "channels last", update the input shape
        if K.image_data_format() == "channels_first":  # for tensorflow
            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