经典神经网络keras实现

收集了一些经典的神经网络的keras实现代码。用于参考学习。网上教程众多，具体原理可以通过网上查找，有很多讲的不错的文章，为了节省时间，在这里不做算法的详细分析。

另外，对于keras使用总结可以参考这篇博客：
https://www.cnblogs.com/wj-1314/p/12510887.html

LeNet-5

大体结构如下，具

代码如下：

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,  Conv2D, MaxPooling2D, Flatten

(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 数据预处理
X_train = X_train.reshape(-1, 28, 28, 1)  # normalize
X_test = X_test.reshape(-1, 28, 28, 1)      # normalize
# 讲述像素值转化到0-1区间
X_train = X_train / 255
X_test = X_test / 255
# 将类别向量转换为二进制（只有0和1）的矩阵类型表示。下面是将其分为10个类别
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)


# 下面是模型结构
model = Sequential()
# 卷积层，5x5卷积核，一共20个，激活函数使用relu
model.add(Conv2D(input_shape=(28, 28, 1), kernel_size=(5, 5), filters=20, activation='relu'))
# 最大池化，2x2核，步伐为2，池化后和原来的大小一样（same），所以需要进行填充
model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='same'))

model.add(Conv2D(kernel_size=(5, 5), filters=50,  activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='same'))

# 将上面最后一个输出展开成一个以为数组
model.add(Flatten())
# 全连接层
model.add(Dense(500, activation='relu'))
model.add(Dense(10, activation='softmax'))
# 优化器使用：rmsprop，损失函数使用多分类常用的categorical_crossentropy：，并且记录准确率
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

print('Training')
# 训练，整个数据集用来训练两次，每次输入32个数据
model.fit(X_train, y_train, epochs=2, batch_size=32)

print('\nTesting')
loss, accuracy = model.evaluate(X_test, y_test)

print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)

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ResNet

优点： 加快收敛/降低训练难度的方法

keras代码：

#coding=utf-8
from keras.models import Model
from keras.layers import Input,Dense,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D,ZeroPadding2D
from keras.layers import add,Flatten
#from keras.layers.convolutional import Conv2D,MaxPooling2D,AveragePooling2D
from keras.optimizers import SGD
import numpy as np
seed = 7
np.random.seed(seed)
 
def Conv2d_BN(x, nb_filter,kernel_size, strides=(1,1), padding='same',name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None
 
    x = Conv2D(nb_filter,kernel_size,padding=padding,strides=strides,activation='relu',name=conv_name)(x)
    x = BatchNormalization(axis=3,name=bn_name)(x)
    return x
 
def Conv_Block(inpt,nb_filter,kernel_size,strides=(1,1), with_conv_shortcut=False):
    x = Conv2d_BN(inpt,nb_filter=nb_filter[0],kernel_size=(1,1),strides=strides,padding='same')
    x = Conv2d_BN(x, nb_filter=nb_filter[1], kernel_size=(3,3), padding='same')
    x = Conv2d_BN(x, nb_filter=nb_filter[2], kernel_size=(1,1), padding='same')
    if with_conv_shortcut:
        shortcut = Conv2d_BN(inpt,nb_filter=nb_filter[2],strides=strides,kernel_size=kernel_size)
        x = add([x,shortcut])
        return x
    else:
        x = add([x,inpt])
        return x
 
inpt = Input(shape=(224,224,3))
x = ZeroPadding2D((3,3))(inpt)
x = Conv2d_BN(x,nb_filter=64,kernel_size=(7,7),strides=(2,2),padding='valid')
x = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same')(x)
 
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3),strides=(1,1),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3))
 
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
 
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
 
x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3))
x = AveragePooling2D(pool_size=(7,7))(x)
x = Flatten()(x)
x = Dense(1000,activation='softmax')(x)
 
model = Model(inputs=inpt,outputs=x)
sgd = SGD(decay=0.0001,momentum=0.9)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
model.summary()

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未完待续…

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