經典神經網絡keras實現

收集了一些經典的神經網絡的keras實現代碼。用於參考學習。網上教程衆多，具體原理可以通過網上查找，有很多講的不錯的文章，爲了節省時間，在這裏不做算法的詳細分析。

另外，對於keras使用總結可以參考這篇博客：
https://www.cnblogs.com/wj-1314/p/12510887.html

LeNet-5

大體結構如下，具

代碼如下：

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()

# 數據預處理
X_train = X_train.reshape(-1, 28, 28, 1)  # normalize
X_test = X_test.reshape(-1, 28, 28, 1)      # normalize
# 講述像素值轉化到0-1區間
X_train = X_train / 255
X_test = X_test / 255
# 將類別向量轉換爲二進制（只有0和1）的矩陣類型表示。下面是將其分爲10個類別
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)


# 下面是模型結構
model = Sequential()
# 卷積層，5x5卷積核，一共20個，激活函數使用relu
model.add(Conv2D(input_shape=(28, 28, 1), kernel_size=(5, 5), filters=20, activation='relu'))
# 最大池化，2x2核，步伐爲2，池化後和原來的大小一樣（same），所以需要進行填充
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'))

# 將上面最後一個輸出展開成一個以爲數組
model.add(Flatten())
# 全連接層
model.add(Dense(500, activation='relu'))
model.add(Dense(10, activation='softmax'))
# 優化器使用：rmsprop，損失函數使用多分類常用的categorical_crossentropy：，並且記錄準確率
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

print('Training')
# 訓練，整個數據集用來訓練兩次，每次輸入32個數據
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)

---

ResNet

優點： 加快收斂/降低訓練難度的方法

keras代碼：

#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()

---

未完待續…

撩我，可以搜索我的公衆號：Kyda