highway network及mnist数据集测试

先说结论：没经过仔细调参，打不开论文所说代码链接（fq也没打开），结果和普通卷积网络比较没有优势。反倒是BN对网络起着非常重要的作用，达到了99.17%的测试精度（训练轮数还没到过拟合）。

论文为《Training Very Deep Networks》，一说其在resnet前发表，resnet模仿了它。

 如上式，对于每个输入，都用一个layer去计算T（sigmoid激活），初始设置T的偏置为负，这样使得激活值开始比较小，便于信息流通。

以下对此做了2个测试，一个将图片Flatten后训练，一个使用卷积层。

1，Flatten

from keras.models import Model,Input
from keras.datasets import mnist
from keras.layers import Dense,Multiply,Add,Layer,Conv2D,Subtract,Lambda,Flatten,MaxPooling2D,BatchNormalization,Activation
from keras.losses import categorical_crossentropy
from keras import optimizers
from keras.utils import to_categorical
from keras import initializers
import keras.backend as K

(x_train,y_train),(x_test,y_test)=mnist.load_data()

X_train=x_train.reshape(60000,-1)/255.
X_test=x_test.reshape(10000,-1)/255.

y_train=to_categorical(y_train,num_classes=10)
y_test=to_categorical(y_test,num_classes=10)

自定义Block层，对应上面的公式

# 自定义highway-network的一个block
class Block(Layer):
    def __init__(self,units,**kwargs):
        self.units=units
        self.weight_initializer=initializers.truncated_normal()
        self.bh_initializer=initializers.constant(0.01)
        # 根据论文，转换层使用负的偏置。这样开始训练时转换层输出小，信息基本原样流通
        # 开始设置为-1，不行，这个参数还是敏感的
        self.bt_initializer=initializers.constant(-0.3)
        super(Block,self).__init__(**kwargs)
    def build(self,input_shape):
        self.h_w=self.add_weight(name='hw',shape=(input_shape[-1],self.units),initializer=self.weight_initializer,trainable=True)
        self.t_w=self.add_weight(name='tw',shape=(input_shape[-1],self.units),initializer=self.weight_initializer,trainable=True)
        self.h_b=self.add_weight(name='hb',shape=(input_shape[-1],),initializer=self.bh_initializer,trainable=True)
        self.t_b=self.add_weight(name='tb',shape=(input_shape[-1],),initializer=self.bt_initializer,trainable=True)
        super(Block,self).build(input_shape)
    def call(self,inputs):
        h_out=K.relu(K.dot(inputs,self.h_w)+self.h_b)
        t_out=K.sigmoid(K.dot(inputs,self.t_w)+self.t_b)
        out1=t_out*h_out
        out2=(1-t_out)*inputs
        return out1+out2

先降维一下，再叠加10个Block，使用带动量的SGD训练，参数为134,090

block_layers=10
inputs=Input(shape=(784,))
x=Dense(64,activation='relu')(inputs)
for i in range(block_layers):
    x=Block(64)(x)
x=Dense(10,activation='softmax')(x)
model=Model(inputs,x)

model.compile(optimizer=optimizers.Adam(),loss=categorical_crossentropy,metrics=['accuracy'])

model.summary()
model.fit(X_train,y_train,batch_size=32,epochs=20,verbose=2,validation_split=0.3)

观测训练后，选定epochs为6，再用完整数据集训练一遍，测试精度97.5%

model.fit(X_train,y_train,batch_size=32,epochs=6,verbose=2)
model.evaluate(X_test,y_test)# 97.5%

作为对比，以下简单卷积网络测试精度即可达到97.83%，参数159,010

inputs=Input(shape=(784,))
x=Dense(200,activation='relu')(inputs)
x=Dense(10,activation='softmax')(x)
model=Model(inputs,x)

2，CNN

此处不再赘述，仅对模型创建过程说明一下，叠加10层，参数172,010，batch_size调整为128，训练后未在完整数据集再训练一遍，直接测试集精度为98.48%。但这也不够高。

layer_size=10
inputs=Input((28,28,1))
x=Conv2D(16,3,activation='relu',padding='same')(inputs)
for i in range(layer_size):
    t=Conv2D(16,3,activation='sigmoid',padding='same',bias_initializer=initializers.constant(-1.))(x)
    h=Conv2D(16,3,activation='relu',padding='same',bias_initializer=initializers.random_uniform())(x)
    out1=Multiply()([t,h])
    sub=Lambda(lambda p:1-p)(t)
    out2=Multiply()([x,sub])
    x=Add()([out1,out2])
x=Flatten()(x)
x=Dense(10,activation='softmax')(x)
model=Model(inputs,x)
model.summary()

3，BatchNormalization

调整验证集比例为20%

inputs=Input((28,28,1))
x=Conv2D(32,3,padding='same')(inputs)
x=BatchNormalization()(x)
x=Activation('tanh')(x)
x=Conv2D(32,3,padding='same',activation='tanh')(x)
x=MaxPooling2D()(x)
x=Conv2D(64,3,padding='same')(x)
x=BatchNormalization()(x)
x=Activation('tanh')(x)
x=Conv2D(64,3,padding='same',activation='tanh')(x)
x=MaxPooling2D()(x)
x=Flatten()(x)
x=Dense(10,activation='softmax')(x)
model=Model(inputs,x)
model.compile(optimizer=optimizers.SGD(momentum=0.8,nesterov=True),loss=categorical_crossentropy,metrics=['accuracy'])

model.fit(X_train,y_train,batch_size=128,epochs=30,validation_split=0.2,verbose=2)

看看训练过程，可以看到，到训练30轮为止，验证损失仍没有上升迹象

Train on 48000 samples, validate on 12000 samples
Epoch 1/30
 - 23s - loss: 0.2314 - accuracy: 0.9346 - val_loss: 0.1393 - val_accuracy: 0.9606
Epoch 2/30
 - 23s - loss: 0.0756 - accuracy: 0.9778 - val_loss: 0.0675 - val_accuracy: 0.9810
Epoch 3/30
 - 23s - loss: 0.0565 - accuracy: 0.9843 - val_loss: 0.0570 - val_accuracy: 0.9834
Epoch 4/30
 - 23s - loss: 0.0451 - accuracy: 0.9870 - val_loss: 0.0519 - val_accuracy: 0.9851
Epoch 5/30
 - 23s - loss: 0.0390 - accuracy: 0.9890 - val_loss: 0.0444 - val_accuracy: 0.9871
Epoch 6/30
 - 23s - loss: 0.0335 - accuracy: 0.9905 - val_loss: 0.0431 - val_accuracy: 0.9878
Epoch 7/30
 - 23s - loss: 0.0294 - accuracy: 0.9921 - val_loss: 0.0413 - val_accuracy: 0.9883
Epoch 8/30
 - 23s - loss: 0.0267 - accuracy: 0.9928 - val_loss: 0.0396 - val_accuracy: 0.9899
Epoch 9/30
 - 23s - loss: 0.0241 - accuracy: 0.9933 - val_loss: 0.0385 - val_accuracy: 0.9886
Epoch 10/30
 - 23s - loss: 0.0222 - accuracy: 0.9942 - val_loss: 0.0391 - val_accuracy: 0.9877
Epoch 11/30
 - 23s - loss: 0.0200 - accuracy: 0.9950 - val_loss: 0.0378 - val_accuracy: 0.9887
Epoch 12/30
 - 23s - loss: 0.0188 - accuracy: 0.9950 - val_loss: 0.0381 - val_accuracy: 0.9881
Epoch 13/30
 - 23s - loss: 0.0166 - accuracy: 0.9960 - val_loss: 0.0354 - val_accuracy: 0.9902
Epoch 14/30
 - 23s - loss: 0.0156 - accuracy: 0.9961 - val_loss: 0.0379 - val_accuracy: 0.9886
Epoch 15/30
 - 23s - loss: 0.0145 - accuracy: 0.9967 - val_loss: 0.0341 - val_accuracy: 0.9906
Epoch 16/30
 - 23s - loss: 0.0133 - accuracy: 0.9971 - val_loss: 0.0345 - val_accuracy: 0.9902
Epoch 17/30
 - 23s - loss: 0.0122 - accuracy: 0.9973 - val_loss: 0.0341 - val_accuracy: 0.9908
Epoch 18/30
 - 23s - loss: 0.0113 - accuracy: 0.9978 - val_loss: 0.0346 - val_accuracy: 0.9900
Epoch 19/30
 - 23s - loss: 0.0102 - accuracy: 0.9983 - val_loss: 0.0334 - val_accuracy: 0.9902
Epoch 20/30
 - 23s - loss: 0.0097 - accuracy: 0.9982 - val_loss: 0.0326 - val_accuracy: 0.9910
Epoch 21/30
 - 23s - loss: 0.0091 - accuracy: 0.9984 - val_loss: 0.0325 - val_accuracy: 0.9907
Epoch 22/30
 - 23s - loss: 0.0083 - accuracy: 0.9987 - val_loss: 0.0325 - val_accuracy: 0.9905
Epoch 23/30
 - 23s - loss: 0.0077 - accuracy: 0.9989 - val_loss: 0.0324 - val_accuracy: 0.9908
Epoch 24/30
 - 23s - loss: 0.0073 - accuracy: 0.9990 - val_loss: 0.0330 - val_accuracy: 0.9900
Epoch 25/30
 - 23s - loss: 0.0067 - accuracy: 0.9992 - val_loss: 0.0337 - val_accuracy: 0.9913
Epoch 26/30
 - 23s - loss: 0.0065 - accuracy: 0.9992 - val_loss: 0.0318 - val_accuracy: 0.9907
Epoch 27/30
 - 23s - loss: 0.0062 - accuracy: 0.9993 - val_loss: 0.0328 - val_accuracy: 0.9907
Epoch 28/30
 - 23s - loss: 0.0056 - accuracy: 0.9995 - val_loss: 0.0316 - val_accuracy: 0.9914
Epoch 29/30
 - 23s - loss: 0.0052 - accuracy: 0.9996 - val_loss: 0.0313 - val_accuracy: 0.9912
Epoch 30/30
 - 23s - loss: 0.0049 - accuracy: 0.9997 - val_loss: 0.0313 - val_accuracy: 0.9911

完整数据集训练30轮后，测试精度99.17%。

后续可以尝试更深的网络，使用resnet。或者直接使用inception等。