Keras搭建常用深度学习模型LeNet-5\AlexNet\ZFNet\VGGNet\GoogleNet\Resnet----(已解决包含代码和论文)

找了好多大神的代码和总结的论文，先在此谢过啦，总结记录一下，也希望能帮到你和以后的我。

LeNet-5：（论文地址）

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author：'ZhangML'
# Time:2020/04/15  05:23


from keras.models import Sequential
from keras.layers import Dense,Flatten
from keras.layers.convolutional import Conv2D,MaxPooling2D
from keras.utils.np_utils import to_categorical
import cPickle
import gzip
import numpy as np
seed = 7
np.random.seed(seed)
 
data = gzip.open(r'/Digit Recognizer/mnist.pkl.gz')
train_set,valid_set,test_set = cPickle.load(data)

train_x = train_set[0].reshape((-1,28,28,1))
train_y = to_categorical(train_set[1])
 
valid_x = valid_set[0].reshape((-1,28,28,1))
valid_y = to_categorical(valid_set[1])
 
test_x = test_set[0].reshape((-1,28,28,1))
test_y = to_categorical(test_set[1])
 
 # 模型部分
model = Sequential()
model.add(Conv2D(32,(5,5),strides=(1,1),input_shape=(28,28,1),padding='valid',activation='relu',kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64,(5,5),strides=(1,1),padding='valid',activation='relu',kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(100,activation='relu'))
model.add(Dense(10,activation='softmax'))
model.compile(optimizer='sgd',loss='categorical_crossentropy',metrics=['accuracy'])
model.summary()
 
# model.fit(train_x,train_y,validation_data=(valid_x,valid_y),batch_size=20,epochs=20,verbose=2)
# print (model.evaluate(test_x,test_y,batch_size=20,verbose=2))

AlexNet：（论文地址）

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author：'ZhangML'
# Time:2020/04/18  12:14


from keras.models import Sequential
from keras.layers import Dense,Flatten,Dropout
from keras.layers.convolutional import Conv2D,MaxPooling2D
from keras.utils.np_utils import to_categorical
import numpy as np
seed = 7
np.random.seed(seed)
 
 #  模型部分
model = Sequential()
model.add(Conv2D(96,(11,11),strides=(4,4),input_shape=(227,227,3),padding='valid',activation='relu',kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))
model.add(Conv2D(256,(5,5),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))
model.add(Conv2D(384,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))
model.add(Conv2D(384,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))
model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))
model.add(Flatten())
model.add(Dense(4096,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1000,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])
model.summary()

ZFNet：（论文地址）

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author：'ZhangML'
# Time:2020/04/20  02:11

from keras.models import Sequential
from keras.layers import Dense,Flatten,Dropout
from keras.layers.convolutional import Conv2D,MaxPooling2D
from keras.utils.np_utils import to_categorical
import numpy as np
seed = 7
np.random.seed(seed)
 
 #  模型部分
model = Sequential()
model.add(Conv2D(96,(7,7),strides=(2,2),input_shape=(224,224,3),padding='valid',activation='relu',kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))
model.add(Conv2D(256,(5,5),strides=(2,2),padding='same',activation='relu',kernel_initializer=<