keras实现常用深度学习模型LeNet，AlexNet，ZFNet，VGGNet，GoogleNet，Resnet

LeNet

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#coding=utf-8

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'/media/wmy/document/BigData/kaggle/Digit Recognizer/mnist.pkl.gz')

train_set,valid_set,test_set = cPickle.load(data)

#train_x is [0,1]

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])

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)

#[0.031825309940411217, 0.98979999780654904]

print model.evaluate(test_x,test_y,batch_size=20,verbose=2)

AlexNet

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#coding=utf-8

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)

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

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#coding=utf-8

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)

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='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()

VGG-13

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#coding=utf-8

from keras.models import Sequential

from keras.layers import Dense,Flatten,Dropout

from keras.layers.convolutional import Conv2D,MaxPooling2D

import numpy as np

seed = 7

np.random.seed(seed)

model = Sequential()

model.add(Conv2D(64,(3,3),strides=(1,1),input_shape=(224,224,3),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(64,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(128,(3,2),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(128,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(256,(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=(2,2)))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(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()

VGG-16

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#coding=utf-8

from keras.models import Sequential

from keras.layers import Dense,Flatten,Dropout

from keras.layers.convolutional import Conv2D,MaxPooling2D

import numpy as np

seed = 7

np.random.seed(seed)

model = Sequential()

model.add(Conv2D(64,(3,3),strides=(1,1),input_shape=(224,224,3),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(64,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(128,(3,2),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(128,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(256,(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(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))

model.add(MaxPooling2D(pool_size=(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()

GoogleNet

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#coding=utf-8

from keras.models import Model

from keras.layers import Input,Dense,Dropout,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D,concatenate

from keras.layers.convolutional import Conv2D,MaxPooling2D,AveragePooling2D

import numpy as np

seed = 7

np.random.seed(seed)

def Conv2d_BN(x, nb_filter,kernel_size, padding='same',strides=(1,1),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 Inception(x,nb_filter):

branch1x1 = Conv2d_BN(x,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

branch3x3 = Conv2d_BN(x,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

branch3x3 = Conv2d_BN(branch3x3,nb_filter,(3,3), padding='same',strides=(1,1),name=None)

branch5x5 = Conv2d_BN(x,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

branch5x5 = Conv2d_BN(branch5x5,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

branchpool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same')(x)

branchpool = Conv2d_BN(branchpool,nb_filter,(1,1),padding='same',strides=(1,1),name=None)

x = concatenate([branch1x1,branch3x3,branch5x5,branchpool],axis=3)

return x

inpt = Input(shape=(224,224,3))

#padding = 'same'，填充为(步长-1）/2,还可以用ZeroPadding2D((3,3))

x = Conv2d_BN(inpt,64,(7,7),strides=(2,2),padding='same')

x = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same')(x)

x = Conv2d_BN(x,192,(3,3),strides=(1,1),padding='same')

x = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same')(x)

x = Inception(x,64)#256

x = Inception(x,120)#480

x = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same')(x)

x = Inception(x,128)#512

x = Inception(x,128)

x = Inception(x,128)

x = Inception(x,132)#528

x = Inception(x,208)#832

x = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same')(x)

x = Inception(x,208)

x = Inception(x,256)#1024

x = AveragePooling2D(pool_size=(7,7),strides=(7,7),padding='same')(x)

x = Dropout(0.4)(x)

x = Dense(1000,activation='relu')(x)

x = Dense(1000,activation='softmax')(x)

model = Model(inpt,x,name='inception')

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

model.summary()

Resnet-34

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#coding=utf-8

from keras.models import Model

from keras.layers import Input,Dense,Dropout,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D,concatenate,Activation,ZeroPadding2D

from keras.layers import add,Flatten

#from keras.layers.convolutional import Conv2D,MaxPooling2D,AveragePooling2D

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,kernel_size=kernel_size,strides=strides,padding='same')

x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size,padding='same')

if with_conv_shortcut:

shortcut = Conv2d_BN(inpt,nb_filter=nb_filter,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)

#(56,56,64)

x = Conv_Block(x,nb_filter=64,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=64,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=64,kernel_size=(3,3))

#(28,28,128)

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3))

#(14,14,256)

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

#(7,7,512)

x = Conv_Block(x,nb_filter=512,kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)

x = Conv_Block(x,nb_filter=512,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=512,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)

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

model.summary()

Resnet-50

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#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()