LeNet
#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
#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
#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 #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 #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 #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 #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 #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()
