keras實現 vgg16
阿新 • • 發佈：2018-12-13
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Sep 30 17:12:12 2018 這是用keras搭建的vgg16網路 這是很經典的cnn,在影象和時間序列分析方面有很多的應用 @author: lg """ ################# import keras from keras.datasets import cifar10 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, BatchNormalization from keras import optimizers import numpy as np from keras.layers.core import Lambda from keras import backend as K from keras.optimizers import SGD from keras import regularizers from keras.models import load_model #import data (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10) #用於正則化時權重降低的速度 weight_decay = 0.0005 nb_epoch=100 batch_size=32 #layer1 32*32*3 model = Sequential() #第一個 卷積層 的卷積核的數目是32 ，卷積核的大小是3*3，stride沒寫，預設應該是1*1 #對於stride=1*1,並且padding ='same',這種情況卷積後的影象shape與卷積前相同，本層後shape還是32*32 model.add(Conv2D(64, (3, 3), padding='same', input_shape=(32,32,3),kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) #進行一次歸一化 model.add(BatchNormalization()) model.add(Dropout(0.3)) #layer2 32*32*64 model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) #下面兩行程式碼是等價的，#keras Pool層有個奇怪的地方，stride,預設是(2*2), #padding預設是valid，在寫程式碼是這些引數還是最好都加上,這一步之後,輸出的shape是16*16*64 #model.add(MaxPooling2D(pool_size=(2, 2))) model.add(MaxPooling2D(pool_size=(2, 2),strides=(2,2),padding='same') ) #layer3 16*16*64 model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer4 16*16*128 model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #layer5 8*8*128 model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer6 8*8*256 model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer7 8*8*256 model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #layer8 4*4*256 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer9 4*4*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer10 4*4*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #layer11 2*2*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer12 2*2*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) #layer13 2*2*512 model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.5)) #layer14 1*1*512 model.add(Flatten()) model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) #layer15 512 model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay))) model.add(Activation('relu')) model.add(BatchNormalization()) #layer16 512 model.add(Dropout(0.5)) model.add(Dense(10)) model.add(Activation('softmax')) # 10 model.summary() sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy']) model.fit(x_train,y_train,epochs=nb_epoch, batch_size=batch_size, validation_split=0.1, verbose=1) model.save('my_model_bp.h5')