keras入门(三) VGG 10折交叉验证实现cifar10的分类

ゞ浴缸里的玫瑰 2021-12-17 07:19 653阅读 0赞

#!/usr/bin/env python3
    # -*- coding: utf-8 -*-
    """
    Created on Sun Sep 30 17:12:12 2018
    
    
    这是用keras搭建的vgg16网络
    这是很经典的cnn,在图像和时间序列分析方面有很多的应用
    @author: lg
    """
    #################
    
    import keras
    from keras import regularizers
    from keras.datasets import cifar10
    from keras.models import Sequential
    from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, BatchNormalization
    from keras.optimizers import SGD
    import os
    import argparse
    import random
    import numpy as np
    from scipy.misc import imread, imresize, imsave
    import pickle
    
    from sklearn.model_selection import StratifiedKFold
    
    parser = argparse.ArgumentParser()
    parser.add_argument('--train_dir', default='./train/')
    parser.add_argument('--test_dir', default='./test/')
    parser.add_argument('--log_dir', default='./')
    parser.add_argument('--batch_size', default=16)
    parser.add_argument('--gpu', type=int, default=0)
    args = parser.parse_args()
    
    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
    type_list = ['cat', 'dog']
    
    
    def vgg_16_net():
        model = Sequential()
        model.add(Conv2D(64, (3, 3), input_shape=(32, 32, 3), padding='same', activation='relu', name='conv1_block'))
        model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='conv2_block'))
        model.add(BatchNormalization())
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='conv3_block'))
        model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='conv4_block'))
        model.add(BatchNormalization())
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='conv5_block'))
        model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='conv6_block'))
        model.add(Conv2D(256, (1, 1), activation='relu', padding='same', name='conv7_block'))
        model.add(BatchNormalization())
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='conv8_block'))
        model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='conv9_block'))
        model.add(Conv2D(512, (1, 1), activation='relu', padding='same', name='conv10_block'))
        model.add(BatchNormalization())
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='conv11_block'))
        model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='conv12_block'))
        model.add(Conv2D(512, (1, 1), activation='relu', padding='same', name='conv13_block'))
        model.add(BatchNormalization())
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        model.add(Flatten())
        model.add(Dense(2048, activation='relu'))
        model.add(Dropout(0.5))
        model.add(Dense(4096, activation='relu'))
        model.add(Dropout(0.5))
        model.add(Dense(10, activation='softmax'))
        #model.add(Dense(1, activation='sigmoid'))
        return model
    
    
    def prepare_data():
        file_dict1 = unpickle('F:/cifar10/cifar-10-batches-py/data_batch_1')
        label = file_dict1[b'labels']
        image = file_dict1[b'data']
        print(type(image))
        file_dict2 = unpickle('F:/cifar10/cifar-10-batches-py/data_batch_2')
        label = label + file_dict2[b'labels']
        image = np.vstack((image, file_dict2[b'data']))
        file_dict3 = unpickle('F:/cifar10/cifar-10-batches-py/data_batch_3')
        label = label + file_dict3[b'labels']
        image = np.vstack((image, file_dict3[b'data']))
        file_dict4 = unpickle('F:/cifar10/cifar-10-batches-py/data_batch_4')
        label = label + file_dict4[b'labels']
        image = np.vstack((image, file_dict4[b'data']))
        file_dict5 = unpickle('F:/cifar10/cifar-10-batches-py/data_batch_5')
        label = label + file_dict5[b'labels']
        image = np.vstack((image, file_dict5[b'data']))
        image = np.reshape(image/255, (-1, 32, 32, 3))
        label = keras.utils.to_categorical(label, 10)
        #seed = 7
        #np.random.seed(seed)
        #train_data, test_data, train_label, test_label = train_test_split(image, label, test_size=0.2, random_state=0)
        #train_num = int(len(label) * 0.8 )
        #train_data, train_label, test_data, test_label = image[0:train_num], label[0:train_num], image[train_num:], label[train_num:]
        # (X_train, y_train), (X_test, y_test) = cifar10.load_data()
        # train_data = np.reshape(X_train/255, (-1, 32, 32, 3))
        # train_label = keras.utils.to_categorical(y_train, 10)
        # test_data = np.reshape(X_test/255, (-1, 32, 32, 3))
        # test_label = keras.utils.to_categorical(y_test, 10)
        # return train_data, train_label, test_data, test_label
        return image, label
    
    def unpickle(file):
        with open(file, 'rb') as fo:
            file_dict = pickle.load(fo, encoding='bytes')
        return file_dict
    
    
    def train():
        kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)
        data, label = prepare_data()
        index = 1
        for train, test in kfold.split(data, label.argmax(1)):
            model = vgg_16_net()
            sgd = SGD(lr=0.001, decay=1e-8, momentum=0.9, nesterov=True)
            model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
            model.fit(data[train], label[train], validation_data=(data[test], label[test]), epochs=20, batch_size=32, shuffle=True)
            #train_data, train_label = prepare_data()
            #model.fit(train_data, train_label, batch_size=64, epochs=20, shuffle=True, validation_split=0.2)
            model.save_weights(args.log_dir + 'model_' + str(index) + '.h5')
            index = index + 1
    
    
    
    if __name__ == '__main__':
        try:
            train()
            #model.load_weights(args.log_dir + '/model.h5')
            #predict(model)
        except Exception  as err:
            print(err)

最开始没有使用交叉验证，但是测试集与验证集的准确率一直维持在50%~60%，基本属于盲猜系列。原因大概是数据量太多，进行随机划分时，测试数据的分类不是很均匀。所以采用了交叉验证的方式，最终测试集与训练集的准确率能够达到99%，应该是有点过拟合了，结果还是非常满意的。