Tensorflow2.0VGG实现图片分类qq38412266的博客-

VGG16网络

论文链接：https://arxiv.org/abs/1409.1556
VGG网络是在2014年由牛津大学计算机视觉组和谷歌公司的研究员共同开发的。VGG由5层卷积层、3层全连接层、softmax输出层构成，层与层之间使用最大池化分开，所有隐层的激活单元都采用ReLU函数。通过反复堆叠3×3的小卷积核和2×2的最大池化层，VGGNet成功的搭建了16-19层的深度卷积神经网络。VGG的结构图如下：

数据集

数据集下载：https://download.tensorflow.org/example_images/flower_photos.tgz
采用五类花卉数据集，类别是：雏菊，蒲公英，玫瑰，向日葵，郁金香，如下图所示：

本次实验样本数量如下表：

对数据集进行分类，90%训练集，10%验证集

代码

#导入相应的库 from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras import layers, models, Model, Sequential import matplotlib.pyplot as plt from tensorflow.keras.callbacks import ReduceLROnPlateau,EarlyStopping import tensorflow as tf import json import os   # GPU 设置 gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: for gpu in gpus:         tf.config.experimental.set_memory_growth(gpu, True) #设置图片的大小，路径，batch_size,epoch im_height = 224 im_width = 224 batch_size = 128 epochs = 15  image_path = "./data_set/flower_data/" train_dir = image_path + "train" validation_dir = image_path + "val" # 图像预处理（训练集图片运用图像增强） train_image_generator = ImageDataGenerator( rescale=1./255,                                             rotation_range=40,                                             width_shift_range=0.2,                                             height_shift_range=0.2,                                             zoom_range=0.2,                                             horizontal_flip=True,                                             fill_mode='nearest') # 图像预处理 validation_image_generator = ImageDataGenerator(rescale=1./255) #训练集数据生成器，one-hot编码，打乱数据 train_data_gen = train_image_generator.flow_from_directory(directory=train_dir,                                                            batch_size=batch_size,                                                            shuffle=True,                                                            target_size=(im_height, im_width),                                                            class_mode='categorical') #训练集图片总数 total_train = train_data_gen.n #验证集数据生成器 val_data_gen = validation_image_generator.flow_from_directory(directory=validation_dir,                                                               batch_size=batch_size,                                                               shuffle=False,                                                               target_size=(im_height, im_width),                                                               class_mode='categorical') #验证集图片总数 total_val = val_data_gen.n  #VGG16预训练网络 covn_base = tf.keras.applications.vgg16.VGG16(weights='imagenet',include_top=False) covn_base.trainable = True #冻结前面的层，训练最后四层 for layers in covn_base.layers[:-4]:     layers.trainable = False #构建模型 model = tf.keras.Sequential() model.add(covn_base) model.add(tf.keras.layers.GlobalAveragePooling2D()) model.add(tf.keras.layers.Dense(256, activation='relu')) model.add(tf.keras.layers.Dropout(0.2)) model.add(tf.keras.layers.Dense(5, activation='softmax')) model.summary() #编译模型，初始学习率0.001 model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),               loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),               metrics=["accuracy"]) #监视'val_loss'，当两个epoch不变时，学习率减小为1/10 reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, verbose=1) #开始训练 history = model.fit(x=train_data_gen,                     steps_per_epoch=total_train // batch_size,                     epochs=epochs,                     validation_data=val_data_gen,                     validation_steps=total_val // batch_size,                     callbacks=[reduce_lr]) # 记录准确率和损失值 history_dict = history.history train_loss = history_dict["loss"] train_accuracy = history_dict["accuracy"] val_loss = history_dict["val_loss"] val_accuracy = history_dict["val_accuracy"] # 绘制损失值 plt.figure() plt.plot(range(epochs), train_loss, label='train_loss') plt.plot(range(epochs), val_loss, label='val_loss') plt.legend() plt.xlabel('epochs') plt.ylabel('loss') # 绘制准确率 plt.figure() plt.plot(range(epochs), train_accuracy, label='train_accuracy') plt.plot(range(epochs), val_accuracy, label='val_accuracy') plt.legend() plt.xlabel('epochs') plt.ylabel('accuracy') plt.show() 

训练结果

训练到第15个epoch是时准确率达到92.5%
准确率

损失值