基于CNN的二分类识别

# python练习 # 重新学习时间：2023/5/3 22:07 from keras.models import Sequential from keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPool2D from keras.optimizers import RMSprop, Adam from keras.preprocessing.image import ImageDataGenerator import sys, os # 目录结构 import matplotlib.pyplot as plt from keras.callbacks import EarlyStopping, ReduceLROnPlateau "1.模型建立" # 1.卷积层，输入图片大小(150, 150, 3), 卷积核个数16,卷积核大小(5, 5), 激活函数'relu' conv_layer1 = Conv2D(input_shape=(150, 150, 3), filters=16, kernel_size=(5, 5), activation='relu') # 2.最大池化层，池化层大小(2, 2), 步长为2 max_pool1 = MaxPool2D(pool_size=(2, 2), strides=2) # 3.卷积层，卷积核个数32,卷积核大小(5, 5), 激活函数'relu' conv_layer2 = Conv2D(filters=32, kernel_size=(5, 5), activation='relu') # 4.最大池化层，池化层大小(2, 2), 步长为2 max_pool2 = MaxPool2D(pool_size=(2, 2), strides=2) # 5.卷积层，卷积核个数64,卷积核大小(5, 5), 激活函数'relu' conv_layer3 = Conv2D(filters=64, kernel_size=(5, 5), activation='relu') # 6.最大池化层，池化层大小(2, 2), 步长为2 max_pool3 = MaxPool2D(pool_size=(2, 2), strides=2) # 7.卷积层，卷积核个数128,卷积核大小(5, 5), 激活函数'relu' conv_layer4 = Conv2D(filters=128, kernel_size=(5, 5), activation='relu') # 8.最大池化层，池化层大小(2, 2), 步长为2 max_pool4 = MaxPool2D(pool_size=(2, 2), strides=2) # 9.展平层 flatten_layer = Flatten() # 10.Dropout层, Dropout(0.2) third_dropout = Dropout(0.2) # 11.全连接层/隐藏层1，240个节点, 激活函数'relu' hidden_layer1 = Dense(240, activation='relu') # 12.全连接层/隐藏层2，84个节点, 激活函数'relu' hidden_layer3 = Dense(84, activation='relu') # 13.Dropout层, Dropout(0.2) fif_dropout = Dropout(0.5) # 14.输出层，输出节点个数1, 激活函数'sigmoid' output_layer = Dense(1, activation='sigmoid') model = Sequential([conv_layer1, max_pool1, conv_layer2, max_pool2, conv_layer3, max_pool3, conv_layer4, max_pool4, flatten_layer, third_dropout, hidden_layer1, hidden_layer3, fif_dropout, output_layer]) "2.模型编译" # 模型编译，2分类：binary_crossentropy model.compile(optimizer=Adam(lr=0.0001), # 优化器选择Adam，初始学习率设置为0.0001 loss='binary_crossentropy', # 代价函数选择 binary_crossentropy metrics=['accuracy']) # 设置指标为准确率 model.summary() # 模型统计 # 回调机制 动态调整学习率 reduce = ReduceLROnPlateau(monitor='val_accuracy', # 设置监测的值为val_accuracy patience=2, # 设置耐心容忍次数为2 verbose=1, # factor=0.5, # 缩放学习率的值为0.5，学习率将以lr = lr*factor的形式被减少 min_lr=0.000001 # 学习率最小值0.000001 ) # 监控val_accuracy增加趋势 # 生成器对象1: 归一化 gen = ImageDataGenerator(rescale=1 / 255.0) # 生成器对象2: 归一化 + 数据加强 gen1 = ImageDataGenerator( rescale=1 / 255.0, rotation_range=5, # 图片随机旋转的角度5度 width_shift_range=0.1, height_shift_range=0.1, # 水平和竖直方向随机移动0.1 shear_range=0.1, # 剪切变换的程度0.1 zoom_range=0.1, # 随机放大的程度0.1 fill_mode='nearest') # 当需要进行像素填充时选择最近的像素进行填充 # 拼接训练和验证的两个路径 train_path = os.path.join(sys.path[0], 'imgs', 'train') val_path = os.path.join(sys.path[0], 'imgs', 'val') print('训练数据路径： ', train_path) print('验证数据路径: ', val_path) # 训练和验证的两个迭代器 train_iter = gen1.flow_from_directory(train_path, # 训练train目录路径 target_size=(150, 150), # 目标图像大小统一尺寸150 batch_size=8, # 设置每次加载到内存的图像大小 class_mode='binary', # 设置分类模型(默认one-hot编码) shuffle=True) # 是否打乱 val_iter = gen.flow_from_directory(val_path, # 测试val目录路径 target_size=(150, 150), # 目标图像大小统一尺寸150 batch_size=8, # 设置每次加载到内存的图像大小 class_mode='binary', # 设置分类模型(默认one-hot编码) shuffle=True) # 是否打乱 "3.模型训练" # 模型的训练， model.fit result = model.fit(train_iter, # 设置训练数据的迭代器 epochs=20, # 循环次数12次 validation_data=val_iter, # 验证数据的迭代器 callbacks=[reduce], # 回调机制设置为reduce verbose=1) "4.模型保存" # 保存训练好的模型 model.save('my_ikun.h5') "5.模型训练时的可视化" # 显示训练集和验证集的acc和loss曲线 acc = result.history['accuracy'] # 获取模型训练中的accuracy val_acc = result.history['val_accuracy'] # 获取模型训练中的val_accuracy loss = result.history['loss'] # 获取模型训练中的loss val_loss = result.history['val_loss'] # 获取模型训练中的val_loss # 绘值acc曲线 plt.figure(1) plt.plot(acc, label='Training Accuracy') plt.plot(val_acc, label='Validation Accuracy') plt.title('Training and Validation Accuracy') plt.legend() plt.savefig('my_ikun_acc.png', dpi=600) # 绘制loss曲线 plt.figure(2) plt.plot(loss, label='Training Loss') plt.plot(val_loss, label='Validation Loss') plt.title('Training and Validation Loss') plt.legend() plt.savefig('my_ikun_loss.png', dpi=600) plt.show() # 将结果显示出来