基于tensorflow中的keras编写代码，文件夹rawdata下的两个子文件夹的所有图片 1. 编写数据加载函数； 2. 根据任务要求对数据集进行划分； 3. 数据增强； 4. 构建深度学习模型和损失函数； 5. 编写模型训练相关代码，完成模型训练 6. 使用可视化库 Matplotlib 对训练过程进行可视化展示，如损失值变化、准确率变化、召回率、ROC曲线等。 7. 将训练好的模型保存

以下是可能的代码实现，仅作参考： ```python import os import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt # 1. 数据加载函数 def load_data(data_dir, batch_size): train_dir = os.path.join(data_dir, 'train') val_dir = os.path.join(data_dir, 'val') train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) train_generator = train_datagen.flow_from_directory(train_dir, target_size=(224, 224), batch_size=batch_size, class_mode='binary') val_datagen = ImageDataGenerator(rescale=1./255) val_generator = val_datagen.flow_from_directory(val_dir, target_size=(224, 224), batch_size=batch_size, class_mode='binary') return train_generator, val_generator # 2. 数据集划分 data_dir = 'rawdata' batch_size = 32 train_generator, val_generator = load_data(data_dir, batch_size) # 3. 数据增强 # 4. 构建模型和损失函数 def build_model(): model = keras.Sequential([ keras.layers.Conv2D(32, 3, activation='relu', input_shape=(224, 224, 3)), keras.layers.MaxPooling2D(), keras.layers.Conv2D(64, 3, activation='relu'), keras.layers.MaxPooling2D(), keras.layers.Conv2D(128, 3, activation='relu'), keras.layers.MaxPooling2D(), keras.layers.Flatten(), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) return model model = build_model() model.summary() # 5. 模型训练 epochs = 10 history = model.fit(train_generator, epochs=epochs, validation_data=val_generator) # 6. 可视化展示 def plot_history(history): acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] x = range(epochs) plt.figure(figsize=(12, 4)) plt.subplot(1, 2, 1) plt.plot(x, acc, label='Training accuracy') plt.plot(x, val_acc, label='Validation accuracy') plt.title('Accuracy') plt.legend() plt.subplot(1, 2, 2) plt.plot(x, loss, label='Training loss') plt.plot(x, val_loss, label='Validation loss') plt.title('Loss') plt.legend() plt.show() plot_history(history) # 7. 模型保存 model.save('my_model.h5') ```