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：

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')