中医舌诊代码

### 中医舌诊相关代码实现 以下是基于深度学习框架 TensorFlow 和 PyTorch 的简单示例，用于实现中医舌诊的图像分类任务。这些代码可以作为基础模板来开发更复杂的模型。 #### 基于 TensorFlow 的舌诊图像分类代码 此代码展示了如何加载数据集并构建一个简单的卷积神经网络 (CNN) 来对舌头图像进行分类[^1]。 ```python import tensorflow as tf from tensorflow.keras import layers, models # 数据预处理 def preprocess_data(): # 加载自定义的数据集（替换为实际路径） dataset_url = "https://example.com/tongue_dataset.zip" data_dir = tf.keras.utils.get_file('tongue_dataset', origin=dataset_url, extract=True) train_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split=0.2, subset="training", seed=123, image_size=(180, 180), batch_size=32) val_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, validation_split=0.2, subset="validation", seed=123, image_size=(180, 180), batch_size=32) return train_ds, val_ds train_ds, val_ds = preprocess_data() # 构建 CNN 模型 model = models.Sequential([ layers.Rescaling(1./255, input_shape=(180, 180, 3)), layers.Conv2D(16, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Conv2D(32, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Conv2D(64, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(len(class_names)) # 替换 class_names 为实际类别数 ]) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) # 训练模型 epochs = 10 history = model.fit(train_ds, validation_data=val_ds, epochs=epochs) ``` --- #### 基于 PyTorch 的舌诊图像分类代码 以下是一个使用 PyTorch 实现的简单 CNN 模型，适用于中医舌诊中的图像分类任务[^2]。 ```python import torch import torchvision import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms # 数据预处理 transform = transforms.Compose([ transforms.Resize((180, 180)), # 调整大小 transforms.ToTensor(), # 转换为张量 transforms.Normalize([0.5], [0.5]) # 归一化 ]) train_dataset = datasets.ImageFolder(root='./data/train/', transform=transform) val_dataset = datasets.ImageFolder(root='./data/val/', transform=transform) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=32, shuffle=True) val_loader = torch.utils.data.DataLoader(dataset=val_dataset, batch_size=32, shuffle=False) # 定义 CNN 模型 class TongueNet(nn.Module): def __init__(self): super(TongueNet, self).__init__() self.conv_layers = nn.Sequential( nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2) ) self.fc_layers = nn.Sequential( nn.Linear(64 * 22 * 22, 128), nn.ReLU(), nn.Linear(128, num_classes) # 替换 num_classes 为实际类别数 ) def forward(self, x): x = self.conv_layers(x) x = x.view(-1, 64 * 22 * 22) x = self.fc_layers(x) return x device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = TongueNet().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 训练循环 for epoch in range(10): for i, (images, labels) in enumerate(train_loader): images, labels = images.to(device), labels.to(device) outputs = model(images) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() ``` --- #### 关键点说明 - **数据集准备**：需要准备好高质量的舌头图像数据集，并标注好对应的特征标签，例如苔色、裂纹等。 - **模型架构**：可以根据具体需求调整模型复杂度，引入迁移学习方法（如 ResNet 或 VGG）可能进一步提高性能。 - **优化策略**：通过调节超参数（如学习率、批量大小）、增加正则化手段等方式改善模型泛化能力。