实现糖尿病诱导的眼底视网膜病变等级判定的python代码使得结果得到我们可以使用matplotlib来绘制训练和验证的准确率（accuracy）的曲线

要在Python中实现糖尿病诱导眼底视网膜病变等级判定，并通过Matplotlib绘制训练和验证准确率曲线，你可以按照以下步骤操作： 首先，你需要安装所需的库，如`pandas`, `numpy`, `torch`, `sklearn`, 和 `matplotlib`。如果你还没有安装，可以使用`pip install`命令安装： ```bash pip install pandas numpy torch scikit-learn matplotlib ``` 然后，假设你已经有了训练集和测试集数据，并已经预处理过。这里我们将使用一个简单的分类模型，例如支持向量机（SVM）或深度学习模型（如ResNet等）。这里我们以Keras为例，因为其易于搭建神经网络并集成到PyTorch框架中。 ```python import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from torch.utils.data import TensorDataset, DataLoader from torch.optim import Adam import torch.nn as nn import torch from torch import optim import matplotlib.pyplot as plt # 加载数据（这里假设df是包含特征和标签的数据框） # df = pd.read_csv('your_data.csv') # 数据预处理 X = df.drop('label_column', axis=1) # 假设'label_column'是目标列 y = df['label_column'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 标准化数据 scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 将数据转换为Tensor train_dataset = TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train).long()) val_dataset = TensorDataset(torch.from_numpy(X_test), torch.from_numpy(y_test).long()) # 定义Dataloader train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) # 使用PyTorch构建模型 class CustomModel(nn.Module): # ... (模型定义部分) model = CustomModel() optimizer = Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() # 训练过程 epochs = 50 train_accuracies, val_accuracies = [], [] for epoch in range(epochs): model.train() # 进入训练模式 running_loss = 0.0 for inputs, labels in train_loader: optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() * inputs.size(0) model.eval() # 进入评估模式 with torch.no_grad(): correct = 0 total = 0 for inputs, labels in val_loader: outputs = model(inputs) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() train_acc = correct / len(train_loader.dataset) val_acc = correct / total train_accuracies.append(train_acc) val_accuracies.append(val_acc) print(f"Epoch {epoch + 1}/{epochs}, Train Accuracy: {train_acc:.4f}, Val Accuracy: {val_acc:.4f}") # 绘制训练和验证准确率曲线 plt.plot(range(1, epochs+1), train_accuracies, label='Training Accuracy') plt.plot(range(1, epochs+1), val_accuracies, label='Validation Accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.title('Diabetic Retinopathy Classification Accuracy') plt.legend() plt.show() ``` 记得替换上述代码中的`your_data.csv`、`label_column`以及`CustomModel`（自定义模型的具体实现），根据实际情况调整模型参数和数据预处理步骤。