- 🍨 本文为🔗365天深度学习训练营中的学习记录博客
- 🍖 原作者：K同学啊
一、前期准备
1.数据导入
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
import seaborn as sns
df = pd.read_csv("alzheimers_disease_data.csv")
# 删除第一列和最后一列
df = df.iloc[:, 1:-1]
df
二、构建数据集
1.标准化
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
X = df.iloc[:,:-1]
y = df.iloc[:,-1]
# 将每一列特征标准化为标准正太分布，注意，标准化是针对每一列而言的
sc = StandardScaler()
X = sc.fit_transform(X)2.划分数据集
X = torch.tensor(np.array(X), dtype=torch.float32)
y = torch.tensor(np.array(y), dtype=torch.int64)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 1)
X_train.shape, y_train.shape
3.构建数据加载器
from torch.utils.data import TensorDataset, DataLoader
train_dl = DataLoader(TensorDataset(X_train, y_train),
batch_size=64,
shuffle=False)
test_dl = DataLoader(TensorDataset(X_test, y_test),
batch_size=64,
shuffle=False)三、模型训练
1.构建模型
class model_rnn(nn.Module):
def __init__(self):
super(model_rnn, self).__init__()
self.rnn0 = nn.RNN(input_size=32, hidden_size=200,
num_layers=1, batch_first=True)
self.fc0 = nn.Linear(200, 50)
self.fc1 = nn.Linear(50, 2)
def forward(self, x):
out, hidden1 = self.rnn0(x)
out = self.fc0(out)
out = self.fc1(out)
return out
model = model_rnn()
model
model(torch.rand(30,32)).shape
2.定义训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 获取图片及其标签
X, y = X.to(device), y.to(device)
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # grad属性归零
loss.backward() # 反向传播
optimizer.step() # 每一步自动更新
# 记录acc与loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss
3.定义测试函数
def test (dataloader, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
test_loss, test_acc = 0, 0
# 当不进行训练时，停止梯度更新，节省计算内存消耗
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 计算loss
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
4.训练模型
loss_fn = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 5e-5 # 学习率
opt = torch.optim.Adam(model.parameters(),lr=learn_rate)
epochs = 50
train_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
# 获取当前的学习率
lr = opt.state_dict()['param_groups'][0]['lr']
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss,
epoch_test_acc*100, epoch_test_loss, lr))
print("="*20, 'Done', "="*20)
四、模型评估
1.Loss与Accuracy图
import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 200 #分辨率
from datetime import datetime
current_time = datetime.now() # 获取当前时间
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
2.混淆矩阵
print("==============输入数据Shape为==============")
print("X_test.shape：",X_test.shape)
print("y_test.shape：",y_test.shape)
pred = model(X_test.to(device)).argmax(1).cpu().numpy()
print("\n==============输出数据Shape为==============")
print("pred.shape：",pred.shape)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
# 计算混淆矩阵
cm = confusion_matrix(y_test, pred)
plt.figure(figsize=(6,5))
plt.suptitle('')
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
# 修改字体大小
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.title("Confusion Matrix", fontsize=12)
plt.xlabel("Predicted Label", fontsize=10)
plt.ylabel("True Label", fontsize=10)
# 显示图
plt.tight_layout() # 调整布局防止重叠
plt.show()
3.调用模型进行预测
test_X = X_test[0].reshape(1, -1) # X_test[0]即我们的输入数据
pred = model(test_X.to(device)).argmax(1).item()
print("模型预测结果为：",pred)
print("=="*20)
print("0：未患病")
print("1：已患病")
四、总结
阿尔茨海默病的诊断难点在于早期识别和病程追踪，RNN恰好擅长处理这类时序数据。RNN 能够学习这些不同模态特征在时间维度上的相互作用及其共同演变对疾病状态的影响。
然基础 RNN 处理缺失数据有挑战，但可以结合以下技术使其更鲁棒：
序列填充与掩码： 处理不同长度序列。
插值技术： 估算缺失时间点的值（要谨慎）。
注意力机制： 让模型学会关注更可靠或信息更丰富的时间点和模态，降低对缺失数据的敏感性。
图神经网络结合： 将时间点视为图节点，利用图结构处理不规则时序和缺失。