R8周：RNN实现阿尔茨海默病诊断

•      🍨 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.设置GPU

import numpy as np
import pandas as pd
import torch 
from torch import nn
import torch.nn as nn
import torch.nn.functional as F
import seaborn as sns

#设置硬件设备，如果有GPU则使用，没有则使用cpu
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

device(type='cuda')

2.数据导入

df = pd.read_csv("F:/jupyter lab/DL-100-days/datasets/alzheimers_dig/alzheimers_disease_data.csv")
# 删除最后一列和第一列
df = df.iloc[:, 1:-1]
df

​​

二、数据分析

1.标准化

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

X = df.iloc[:, :-1]
y = df.iloc[:, -1]

# 将每一列特征标准化为标准正态分布，注意，标准化是针对每一列而言的
scaler = StandardScaler()
X = scaler.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

(torch.Size([1934, 32]), torch.Size([1934]))

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=32, 
                      shuffle=False)
test_dl = DataLoader(TensorDataset(X_test, y_test), 
                     batch_size=32, 
                     shuffle=False)

三、训练模型

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):
        # 如果 x 是 2D 的，转换为 3D 张量，假设 seq_len=1
        if x.dim() == 2:
            x = x.unsqueeze(1)  # [batch_size, 1, input_size]
    
        # RNN 处理数据
        out, h_n = self.rnn0(x)  # 第一层 RNN
            
        # out 维度: [batch_size, seq_len, hidden_size]
        # 过 fc0 是线性层
        out = self.fc0(out)  # [batch_size, seq_len, 50]
    
        # 获取最后一个时间步的输出
        out = out[:, -1, :]  # 选择序列的最后一个时间步的输出 [batch_size, 50]
    
        out = self.fc1(out)  # [batch_size, 2]
        return out

model = model_rnn().to(device)
model

model_rnn(
  (rnn0): RNN(32, 200, batch_first=True)
  (fc0): Linear(in_features=200, out_features=50, bias=True)
  (fc1): Linear(in_features=50, out_features=2, bias=True)
)

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
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('Done')

Epoch: 1, Train_acc:63.4%, Train_loss:0.671, Test_acc:65.6%, Test_loss:0.659, Lr:5.00E-05
Epoch: 2, Train_acc:75.7%, Train_loss:0.632, Test_acc:74.4%, Test_loss:0.621, Lr:5.00E-05
Epoch: 3, Train_acc:78.4%, Train_loss:0.592, Test_acc:74.4%, Test_loss:0.580, Lr:5.00E-05
Epoch: 4, Train_acc:79.8%, Train_loss:0.550, Test_acc:74.9%, Test_loss:0.540, Lr:5.00E-05
Epoch: 5, Train_acc:81.4%, Train_loss:0.509, Test_acc:77.2%, Test_loss:0.502, Lr:5.00E-05
..........

Epoch:46, Train_acc:85.1%, Train_loss:0.368, Test_acc:81.4%, Test_loss:0.378, Lr:5.00E-05
Epoch:47, Train_acc:85.1%, Train_loss:0.368, Test_acc:81.4%, Test_loss:0.378, Lr:5.00E-05
Epoch:48, Train_acc:85.1%, Train_loss:0.368, Test_acc:81.4%, Test_loss:0.378, Lr:5.00E-05
Epoch:49, Train_acc:85.1%, Train_loss:0.368, Test_acc:81.4%, Test_loss:0.378, Lr:5.00E-05
Epoch:50, Train_acc:85.1%, Train_loss:0.368, Test_acc:81.4%, Test_loss:0.378, Lr:5.00E-05
==================== Done ====================

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']         = 100        #分辨率

from datetime import datetime
current_time = datetime.now()

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)

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()

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)

============输入数据shape为==============
X_test.shape: torch.Size([215, 32])
y_test.shape: torch.Size([215])

==========输出数据Shape为=============
pred.shape: (215,)

2.混淆矩阵

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")

模型预测结果为: 0
========================================
0:未患病
1:已患病

五、学习心得

1.本周使用RNN开展了阿尔兹海默症预测，使用阿尔兹海默症诊断状态0/1表示，同时加入混淆矩阵。

2.RNN与LSTM相比较，RNN的参数较少，计算量小；而LSTM的参数相对较多，时间长，但是记忆力保持的比较好。