深度学习训练camp-第R3周：RNN-心脏病预测
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阅读量
- 🍨 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
🍺要求：
- 从本地设备读取并加载数据。
- 深入解析循环神经网络（RNN）的构建机制。
- 测试集上的准确率达到了87%。
🍻拔高：
- 测试集accuracy到达89%
🏡 我的环境：
● 当前编程环境使用的是Python 3.12.4版本。
● 该项目的开发和运行基于Jupyter Lab平台。
● 深度学习相关的开发采用了PyTorch库。
● 数据存储位置位于百度网盘的共享链接处。
一、RNN简介
传统神经网络的架构相对单一：仅包含输入层-隐藏层-输出层三层结构。
在每次迭代过程中，RNN与传统神经网络的主要区别在于会将上一个时间步输出结果纳入当前隐藏层进行协同训练。
循环神经网络（RNN）专为处理序列数据（sequence data）设计，在其核心机制上具有独特的循环连接特征：能够通过前一时刻的信息影响当前时刻的计算过程。
这种特性使其特别适合应用于时间序列分析、自然语言处理（NLP）以及各种序列预测任务。
二、心脏病的预测
一、前期准备
import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
import seaborn as sns
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device输出：
device(type='cuda')我的GPU是Nvdia RTX 4070Ti super
二、导入数据
🥂 数据介绍：
● age: 1. 年龄
● sex: 2. 性别
● cp: 3. 胸痛类别 (4种分类)
● trestbps: 4. 静息血压（单位:mmHg）
● chol: 5. 血清胆固醇水平 (单位:mg/dl)
● fbs: 6. 空腹血糖水平超出正常范围（>120 mg/dl）
● restecg:7. 静息心电图结果（值为0、1、2）
● thalach:8. 最大心率值
● exang:9. 运动期间出现的心绞痛事件
● oldpeak:10. 运动与静止状态下ST段压低程度
● slope:11.ST段上升斜率
● ca:12主要动脉供血缺血程度（无血管→缺血→部分缺血）
● thal:13冠状动脉状况评估结果（完全正常→部分阻塞→完全阻塞）
● target :14心脏疾病风险评估结果（低风险→高风险）
源数据属于K同学🍖 原作者：K同学啊
file_path = 'D:/OneDrive/code learning(python and matlab and latex)/365camp/data/heart.csv'
df = pd.read_csv(file_path)
df代码输出：
age sex cp trestbps chol fbs restecg thalach exang oldpeak \
0 63 1 3 145 233 1 0 150 0 2.3
1 37 1 2 130 250 0 1 187 0 3.5
2 41 0 1 130 204 0 0 172 0 1.4
3 56 1 1 120 236 0 1 178 0 0.8
4 57 0 0 120 354 0 1 163 1 0.6
.. ... ... .. ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2
299 45 1 3 110 264 0 1 132 0 1.2
300 68 1 0 144 193 1 1 141 0 3.4
301 57 1 0 130 131 0 1 115 1 1.2
302 57 0 1 130 236 0 0 174 0 0.0
slope ca thal target
0 0 0 1 1
1 0 0 2 1
2 2 0 2 1
3 2 0 2 1
4 2 0 2 1
.. ... .. ... ...
298 1 0 3 0
299 1 0 3 0
300 1 2 3 0
301 1 1 3 0
302 1 1 2 0
[303 rows x 14 columns]三、数据预处理
1、划分训练集和测试集
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
x = df.iloc[:, :-1]
y = df.iloc[:,-1]
sc = StandardScaler()
X = sc.fit_transform(x)
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代码输出：
(torch.Size([272, 13]), torch.Size([272]))2、标准化
from torch.utils.data import TensorDataset, DataLoader, 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)输出：
(<torch.utils.data.dataloader.DataLoader at 0x21cbfd08b90>,
<torch.utils.data.dataloader.DataLoader at 0x21cbfd0adb0>)四、构建RNN模型
class model_RNN(nn.Module):
def __init__(self):
super(model_RNN, self).__init__()
self.rnn0 = nn.RNN(input_size=13, hidden_size=200, num_layers=1, batch_first=True)
self.fc0 = nn.Linear(in_features=200, out_features=50)
self.fc1 = nn.Linear(in_features=50,out_features=2)
def forward(self, x):
out, hidden1 = self.rnn0(x)
out = self.fc0(out)
out = self.fc1(out)
return out
model = model_RNN().to(device)
model代码输出：
model_RNN(
(rnn0): RNN(13, 200, batch_first=True)
(fc0): Linear(in_features=200, out_features=50, bias=True)
(fc1): Linear(in_features=50, out_features=2, bias=True)
)五、编写训练函数和测试函数
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
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六、正式训练
loss_fn = nn.CrossEntropyLoss()
learning_rate = 1e-4
opt = torch.optim.Adam(model.parameters(), lr=learning_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_loss.append(epoch_train_loss)
train_acc.append(epoch_train_acc)
test_loss.append(epoch_test_loss)
test_acc.append(epoch_test_acc)
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)代码输出：
Epoch: 1, Train_acc:47.1%, Train_loss:0.689, Test_acc:67.7%, Test_loss:0.639, Lr:1.00E-04
Epoch: 2, Train_acc:54.0%, Train_loss:0.672, Test_acc:83.9%, Test_loss:0.622, Lr:1.00E-04
Epoch: 3, Train_acc:66.5%, Train_loss:0.653, Test_acc:80.6%, Test_loss:0.606, Lr:1.00E-04
Epoch: 4, Train_acc:76.5%, Train_loss:0.638, Test_acc:80.6%, Test_loss:0.590, Lr:1.00E-04
Epoch: 5, Train_acc:79.4%, Train_loss:0.621, Test_acc:77.4%, Test_loss:0.574, Lr:1.00E-04
Epoch: 6, Train_acc:80.1%, Train_loss:0.620, Test_acc:80.6%, Test_loss:0.558, Lr:1.00E-04
Epoch: 7, Train_acc:79.0%, Train_loss:0.604, Test_acc:77.4%, Test_loss:0.543, Lr:1.00E-04
Epoch: 8, Train_acc:82.0%, Train_loss:0.603, Test_acc:83.9%, Test_loss:0.529, Lr:1.00E-04
Epoch: 9, Train_acc:79.8%, Train_loss:0.585, Test_acc:83.9%, Test_loss:0.516, Lr:1.00E-04
Epoch:10, Train_acc:80.1%, Train_loss:0.564, Test_acc:83.9%, Test_loss:0.502, Lr:1.00E-04
Epoch:11, Train_acc:77.9%, Train_loss:0.559, Test_acc:83.9%, Test_loss:0.488, Lr:1.00E-04
Epoch:12, Train_acc:81.2%, Train_loss:0.542, Test_acc:83.9%, Test_loss:0.473, Lr:1.00E-04
Epoch:13, Train_acc:81.2%, Train_loss:0.543, Test_acc:87.1%, Test_loss:0.459, Lr:1.00E-04
Epoch:14, Train_acc:80.9%, Train_loss:0.529, Test_acc:83.9%, Test_loss:0.447, Lr:1.00E-04
Epoch:15, Train_acc:81.6%, Train_loss:0.520, Test_acc:83.9%, Test_loss:0.435, Lr:1.00E-04
Epoch:16, Train_acc:80.1%, Train_loss:0.506, Test_acc:83.9%, Test_loss:0.423, Lr:1.00E-04
Epoch:17, Train_acc:81.6%, Train_loss:0.507, Test_acc:83.9%, Test_loss:0.412, Lr:1.00E-04
Epoch:18, Train_acc:82.0%, Train_loss:0.472, Test_acc:83.9%, Test_loss:0.401, Lr:1.00E-04
Epoch:19, Train_acc:82.0%, Train_loss:0.478, Test_acc:83.9%, Test_loss:0.393, Lr:1.00E-04
Epoch:20, Train_acc:82.4%, Train_loss:0.485, Test_acc:83.9%, Test_loss:0.385, Lr:1.00E-04
Epoch:21, Train_acc:81.2%, Train_loss:0.459, Test_acc:83.9%, Test_loss:0.378, Lr:1.00E-04
Epoch:22, Train_acc:84.2%, Train_loss:0.465, Test_acc:83.9%, Test_loss:0.370, Lr:1.00E-04
Epoch:23, Train_acc:82.0%, Train_loss:0.442, Test_acc:83.9%, Test_loss:0.363, Lr:1.00E-04
Epoch:24, Train_acc:81.6%, Train_loss:0.433, Test_acc:83.9%, Test_loss:0.357, Lr:1.00E-04
Epoch:25, Train_acc:82.0%, Train_loss:0.425, Test_acc:87.1%, Test_loss:0.351, Lr:1.00E-04
Epoch:26, Train_acc:82.7%, Train_loss:0.423, Test_acc:87.1%, Test_loss:0.345, Lr:1.00E-04
Epoch:27, Train_acc:82.4%, Train_loss:0.428, Test_acc:87.1%, Test_loss:0.339, Lr:1.00E-04
Epoch:28, Train_acc:83.1%, Train_loss:0.443, Test_acc:87.1%, Test_loss:0.334, Lr:1.00E-04
Epoch:29, Train_acc:83.1%, Train_loss:0.433, Test_acc:87.1%, Test_loss:0.330, Lr:1.00E-04
Epoch:30, Train_acc:83.1%, Train_loss:0.407, Test_acc:87.1%, Test_loss:0.326, Lr:1.00E-04
Epoch:31, Train_acc:83.5%, Train_loss:0.415, Test_acc:87.1%, Test_loss:0.321, Lr:1.00E-04
Epoch:32, Train_acc:83.5%, Train_loss:0.389, Test_acc:87.1%, Test_loss:0.316, Lr:1.00E-04
Epoch:33, Train_acc:82.4%, Train_loss:0.400, Test_acc:87.1%, Test_loss:0.312, Lr:1.00E-04
Epoch:34, Train_acc:85.3%, Train_loss:0.380, Test_acc:87.1%, Test_loss:0.311, Lr:1.00E-04
Epoch:35, Train_acc:83.8%, Train_loss:0.388, Test_acc:87.1%, Test_loss:0.310, Lr:1.00E-04
Epoch:36, Train_acc:83.1%, Train_loss:0.377, Test_acc:87.1%, Test_loss:0.311, Lr:1.00E-04
Epoch:37, Train_acc:83.8%, Train_loss:0.382, Test_acc:87.1%, Test_loss:0.310, Lr:1.00E-04
Epoch:38, Train_acc:83.8%, Train_loss:0.379, Test_acc:87.1%, Test_loss:0.309, Lr:1.00E-04
Epoch:39, Train_acc:84.6%, Train_loss:0.388, Test_acc:87.1%, Test_loss:0.307, Lr:1.00E-04
Epoch:40, Train_acc:84.2%, Train_loss:0.373, Test_acc:87.1%, Test_loss:0.306, Lr:1.00E-04
Epoch:41, Train_acc:83.8%, Train_loss:0.373, Test_acc:87.1%, Test_loss:0.307, Lr:1.00E-04
Epoch:42, Train_acc:84.2%, Train_loss:0.371, Test_acc:87.1%, Test_loss:0.309, Lr:1.00E-04
Epoch:43, Train_acc:84.2%, Train_loss:0.363, Test_acc:87.1%, Test_loss:0.310, Lr:1.00E-04
Epoch:44, Train_acc:83.5%, Train_loss:0.361, Test_acc:87.1%, Test_loss:0.311, Lr:1.00E-04
Epoch:45, Train_acc:84.2%, Train_loss:0.394, Test_acc:87.1%, Test_loss:0.313, Lr:1.00E-04
Epoch:46, Train_acc:83.8%, Train_loss:0.380, Test_acc:87.1%, Test_loss:0.314, Lr:1.00E-04
Epoch:47, Train_acc:84.6%, Train_loss:0.365, Test_acc:87.1%, Test_loss:0.314, Lr:1.00E-04
Epoch:48, Train_acc:84.6%, Train_loss:0.338, Test_acc:87.1%, Test_loss:0.313, Lr:1.00E-04
Epoch:49, Train_acc:83.5%, Train_loss:0.373, Test_acc:87.1%, Test_loss:0.314, Lr:1.00E-04
Epoch:50, Train_acc:84.6%, Train_loss:0.355, Test_acc:87.1%, Test_loss:0.314, Lr:1.00E-04
==================== Done ====================七、数据可视化
1、Acc以及Loss图
import matplotlib.pyplot as plt
from datetime import datetime
#隐藏警告
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
current_time = datetime.now() # 获取当前时间
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
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()输出：
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()代码输出：
八、总结与提升
RNN可以简单理解为读书，在上一页的基础上对下一页进行理解
- RNN 的 Forward 过程：模拟人类阅读故事
假设你有一个句子：
“I love deep learning and PyTorch is amazing!”
RNN 的工作方式类似于逐字阅读：
感知到 “I” ，便获得了基础认知。
融合 “love” 的信息后会进一步强化认知。
通过融合全部信息将能实现对整体的优化。
当完整解析这句话时会基于最终的认知作出判断（例如预测其情感倾向）。这一流程与 RNN 的 前向传播（forward pass）过程具有相似性。
实验表明，在 epoch 次数增加时损失反而是上升了。因此我们对数据预处理设置了 shuffle 为 true 并降低了训练轮次。
Epoch: 1, Train_acc:51.8%, Train_loss:0.696, Test_acc:54.8%, Test_loss:0.671, Lr:1.00E-04
Epoch: 2, Train_acc:64.3%, Train_loss:0.679, Test_acc:77.4%, Test_loss:0.653, Lr:1.00E-04
Epoch: 3, Train_acc:75.0%, Train_loss:0.660, Test_acc:87.1%, Test_loss:0.622, Lr:1.00E-04
Epoch: 4, Train_acc:77.2%, Train_loss:0.644, Test_acc:90.3%, Test_loss:0.599, Lr:1.00E-04
Epoch: 5, Train_acc:79.4%, Train_loss:0.625, Test_acc:87.1%, Test_loss:0.578, Lr:1.00E-04
Epoch: 6, Train_acc:78.7%, Train_loss:0.611, Test_acc:87.1%, Test_loss:0.566, Lr:1.00E-04
Epoch: 7, Train_acc:81.6%, Train_loss:0.595, Test_acc:93.5%, Test_loss:0.521, Lr:1.00E-04
Epoch: 8, Train_acc:77.9%, Train_loss:0.580, Test_acc:90.3%, Test_loss:0.520, Lr:1.00E-04
Epoch: 9, Train_acc:79.0%, Train_loss:0.565, Test_acc:90.3%, Test_loss:0.496, Lr:1.00E-04
Epoch:10, Train_acc:80.1%, Train_loss:0.545, Test_acc:90.3%, Test_loss:0.455, Lr:1.00E-04
Epoch:11, Train_acc:82.7%, Train_loss:0.528, Test_acc:87.1%, Test_loss:0.444, Lr:1.00E-04
Epoch:12, Train_acc:81.2%, Train_loss:0.512, Test_acc:93.5%, Test_loss:0.422, Lr:1.00E-04
Epoch:13, Train_acc:82.0%, Train_loss:0.493, Test_acc:90.3%, Test_loss:0.405, Lr:1.00E-04
Epoch:14, Train_acc:82.4%, Train_loss:0.477, Test_acc:90.3%, Test_loss:0.381, Lr:1.00E-04
Epoch:15, Train_acc:82.7%, Train_loss:0.469, Test_acc:87.1%, Test_loss:0.365, Lr:1.00E-04
Epoch:16, Train_acc:83.1%, Train_loss:0.457, Test_acc:90.3%, Test_loss:0.356, Lr:1.00E-04
Epoch:17, Train_acc:84.2%, Train_loss:0.431, Test_acc:87.1%, Test_loss:0.342, Lr:1.00E-04
Epoch:18, Train_acc:83.5%, Train_loss:0.435, Test_acc:87.1%, Test_loss:0.340, Lr:1.00E-04
Epoch:19, Train_acc:83.8%, Train_loss:0.424, Test_acc:90.3%, Test_loss:0.324, Lr:1.00E-04
Epoch:20, Train_acc:82.4%, Train_loss:0.416, Test_acc:90.3%, Test_loss:0.321, Lr:1.00E-04
Epoch:21, Train_acc:85.3%, Train_loss:0.422, Test_acc:90.3%, Test_loss:0.310, Lr:1.00E-04
Epoch:22, Train_acc:84.2%, Train_loss:0.408, Test_acc:90.3%, Test_loss:0.294, Lr:1.00E-04
Epoch:23, Train_acc:86.0%, Train_loss:0.388, Test_acc:87.1%, Test_loss:0.307, Lr:1.00E-04
Epoch:24, Train_acc:84.6%, Train_loss:0.385, Test_acc:90.3%, Test_loss:0.290, Lr:1.00E-04
Epoch:25, Train_acc:84.9%, Train_loss:0.381, Test_acc:87.1%, Test_loss:0.311, Lr:1.00E-04
Epoch:26, Train_acc:85.3%, Train_loss:0.368, Test_acc:87.1%, Test_loss:0.293, Lr:1.00E-04
Epoch:27, Train_acc:84.2%, Train_loss:0.388, Test_acc:87.1%, Test_loss:0.315, Lr:1.00E-04
Epoch:28, Train_acc:84.6%, Train_loss:0.376, Test_acc:87.1%, Test_loss:0.285, Lr:1.00E-04
Epoch:29, Train_acc:83.1%, Train_loss:0.375, Test_acc:87.1%, Test_loss:0.306, Lr:1.00E-04
Epoch:30, Train_acc:84.2%, Train_loss:0.379, Test_acc:87.1%, Test_loss:0.278, Lr:1.00E-04
Epoch:31, Train_acc:84.6%, Train_loss:0.374, Test_acc:87.1%, Test_loss:0.308, Lr:1.00E-04
Epoch:32, Train_acc:84.6%, Train_loss:0.368, Test_acc:90.3%, Test_loss:0.289, Lr:1.00E-04
==================== Done ====================测试集的准确度超过了90%
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