•  🍨 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

import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
import seaborn as sns
#设置GPU训练，也可以使用CPU
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

df = pd.read_csv("heart.csv")
df

二、构建数据集

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)

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)

# 维度扩增使其符合RNN模型可接受shape
X_train = X_train.unsqueeze(1)
X_test = X_test.unsqueeze(1)
X_train.shape, y_train.shape

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

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(200, 50)
        self.fc1 = nn.Linear(50, 2)

    def forward(self, x):
        out, _ = self.rnn0(x)
        out = out[:, -1, :]  # 只取最后一个时间步的输出
        out = self.fc0(out)
        out = self.fc1(out)
        return out

model = model_rnn().to(device)
model

# 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:  # 获取数据及其标签
        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

loss_fn    = nn.CrossEntropyLoss()  # 创建损失函数
learn_rate = 1e-4                   # 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

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(str(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()

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import seaborn as sns

# 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()

test_X = X_test[0].unsqueeze(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：不会患心脏病")
print("1：可能患心脏病")