李宏毅机器学习Homework1

• 题意大概是（我具体没怎么听，觉得有问题的小伙伴可以提醒我一句），给你前四天的数据，根据第5天的身体状况预测第五天的test_positive，具体数据b站有，懒得放了
• 因为电脑没有GPU所以我懒得管那些GPU的设置了，然后代码主要是训练模块，测试模块实际上就是把网络设置成推理模式，然后自己跑，很简单的我就不写了
• 这个问题单纯是练手的，我也没看老师的代码是怎么做的，反正训练效果我感觉差不多就行了，就不多说废话，调整超参数那些自己可以尝试搞一搞
• 最后大家感兴趣的话可以关注我的个人公众号右转的第二排架子，里面（将）有相关内容

import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import csv import numpy as np import matplotlib.pyplot as plttrain_path = 'covid.train.csv' test_path = 'covid.test.csv'class Net(nn.Module):def __init__(self, input_dim, output_dim):super(Net, self).__init__()self.fc1 = nn.Linear(input_dim, 100)self.fc2 = nn.Linear(100, 50)self.fc3 = nn.Linear(50, 30)self.fc4 = nn.Linear(30, output_dim)def forward(self, state):output = F.relu((self.fc1(state)))output = F.relu((self.fc2(output)))output = F.relu((self.fc3(output)))output = self.fc4(output)return outputclass Covid19_Dataset:def __init__(self, train_path, test_path):self.train_path = train_pathself.test_path = test_path_, self.train_data = self.load_data(train_path)_, self.test_data = self.load_data(test_path) '''*******************************************************************id : line 1location : line 2 - 38day1 : line 39 - 54day2 : line 55 - 70day3 : line 71 - 86day4 : line 87 - 102if train_data:day5 : line 103 - 118if test_data: id + 116day5 : line 103 - 117 (need to predict 'tested_positve' according to relevant parameters)*******************************************************************'''def load_data(self, filepath):with open(filepath, 'r') as f:reader = csv.reader(f)index = next(reader)data = np.array([*reader]).astype(np.float64)return index, datadef sample_data(self, Batch_size, data):index = np.random.choice(len(data), size = Batch_size, replace = False) #不放回抽样sample_data = []for sample_index in index:sample_data.append(data[sample_index])return np.array(sample_data)if __name__ == '__main__':Batch_size = 16input_dim = 116output_dim = 1learning_rate = 0.001net = Net(input_dim, output_dim)dataset = Covid19_Dataset(train_path, test_path)criterion = nn.MSELoss(reduction = 'mean')optimizer = optim.Adam(net.parameters(), lr = learning_rate)Episode = np.arange(1, 1000)Episode_loss = []for epoch in Episode:sample_train_data = dataset.sample_data(Batch_size, dataset.train_data)sample_input_data = torch.FloatTensor(sample_train_data[:, 1:117])sample_label_data = torch.FloatTensor(sample_train_data[:, -1]).unsqueeze(1)net.eval()predict_data = net(sample_input_data)net.train()loss = criterion(predict_data, sample_label_data)Episode_loss.append(loss.item())optimizer.zero_grad()loss.backward()optimizer.step()Episode_loss = np.array(Episode_loss)plt.plot(Episode, Episode_loss)plt.show()

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