我试图建立一个预测足球比分的模型。
我使用了这个数据集：https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017
然后，我给每个团队名称一个唯一的整数。
下面是NN的代码：

import csv
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
import pandas as pd

data = []
with open('/Volumes/Drive 2/Football nn/output.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    for row in reader:
        data.append(row)

# team names
home_team = [str(row[1]) for row in data]
away_team = [str(row[2]) for row in data]
#team scores
old_home_score = [str(row[3]) for row in data]
old_away_score = [str(row[4]) for row in data]

home_score = []
away_score = []
print(home_team[44762])
print(away_team[44762])
home_team.pop(0)
away_team.pop(0)

old_home_score.pop(0)
old_away_score.pop(0)

for item in (old_home_score):
    iteam = int(item)
    iteam /=10
    home_score.append(iteam)

for item in (old_away_score):
    iteam = int(item)
    iteam /=10
    away_score.append(iteam)

print(away_score[44761])
print(home_score[44761])
home_team = [eval(i) for i in home_team]
away_team = [eval(i) for i in away_team]
class SoccerNN(nn.Module):
    def __init__(self):
        super(SoccerNN, self).__init__()
        self.fc1 = nn.Linear(2, 15)  
        self.fc2 = nn.Linear(15, 20)
        self.fc3 = nn.Linear(20, 10)
        self.fc4 = nn.Linear(10, 2) 

    def forward(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        x = self.fc4(x)
        x = torch.sigmoid(x)
        return x

# Convert the columns to PyTorch tensors
input_data = torch.tensor(np.column_stack((home_team, away_team)), dtype=torch.int16)
output_data = torch.tensor(np.column_stack((home_score, away_score)), dtype=torch.int16)

model = SoccerNN()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
epochs = 50  # Adjust the number of epochs as needed
batch_size = 10  # Adjust the batch size as needed
for epoch in range(epochs):
    for batch_start in range(0, len(input_data), batch_size):
        batch_end = batch_start + batch_size
        batch_input = input_data[batch_start:batch_end]
        batch_output = output_data[batch_start:batch_end]
        # Convert batch_input and batch_output to the same dtype as the model's parameters
        batch_input = batch_input.to(model.fc1.weight.dtype)
        batch_output = batch_output.to(model.fc2.weight.dtype)

        # Zero the gradients
        optimizer.zero_grad()

        # Forward pass
        predictions = model(batch_input)

        # Calculate loss
        loss = criterion(predictions, batch_output)

        # Backpropagation
        loss.backward()

        # Update weights
        optimizer.step()

    
        print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}")

# After training, you can use the model for predictions
home_team = [16]
away_team = [37]
new_data = torch.tensor(np.column_stack((home_team, away_team)), dtype=torch.int16)
new_data = torch.tensor(new_data, dtype=torch.float32)  # Use the appropriate data type

# Put the model in evaluation mode
model.eval()

# Make predictions
with torch.no_grad():
    predictions = model(new_data)

# Convert predictions to numpy array
predictions_np = predictions.numpy()

# Print or use the predictions as needed
print(predictions_np)

我给网络两个int作为输入（每个int代表一个团队），网络应该输出游戏的预测得分。
当我训练模型时，它说损失为0，但预测完全错误。
为什么它不工作（我的猜测是，它进入了一个局部最小值，它不能出去，但我可能完全错了，因为我是非常新的整个事情），我应该如何改变模型，使它工作？
已尝试更改批处理大小、层数、时期.
还是不行...