import torch import torch.nn as nn import numpy as np from torch.utils.data import DataLoader, TensorDataset import pandas as pd class RNN(nn.Module): def init(self, input_size, hidden_size, output_size): super(RNN, self).init() self.hidden_size = hidden_size self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h0 = torch.zeros(1, x.size(0), self.hidden_size) out, _ = self.rnn(x, h0) out = self.fc(out[:, -1, :]) return out input_size = 1 hidden_size = 32 output_size = 1 model = RNN(input_size, hidden_size, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) df = pd.read_csv('beijing_wangjing_125_new.csv') congestion_index = df['idx'].values congestion_index_tensor= torch.tensor(congestion_index, dtype=torch.float32) print(congestion_index_tensor) for epoch in range(100): outputs = model(congestion_index_tensor) loss = criterion(outputs, congestion_index_tensor) optimizer.zero_grad() loss.backward() optimizer.step()的代码中为什么会出现张量不匹配的情况，应该怎么修改？

在代码中出现张量不匹配的情况可能是因为输入数据的维度不匹配导致的。具体来说，模型的输入是一个三维张量，形状为(batch_size, sequence_length, input_size)，而数据集转化为Tensor后，形状可能不是这个形状，导致模型无法进行前向计算。建议在将数据集转化为Tensor时，将数据集的维度调整为(batch_size, sequence_length, input_size)的形状。代码中RNN模型的init方法应该是__init__方法，而不是init方法。正确的代码如下：

import torch
import torch.nn as nn
import numpy as np
from torch.utils.data import DataLoader, TensorDataset
import pandas as pd

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        h0 = torch.zeros(1, x.size(0), self.hidden_size)
        out, _ = self.rnn(x, h0)
        out = self.fc(out[:, -1, :])
        return out

input_size = 1
hidden_size = 32
output_size = 1

model = RNN(input_size, hidden_size, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

df = pd.read_csv('beijing_wangjing_125_new.csv')
congestion_index = df['idx'].values
congestion_index_tensor = torch.tensor(congestion_index, dtype=torch.float32).view(-1, 1, 1) # 调整数据集维度

print(congestion_index_tensor)

for epoch in range(100):
    outputs = model(congestion_index_tensor)
    loss = criterion(outputs, congestion_index_tensor)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

在这里，我们将数据集转化为Tensor后，使用view方法将其调整为(batch_size, sequence_length, input_size)的形状，其中batch_size表示数据集的样本数，sequence_length表示序列的长度，input_size表示每个时间步的输入大小。这样就可以避免了张量不匹配的问题。