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) 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()代码如何修改初始隐藏层状态为3

要将初始隐藏层状态设置为3，需要在RNN类的__init__函数中增加一个参数，并在forward函数中使用该参数来初始化隐藏层状态。具体修改如下：

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.RNN(input_size, hidden_size, num_layers=num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, h=None):
        if h is None:
            h = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
        out, h = self.rnn(x, h)
        out = self.fc(out[:, -1, :])
        return out, h

input_size = 1
hidden_size = 32
output_size = 1
num_layers = 1

model = RNN(input_size, hidden_size, output_size, num_layers)
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)

h = torch.zeros(num_layers, 1, hidden_size) + 3  # 初始隐藏层状态设置为3

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

在该代码中，我们增加了一个num_layers参数，用于指定RNN层的层数。在forward函数中，我们增加了一个h参数，用于初始化隐藏层状态，同时在第一次调用forward函数时，如果h参数为None，则将其初始化为全零张量。在训练过程中，我们通过将h参数设置为一个值为3的全零张量，来将初始隐藏层状态设置为3。