import torch from torch import nn class RNN(nn.Module): def init(self): super().init() def forward(self, input): output = input + 1 return output Network = RNN() x = 1 x = RNN(x) print(x)为什么报错这个 init() takes 1 positional argument but 2 were given

这是因为在创建 RNN 实例时，传入了一个参数 x，但是 RNN 类的构造函数 init() 并不接受任何参数，因此出现了 init() takes 1 positional argument but 2 were given 的错误提示。正确的创建方式应该是 Network = RNN()，然后再调用 forward() 方法来进行计算。

相关问题

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。

import torch import torch.nn as nn # 定义序列数据 sequence_data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 定义RNN模型 class RNN1(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size): super(RNN1, self).__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size) # 修改这里的h0初始化 lu = x.unsqueeze(0) out, _ = self.rnn(lu, h0) # 添加unsqueeze(0)来增加batch维度 out = self.fc(out[:, -1, :]) # 取序列最后一个时间步的输出 return out # 定义模型参数 input_size = 3 # 输入大小，即序列的特征维度 hidden_size = 5 # 隐藏层大小 num_layers = 1 # RNN隐层数 output_size = 1 # 输出大小 # 创建模型实例 model = RNN1(input_size, hidden_size, num_layers, output_size) # 运行模型 outputs = model(sequence_data) # 输出结果 print(outputs) 这段代码报错：RuntimeError: Expected hidden size (1, 1, 5), got [1, 3, 5]， 请分析原因

这个错误是因为对于 RNN 模型的初始隐藏状态 h0 没有正确初始化，导致期望的维度与实际维度不匹配。在这段代码中，h0 的维度应该是 (num_layers, batch_size, hidden_size)，而 batch_size 的大小应该是序列数据的样本数，即3。因此，h0 应该被初始化为 torch.zeros(num_layers, sequence_data.size(0), hidden_size)，而不是 torch.zeros(self.num_layers, x.size(0), self.hidden_size)。此外，为了让输入数据具有 batch 维度，需要在输入数据上添加一个维度，即使用 unsqueeze(0)。因此，代码应该修改为：

import torch 
import torch.nn as nn 

# 定义序列数据 
sequence_data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) 

# 定义RNN模型 
class RNN1(nn.Module): 
    def __init__(self, input_size, hidden_size, num_layers, output_size): 
        super(RNN1, self).__init__() 
        self.hidden_size = hidden_size 
        self.num_layers = num_layers 
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True) 
        self.fc = nn.Linear(hidden_size, output_size) 

    def forward(self, x): 
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size) 
        lu = x.unsqueeze(0)  # 添加unsqueeze(0)来增加batch维度
        out, _ = self.rnn(lu, h0) 
        out = self.fc(out[:, -1, :]) 
        return out 

# 定义模型参数 
input_size = 3 
hidden_size = 5 
num_layers = 1 
output_size = 1 

# 创建模型实例 
model = RNN1(input_size, hidden_size, num_layers, output_size) 

# 运行模型 
outputs = model(sequence_data) 

# 输出结果 
print(outputs)

这样应该就可以运行了。