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, hinput_size = 1hidden_size = 32output_size = 1num_layers = 1model = 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'].valuescongestion_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3for epoch in range(100): outputs, h = model(congestion_index_tensor, h) loss = criterion(outputs, congestion_index_tensor) optimizer.zero_grad() loss.backward() optimizer.step()为什么会出现cannot unpack non-iterable NoneType object的问题,怎么解决
时间: 2024-05-02 18:22:06 浏览: 139
这个错误通常是因为 `model` 的 `forward` 函数中返回的第二个变量 `h` 没有被正确地赋值。在这个例子中,当 `h=None` 时,`out, h = self.rnn(x, h)` 会将 `h` 的值设为 `None`,因此在第二次迭代时,会出现错误。为了解决这个问题,可以将 `h` 的默认值设为一个张量,例如`h = torch.zeros(num_layers, x.size(0), self.hidden_size)`。这样,在第二次迭代时,`h` 会被正确地赋值,避免了出现上述错误。
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, labels) optimizer.zero_grad() loss.backward() optimizer.step()为什么会说张量维度不匹配,应该如何修改
从你的代码中可以看出,"labels"这个变量没有被定义或赋值。因此,当你调用`criterion(outputs, labels)`时会出现维度不匹配的错误。
为了解决这个问题,你需要指定一个标签张量来计算损失。你可以从你的CSV文件中提取标签数据,并将其转换为张量。例如,如果你的标签数据存储在名为“labels”的列中,则可以使用以下代码将其转换为张量:
```python
labels = df['labels'].values
labels_tensor = torch.tensor(labels, dtype=torch.float32)
```
然后,你可以在训练循环中使用这个标签张量来计算损失:
```python
for epoch in range(100):
outputs = model(congestion_index_tensor)
loss = criterion(outputs, labels_tensor)
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
这样就可以解决维度不匹配的错误了。请注意,如果你的标签张量的维度和模型输出的维度不匹配,你仍然会遇到类似的错误。在这种情况下,你需要确保标签张量的形状与模型输出的形状匹配。
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。
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