User: Traceback (most recent call last):
  File "d:\important\python\machineLearn\pytorch.py", line 78, in <module>
    seq = torch.FloatTensor(test_inputs[-seq_length:])
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: not a sequence

Assistant: The error message you're encountering is a `TypeError` indicating that the argument you're providing to the `torch.FloatTensor()` constructor is "not a sequence". This likely means that `test_inputs[-seq_length:]` is not returning a list, tuple, or another iterable that `torch.FloatTensor()` can convert into a tensor. Let's troubleshoot and fix this issue:

1. **Check `test_inputs`:**
   - Ensure that `test_inputs` is a sequence (such as a list, tuple, or another iterable) before you attempt slicing it. You can print its type to verify:

     ```python
     print(type(test_inputs))
     ```

2. **Check `seq_length`:**
   - Verify that `seq_length` is an integer and that it's a valid index for slicing `test_inputs`. Ensure it is not negative or larger than the length of `test_inputs`.

3. **Verify Slicing:**
   - Check if `test_inputs[-seq_length:]` is indeed producing a slice. You can try printing `test_inputs[-seq_length:]` to inspect its contents:

     ```python
     print(test_inputs[-seq_length:])
     ```

4. **Handle Non-Sequence:**
   - If `test_inputs` is not a sequence, you'll need to determine why. Make sure that wherever `test_inputs` is being set, it is assigned a sequence. Double-check the logic if `test_inputs` is being updated dynamically.

Here's how part of your code might look with these checks in place:

```python
import torch

# Example variables for context
test_inputs = [...]  # Make sure this is defined properly as a list or similar
seq_length = 5       # Example value, choose an appropriate length

# Check types and slicing
print("Type of test_inputs:", type(test_inputs))
print("Contents of test_inputs slice:", test_inputs[-seq_length:])

# Create tensor if everything is correct
if isinstance(test_inputs, (list, tuple)) and isinstance(seq_length, int):
    seq = torch.FloatTensor(test_inputs[-seq_length:])

# Rest of your machine learning code...
```

Make sure to replace the example content with the actual data and logic from your program. Once you address the non-sequence issue, your code should proceed without this error.
User: import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

# 示例数据（假设你有一个气温数据的列表）
data = np.sin(np.arange(0, 100, 0.1))  # 用正弦波模拟气温数据

# 数据预处理
scaler = MinMaxScaler(feature_range=(-1, 1))
data_normalized = scaler.fit_transform(data.reshape(-1, 1))

def create_sequences(data, seq_length):
    xs, ys = [], []
    for i in range(len(data)-seq_length):
        x = data[i:i+seq_length]
        y = data[i+seq_length]
        xs.append(x)
        ys.append(y)
    return np.array(xs), np.array(ys)

seq_length = 20
X, y = create_sequences(data_normalized, seq_length)

X_train = torch.from_numpy(X).float()
y_train = torch.from_numpy(y).float()

# 定义LSTM模型
class LSTMModel(nn.Module):
    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):
        super().__init__()
        self.hidden_layer_size = hidden_layer_size
        self.lstm = nn.LSTM(input_size, hidden_layer_size)
        self.linear = nn.Linear(hidden_layer_size, output_size)
        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),
                            torch.zeros(1,1,self.hidden_layer_size))

    def forward(self, input_seq):
        lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq), 1, -1), self.hidden_cell)
        predictions = self.linear(lstm_out.view(len(input_seq), -1))
        return predictions[-1]

model = LSTMModel()
loss_function = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
epochs = 150
for i in range(epochs):
    for seq, labels in zip(X_train, y_train):
        optimizer.zero_grad()
        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),
                        torch.zeros(1, 1, model.hidden_layer_size))

        y_pred = model(seq)

        single_loss = loss_function(y_pred, labels)
        single_loss.backward()
        optimizer.step()

    if i % 25 == 0:
        print(f'Epoch {i} Loss: {single_loss.item()}')

# 测试模型
model.eval()
test_inputs = data_normalized[-seq_length:].tolist()

for i in range(100):
   
# 创建 seq 张量
    seq = torch.FloatTensor(test_inputs[-seq_length:])
    with torch.no_grad():
        model.hidden = (torch.zeros(1, 1, model.hidden_layer_size),
                        torch.zeros(1, 1, model.hidden_layer_size))
        test_inputs.append(model(seq).item())

# 逆缩放输出
actual_predictions = scaler.inverse_transform(np.array(test_inputs[seq_length:]).reshape(-1, 1))

# 绘制结果
plt.plot(data, label='实际气温')
plt.plot(range(80, 180), actual_predictions, label='预测气温')
plt.legend()
plt.show()

Assistant: