Pytorch predict Result is Almost the same

I have only seven characteristics in a data forecasting the amount of bicycles. Why do I predict that the results are almost the same?

Out:
tensor([[50.9622],
[50.7544],
[50.2624],
[49.4988],
[50.6219],
[50.3502],
[50.2156],
[50.7703],
[51.0407],
[50.7978]])

Maybe your model is too simple. Better you show your full code.

Thank you

If you need all the data, I can give it to you.

``````import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch˽
import torch.nn as nn
import warnings
warnings.filterwarnings('ignore')
``````
``````train = pd.read_csv("../../Data/3-sofa公共自行车使用量预测/train.csv",index_col='id')
``````
``````train.columns
``````

OUt
Index([‘city’, ‘hour’, ‘is_workday’, ‘weather’, ‘temp_1’, ‘temp_2’, ‘wind’, ‘y’], dtype=‘object’)

``````train.head()
``````

``````# 取出训练集的y
y_train = train.pop('y')
``````
``````train = torch.FloatTensor(train.values)
test = torch.FloatTensor(test.values)
y_train = torch.FloatTensor(y_train.values)
``````
``````net = torch.nn.Sequential(
torch.nn.Linear(7,100),
torch.nn.Sigmoid(),
torch.nn.Linear(100,1),
)
loss_func = torch.nn.MSELoss()
``````
``````for i in range(60):
prediction = net(train)
loss = loss_func(prediction,y_train)
# 梯度归零
# 计算梯度
loss.backward()
# 更新结点
optimizer.step()
if i % 20 == 0:
print(loss)
``````

OUt˽˽

``````tensor(4836.1333, grad_fn=<MseLossBackward>)
``````
``````y_train[:10]
``````
``````tensor([15., 48., 21., 11., 39., 12., 11., 67., 77.,  2.])
``````
``````net(train[:10])
``````

Out
tensor([[51.4017],
[49.5164],
[48.2066],
[49.3453],
[49.4258],
[48.9165],
[48.8166],
[49.5939],
[50.7129],
[49.8309]])