I have applied a simple NN  for regression to the traditional boston housing price dataset. The problem I’m getting is, when I make predictions using the trained model it always predicts the same numbers. Here is my code:
import numpy as  np
import pandas as pd
from sklearn import datasets
data = datasets.load_boston()

X = pd.DataFrame(data.data, columns=data.feature_names)
Y = pd.DataFrame(data.target, columns=["MEDV"])

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=1234)

import torch

x_train = torch.tensor(X_train.values, dtype=torch.float)
y_train = torch.tensor(y_train.values, dtype=torch.float)
x_test = torch.tensor(X_test.values, dtype=torch.float)
y_test = torch.tensor(y_test.values, dtype=torch.float)

import torch.nn.functional as F

class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)
        self.predict = torch.nn.Linear(n_hidden, n_output)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x

net = Net(n_feature=13, n_hidden=50, n_output=1)
optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
loss_func = torch.nn.MSELoss() 


for t in range(200):
    prediction = net(x_train)
    
    loss = loss_func(prediction, y_train)
    print(t, loss.item())
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Here is the loss per epoch log:
0 6414.029296875
1 3.883837028532696e+19
2 5.963952279684907e+18
3 2.1470207536047063e+18
4 7.729279495852524e+17
5 2.7825424945486234e+17
6 1.001715136546734e+17
7 3.606170935335322e+16
8 1.2982211888283648e+16
9 4673591211720704.0
10 1682493577101312.0
11 605696890503168.0
12 218050926215168.0
13 78498328739840.0
14 28259370663936.0
15 10173364043776.0
16 3662410940416.0
17 1318467796992.0
18 474648543232.0
19 170873470976.0
20 61514457088.0
21 22145208320.0
22 7972275200.0
23 2870019072.0
24 1033206912.0
25 371954528.0
26 133903616.0
27 48205360.0
28 17353982.0
29 6247483.5
30 2249145.25
31 809743.25
32 291558.65625
33 105012.1171875
34 37855.3984375
35 13678.982421875
36 4975.46826171875
37 1842.20361328125
38 714.2283325195312
39 308.15716552734375
40 161.97158813476562
41 109.34477233886719
42 90.39911651611328
43 83.57867431640625
44 81.12332153320312
45 80.23938751220703
46 79.92117309570312
47 79.8066177368164
48 79.76537322998047
49 79.75053405761719
50 79.74518585205078
51 79.7432632446289
52 79.74256896972656
53 79.74231719970703
54 79.74223327636719
55 79.74219512939453
56 79.7421875
57 79.74217987060547
58 79.74217987060547
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199 79.74217987060547
After training the model, I’ve used the following code to make predictions:
with torch.no_grad():
    y_val = net(x_test)

When I print the predictions I get the following results:
tensor([[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099],
[22.4099]])