I am trying to writing a regressor which uses a series of dense layers to predict a value.
The code is as follows:
The Network
class deep_regressor(nn.Module):
  def __init__(self):
    super(deep_regressor,self).__init__()
    self.linear_1 = nn.Linear(
                      in_features = 8,
                      out_features = 16
                    )
    self.linear_2 = nn.Linear(
                      in_features = 16,
                      out_features = 32
                    )
    self.output = nn.Linear(
                      in_features = 32,
                      out_features = 1
                    )
    
  def forward(self, input_tensor):
    tensor = self.linear_1(input_tensor)
    tensor = f.relu(tensor)

    tensor = self.linear_2(tensor)
    tensor = f.relu(tensor)

    tensor = self.output(tensor)
    return tensor

The Script:
neurons = deep_regressor()
optimizer = torch.optim.Adam(neurons.parameters(),lr = 0.01)
loss_function = nn.MSELoss()
for epoch in range(10):
    number = 0
    total_loss = 0 
    accuracy = 0
    for data_rows,labels in train_loader:
        predictions = neurons(data_rows)
        calc_loss = loss_function(predictions,labels)

        optimizer.zero_grad() #resets the gradients

        calc_loss.backward()
        optimizer.step()

    print(
        "Epochs =\t" + str(epoch + 1)
    )
    preds = neurons(torch.as_tensor(df, dtype = torch.float32))
    preds = preds.detach().numpy()
    rmse = math.sqrt(mean_squared_error(preds,target))
    print(rmse)

The output per epoch is as follows:
Epochs =	1
78.64161989373262
Epochs =	2
78.3528279726827
Epochs =	3
78.3800896760905
Epochs =	4
78.35181985605024
Epochs =	5
78.24301382034795
Epochs =	6
78.52033108135714
Epochs =	7
78.39965093292
Epochs =	8
78.2766145950348
Epochs =	9
78.39979733373761
Epochs =	10
78.39885957650147

Why is this happening? How do I fix it?