NN regression loss value not decreasing
I'm training a NN with Pytorch to predict the expected price for the Boston dataset.
The network looks like this:

from sklearn.datasets import load_boston
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch
import torch.nn as nn
import torch.optim as optim

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(13, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 32)
        self.fc4 = nn.Linear(32, 16)
        self.fc5 = nn.Linear(16,1)

    def forward(self, x):
       x = self.fc1(x)
       x = self.fc2(x)
       x = F.relu(x)
       x = self.fc3(x)
       x = F.relu(x)
       x = self.fc4(x)
       x = F.relu(x)
       return self.fc5(x)


And the data loader:

class BostonData(Dataset):
    __xs = []
    __ys = []

    def __init__(self, train = True):
        df = load_boston()
        index = int(len(df["data"]) * 0.7)
        if train:
            self.__xs = df["data"][0:index]
            self.__ys = df["target"][0:index]
        else:
            self.__xs = df["data"][index:]
            self.__ys = df["target"][index:]

    def __getitem__(self, index):
        return self.__xs[index], self.__ys[index]

    def __len__(self):
        return len(self.__xs)


In my first attempt I didn't add the ReLU units, but after a little bit of research I saw that adding them is a common practice, but It didn't work out for me.

Here is the training code:

dset_train = BostonData(train = True)
dset_test = BostonData(train = False)
train_loader = DataLoader(dset_train, batch_size=30, shuffle=True)
test_loader = DataLoader(dset_train, batch_size=30, shuffle=True)


optimizer = optim.Adam(net.parameters(), lr = 0.001)
criterion = torch.nn.MSELoss() 
EPOCHS = 10000

lloss = []

for epoch in range(EPOCHS):
    for trainbatch in train_loader:
        X,y = trainbatch
        net.zero_grad()
        output = net(X.float())
        loss = criterion(output, y)
        loss.backward()
        optimizer.step()
    lloss.append(loss)
    print(loss)


After 10k epochs, the loss graph looks like the following



where I don't see any clear decrease. 
I don't know if I'm messing up with the torch.nn.MSELoss(), the optimizer or maybe with the net topology, so any help will be appreciated.

Edit:
Changing the learning rate and normalizing the data didn't work for me. I added the line self.__xs = (self.__xs - self.__xs.mean()) / self.__xs.std()
 and a change to lr = 0.01. The loss plot is very similar to the first one.

Same plot for lr = 0.01 and normalizing after 1000 epochs:



you are appending lloss once per epoch and that correct but you are appending with loss (using only last batch) where you should append with avg_train_loss

Try:

for epoch in range(EPOCHS):
    avg_train_loss = 0
    for trainbatch in train_loader:
        X,y = trainbatch
        net.zero_grad()
        output = net(X.float())
        loss = criterion(output, y)
        loss.backward()
        optimizer.step()
        avg_train_loss += loss.item() / len(train_loader)
    lloss.append(avg_train_loss)

