How to improve my model accuracy?

autograd
1

I’m learning PyTorch and tried my concepts on my own custom data. I created two lists of random numbers and combined the corresponding items in a new list as X and another list containing the sum of corresponding items as labels y. In simple I want to train a model in such a way that to make it work as an addition calculator. But, I’m getting a very huge loss, I tried tweaking different parameters but getting almost the same results.

Here’s my code:

import torch as T
import torch.nn as nn
import torch.nn.functional as F
import random

randomList1 = random.sample(range(0, 10000), 10000)
randomList2 = random.sample(range(0, 10000), 10000)

xList = []
yList = []

for (item1, item2) in zip(randomList1, randomList2):
    xList.append([item1, item2])

for x in xList:
    yList.append(x[0] + x[1])


dataset = T.torch.utils.data.TensorDataset(
    T.tensor(xList, dtype=T.float), T.tensor(yList, dtype=T.float))

train_batch = T.torch.utils.data.DataLoader(
    dataset, batch_size=10, shuffle=True)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(2, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, 128)
        self.fc4 = nn.Linear(128, 1)

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


net = Net()


loss_fn = nn.MSELoss()
optimizer = T.optim.Adam(net.parameters(), lr=0.0001)


for epoch in range(3):
    for data in train_batch:
        X, y = data
        net.zero_grad()
        output = net(X.view(-1, 2))
        loss = T.sqrt(loss_fn(output, y))       
        loss.backward()
        optimizer.step()
    print(loss)

Output:

tensor(11254.4688, grad_fn=<SqrtBackward0>)
tensor(9933.7793, grad_fn=<SqrtBackward0>)
tensor(11308.9463, grad_fn=<SqrtBackward0>)