PyTorch Binary classification not learning

I state that I am new on PyTorch. I wrote this simple program for binary classification. I also created the CSV with two columns of random values, with the "ok" column whose value is 1 only if the other two values are included between two values I decided at the same time. Example:

diam_int,diam_est,ok
37.782,125.507,0
41.278,115.15,1
42.248,115.489,1
29.582,113.141,0
37.428,107.247,0
32.947,123.233,0
37.146,121.537,0
38.537,110.032,0
26.553,113.752,0
27.369,121.144,0
41.632,108.178,0
27.655,111.279,0
29.779,109.268,0
43.695,115.649,1
44.587,116.126,0

It seems to me all done correctly, loss actually lowers (it comes back up slightly after many epochs but I don't think it's a problem), but when I try to test my Net after the training, with a sample batch of the trainset, what I got is always a prediction below 0.5 (so always 0 as estimated output) with a completely random trend.

with torch.no_grad():
        pred = net(trainSet[10])
        trueVal = ySet[10]
        for i in range(len(trueVal)):
            print(trueVal[i], pred[i])

Here is my Net class:

import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    
    def __init__(self) :
        super().__init__()
        self.fc1 = nn.Linear(2, 32)
        self.fc2 = nn.Linear(32, 64)
        self.fc3 = nn.Linear(64, 1)

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

Here is my Main class:

import torch
import torch.optim as optim
import torch.nn.functional as F
import pandas as pd

from net import Net 

df = pd.read_csv("test.csv")
y = torch.Tensor(df["ok"])
ySet = torch.split(y, 32)
df.drop(["ok"], axis=1, inplace=True)
data = F.normalize(torch.Tensor(df.values), dim=1)
trainSet = torch.split(data, 32)

net = Net()
optimizer = optim.Adam(net.parameters(), lr=0.001)
lossFunction = torch.nn.BCELoss()
EPOCHS = 300

for epoch in range(EPOCHS):
    for i, X in enumerate(trainSet):
        optimizer.zero_grad()
        output = net(X)
        target = ySet[i].reshape(-1, 1)
        loss = lossFunction(output, target)
        loss.backward()
        optimizer.step()

    if epoch % 20 == 0:
        print(loss)

What am I doing wrong? Thanks in advance for the help