I’m trying to fit this very simple data with targets in the range (0, 1), but even when two values are far apart, like .2 and .45, the loss is very small. So I end up with a very small loss, but the predictions are very wrong. How can improve my predictions?
Here’s my data and the model predictions after training for 1000 epochs with Adam, MSE, and a learning rate of .001. Trying BCELoss didn’t improve the results.

Inputs = [[14.0110, 2.0000], [18.9990, 3.0000], [12.0110, 0.0000], [16.9990, 1.0000]]
Targets = [0.6774895, 0.12747164, 0.02246823, 0.00056751847]
Predictions = [0.37114963, 0.31669545, 0.1016788, 0.09256815]

The last sample’s prediction is 163 times bigger than its target, but the loss is tiny (0.0084).
Here’s my model:
class Net(torch.nn.Module):
    def __init__(self, num_features):
        super().__init__()

        self.linear_hidden_1 = torch.nn.Linear(num_features, num_features * 2)
        self.linear_out = torch.nn.Linear(num_features * 2, 1)

    def forward(self, inputs):
        hidden = torch.nn.ReLU()(self.linear_hidden_1(inputs))

        out = torch.nn.Sigmoid()(self.linear_out(hidden)).flatten()

        return out

I would really appreciate any suggestions how to get it to predict properly.