I understand that you want to train a custom ANN such that it meets the desired constraints.
In order to achieve desirable predictions from a neural network, a sufficiently large training dataset and target labels adhered to the specified constraints can help train the model such that the predictions naturally tend to be bound by the constraints.
However, in the given example, the training data is limited, which could be a contributing factor to the failure in meeting the constraints.
To address this issue and ensure that the network is trained while enforcing the specified conditions, the following strategies can be employed:
- Incorporate an additional expression into the cost function. This expression should be a differentiable function that evaluates to zero when the constraints are satisfied, and a large positive value otherwise. By including this expression, the network is encouraged to prioritize meeting the constraints during the training process.
- Select an appropriate activation function that enforces the allowed range of predictions. For instance, if the sigmoid function is utilized, the output can be scaled to fall within the desired range [a, b] by applying the formula: output * (b - a) + a.
For further help, kindly refer to the below answer:
I hope this helps!
