using Tensorflow.Keras.Utils;

namespace Tensorflow.Keras.Losses;

/// <summary>

/// Loss base class.

/// </summary>

public abstract class Loss : ILossFunc

{

protected string reduction;

protected string name;

bool _allow_sum_over_batch_size;

protected bool from_logits = false;

string _name_scope;

public string Reduction => reduction;

public string Name => name;

public Loss(string reduction = ReductionV2.AUTO,

string name = null,

bool from_logits = false)

{

this.reduction = reduction == null ? ReductionV2.SUM_OVER_BATCH_SIZE : reduction;

this.name = name;

this.from_logits = from_logits;

_allow_sum_over_batch_size = false;

}

public abstract Tensor Apply(Tensor y_true, Tensor y_pred, bool from_logits = false, int axis = -1);

public Tensor Call(Tensor y_true, Tensor y_pred, Tensor sample_weight = null)

{

var losses = Apply(y_true, y_pred, from_logits: from_logits);

var reduction = GetReduction();

return losses_utils.compute_weighted_loss(losses, reduction: reduction, sample_weight: sample_weight);

}

string GetReduction()

{

return reduction switch

{

ReductionV2.AUTO => ReductionV2.SUM_OVER_BATCH_SIZE,

_ => reduction

};

}

void _set_name_scope()

{

_name_scope = name;

}

}