This function computes the Binary Cross-Entropy(stable_bce) Cost function the way Keras implements it

computer_keras_like_bce_cost.py

def compute_keras_like_bce_cost(Y, P_hat, from_logits=False):
    """
    This function computes the Binary Cross-Entropy(stable_bce) Cost function the way Keras
    implements it. Accepting either probabilities(P_hat) from the sigmoid neuron or values direct
    from the linear node(Z)

    Args:
        Y: labels of data
        P_hat: Probabilities from sigmoid function
        from_logits: flag to check if logits are being provided or not(Default: False)

    Returns:
        cost: The "Stable" Binary Cross-Entropy Cost result
        dZ_last: gradient of Cost w.r.t Z_last

    """
    if from_logits:
        # assume that P_hat contains logits and not probabilities
        return compute_stable_bce_cost(Y, Z=P_hat)

    else:
        # Assume P_hat contains probabilities. So make logits out of them

        # First clip probabilities to stable range
        EPSILON = 1e-07
        P_MAX = 1- EPSILON  # 0.9999999

        P_hat = np.clip(P_hat, a_min=EPSILON, a_max=P_MAX)

        # Second, Convert stable probabilities to logits(Z)
        Z = np.log(P_hat/(1-P_hat))

        # now call compute_stable_bce_cost
        return compute_stable_bce_cost(Y, Z)