def custom_loss(data, y_pred):

    y_true = data[:, 0]
    d = data[:, 1:]
    # condition
    mask2 = keras.backend.not_equal(y_true, 0) #i.e. y_true != 0
    mask2 = KB.cast(mask2, KB.floatx())
    # returns 0 when y_true =0, 1 otherwise
    #calculate loss using d...
    loss_value = 2 * d * y_pred + mask2 * (2 * d * y_true * KB.log(y_true) + 2 * d * y_true * KB.log(y_pred) - 2 * d * y_true)
    return loss_value