Keras错误:无法将符号Keras输入/输出转换为numpy数组

Question

我有来自RaGan (相对论平均Gan)的代码(部分代码)：

def get_ragan_network(generator,discriminator,optimizer):
    imgs_hr = Input(image_shape)
    generated_hr = Input(image_shape)

    Discriminator_real_out = discriminator(imgs_hr)
    Discriminator_fake_out = discriminator(generated_hr)


    Real_Fake_relativistic_average_out = tf.add(Discriminator_real_out,-(K.mean(Discriminator_fake_out, axis=0)))
    Fake_Real_relativistic_average_out = tf.add(Discriminator_fake_out,-(K.mean(Discriminator_real_out, axis=0)))

    epsilon=0.000001 
    def relativistic_discriminator_loss(y_true, y_pred):
        if isinstance(Real_Fake_relativistic_average_out, np.ndarray):
            return -(K.mean(K.log(K.sigmoid(Real_Fake_relativistic_average_out)+epsilon ),axis=0)
                 +K.mean(K.log(1-K.sigmoid(Fake_Real_relativistic_average_out)+epsilon),axis=0))
        else:
            return -(K.mean(K.log(K.sigmoid(Real_Fake_relativistic_average_out)+epsilon ),axis=0)
                 +K.mean(K.log(1-K.sigmoid(Fake_Real_relativistic_average_out)+epsilon),axis=0))

    model = Model([generated_hr,imgs_hr],[Discriminator_real_out,Discriminator_fake_out])

    model.compile(optimizer=optimizer, loss=[relativistic_discriminator_loss,None])
    return model

但是当我执行代码时，我得到了这个错误：

Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.

我不知道如何解决这个问题。

Numpy版本= 1.19

tensorflow版本= 2.6

Answer 1

我认为错误来自下面这一行：isinstance(Real_Fake_relativistic_average_out, np.ndarray)，因为Real_Fake_relativistic_average_out来自鉴别器输出，而np.ndarray是一个numpy对象。显然，条件if else语句是无用的。