python - Keras my_layer.output 返回 KerasTensor 对象而不是 Tensor 对象(在自定义损失函数中)

Question

我正在尝试在 Keras v2.4.3 中构建自定义损失函数:(如本 answer 中所述)

def vae_loss(x: tf.Tensor, x_decoded_mean: tf.Tensor,
            original_dim=original_dim):
    z_mean = encoder.get_layer('mean').output
    z_log_var = encoder.get_layer('log-var').output

    xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)
    kl_loss = - 0.5 * K.sum(
        1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
    vae_loss = K.mean(xent_loss + kl_loss)
    return vae_loss

但我认为它的行为与预期有很大不同(可能是因为我的 Keras 版本？)，我收到此错误:

TypeError: 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.

我认为这是因为 encoder.get_layer('mean').output 返回的是 KerasTensor 对象而不是 tf.Tensor对象(如其他答案所示)。

我在这里做错了什么？如何从自定义损失函数内部访问给定层的输出？

Answer 1

我认为使用 model.add_loss() 非常简单。此功能使您能够将多个输入传递给您的自定义损失。

为了提供一个可靠的示例，我生成了一个简单的 VAE，其中我使用 model.add_loss()

添加了 VAE 损失

完整的模型结构如下:

def sampling(args):
    
    z_mean, z_log_sigma = args
    batch_size = tf.shape(z_mean)[0]
    epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0., stddev=1.)
    
    return z_mean + K.exp(0.5 * z_log_sigma) * epsilon

def vae_loss(x, x_decoded_mean, z_log_var, z_mean):

    xent_loss = original_dim * K.binary_crossentropy(x, x_decoded_mean)
    kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var))
    vae_loss = K.mean(xent_loss + kl_loss)

    return vae_loss

def get_model():
    
    ### encoder ###
    
    inp = Input(shape=(n_features,))
    enc = Dense(64)(inp)
    
    z = Dense(32, activation="relu")(enc)
    z_mean = Dense(latent_dim)(z)
    z_log_var = Dense(latent_dim)(z)
            
    encoder = Model(inp, [z_mean, z_log_var])
    
    ### decoder ###
    
    inp_z = Input(shape=(latent_dim,))
    dec = Dense(64)(inp_z)

    out = Dense(n_features)(dec)
    
    decoder = Model(inp_z, out)   
    
    ### encoder + decoder ###
    
    z_mean, z_log_sigma = encoder(inp)
    z = Lambda(sampling)([z_mean, z_log_var])
    pred = decoder(z)
    
    vae = Model(inp, pred)
    vae.add_loss(vae_loss(inp, pred, z_log_var, z_mean))  # <======= add_loss
    vae.compile(loss=None, optimizer='adam')
    
    return vae, encoder, decoder

运行的笔记本在这里可用:https://colab.research.google.com/drive/18day9KMEbH8FeYNJlCum0xMLOtf1bXn8?usp=sharing