在创建VAE模型期间，抛出异常“您应该实现`call`方法. [英] During creating VAE model throws exception "you should implement a `call` method."

问题描述

我想创建VAE(可变自动编码器).在模型创建期间，它将引发异常. 子类化Model类时，应实现call方法.

I want to create VAE(variational autoencoder). During model creating it throws exception. When subclassing the Model class, you should implement a call method.

我正在使用Tensorflow 2.0

I am using Tensorflow 2.0

def vae():
    models ={}

    def apply_bn_and_dropout(x):
        return l.Dropout(dropout_rate)(l.BatchNormalization()(x))
    input_image = l.Input(batch_shape=(batch_size,28,28,1))
    x = l.Flatten()(input_image)
    x = l.Dense(256,activation="relu")(x)
    x = apply_bn_and_dropout(x)
    x = l.Dense(128,activation="relu")(x)
    x = apply_bn_and_dropout(x)

    z_mean = l.Dense(latent_dim)(x)
    z_log_var = l.Dense(latent_dim)(x)

    def sampling(args):
        z_mean, z_log_var = args
        epsilon = K.random_normal(shape=(batch_size,latent_dim),mean=0., stddev=1.0)
        return z_mean + K.exp(z_log_var/2) * epsilon

    lambda_layer = l.Lambda(sampling,output_shape=(latent_dim,))([z_mean,z_log_var])

    models["encoder"] = Model(input_image,lambda_layer,"Encoder")
    models["z_meaner"] = Model(input_image,z_mean,"Enc_z_mean")
    models["z_lvarer"] = Model(input_image, z_log_var,"Enc_z_log_var")

    z = l.Input(shape=(latent_dim,))
    x = l.Dense(128)(z)
    x = l.LeakyReLU()(x)
    x = apply_bn_and_dropout(x)
    x = l.Dense(256)(x)
    x = l.LeakyReLU()(x)
    x = apply_bn_and_dropout(x)
    x = l.Dense(28*28,activation="sigmoid")(x)
    decoded = l.Reshape((28,28,1))(x)

    models["decoder"] = Model(z,decoded,name="Decoder")
    models["vae"] = Model(input_image, models["decoder"](models["encoder"](input_image)), name="VAE")

    def vae_loss(x,decoded):
        x = K.reshape(x,shape=(batch_size,28*28))
        decoded = K.reshape(decoded,shape=(batch_size,28*28))
        xent_loss = 28*28*binary_crossentropy(x, decoded)
        kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
        return (xent_loss + kl_loss)/2/28/28

    return models, vae_loss

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-34-186b31069dc3> in <module>
----> 1 models, vae_loss = vae()
      2 vae = models["vae"]

<ipython-input-33-0fa06b39e41c> in vae()
     36 
     37     models["decoder"] = Model(z,decoded,name="Decoder")
---> 38     models["vae"] = Model(input_image, models["decoder"](models["encoder"](input_image)), name="VAE")
     39 
     40     def vae_loss(x,decoded):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, inputs, *args, **kwargs)
    610                       base_layer_utils.AutoAddUpdates(self,
    611                                                       inputs)) as auto_updater:
--> 612                 outputs = self.call(inputs, *args, **kwargs)
    613                 auto_updater.set_outputs(outputs)
    614 

~\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\keras\engine\network.py in call(self, inputs, training, mask)
    865     """
    866     if not self._is_graph_network:
--> 867       raise NotImplementedError('When subclassing the `Model` class, you should'
    868                                 ' implement a `call` method.')
    869 

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method.

具有名称的模型

def create_dense_ae():
    encoding_dim = 64

    input_img = layers.Input(shape=(28, 28, 1))
    flat_img = layers.Flatten()(input_img)

    encoded = layers.Dense(encoding_dim, activation='relu')(flat_img)


    input_encoded = layers.Input(shape=(encoding_dim,))
    flat_decoded = layers.Dense(28*28, activation='sigmoid')(input_encoded)
    decoded = layers.Reshape((28, 28, 1))(flat_decoded)


    encoder = tf.keras.Model(input_img, encoded, name="encoder")
    decoder = tf.keras.Model(input_encoded, decoded, name="decoder")
    autoencoder = tf.keras.Model(input_img, decoder(encoder(input_img)), name="autoencoder")
    return encoder, decoder, autoencoder

我想得到模特.

推荐答案

问题在这里:

models["encoder"] = Model(input_image,lambda_layer,"Encoder")
models["z_meaner"] = Model(input_image,z_mean,"Enc_z_mean")
models["z_lvarer"] = Model(input_image, z_log_var,"Enc_z_log_var")

您正在向构造传递三个参数，其中仅需要两个参数(输入和输出).模型没有名称.问题在于，三个参数将破坏对网络或子类模型的检测，如

You are passing three arguments to the construction, where only two are needed (inputs and outputs). Models do not have names. The problem is that three parameters will break the detection of network or sub-classed model as shown in the keras source code.

所以只需将代码替换为:

So just replace the code with:

models["encoder"] = Model(input_image,lambda_layer)
models["z_meaner"] = Model(input_image,z_mean)
models["z_lvarer"] = Model(input_image, z_log_var)

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