在创建 VAE 模型期间抛出异常“你应该实现一个 `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(变分自编码器).在模型创建过程中它抛出异常.在继承 Model 类时，您应该实现一个 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.

我使用的是 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):

~AppDataLocalContinuumanaconda3libsite-packages	ensorflowpythonkerasenginease_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 

~AppDataLocalContinuumanaconda3libsite-packages	ensorflowpythonkerasengine
etwork.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.

带名字的模型

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

我想得到模型.

推荐答案

问题出在这里:

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，其中只需要两个参数(输入和输出).模型没有名称.问题是三个参数会破坏网络或子类模型的检测，如 keras 源代码.

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)

这篇关于在创建 VAE 模型期间抛出异常“你应该实现一个 `call` 方法."的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！