KerasTensor和Tensor如何处理？

Question

我正在尝试创建变分自动编码器，这意味着我需要自定义损失函数。问题是在损失函数内部我有两种不同的损失——mse 和发散。 mse 是 Tensor，divergence 是 KerasTensor（因为色散和 mu，我从编码器中出来了）。我收到这样的错误：

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.

这是我的架构：

import tensorflow.keras as keras
from keras.layers import Input, Dense, Flatten, Reshape
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Conv2DTranspose
from keras.models import Model
import tensorflow as tf
import keras.backend as K


encoded_dim = 2

class Sampling(keras.layers.Layer):
    """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""

    def call(self, inputs):
        z_mean, z_log_var = inputs
        batch = tf.shape(z_mean)[0]
        dim = tf.shape(z_mean)[1]
        epsilon = K.random_normal(shape=(batch, dim))
        return z_mean + tf.exp(0.5 * z_log_var) * epsilon


img = Input((28,28,1), name='img')

x = Conv2D(32, (3,3), padding='same', activation='relu')(img)
x = MaxPooling2D()(x)
x = Conv2D(64, (3,3), padding='same', activation='relu')(x)
x = MaxPooling2D()(x)
x = Flatten()(x)
x = Dense(16, activation='relu')(x)
mu = Dense(encoded_dim, name='mu')(x)
sigma = Dense(encoded_dim, name='sigma')(x)
z = Sampling()([mu,sigma])
# print(mu)
xx = Input((encoded_dim,))

x = Dense(7*7*64, activation='relu')(xx)
x = Reshape((7,7,64))(x)

x = Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same")(x)
x = Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same")(x)

out = Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x)

encoder = Model(img,z, name='encoder')
decoder = Model(xx,out,name='decoder')

autoencoder = Model(img,decoder(encoder(img)),name='autoencoder')

以及损失函数：

def vae_loss(x, y):
    loss = tf.reduce_mean(K.square(x-y))
    kl_loss = -0.5 * tf.reduce_mean(1 + sigma - tf.square(mu) - tf.exp(sigma))
    print(type(loss))
    print(type(kl_loss))
    return loss + kl_loss

autoencoder.compile(optimizer='adam',
                    loss = vae_loss)

autoencoder.fit(train,train,
                epochs=1,
                batch_size=60,
                shuffle=True,
                verbose = 2)

损失类型和lk_loss：

class 'tensorflow.python.framework.ops.Tensor'

class 'tensorflow.python.keras.engine.keras_tensor.KerasTensor'

Answer 1

你需要将 mu 和 sigma 传递给你的损失函数。 vae_loss 现在接受 4 个输入：

def vae_loss(x, y, mu, sigma):
    loss = tf.reduce_mean(K.square(x-y))
    kl_loss = -0.5 * tf.reduce_mean(1 + sigma - tf.square(mu) - tf.exp(sigma))
    return loss + kl_loss

您可以在模型中使用它，只需使用 autoencoder.add_loss。

同样重要的是 encoder returns 不仅 z 而且 mu 和 sigma.

z, mu, sigma = encoder(img)
out = decoder(z)
autoencoder = Model(img, out, name='autoencoder')
autoencoder.add_loss(vae_loss(img, out, mu, sigma))  # <======= add_loss
autoencoder.compile(loss=None, optimizer='adam')

这里是运行笔记本 https://colab.research.google.com/drive/1r5lMZ2Dc_Lq4KJDqiirXla1qfDVmdwxW?usp=sharing

KerasTensor和Tensor如何处理？

How to deal with KerasTensor and Tensor?

python

tensorflow

keras

deep-learning

autoencoder