给负损失的变分AutoEncoder

Question

我正在学习变分自动编码器，并在keras中实现了一个简单的例子，下面是模型摘要。我从Francois的一篇博客文章中复制了损失函数，现在我的损失非常严重。我在这里错过了什么？

    Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_1 (InputLayer)            [(None, 224)]        0
__________________________________________________________________________________________________
encoding_flatten (Flatten)      (None, 224)          0           input_1[0][0]
__________________________________________________________________________________________________
encoding_layer_2 (Dense)        (None, 256)          57600       encoding_flatten[0][0]
__________________________________________________________________________________________________
encoding_layer_3 (Dense)        (None, 128)          32896       encoding_layer_2[0][0]
__________________________________________________________________________________________________
encoding_layer_4 (Dense)        (None, 64)           8256        encoding_layer_3[0][0]
__________________________________________________________________________________________________
encoding_layer_5 (Dense)        (None, 32)           2080        encoding_layer_4[0][0]
__________________________________________________________________________________________________
encoding_layer_6 (Dense)        (None, 16)           528         encoding_layer_5[0][0]
__________________________________________________________________________________________________
encoder_mean (Dense)            (None, 16)           272         encoding_layer_6[0][0]
__________________________________________________________________________________________________
encoder_sigma (Dense)           (None, 16)           272         encoding_layer_6[0][0]
__________________________________________________________________________________________________
lambda (Lambda)                 (None, 16)           0           encoder_mean[0][0]
                                                                 encoder_sigma[0][0]
__________________________________________________________________________________________________
decoder_layer_1 (Dense)         (None, 16)           272         lambda[0][0]
__________________________________________________________________________________________________
decoder_layer_2 (Dense)         (None, 32)           544         decoder_layer_1[0][0]
__________________________________________________________________________________________________
decoder_layer_3 (Dense)         (None, 64)           2112        decoder_layer_2[0][0]
__________________________________________________________________________________________________
decoder_layer_4 (Dense)         (None, 128)          8320        decoder_layer_3[0][0]
__________________________________________________________________________________________________
decoder_layer_5 (Dense)         (None, 256)          33024       decoder_layer_4[0][0]
__________________________________________________________________________________________________
decoder_mean (Dense)            (None, 224)          57568       decoder_layer_5[0][0]
==================================================================================================
Total params: 203,744
Trainable params: 203,744
Non-trainable params: 0
__________________________________________________________________________________________________
Train on 3974 samples, validate on 994 samples
Epoch 1/10
3974/3974 [==============================] - 3s 677us/sample - loss: -28.1519 - val_loss: -33.5864
Epoch 2/10
3974/3974 [==============================] - 1s 346us/sample - loss: -137258.8175 - val_loss: -3683802.1489
Epoch 3/10
3974/3974 [==============================] - 1s 344us/sample - loss: -14543022903.6056 - val_loss: -107811177469.9396
Epoch 4/10
3974/3974 [==============================] - 1s 363us/sample - loss: -3011718676570.7012 - val_loss: -13131454938476.6816
Epoch 5/10
3974/3974 [==============================] - 1s 350us/sample - loss: -101442605943572.4844 - val_loss: -322685056398605.9375
Epoch 6/10
3974/3974 [==============================] - 1s 344us/sample - loss: -1417424385529640.5000 - val_loss: -3687688508198145.5000
Epoch 7/10
3974/3974 [==============================] - 1s 358us/sample - loss: -11794297368126698.0000 - val_loss: -26632844827070784.0000
Epoch 8/10
3974/3974 [==============================] - 1s 339us/sample - loss: -69508229806130784.0000 - val_loss: -141312065640756336.0000
Epoch 9/10
3974/3974 [==============================] - 1s 345us/sample - loss: -319838384005810432.0000 - val_loss: -599553350073361152.0000
Epoch 10/10
3974/3974 [==============================] - 1s 342us/sample - loss: -1221653451351326464.0000 - val_loss: -2147128507956525312.0000

潜在样本漏斗：

def sampling(self,args):
    """Reparameterization trick by sampling fr an isotropic unit Gaussian.
    # Arguments
        args (tensor): mean and log of variance of Q(z|X)
    # Returns
        z (tensor): sampled latent vector
    """

    z_mean, z_log_var = args
    set = tf.shape(z_mean)[0]
    batch = tf.shape(z_mean)[1]
    dim = tf.shape(z_mean)[-1]
    # by default, random_normal has mean=0 and std=1.0
    epsilon = tf.random.normal(shape=(set, dim))#tfp.distributions.Normal(mean=tf.zeros(shape=(batch, dim)),loc=tf.ones(shape=(batch, dim)))
    return z_mean + (z_log_var * epsilon)

损失基金：

def vae_loss(self,input, x_decoded_mean):
    xent_loss = tf.reduce_mean(tf.keras.backend.binary_crossentropy(input, x_decoded_mean))
    kl_loss = -0.5 * tf.reduce_sum(tf.square(self.encoded_mean) + tf.square(self.encoded_sigma) - tf.math.log(tf.square(self.encoded_sigma)) - 1, -1)
    return xent_loss + kl_loss

另一个vae_loss实现：

def vae_loss(self,input, x_decoded_mean):
    gen_loss = tf.reduce_sum(tf.keras.backend.binary_crossentropy(input, x_decoded_mean))
    #gen_loss = tf.losses.mean_squared_error(input,x_decoded_mean)
    kl_loss = -0.5 * tf.reduce_sum(1 + self.encoded_sigma - tf.square(self.encoded_mean) - tf.exp(self.encoded_sigma), -1)
    return tf.reduce_mean(gen_loss + kl_loss)

log_sigma kl_loss：

kl_loss = 0.5 * tf.reduce_sum(tf.square(self.encoded_mean) + tf.square(tf.exp(self.encoded_sigma)) - self.encoded_sigma - 1, axis=-1)

Answer 1

您的数据是二进制数据。我认为binary_crossentropy损失适用于二进制输入。MNIST中的所有数据都是二进制的。如果您的输入是连续的，如彩色图像，尝试MSE丢失或其他。看看这里，https://github.com/Lasagne/Recipes/issues/54