Keras LSTM-VAE可变自动编码器，用于时间序列异常检测

如何解决Keras LSTM-VAE可变自动编码器，用于时间序列异常检测

我正在尝试为LSTM-VAE建模以使用Keras进行时间序列重建。

我曾参考https://github.com/twairball/keras_lstm_vae/blob/master/lstm_vae/vae.py和https://machinelearningmastery.com/lstm-autoencoders/来创建LSTM-VAE体系结构。

我在训练网络时遇到麻烦，在热切的执行模式下训练时遇到以下错误：

  InvalidArgumentError: Incompatible shapes: [8,1] vs. [32,1] [Op:Mul]

这里的输入形状为(7752,30,1)，这里有30个时间步长和1个特征。

模型编码器：

# encoder
latent_dim = 1
inter_dim = 32

#sample,timesteps,features
input_x = keras.layers.Input(shape= (X_train.shape[1],X_train.shape[2])) 

#intermediate dimension 
h = keras.layers.LSTM(inter_dim)(input_x)

#z_layer
z_mean = keras.layers.Dense(latent_dim)(h)
z_log_sigma = keras.layers.Dense(latent_dim)(h)
z = Lambda(sampling)([z_mean,z_log_sigma])

模型解码器：

# Reconstruction decoder
decoder1 = RepeatVector(X_train.shape[1])(z)
decoder1 = keras.layers.LSTM(100,activation='relu',return_sequences=True)(decoder1)
decoder1 = keras.layers.TimeDistributed(Dense(1))(decoder1)

采样功能：

batch_size = 32
def sampling(args):
    z_mean,z_log_sigma = args
    epsilon = K.random_normal(shape=(batch_size,latent_dim),mean=0.,stddev=1.)
    return z_mean + z_log_sigma * epsilon

VAE损失功能：

def vae_loss2(input_x,decoder1):
    """ Calculate loss = reconstruction loss + KL loss for each data in minibatch """
    # E[log P(X|z)]
    recon = K.sum(K.binary_crossentropy(input_x,decoder1),axis=1)
    # D_KL(Q(z|X) || P(z|X)); calculate in closed form as both dist. are Gaussian
    kl = 0.5 * K.sum(K.exp(z_log_sigma) + K.square(z_mean) - 1. - z_log_sigma,axis=1)

    return recon + kl

LSTM-VAE model architecture

有什么建议可以使模型起作用？

解决方法

您需要推断采样函数中的batch_dim，并且需要注意损失...您的损失函数使用先前图层的输出，因此您需要注意这一点。我使用model.add_loss(...)

来实现

# encoder
latent_dim = 1
inter_dim = 32
timesteps,features = 100,1

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 + z_log_sigma * epsilon

# timesteps,features
input_x = Input(shape= (timesteps,features)) 

#intermediate dimension 
h = LSTM(inter_dim,activation='relu')(input_x)

#z_layer
z_mean = Dense(latent_dim)(h)
z_log_sigma = Dense(latent_dim)(h)
z = Lambda(sampling)([z_mean,z_log_sigma])

# Reconstruction decoder
decoder1 = RepeatVector(timesteps)(z)
decoder1 = LSTM(inter_dim,activation='relu',return_sequences=True)(decoder1)
decoder1 = TimeDistributed(Dense(features))(decoder1)

def vae_loss2(input_x,decoder1,z_log_sigma,z_mean):
    """ Calculate loss = reconstruction loss + KL loss for each data in minibatch """
    # E[log P(X|z)]
    recon = K.sum(K.binary_crossentropy(input_x,decoder1))
    # D_KL(Q(z|X) || P(z|X)); calculate in closed form as both dist. are Gaussian
    kl = 0.5 * K.sum(K.exp(z_log_sigma) + K.square(z_mean) - 1. - z_log_sigma)

    return recon + kl

m = Model(input_x,decoder1)
m.add_loss(vae_loss2(input_x,z_mean)) #<===========
m.compile(loss=None,optimizer='adam')

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