创建LSTM模型，代码为model = Sequential() model.add(LSTM(10, activation = "tanh",input_shape=(window_size, 1))) model.add(Dense(1)) model.add(tfp.layers.DistributionLambda( lambda t: tfd.Normal(loc=t[...,0], scale=0.01*tf.math.softplus(t[...,1])), name='normal_dist')) # note this negloglik = lambda y, p_y: -p_y.log_prob(y) # note this opt = keras.optimizers.Adam(learning_rate=0.001) model.compile(loss=negloglik, optimizer= opt)，报错显示Exception encountered when calling layer "normal_dist" (type DistributionLambda).如果要model.add(Dense(1))怎么办

你可以将`model.add(tfp.layers.DistributionLambda(...))`替换为`model.add(tfp.layers.DenseVariational(2, activation=None, name='dense_variational'))`，并在模型的最后添加一个Dense层，如下所示：

model = Sequential()
model.add(LSTM(10, activation="tanh", input_shape=(window_size, 1)))
model.add(tfp.layers.DenseVariational(2, activation=None, name='dense_variational'))
model.add(Dense(1))

然后，你需要重新定义`negloglik`函数以使用`DenseVariational`层。下面是一个示例：

def negloglik(y, y_pred):
    dist = tfd.Normal(loc=y_pred[..., 0], scale=0.01 * tf.math.softplus(y_pred[..., 1]))
    return -tf.reduce_mean(dist.log_prob(y))

opt = keras.optimizers.Adam(learning_rate=0.001)
model.compile(loss=negloglik, optimizer=opt)

这样就可以定义一个具有LSTM层和正态分布输出的变分自编码器模型了。