为什么不连接到Keras模型中的预期层

我想在Keras中构建一个可变自动编码器(2.2.4,带有TensorFlow后端),下面是我的代码:

dims = [1000, 256, 64, 32]
x_inputs = Input(shape=(dims[0],), name='inputs')
h = x_inputs

# internal layers in encoder
for i in range(n_stacks-1):
    h = Dense(dims[i + 1], activation='relu', kernel_initializer='glorot_uniform', name='encoder_%d' % i)(h)

# hidden layer
z_mean = Dense(dims[-1], kernel_initializer='glorot_uniform', name='z_mean')(h)
z_log_var = Dense(dims[-1], kernel_initializer='glorot_uniform', name='z_log_var')(h)

z = Lambda(sampling, output_shape=(dims[-1],), name='z')([z_mean, z_log_var])

encoder = Model(inputs=x_inputs, outputs=z, name='encoder')
encoder_z_mean = Model(inputs=x_inputs, outputs=z_mean, name='encoder_z_mean')


# internal layers in decoder
latent_inputs = Input(shape=(dims[-1],), name='latent_inputs')
h = latent_inputs
for i in range(n_stacks-1, 0, -1):
    h = Dense(dims[i], activation='relu', kernel_initializer='glorot_uniform', name='decoder_%d' % i)(h)

# output
outputs = Dense(dims[0], activation='relu', kernel_initializer='glorot_uniform' name='mean')

decoder = Model(inputs=latent_inputs, outputs=outputs, name='decoder')

ae_output = decoder(encoder_z_mean(x_inputs))
ae = Model(inputs=x_inputs, outputs=ae_output, name='ae')
ae.summary()

vae_output = decoder(encoder(x_inputs))
vae = Model(inputs=x_inputs, outputs=vae_output, name='vae')
vae.summary()

问题是我可以打印“ae”和“vae”模型的摘要,但是当我训练ae模型时,它说

tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'latent_inputs' with dtype float and shape [?,32]

在模型中,“解码器”应连接到ae模型中“编码器z_均值”层的输出。但当我打印“ae”模型的摘要时,“decoder”实际上连接到“encoder_z_mean[1][0]”。是否应该是“编码器z_表示[0][0]”?