重用中间层作为Keras中另一个模型的输入

我知道首先我应该为解码后的模型做一个输入层来形成这个形状,但是我不知道如何得到我的编码层数据作为解码模型的输入,并让它从编码向量映射到最后一层。

from keras.layers import Input, Dense
from keras.models import Model
from keras.datasets import mnist
import numpy as np

(x_train, _), (x_test, _) = mnist.load_data()

# Prepare data and normalize
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape(len(x_train), -1)
x_test = x_test.reshape(len(x_test), -1)

input_size = 784
hidden_size = 128
coded_size = 64

x = Input(shape=(input_size,))
hidden_1 = Dense(hidden_size, activation='relu')(x)
coded =Dense(coded_size, activation='relu')(hidden_1)
hidden_2 = Dense(hidden_size, activation='relu')(coded)
r = Dense(input_size, activation='sigmoid')(hidden_2)

autoencoder = Model(inputs=x, outputs=r)
encoder = Model(inputs=x, outputs=coded)

decoder_input = Input(shape=(coded_size,))  # should do this, but don't know how to connect it below
decoder = Model(inputs=coded, output=r)