reuse middle layer as input for another model in Keras

Question

I am building a 5-layer autoencoder with Keras. I made the model that maps from input to output which was fine. I built another model that maps from input to the latent coded vector which worked fine. However, I then tried to make a decoding model that maps from the latent coded vector to the output which did not work.

I know that first I should be making an input layer for the decoded model that makes that shape but I can't figure out how to get my coded layers data as an input for the decoded model and to let it map from the coded vector to the final layer.

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)

Answer 1

You can do it like this:

decoder_input = Input(shape=(coded_size,))
next_input = decoder_input

# get the decoder layers and apply them consecutively
for layer in autoencoder.layers[-2:]:
    next_input = layer(next_input)

decoder = Model(inputs=decoder_input, outputs=next_input)

As a side note, there is no h in your model. I think it must be replaced by coded .