mnist_autoencoder_simple.py

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

# input layer
input_img = Input(shape=(784,))

# autoencoder
encoding_dim = 32  
encoded = Dense(encoding_dim, activation='relu')(input_img)
encoded_input = Input(shape=(encoding_dim,))
decoded = Dense(784, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')