Autoencoder

from keras.layers import Dense, Input
from keras.models import Model

encoding_dim = 32

input_img = Input(shape = (784, ))

encoded = Dense(encoding_dim, activation = 'relu')(input_img)

decoded = Dense(784, activation = 'sigmoid')(encoded)

autoencoder = Model(input_img, decoded)

encoder = Model(input_img, encoded)

encoded_input = Input(shape = (encoding_dim, ))

decoder_layer = autoencoder.layers[-1]

decoder = Model(encoded_input, decoder_layer(encoded_input))

autoencoder.compile(optimizer = 'adadelta', loss = 'binary_crossentropy')

from keras.datasets import mnist
import numpy as np
(x_train, _), (x_test, _) = mnist.load_data()

x_train

x_train = x_train.astype('float32')/255.
x_test = x_test.astype('float32')/255.

x_train = x_train.reshape(len(x_train), np.prod(x_train.shape[1:]))
x_test = x_test.reshape(len(x_test), np.prod(x_test.shape[1:]))

x_train.shape

x_test.shape

autoencoder.fit(x_train, x_train,
               epochs = 50,
               batch_size = 256,
               shuffle = True,
               validation_data = (x_test, x_test))

encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)

import matplotlib.pyplot as plt

n = 10
plt.figure(figsize = (20, 4))
for i in range(n):
    ax = plt.subplot(2, n, i+1)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    
    ax = plt.subplot(2, n, i+1 + n)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
plt.show()