sample_keras.py

import numpy as np
import matplotlib.pyplot as plt

from tensorflow import keras 
from tensorflow.keras import layers, regularizers
from tensorflow.keras.datasets import mnist

# dataset & normalize
(x_train, _), (x_test, _) = mnist.load_data()
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:])))

# this is the siz e of our encoded representations
encoding_dim = 32

# encoder
input_img = keras.Input(shape=(784,))
encoded = layers.Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(input_img)
encoder = keras.Model(input_img, encoded)

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

# define network
autoencoder = keras.Model(input_img, decoded)
autoencoder.compile(optimizer="adam", loss="binary_crossentropy")

# decoder
decoded_input = keras.Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = keras.Model(decoded_input, decoder_layer(decoded_input))

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

# network results
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)

# display
n = 10  # How many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
    # Display original
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # Display reconstruction
    ax = plt.subplot(2, n, i + 1 + n)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()