autoencoder_mnist.py

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

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
(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:]))
print(X_train.shape)
print(X_test.shape)

input_img= Input(shape=(784,))
encoded = Dense(units=128, activation='relu')(input_img)
encoded = Dense(units=64, activation='relu')(encoded)
encoded = Dense(units=32, activation='relu')(encoded)
decoded = Dense(units=64, activation='relu')(encoded)
decoded = Dense(units=128, activation='relu')(decoded)
decoded = Dense(units=784, activation='sigmoid')(decoded)


autoencoder=Model(input_img, decoded)

encoder = Model(input_img, encoded)


autoencoder.summary()
encoder.summary()
autoencoder.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
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)

predicted = autoencoder.predict(X_test)
plt.figure(figsize=(40, 4))
for i in range(10):
    # display original images
    ax = plt.subplot(3, 20, 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 encoded images
    ax = plt.subplot(3, 20, i + 1 + 20)
    plt.imshow(encoded_imgs[i].reshape(8, 4))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    # display reconstructed images
    ax = plt.subplot(3, 20, 2 * 20 + i + 1)
    plt.imshow(predicted[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

plt.show()