convautoencoderimage.py

import keras
from keras import layers

input_img = keras.Input(shape=(28, 28, 1))

x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)


#x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = layers.UpSampling2D((2, 2))(encoded)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = keras.Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.summary()

from keras.datasets import mnist
import numpy as np

(x_train, _), (x_test, _) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))

autoencoder.fit(x_train, x_train,
                epochs=50, #5, #50,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test, x_test))
                #callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

for a in (autoencoder.weights):
  print (a.shape )

decoded_imgs = autoencoder.predict(x_test)

import matplotlib.pyplot as plt

n = 10
plt.figure(figsize=(20, 4))
for i in range(1, n + 1):
    # Display original
    ax = plt.subplot(2, n, i)
    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 + 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()

encoder = keras.Model(input_img, encoded)
encoded_imgs = encoder.predict(x_test)

print (encoded_imgs.shape)

n = 10
plt.figure(figsize=(20, 8))
for i in range(1, n + 1):
    ax = plt.subplot(1, n, i)
    plt.imshow(encoded_imgs[i,:,:,4].reshape((14 , 14)))
    #plt.imshow(encoded_imgs[i].reshape((14 * 14 , 16)))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()