AutoEncoder.py

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.contrib.keras.python.keras.callbacks import TensorBoard
from tensorflow.contrib.keras.python.keras.datasets import mnist
from tensorflow.contrib.keras.python.keras.engine import Input, Model
from tensorflow.contrib.keras.python.keras.layers import Dense, Conv2D, MaxPooling2D, UpSampling2D
from tensorflow.contrib.keras.python.keras import backend as K


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

if K.image_data_format() == 'channels_last':
    shape_ord = (28, 28, 1)
else:
    shape_ord = (1, 28, 28)

(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, ((x_train.shape[0],) + shape_ord))
x_test = np.reshape(x_test, ((x_test.shape[0],) + shape_ord))




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

x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

# at this point the representation is (4, 4, 8) i.e. 128-dimensional

x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

conv_autoencoder = Model(input_img, decoded)
conv_autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')


batch_size=128
steps_per_epoch = np.int(np.floor(x_train.shape[0] / batch_size))
conv_autoencoder.fit(x_train, x_train, epochs=50, batch_size=128,
                     shuffle=True, validation_data=(x_test, x_test),
                     callbacks=[TensorBoard(log_dir='./tf_autoencoder_logs')])


decoded_imgs = conv_autoencoder.predict(x_test)

n = 10
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 + n + 1)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()