autoencoder.py

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
import matplotlib.pyplot as plt
from scipy.misc import imsave # imsave es para guardar las segmentaciones de la CNN en gris como se necesita

from keras.datasets import mnist
import numpy as np

input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format
#input_img = Input(shape=(None, None, 1))  # adapt this if using `channels_first` image data format

x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

# at this point the representation is (7, 7, 32)

x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy', metrics=['accuracy'])

(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))  # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format

noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) 
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) 

x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)

autoencoder.summary()

history = autoencoder.fit(x_train_noisy, x_train,
                epochs=10,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test_noisy, x_test))

decoded_imgs = autoencoder.predict(x_test_noisy)

#autoencoder.save('denoising_autoencoder.h5')

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_noisy[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()

history_dict = history.history
print( history_dict.keys() )
print( history_dict )

#for i_predict in range(4):

    
    #imsave( str(i_predict)+'input.png', x_test_noisy[i_predict].reshape(28, 28) )
    #imsave( str(i_predict)+'output.png', decoded_imgs[i_predict].reshape(28, 28) )
    #imsave( str(i_predict)+'ground_truth.png', x_test[i_predict].reshape(28, 28) )