Advanced Deep Learning with Keras

Autoencoders
Figure 3.3.1 shows actual validation data with both the corrupted and denoised test
MNIST digits. We're even able to see that humans will find it difficult to read the
corrupted MNIST digits. Figure 3.3.3 shows a certain level of robustness of DAE as
the level of noise is increased from σ = 0.5 to σ = 0.75 and . At σ = 0.75
, DAE is still able to recover the original images. However, at σ = 1.0 , a few digits
such as 4 and 5 in the second and third sets can no longer be recovered correctly.
Figure 3.3.3: Performance of denoising autoencoder as the noise level is increased
As seen in Listing 3.3.1, denoising-autoencoder-mnist-3.3.1.py shows
us a Denoising autoencoder:
from keras.layers import Dense, Input
from keras.layers import Conv2D, Flatten
from keras.layers import Reshape, Conv2DTranspose
from keras.models import Model
from keras import backend as K
from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
np.random.seed(1337)
# load MNIST dataset
(x_train, _), (x_test, _) = mnist.load_data()
# reshape to (28, 28, 1) and normalize input images
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