Advanced Deep Learning with Keras

Autoencoders
After training the autoencoder for one epoch with a validation loss of 0.01,
we're able to verify if it can encode and decode the MNIST data that it has not seen
before. Figure 3.2.4 shows us eight samples from the test data and the corresponding
decoded images. Except for minor blurring in the images, we're able to easily
recognize that the autoencoder is able to recover the input with good quality.
The results will improve as we train for a larger number of epochs.
Figure 3.2.4: Prediction of the autoencoder from the test data.
The first 2 rows are the original input test data. The last 2 rows are the predicted data.
At this point, we may be wondering how we can visualize the latent vector in space.
A simple method for visualization is to force the autoencoder to learn the MNIST
digits features using a 2-dim latent vector. From there, we're able to project this latent
vector on a 2D space in order to see how the MNIST codes are distributed. By setting
the latent_dim = 2 in autoencoder-mnist-3.2.1.py code and by using the plot_
results() to plot the MNIST digit as a function of the 2-dim latent vector, Figure
3.2.5 and Figure 3.2.6 shows the distribution of MNIST digits as a function of latent
codes. These figures were generated after 20 epochs of training. For convenience,
the program is saved as autoencoder-2dim-mnist-3.2.2.py with the partial code
shown in Listing 3.2.2.
Following is Listing 3.2.2, autoencoder-2dim-mnist-3.2.2.py, which shows the
function for visualization of the MNIST digits distribution over 2-dim latent codes.
The rest of the code is practically similar to Listing 3.2.1 and no longer shown here.
def plot_results(models,
data,
[ 80 ]