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Autoencoders
self.encoder = Encoder(hidden_dim=hidden_dim)
self.decoder = Decoder(hidden_dim=hidden_dim, original_
dim=original_dim)
def call(self, input_features):
encoded = self.encoder(input_features)
reconstructed = self.decoder(encoded)
return reconstructed
In the next section we will use the autoencoder that we defined here to reconstruct
handwritten digits.
Reconstructing handwritten digits using an
autoencoder
Now that we have our model autoencoder with its layer encoder and decoder ready,
let us try to reconstruct handwritten digits. The complete code is available in the
GitHub repo of the chapter in the notebook VanillaAutoencoder.ipynb. The code
will require the NumPy, TensorFlow, and Matplotlib modules:
import numpy as np
import tensorflow as tf
import tensorflow.keras as K
import matplotlib.pyplot as plt
Before starting with the actual implementation, let's also define some
hyperparameters. If you play around with them, you will notice that even though the
architecture of your model remains the same, there is a significant change in model
performance. Hyperparameter tuning (refer to Chapter 1, Neural Network Foundations
with TensorFlow 2.0, for more details) is one of the important steps in deep learning.
For reproducibility, we set the seeds for random calculation:
np.random.seed(11)
tf.random.set_seed(11)
batch_size = 256
max_epochs = 50
learning_rate = 1e-3
momentum = 8e-1
hidden_dim = 128
original_dim = 784
For training data, we are using the MNIST dataset available in the TensorFlow
datasets. We normalize the data so that pixel values lie between [0,1]; this is
achieved by simply dividing each pixel element by 255.
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