Setting up a regularized autoencoder

A regularized autoencoder extends the standard autoencoder by adding a regularization parameter to the cost function.

Getting ready

The regularized autoencoder is an extension of the standard autoencoder. The set-up will require:

1. TensorFlow installation in R and Python.
2. Implementation of a standard autoencoder.

How to do it...

The code setup for the autoencoder can directly be converted to a regularized autoencoder by replacing the cost definition with the following lines:

Lambda=0.01
cost = tf$reduce_mean(tf$pow(x - y_pred, 2))
Regularize_weights = tf$nn$l2_loss(weights)
cost = tf$reduce_mean(cost + lambda * Regularize_weights)

How it works...

As mentioned earlier, a regularized autoencoder extends the standard autoencoder by adding a regularization parameter to the cost function, shown as follows:

Here, λ is the regularization parameter and i and j are the node indexes with W representing the hidden layer weights for the autoencoder. The regularization autoencoder...