Let's look at a basic example of an autoencoder that also happens to be a basic autoencoder. First, we will create an AutoEncoder class and initialize it with the following parameters passed to __init__():
- num_input: Number of input samples
- num_hidden: Number of neurons in the hidden layer
- transfer_function=tf.nn.softplus: Transfer function
- optimizer = tf.train.AdamOptimizer(): Optimizer
You can either pass a custom transfer_function and optimizer or use the default one specified. In our example, we are using softplus as the default transfer_function (also called activation function): f(x)=ln(1+ex).
