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How to perform Hyperparameter tuning in Autoencoders?

Ans: While training an Autoencoder, we will do hyperparameter tuning in order to obtain required output.

Code size: It represents the number of nodes in the middle layer. Smaller size results in more compression.

Number of layers: The autoencoder can consist of as many layers as we want.

Number of nodes per layer: The number of nodes per layer decreases with each subsequent layer of the encoder, and increases back in the decoder. The decoder is symmetric to the encoder in terms of the layer structure.

Loss function: We use mean squared error or binary cross-entropy for Autoencoder. If the input values are in the range [0, 1] then we use cross-entropy, otherwise, we use mean squared error.

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