Hidden Layer

The hidden layer is a layer that processes and transforms data received from the input layer.

It plays a crucial role in transforming simple inputs into complex patterns within the neural network.

In a hidden layer, weights and activation functions are used to adjust input data.

For example, when processing 5×5 sized data from the input layer, the hidden layer applies specific operations to transform these values before passing them to the next layer.

Below is an example showing how input pixel values are transformed by a hidden layer after applying operations.

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[
  [0.1, 0.3, 0.9, 0.3, 0.1],
  [0.3, 0.7, 0.2, 0.7, 0.3],
  [0.9, 0.2, 0.1, 0.2, 0.9],
  [0.3, 0.7, 0.2, 0.7, 0.3],
  [0.1, 0.3, 0.9, 0.3, 0.1]
]

Here, the values changed to between 0-1 due to the activation function.

These changes occur because the weights adjust inputs as the neural network learns from the data.

This transformed data enables the network to learn higher-level features, which are used by the output layer to generate the final prediction.