[source]

Dense

Dense (or fully connected) hidden layers are layers of neurons that connect to each node in the previous layer by a parameterized synapse. They perform a linear transformation on their input and are usually followed by an Activation layer. The majority of the trainable parameters in a standard feed forward neural network are contained within Dense hidden layers. L1 and L2 regularization can be applied to the weights to reduce overfitting; L1 regularization in particular encourages sparsity by driving small weights toward zero.

Parameters

#	Name	Default	Type	Description
1	neurons		int	The number of nodes in the layer.
2	l1Penalty	0.0	float	The amount of L1 regularization applied to the weights.
3	l2Penalty	0.0	float	The amount of L2 regularization applied to the weights.
4	bias	true	bool	Should the layer include a bias parameter?
5	weightInitializer	He	Initializer	The initializer of the weight parameter.
6	biasInitializer	Constant	Initializer	The initializer of the bias parameter.

Example

use Rubix\ML\NeuralNet\Layers\Dense;
use Rubix\ML\NeuralNet\Initializers\He;
use Rubix\ML\NeuralNet\Initializers\Constant;

$layer = new Dense(neurons: 100, l1Penalty: 1e-3, l2Penalty: 1e-4, bias: true, weightInitializer: new He(), biasInitializer: new Constant(0.0));