# FCLayer#

class usencrypt.ai.nn.layers.FCLayer(input_size=None, output_size=None, weight_regularizer=None, bias_regularizer=None, _config=None)#

Fully-connected layer for neural networks, defined by the linear operation $$y = W^TX + b$$, where $$W$$ is the weights matrix, $$X$$ is the features matrix, and $$b$$ is the bias vector.

A fully-connected layer is instantiated with appropriate input and output sizes to ensure a correct linear transformation.

Parameters
Variables
• input_size (int) – The size of the layer input.

• output_size (int) – The number of units in the fully-connected layer.

• weight_regularizer (usencrypt.ai.regularizers.Regularizer) – Regularizer applied to the weights matrix.

• bias_regularizer (usencrypt.ai.regularizers.Regularizer) – Regularizer applied to the bias vector.

• params (dict) – Dictionary containing all trainable parameters.

• params_prime (dict) – Dictionary containing all parameter gradients.

• name (str) – The layer’s string identifier.

Inheritance

usencrypt.ai.nn.layers.Layer

Examples

Like all layers, the fully-connected layer can be added to the top of a neural network architecture stack as follows:

>>> import usencrypt as ue
>>> net = ue.ai.nn.NeuralNetwork()