Layers#

The following layers are supported in USEncrypt.ai:

Layer

Base class for neural network layers.

ActivationLayer

Base class for neural network activation layers.

BatchNormLayer

Applies normalization to the input.

DropoutLayer

Applies dropout to the input.

FCLayer

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.

ReluLayer

Rectified Linear Unit (ReLU) activation function in layer format for neural networks.

SigmoidLayer

Sigmoid activation function in layer format for neural networks.

SoftmaxLayer

Softmax activation function in layer format for neural networks.

TanhLayer

Hyperbolic tangent activation function in layer format for neural networks.