class Num::NN::Network(T)

• Num::NN::Network(T)
• Reference
• Object

Overview

A neural network can be defined as a biologically inspired computational model that consists of a network architecture composed of artificial neurons. This structure contains a set of parameters, which can be adjusted to perform specific tasks.

This class is a loose wrapper that primarily provides syntactic sugar around moving data through a network -> forward, and propogating loss <- backwards

Defined in:

Constructors

• .new(context : Num::Grad::Context(T), **options, &)

  Convenience method to allow for creation of a Network with as little code as possible.

Instance Method Summary

• #forward(train : Num::Grad::Variable(T)) : Num::Grad::Variable(T)

  Propogates an input through a network, returning the final prediction from the network

• #layers : Num::NN::NetworkInfo(T)
• #loss(output : Num::Grad::Variable(T), target : T)

  Uses the Network's loss function to calculate the loss based on the final output from the Network, as well as the target output

• #optimizer

  Return the Network's optimizer to allow updating the weights and biases of the network

Constructor Detail

Network(Float32).new do
  layer(2, 3, :tanh)
  layer(3, 1, :sigmoid)
end

Instance Method Detail

a = [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]].to_tensor
v = ctx.variable(a)
net.forward(v)

epochs.times do |epoch|
  y_pred = net.forward(x)
  loss = net.loss(y_pred, y_actual)
end

epochs.times do |epoch|
  y_pred = net.forward(x)
  loss = net.loss(y_pred, y_actual)
  net.optimizer.update
end