new Group(sizeopt, biasopt)
A Group is an abstraction of Neuron and a tool for creating and manipulating a group of neurons - with Group we can create neural network layers and and build networks faster than neuron-by-neuron construction.
Parameters:
| Name |
Type |
Attributes |
Description |
size |
number
|
<optional>
|
|
bias |
number
|
<optional>
|
|
Properties:
| Name |
Type |
Description |
id |
string
|
|
neurons |
Array.<Neuron>
|
|
- Source:
Methods
activate(inputsopt) → {Array.<number>}
Parameters:
| Name |
Type |
Attributes |
Description |
inputs |
Array.<number>
|
<optional>
|
|
- Source:
Returns:
-
Type
-
Array.<number>
Example
//===============================================
// One Group (No Hidden Layers) =================
//===============================================
const { Group } = require("@liquidcarrot/nn")
const group = new Group(2);
neuron.activate([0, 0]); // [0, 0]
//===============================================
// Three Groups (Hidden Layers) =================
//===============================================
const { Group } = require("@liquidcarrot/nn")
const input = new Group(2); // Input Neuron (Layer)
const hidden = new Group(2,0.1); // Hidden Neuron (Layer)
const output = new Group(2,0.15); // Output Neuron (Layer)
input.connect(hidden, [0.2,0.25,0.3,0.35]); // Connects input layer to hidden layer
hidden.connect(output, [0.4,0.45,0.5,0.55]); // Connects hidden layer to output layer
input.activate([0,0]); // [0,0]
hidden.activate(); // [0.###, 0.###]
output.activate(); // [0.###, 0.###]
connect(target, weightsopt)
Parameters:
| Name |
Type |
Attributes |
Description |
target |
Group
|
|
|
weights |
Array.<number>
|
<optional>
|
|
- Source:
Example
//===============================================
// 2x2 (No Weights) =============================
//===============================================
const { Group } = require("@liquidcarrot/nn")
const group = new Group(2);
const other = new Group(2);
group.connect(other);
//===============================================
// 2x2 (Weights) =============================
//===============================================
const { Group } = require("@liquidcarrot/nn")
const group = new Group(2);
const other = new Group(2);
// group[0] -- weights[0] --> other[0]
// group[0] -- weights[1] --> other[1]
// group[1] -- weights[2] --> other[0]
// group[1] -- weights[3] --> other[1]
group.connect(other, [0.1, 0.2, 0.3, 0.4]);
propagate(targetsopt, rateopt) → {Array.<number>}
Parameters:
| Name |
Type |
Attributes |
Default |
Description |
targets |
Array.<number>
|
<optional>
|
|
|
rate |
number
|
<optional>
|
0.3
|
|
- Source:
Returns:
-
Type
-
Array.<number>
Example
//===============================================
// One Group (No Hidden Layers) =================
//===============================================
const { Group } = require("@liquidcarrot/nn")
const group = new Group(2);
neuron.activate([0, 0]); // [0, 0]
neuron.propagate([0, 1]); // [0, -1]
//===============================================
// Three Groups (Hidden Layers) =================
//===============================================
const { Group } = require("@liquidcarrot/nn")
const input = new Group(2); // Input Neuron (Layer)
const hidden = new Group(2,0.1); // Hidden Neuron (Layer)
const output = new Group(2,0.15); // Output Neuron (Layer)
input.connect(hidden, [0.2,0.25,0.3,0.35]); // Connects input layer to hidden layer
hidden.connect(output, [0.4,0.45,0.5,0.55]); // Connects hidden layer to output layer
input.activate([0,0]); // [0,0]
hidden.activate(); // [0.###, 0.###]
output.activate(); // [0.###, 0.###]
output.propagate([0, 1]); // [0, -1]
hidden.propagate(); // [0.###, 0.###]
input.propagate(); // [0.###, 0.###]