Constructor
new Network(layers, errorFnopt)
Creates a Network of Layers consisting of Neurons. Each array element indicates a layer.
The first element represents the input Layer. The last element represents the output Layer. Each element in between represents a hidden Layer with n Neurons.
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
layers |
Array.<Layer> | An array of Layers. |
||
errorFn |
function |
<optional> |
ERROR.meanSquared | The cost function to be minimized. |
- Source:
Example
// 2 inputs
// 1 output
const net = new Network([
new Layer(2, ACTIVATION.tanh),
new Layer(1, ACTIVATION.softmax)
])Members
allLayers :Layer
An array of all Layers in the Network.
An array of all Layers in the Network. It is a single dimension array
containing the inputLayer, hiddenLayers, and the outputLayer.
Type:
- Source:
error :Number
The result of the errorFn.
The result of the errorFn.
Type:
- Number
- Source:
errorFn :function
The cost function.
The cost function. The function used to calculate Network error.
In other words, to what degree was the Network's output wrong.
Type:
- function
- Source:
hiddenLayers :Array.<Layer>
An array of all layers between the inputLayer and outputLayer.
An array of all layers between the inputLayer and outputLayer.
Type:
- Array.<Layer>
- Source:
inputLayer :Layer
The first Layer of the Network.
The first Layer of the Network. This Layer receives input during activation.
Type:
- Source:
output :Array
The output values of the Neurons in the last layer.
The output values of the Neurons in the last layer.
This is identical to the Network's outputLayer output.
Type:
- Array
- Source:
outputLayer :Layer
The last Layer of the Network.
The last Layer of the Network. The output of this Layer is the "prediction" the Network has made for some given input.
Type:
- Source:
Methods
accumulateGradients()
Calculate and accumulate Neuron Connection weight gradients.
Calculate and accumulate Neuron Connection weight gradients. Does not update weights. Useful during batch/mini-batch training.
- Source:
activate(inputsopt) → {Array.<number>}
Activate the Network with a given set of input values.
Activate the Network with a given set of input values.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
inputs |
Array.<number> |
<optional> |
Values to activate the Network's input Neurons with. |
- Source:
Returns:
output - The output values of each Neuron in the output Layer.
- Type
- Array.<number>
backprop(targetOutputopt)
Set Network error and output Layer deltas and propagate them backward
through the Network.
Set Network error and output Layer deltas and propagate them backward
through the Network. The input Layer has no use for deltas, so it is skipped.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
targetOutput |
Array.<number> |
<optional> |
The expected Network output vector. |
- Source:
updateWeights()
Update Neuron Connection weights and reset their accumulated gradients.
Update Neuron Connection weights and reset their accumulated gradients.
- Source: