unexport network features (neurons, weights, biases)
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parent
a180e09cf0
commit
eee503a405
62
network.go
62
network.go
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@ -11,9 +11,9 @@ type Network struct {
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fed bool // whether the network has the state fed by a forward pass
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Neurons []*mat.Dense // neuron value vector for each layer (size=L)
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Biases []*mat.Dense // neuron bias vector for each layer (size=L-1)
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Weights []*mat.Dense // weights between each 2-layers (size=L-1)
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neurons []*mat.Dense // neuron value vector for each layer (size=L)
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biases []*mat.Dense // neuron bias vector for each layer (size=L-1)
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weights []*mat.Dense // weights between each 2-layers (size=L-1)
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}
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const MaxLayerCount = 255
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@ -34,9 +34,9 @@ func Empty(_layers ...uint) (*Network, error) {
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net := &Network{
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layers: _layers,
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fed: false,
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Neurons: make([]*mat.Dense, 0),
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Biases: make([]*mat.Dense, 0),
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Weights: make([]*mat.Dense, 0),
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neurons: make([]*mat.Dense, 0),
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biases: make([]*mat.Dense, 0),
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weights: make([]*mat.Dense, 0),
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}
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for i, layer := range _layers {
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@ -47,7 +47,7 @@ func Empty(_layers ...uint) (*Network, error) {
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}
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// create neurons
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net.Neurons = append(net.Neurons, mat.NewDense(int(layer), 1, nil))
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net.neurons = append(net.neurons, mat.NewDense(int(layer), 1, nil))
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// do not create weights nor biases for first layer
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// (no previous layer to bound to)
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@ -62,7 +62,7 @@ func Empty(_layers ...uint) (*Network, error) {
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biases = append(biases, rand.Float64())
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}
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biasesVec := mat.NewDense(int(layer), 1, biases)
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net.Biases = append(net.Biases, biasesVec)
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net.biases = append(net.biases, biasesVec)
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rows, cols := int(layer), int(_layers[i-1])
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weights := make([]float64, 0, rows*cols+1)
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@ -71,7 +71,7 @@ func Empty(_layers ...uint) (*Network, error) {
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weights = append(weights, rand.Float64())
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}
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weightsMat := mat.NewDense(rows, cols, weights)
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net.Weights = append(net.Weights, weightsMat)
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net.weights = append(net.weights, weightsMat)
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}
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@ -83,8 +83,8 @@ func Empty(_layers ...uint) (*Network, error) {
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func (net *Network) reset() {
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net.fed = false
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for i, _ := range net.Neurons {
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net.Neurons[i] = mat.NewDense(int(net.layers[i]), 1, nil)
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for i, _ := range net.neurons {
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net.neurons[i] = mat.NewDense(int(net.layers[i]), 1, nil)
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}
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}
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@ -93,26 +93,26 @@ func (net *Network) reset() {
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func (net *Network) forward(_input ...float64) error {
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// check input size
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if len(_input) < net.Neurons[0].ColView(0).Len() {
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if len(_input) < net.neurons[0].ColView(0).Len() {
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return ErrMissingInput
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}
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// reset neuron values
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net.reset()
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// forward input to first layer
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for n, l := 0, net.Neurons[0].ColView(0).Len(); n < l; n++ {
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net.Neurons[0].Set(n, 0, _input[n])
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for n, l := 0, net.neurons[0].ColView(0).Len(); n < l; n++ {
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net.neurons[0].Set(n, 0, _input[n])
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}
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// process each layer from the previous one
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for l, ll := 1, len(net.layers); l < ll; l++ {
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// Z = w^l . a^(l-1) + b^l
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z := net.Neurons[l]
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z := net.neurons[l]
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a := net.Neurons[l-1] // neurons of previous layer
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w := net.Weights[l-1] // shifted by 1 because no weights between layers -1 and 0
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b := net.Biases[l-1] // same shift as weights
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a := net.neurons[l-1] // neurons of previous layer
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w := net.weights[l-1] // shifted by 1 because no weights between layers -1 and 0
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b := net.biases[l-1] // same shift as weights
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z.Mul(w, a)
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z.Add(z, b)
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@ -140,7 +140,11 @@ func (net *Network) Cost(_expect ...float64) (float64, error) {
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// from the given _expect data
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func (net *Network) costVec(_expect ...float64) (*mat.Dense, error) {
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out := net.Neurons[len(net.Neurons)-1]
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if !net.fed {
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return nil, ErrNoState
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}
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out := net.neurons[len(net.neurons)-1]
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// check output size
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if len(_expect) < out.ColView(0).Len() {
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@ -163,7 +167,11 @@ func (net *Network) costVec(_expect ...float64) (*mat.Dense, error) {
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// output (as a vector) from the given _expect data
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func (net *Network) errorVec(_expect ...float64) (*mat.Dense, error) {
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outLayer := net.Neurons[len(net.Neurons)-1]
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if !net.fed {
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return nil, ErrNoState
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}
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outLayer := net.neurons[len(net.neurons)-1]
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// check output size
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if len(_expect) < outLayer.ColView(0).Len() {
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@ -186,7 +194,7 @@ func (net *Network) errorVec(_expect ...float64) (*mat.Dense, error) {
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// and the expected data : _expect
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func (net *Network) backward(_expect ...float64) error {
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out := net.Neurons[len(net.Neurons)-1]
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out := net.neurons[len(net.neurons)-1]
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// 1. fail on no state (no forward pass applied first)
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if !net.fed {
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@ -208,10 +216,10 @@ func (net *Network) backward(_expect ...float64) error {
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// FOR EACH LAYER (from last to 1)
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for l := len(net.layers) - 1; l > 0; l-- {
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neurons := net.Neurons[l]
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previous := net.Neurons[l-1]
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weights := net.Weights[l-1] // from l-1 to l
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biases := net.Biases[l-1] // at l
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neurons := net.neurons[l]
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previous := net.neurons[l-1]
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weights := net.weights[l-1] // from l-1 to l
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biases := net.biases[l-1] // at l
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// calc GRADIENTS = sigmoid'( neuron[l-1] )
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gradients := new(mat.Dense)
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@ -258,7 +266,7 @@ func (net *Network) Guess(_input ...float64) ([]float64, error) {
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// to guess the _expect instead
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func (net *Network) Train(_input []float64, _expect []float64) error {
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out := net.Neurons[len(net.Neurons)-1]
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out := net.neurons[len(net.neurons)-1]
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// check output size
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if len(_expect) != out.ColView(0).Len() {
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@ -283,7 +291,7 @@ func (net Network) Output() ([]float64, error) {
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return nil, ErrNoState
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}
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out := net.Neurons[len(net.Neurons)-1]
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out := net.neurons[len(net.neurons)-1]
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output := make([]float64, 0, net.layers[len(net.layers)-1])
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for n, l := 0, out.ColView(0).Len(); n < l; n++ {
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output = append(output, out.At(n, 0))
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@ -67,31 +67,31 @@ func TestEmptyNetworkSizes(t *testing.T) {
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}
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// 1. Check neuron layer count
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if len(net.Neurons) != len(test) {
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t.Errorf("Expected %d layers of neurons, got %d", len(test), len(net.Neurons))
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if len(net.neurons) != len(test) {
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t.Errorf("Expected %d layers of neurons, got %d", len(test), len(net.neurons))
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continue
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}
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// 2. Check bias layer count (layers-1)
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if len(net.Biases) != len(test)-1 {
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t.Errorf("Expected %d layers of biases, got %d", len(test)-1, len(net.Biases))
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if len(net.biases) != len(test)-1 {
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t.Errorf("Expected %d layers of biases, got %d", len(test)-1, len(net.biases))
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continue
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}
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// 3. Check weights layer count (layers-1)
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if len(net.Weights) != len(test)-1 {
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t.Errorf("Expected %d layers of weights, got %d", len(test)-1, len(net.Weights))
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if len(net.weights) != len(test)-1 {
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t.Errorf("Expected %d layers of weights, got %d", len(test)-1, len(net.weights))
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continue
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}
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// 4. Check each neuron layer count
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for n, neuron := range net.Neurons {
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for n, neuron := range net.neurons {
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if uint(neuron.ColView(0).Len()) != test[n] {
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t.Errorf("Expected %d neurons on layer %d, got %d", test[n], n, neuron.ColView(0).Len())
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}
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}
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// 5. Check each bias layer count
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for b, bias := range net.Biases {
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for b, bias := range net.biases {
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if uint(bias.ColView(0).Len()) != test[b+1] {
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t.Errorf("Expected %d biases on layer %d, got %d", test[b+1], b, bias.ColView(0).Len())
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@ -100,7 +100,7 @@ func TestEmptyNetworkSizes(t *testing.T) {
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}
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// 6. Check each weight layer count
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for w, weight := range net.Weights {
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for w, weight := range net.weights {
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rows, cols := weight.Dims()
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@ -151,19 +151,19 @@ func TestForwardPass(t *testing.T) {
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for l, ll := 1, len(net.layers); l < ll; l++ {
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// each neuron = ( each previous neuron times its weight ) + neuron bias
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for n, nl := 0, net.Neurons[l].ColView(0).Len(); n < nl; n++ {
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sum := net.Biases[l-1].At(n, 0)
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for n, nl := 0, net.neurons[l].ColView(0).Len(); n < nl; n++ {
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sum := net.biases[l-1].At(n, 0)
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// sum each previous neuron*its weight
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for i, il := 0, net.Neurons[l-1].ColView(0).Len(); i < il; i++ {
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sum += net.Neurons[l-1].At(i, 0) * net.Weights[l-1].At(n, i)
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for i, il := 0, net.neurons[l-1].ColView(0).Len(); i < il; i++ {
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sum += net.neurons[l-1].At(i, 0) * net.weights[l-1].At(n, i)
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}
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sum = sigmoid(0, 0, sum)
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// check sum
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if !floats.EqualWithinAbs(net.Neurons[l].At(n, 0), sum, 1e9) {
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t.Fatalf("Expected neuron %d.%d to be %f, got %f", l, n, sum, net.Neurons[l].At(n, 0))
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if !floats.EqualWithinAbs(net.neurons[l].At(n, 0), sum, 1e9) {
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t.Fatalf("Expected neuron %d.%d to be %f, got %f", l, n, sum, net.neurons[l].At(n, 0))
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}
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}
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24
storage.go
24
storage.go
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@ -24,7 +24,7 @@ func (net *Network) MarshalJSON() ([]byte, error) {
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// 2. Biases
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raw.Biases = make([][]float64, 0)
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for _, bias := range net.Biases {
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for _, bias := range net.biases {
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vector := bias.ColView(0)
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biasJSON := make([]float64, 0)
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@ -38,7 +38,7 @@ func (net *Network) MarshalJSON() ([]byte, error) {
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// 3. Weights
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raw.Weights = make([][]float64, 0)
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for _, weight := range net.Weights {
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for _, weight := range net.weights {
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rows, cols := weight.Dims()
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weightJSON := make([]float64, 0)
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@ -70,28 +70,28 @@ func (net *Network) UnmarshalJSON(in []byte) error {
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net.layers = raw.Layers
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// extract biases
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net.Biases = make([]*mat.Dense, 0)
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net.biases = make([]*mat.Dense, 0)
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for l, layer := range net.layers {
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if l == 0 {
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continue
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}
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net.Biases = append(net.Biases, mat.NewDense(int(layer), 1, raw.Biases[l-1]))
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net.biases = append(net.biases, mat.NewDense(int(layer), 1, raw.Biases[l-1]))
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}
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// extract weights
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net.Weights = make([]*mat.Dense, 0)
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net.weights = make([]*mat.Dense, 0)
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for l, layer := range net.layers {
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if l == 0 {
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continue
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}
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net.Weights = append(net.Weights, mat.NewDense(int(layer), int(net.layers[l-1]), raw.Weights[l-1]))
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net.weights = append(net.weights, mat.NewDense(int(layer), int(net.layers[l-1]), raw.Weights[l-1]))
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}
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// mockup neurons
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net.Neurons = make([]*mat.Dense, 0)
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net.neurons = make([]*mat.Dense, 0)
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for _, layer := range net.layers {
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net.Neurons = append(net.Neurons, mat.NewDense(int(layer), 1, nil))
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net.neurons = append(net.neurons, mat.NewDense(int(layer), 1, nil))
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}
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fmt.Printf("neurons: %v\n", net.Neurons)
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fmt.Printf("neurons: %v\n", net.neurons)
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// extract into the current network receiver (net)
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return nil
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@ -130,9 +130,9 @@ func (net *Network) ReadFrom(r io.Reader) (int64, error) {
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// copy values
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net.layers = readNet.layers
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net.fed = readNet.fed
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net.Neurons = readNet.Neurons
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net.Biases = readNet.Biases
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net.Weights = readNet.Weights
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net.neurons = readNet.neurons
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net.biases = readNet.biases
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net.weights = readNet.weights
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return int64(len(raw)), nil
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}
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