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Merge pull request #64 from yaricom/fast-network-solver-serialization
Implementing serialization/deserialization of the model for `FastModularNetworkSolver`
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,114 @@ | ||
| package network | ||
|
|
||
| import ( | ||
| "encoding/json" | ||
| "github.com/yaricom/goNEAT/v4/neat/math" | ||
| "io" | ||
| ) | ||
|
|
||
| // WriteModel is to write this FastModularNetworkSolver as a model to be used later. | ||
| func (s *FastModularNetworkSolver) WriteModel(w io.Writer) error { | ||
| dataHolder := newFastModularNetworkSolverData(s) | ||
| enc := json.NewEncoder(w) | ||
| return enc.Encode(dataHolder) | ||
| } | ||
|
|
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| // ReadFMNSModel allows loading model encoding FastModularNetworkSolver. | ||
| func ReadFMNSModel(reader io.Reader) (*FastModularNetworkSolver, error) { | ||
| var data fastModularNetworkSolverData | ||
| dec := json.NewDecoder(reader) | ||
| if err := dec.Decode(&data); err != nil { | ||
| return nil, err | ||
| } | ||
| activationFunctions := make([]math.NodeActivationType, len(data.ActivationFunctions)) | ||
| for i, f := range data.ActivationFunctions { | ||
| activationFunctions[i] = f.NodeActivation | ||
| } | ||
| var modules []*FastControlNode | ||
| if len(data.Modules) > 0 { | ||
| modules = make([]*FastControlNode, len(data.Modules)) | ||
| for i, m := range data.Modules { | ||
| modules[i] = &FastControlNode{ | ||
| ActivationType: m.ActivationType.NodeActivation, | ||
| InputIndexes: m.InputIndexes, | ||
| OutputIndexes: m.OutputIndexes, | ||
| } | ||
| } | ||
| } | ||
| fmns := NewFastModularNetworkSolver( | ||
| data.BiasNeuronCount, data.InputNeuronCount, data.OutputNeuronCount, | ||
| data.TotalNeuronCount, activationFunctions, | ||
| data.Connections, data.BiasList, modules, | ||
| ) | ||
| fmns.Name = data.Name | ||
| fmns.Id = data.Id | ||
| return fmns, nil | ||
| } | ||
|
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| type NodeActivator struct { | ||
| NodeActivation math.NodeActivationType | ||
| } | ||
|
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| type fastControlNodeData struct { | ||
| ActivationType NodeActivator `json:"activation_type"` | ||
| InputIndexes []int `json:"input_indexes"` | ||
| OutputIndexes []int `json:"output_indexes"` | ||
| } | ||
|
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| type fastModularNetworkSolverData struct { | ||
| Id int `json:"id"` | ||
| Name string `json:"name"` | ||
| InputNeuronCount int `json:"input_neuron_count"` | ||
| SensorNeuronCount int `json:"sensor_neuron_count"` | ||
| OutputNeuronCount int `json:"output_neuron_count"` | ||
| BiasNeuronCount int `json:"bias_neuron_count"` | ||
| TotalNeuronCount int `json:"total_neuron_count"` | ||
| ActivationFunctions []NodeActivator `json:"activation_functions"` | ||
| BiasList []float64 `json:"bias_list"` | ||
| Connections []*FastNetworkLink `json:"connections"` | ||
| Modules []fastControlNodeData `json:"modules,omitempty"` | ||
| } | ||
|
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| func newFastModularNetworkSolverData(n *FastModularNetworkSolver) *fastModularNetworkSolverData { | ||
| data := &fastModularNetworkSolverData{ | ||
| Id: n.Id, | ||
| Name: n.Name, | ||
| InputNeuronCount: n.inputNeuronCount, | ||
| SensorNeuronCount: n.sensorNeuronCount, | ||
| OutputNeuronCount: n.outputNeuronCount, | ||
| BiasNeuronCount: n.biasNeuronCount, | ||
| TotalNeuronCount: n.totalNeuronCount, | ||
| ActivationFunctions: make([]NodeActivator, len(n.activationFunctions)), | ||
| BiasList: n.biasList, | ||
| Connections: n.connections, | ||
| Modules: make([]fastControlNodeData, 0), | ||
| } | ||
| for i, v := range n.activationFunctions { | ||
| data.ActivationFunctions[i] = NodeActivator{ | ||
| NodeActivation: v, | ||
| } | ||
| } | ||
| if n.modules != nil { | ||
| for _, v := range n.modules { | ||
| data.Modules = append(data.Modules, fastControlNodeData{ | ||
| ActivationType: NodeActivator{NodeActivation: v.ActivationType}, | ||
| InputIndexes: v.InputIndexes, | ||
| OutputIndexes: v.OutputIndexes, | ||
| }) | ||
| } | ||
| } | ||
| return data | ||
| } | ||
|
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| func (n *NodeActivator) MarshalText() ([]byte, error) { | ||
| if activationName, err := math.NodeActivators.ActivationNameFromType(n.NodeActivation); err != nil { | ||
| return nil, err | ||
| } else { | ||
| return []byte(activationName), nil | ||
| } | ||
| } | ||
|
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| func (n *NodeActivator) UnmarshalText(text []byte) (err error) { | ||
| n.NodeActivation, err = math.NodeActivators.ActivationTypeFromName(string(text)) | ||
| return err | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,163 @@ | ||
| package network | ||
|
|
||
| import ( | ||
| "bytes" | ||
| "encoding/json" | ||
| "github.com/stretchr/testify/assert" | ||
| "github.com/stretchr/testify/require" | ||
| "testing" | ||
| ) | ||
|
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| const jsonFMNStr = `{"id":123456,"name":"test network","input_neuron_count":2,"sensor_neuron_count":3,"output_neuron_count":2,"bias_neuron_count":1,"total_neuron_count":8,"activation_functions":["SigmoidSteepenedActivation","SigmoidSteepenedActivation","SigmoidSteepenedActivation","SigmoidSteepenedActivation","SigmoidSteepenedActivation","SigmoidSteepenedActivation","SigmoidSteepenedActivation","SigmoidSteepenedActivation"],"bias_list":[0,0,0,0,0,0,1,0],"connections":[{"source_index":1,"target_index":5,"weight":15,"signal":0},{"source_index":2,"target_index":5,"weight":10,"signal":0},{"source_index":2,"target_index":6,"weight":5,"signal":0},{"source_index":6,"target_index":7,"weight":17,"signal":0},{"source_index":5,"target_index":3,"weight":7,"signal":0},{"source_index":7,"target_index":3,"weight":4.5,"signal":0},{"source_index":7,"target_index":4,"weight":13,"signal":0}]}` | ||
| const jsonFNMStrModule = `{"id":123456,"name":"test network","input_neuron_count":2,"sensor_neuron_count":3,"output_neuron_count":2,"bias_neuron_count":1,"total_neuron_count":8,"activation_functions":["SigmoidSteepenedActivation","SigmoidSteepenedActivation","SigmoidSteepenedActivation","LinearActivation","LinearActivation","LinearActivation","LinearActivation","NullActivation"],"bias_list":[0,0,0,0,0,10,1,0],"connections":[{"source_index":1,"target_index":5,"weight":15,"signal":0},{"source_index":2,"target_index":6,"weight":5,"signal":0},{"source_index":7,"target_index":3,"weight":4.5,"signal":0},{"source_index":7,"target_index":4,"weight":13,"signal":0}],"modules":[{"activation_type":"MultiplyModuleActivation","input_indexes":[5,6],"output_indexes":[7]}]}` | ||
|
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| const networkName = "test network" | ||
| const networkId = 123456 | ||
|
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| func TestFastModularNetworkSolver_WriteModel_NoModule(t *testing.T) { | ||
| net := buildNamedNetwork(networkName, networkId) | ||
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| fmm, err := net.FastNetworkSolver() | ||
| require.NoError(t, err, "failed to create fast network solver") | ||
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| outBuf := bytes.NewBufferString("") | ||
| err = fmm.(*FastModularNetworkSolver).WriteModel(outBuf) | ||
| require.NoError(t, err, "failed to write model") | ||
|
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| println(outBuf.String()) | ||
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| var expected interface{} | ||
| err = json.Unmarshal([]byte(jsonFMNStr), &expected) | ||
| require.NoError(t, err, "failed to unmarshal expected json") | ||
| var actual interface{} | ||
| err = json.Unmarshal(outBuf.Bytes(), &actual) | ||
| require.NoError(t, err, "failed to unmarshal actual json") | ||
|
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| assert.EqualValues(t, expected, actual, "model JSON does not match expected JSON") | ||
| } | ||
|
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| func TestFastModularNetworkSolver_WriteModel_WithModule(t *testing.T) { | ||
| net := buildNamedModularNetwork(networkName, networkId) | ||
|
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| fmm, err := net.FastNetworkSolver() | ||
| require.NoError(t, err, "failed to create fast network solver") | ||
|
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| outBuf := bytes.NewBufferString("") | ||
| err = fmm.(*FastModularNetworkSolver).WriteModel(outBuf) | ||
| require.NoError(t, err, "failed to write model") | ||
|
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| println(outBuf.String()) | ||
|
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| var expected interface{} | ||
| err = json.Unmarshal([]byte(jsonFNMStrModule), &expected) | ||
| require.NoError(t, err, "failed to unmarshal expected json") | ||
| var actual interface{} | ||
| err = json.Unmarshal(outBuf.Bytes(), &actual) | ||
| require.NoError(t, err, "failed to unmarshal actual json") | ||
|
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| assert.EqualValues(t, expected, actual, "model JSON does not match expected JSON") | ||
| } | ||
|
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| func TestReadFMNSModel_NoModule(t *testing.T) { | ||
| buf := bytes.NewBufferString(jsonFMNStr) | ||
|
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| fmm, err := ReadFMNSModel(buf) | ||
| assert.NoError(t, err, "failed to read model") | ||
| assert.NotNil(t, fmm, "failed to deserialize model") | ||
|
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| assert.Equal(t, fmm.Name, networkName, "wrong network name") | ||
| assert.Equal(t, fmm.Id, networkId, "wrong network id") | ||
|
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| data := []float64{1.5, 2.0} // bias inherent | ||
| err = fmm.LoadSensors(data) | ||
| require.NoError(t, err, "failed to load sensors") | ||
|
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| // test that it operates as expected | ||
| // | ||
| net := buildNetwork() | ||
| depth, err := net.MaxActivationDepth() | ||
| require.NoError(t, err, "failed to calculate max depth") | ||
|
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| t.Logf("depth: %d\n", depth) | ||
| logNetworkActivationPath(net, t) | ||
|
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| data = append(data, 1.0) // BIAS is third object | ||
| err = net.LoadSensors(data) | ||
| require.NoError(t, err, "failed to load sensors") | ||
| res, err := net.ForwardSteps(depth) | ||
| require.NoError(t, err, "error when trying to activate objective network") | ||
| require.True(t, res, "failed to activate objective network") | ||
|
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| // do forward steps through the solver and test results | ||
| // | ||
| res, err = fmm.Relax(depth, .1) | ||
| require.NoError(t, err, "error while do forward steps") | ||
| require.True(t, res, "forward steps returned false") | ||
|
|
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| // check results by comparing activations of objective network and fast network solver | ||
| // | ||
| outputs := fmm.ReadOutputs() | ||
| for i, out := range outputs { | ||
| assert.Equal(t, net.Outputs[i].Activation, out, "wrong activation at: %d", i) | ||
| } | ||
| } | ||
|
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| func TestReadFMNSModel_ModularNetwork(t *testing.T) { | ||
| buf := bytes.NewBufferString(jsonFNMStrModule) | ||
|
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| fmm, err := ReadFMNSModel(buf) | ||
| assert.NoError(t, err, "failed to read model") | ||
| assert.NotNil(t, fmm, "failed to deserialize model") | ||
|
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| assert.Equal(t, fmm.Name, networkName, "wrong network name") | ||
| assert.Equal(t, fmm.Id, networkId, "wrong network id") | ||
|
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| data := []float64{1.0, 2.0} // bias inherent | ||
| err = fmm.LoadSensors(data) | ||
| require.NoError(t, err, "failed to load sensors") | ||
|
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| // test that it operates as expected | ||
| // | ||
| net := buildModularNetwork() | ||
| depth, err := net.MaxActivationDepth() | ||
| require.NoError(t, err, "failed to calculate max depth") | ||
|
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| t.Logf("depth: %d\n", depth) | ||
| logNetworkActivationPath(net, t) | ||
|
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| // activate objective network | ||
| // | ||
| data = append(data, 1.0) // BIAS is third object | ||
| err = net.LoadSensors(data) | ||
| require.NoError(t, err, "failed to load sensors") | ||
| res, err := net.ForwardSteps(depth) | ||
| require.NoError(t, err, "error when trying to activate objective network") | ||
| require.True(t, res, "failed to activate objective network") | ||
|
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| // do forward steps through the solver and test results | ||
| // | ||
| res, err = fmm.Relax(depth, 1) | ||
| require.NoError(t, err, "error while do forward steps") | ||
| require.True(t, res, "forward steps returned false") | ||
|
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| // check results by comparing activations of objective network and fast network solver | ||
| // | ||
| outputs := fmm.ReadOutputs() | ||
| for i, out := range outputs { | ||
| assert.Equal(t, net.Outputs[i].Activation, out, "wrong activation at: %d", i) | ||
| } | ||
|
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| } | ||
| func buildNamedNetwork(name string, id int) *Network { | ||
| net := buildNetwork() | ||
| net.Name = name | ||
| net.Id = id | ||
| return net | ||
| } | ||
|
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| func buildNamedModularNetwork(name string, id int) *Network { | ||
| net := buildModularNetwork() | ||
| net.Name = name | ||
| net.Id = id | ||
| return net | ||
| } |
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