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dbcooper/Matlab neural net object console dump.txt

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Matlab neural net stuff
Raw
Matlab neural net object console dump.txt
>> net
net =
Neural Network
name: 'Function Fitting Neural Network'
userdata: (your custom info)
dimensions:
numInputs: 1
numLayers: 2
numOutputs: 1
numInputDelays: 0
numLayerDelays: 0
numFeedbackDelays: 0
numWeightElements: 73
sampleTime: 1
connections:
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
subobjects:
input: Equivalent to inputs{1}
output: Equivalent to outputs{2}
inputs: {1x1 cell array of 1 input}
layers: {2x1 cell array of 2 layers}
outputs: {1x2 cell array of 1 output}
biases: {2x1 cell array of 2 biases}
inputWeights: {2x1 cell array of 1 weight}
layerWeights: {2x2 cell array of 1 weight}
functions:
adaptFcn: 'adaptwb'
adaptParam: (none)
derivFcn: 'defaultderiv'
divideFcn: 'dividerand'
divideParam: .trainRatio, .valRatio, .testRatio
divideMode: 'sample'
initFcn: 'initlay'
performFcn: 'mse'
performParam: .regularization, .normalization
plotFcns: {'plotperform', plottrainstate, ploterrhist,
plotregression, plotfit}
plotParams: {1x5 cell array of 5 params}
trainFcn: 'trainlm'
trainParam: .showWindow, .showCommandLine, .show, .epochs,
.time, .goal, .min_grad, .max_fail, .mu, .mu_dec,
.mu_inc, .mu_max
weight and bias values:
IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell} containing 2 bias vectors
methods:
adapt: Learn while in continuous use
configure: Configure inputs & outputs
gensim: Generate Simulink model
init: Initialize weights & biases
perform: Calculate performance
sim: Evaluate network outputs given inputs
train: Train network with examples
view: View diagram
unconfigure: Unconfigure inputs & outputs
evaluate: outputs = net(inputs)
>> net.inputs{1}
ans =
Neural Network Input
name: 'Input'
feedbackOutput: []
processFcns: {'mapminmax'}
processParams: {1x1 cell array of 1 param}
processSettings: {1x1 cell array of 1 setting}
processedRange: [34x2 double]
processedSize: 34
range: [34x2 double]
size: 34
userdata: (your custom info)
>> net.layers{1}
ans =
Neural Network Layer
name: 'Hidden'
dimensions: 2
distanceFcn: (none)
distanceParam: (none)
distances: []
initFcn: 'initnw'
netInputFcn: 'netsum'
netInputParam: (none)
positions: []
range: [2x2 double]
size: 2
topologyFcn: (none)
transferFcn: 'tansig'
transferParam: (none)
userdata: (your custom info)
>> net.layers{2}
ans =
Neural Network Layer
name: 'Output'
dimensions: 1
distanceFcn: (none)
distanceParam: (none)
distances: []
initFcn: 'initnw'
netInputFcn: 'netsum'
netInputParam: (none)
positions: []
range: [1x2 double]
size: 1
topologyFcn: (none)
transferFcn: 'purelin'
transferParam: (none)
userdata: (your custom info)
>> net.outputs{1}
Error using network/subsref (line 212)
Dot indexing is not supported for variables of this type.
>> net.biases
ans =
2×1 cell array
{1×1 nnetBias}
{1×1 nnetBias}
>> net.outputs
ans =
1×2 cell array
{0×0 double} {1×1 nnetOutput}
>> net.outputs{1}
Error using network/subsref (line 212)
Dot indexing is not supported for variables of this type.
>> net.outputs{2}
ans =
Neural Network Output
name: 'Output'
feedbackInput: []
feedbackDelay: 0
feedbackMode: 'none'
processFcns: {'mapminmax'}
processParams: {1x1 cell array of 1 param}
processSettings: {1x1 cell array of 1 setting}
processedRange: [1x2 double]
processedSize: 1
range: [1x2 double]
size: 1
userdata: (your custom info)
>> net.biases{1}
ans =
Neural Network Bias
initFcn: (none)
learn: true
learnFcn: 'learngdm'
learnParam: .lr, .mc
size: 2
userdata: (your custom info)
>> net.biases{2}
ans =
Neural Network Bias
initFcn: (none)
learn: true
learnFcn: 'learngdm'
learnParam: .lr, .mc
size: 1
userdata: (your custom info)
>> net.inputWeights
ans =
2×1 cell array
{1×1 nnetWeight}
{0×0 double }
>> net.inputWeights{1}
ans =
Neural Network Weight
delays: 0
initFcn: (none)
initSettings: .range
learn: true
learnFcn: 'learngdm'
learnParam: .lr, .mc
size: [2 34]
weightFcn: 'dotprod'
weightParam: (none)
userdata: (your custom info)
>> net.inputWeights{1}(:)
ans =
struct with fields:
delays: 0
initFcn: ''
initSettings: [1×1 struct]
learn: 1
learnFcn: 'learngdm'
learnParam: [1×1 struct]
size: [2 34]
userdata: [1×1 struct]
weightFcn: 'dotprod'
weightParam: [1×1 struct]
>> net.inputWeights{1}()
ans =
struct with fields:
delays: 0
initFcn: ''
initSettings: [1×1 struct]
learn: 1
learnFcn: 'learngdm'
learnParam: [1×1 struct]
size: [2 34]
userdata: [1×1 struct]
weightFcn: 'dotprod'
weightParam: [1×1 struct]
>> x = net.inputWeights{1}
x =
Neural Network Weight
delays: 0
initFcn: (none)
initSettings: .range
learn: true
learnFcn: 'learngdm'
learnParam: .lr, .mc
size: [2 34]
weightFcn: 'dotprod'
weightParam: (none)
userdata: (your custom info)
>> x.disp()
Neural Network Weight
delays: 0
initFcn: (none)
initSettings: .range
learn: true
learnFcn: 'learngdm'
learnParam: .lr, .mc
size: [2 34]
weightFcn: 'dotprod'
weightParam: (none)
userdata: (your custom info)
>> x.struct()
ans =
struct with fields:
delays: 0
initFcn: ''
initSettings: [1×1 struct]
learn: 1
learnFcn: 'learngdm'
learnParam: [1×1 struct]
size: [2 34]
userdata: [1×1 struct]
weightFcn: 'dotprod'
weightParam: [1×1 struct]
>> disp(x)
Neural Network Weight
delays: 0
initFcn: (none)
initSettings: .range
learn: true
learnFcn: 'learngdm'
learnParam: .lr, .mc
size: [2 34]
weightFcn: 'dotprod'
weightParam: (none)
userdata: (your custom info)
>> x.size
ans =
2 34
>>
Raw
Matlab neural net object.json
{
"version": "8",
"name": "Function Fitting Neural Network",
"efficiency": {
"cacheDelayedInputs": true,
"flattenTime": true,
"memoryReduction": 1,
"flattenedTime": true
},
"userdata": {
"note": "Put your custom network information here."
},
"numInputs": 1,
"numLayers": 2,
"numOutputs": 1,
"numInputDelays": 0,
"numLayerDelays": 0,
"numFeedbackDelays": 0,
"numWeightElements": 73,
"sampleTime": 1,
"biasConnect": [
true,
true
],
"inputConnect": [
true,
false
],
"layerConnect": [
[
false,
false
],
[
true,
false
]
],
"outputConnect": [
false,
true
],
"inputs": [
{
"name": "Input",
"feedbackOutput": [],
"processFcns": [
"mapminmax"
],
"processParams": [
{
"ymin": -1,
"ymax": 1
}
],
"processSettings": [
{
"name": "mapminmax",
"xrows": 34,
"xmax": [
33.9,
31.7,
31.7,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1,
32.1
],
"xmin": [
5.8,
8.9,
8.9,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6
],
"xrange": [
28.099999999999998,
22.799999999999997,
22.799999999999997,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5,
31.5
],
"yrows": 34,
"ymax": 1,
"ymin": -1,
"yrange": 2,
"gain": [
0.0711743772241993,
0.08771929824561404,
0.08771929824561404,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349,
0.06349206349206349
],
"xoffset": [
5.8,
8.9,
8.9,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6,
0.6
],
"no_change": false
}
],
"processedRange": [
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
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-1,
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-1,
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-1,
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-1,
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-1,
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-1,
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-1,
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-1,
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[
-1,
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[
-1,
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],
[
-1,
1
],
[
-1,
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],
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-1,
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[
-1,
1
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-1,
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-1,
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-1,
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-1,
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-1,
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-1,
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-1,
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[
-1,
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[
-1,
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]
],
"processedSize": 34,
"range": [
[
5.8,
33.9
],
[
8.9,
31.7
],
[
8.9,
31.7
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
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[
0.6,
32.1
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[
0.6,
32.1
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[
0.6,
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[
0.6,
32.1
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[
0.6,
32.1
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[
0.6,
32.1
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[
0.6,
32.1
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[
0.6,
32.1
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[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
],
[
0.6,
32.1
]
],
"size": 34,
"userdata": {
"note": "Put your custom input information here."
},
"exampleInput": []
}
],
"layers": [
{
"dimensions": 2,
"distanceFcn": "",
"distanceParam": {},
"distances": [],
"initFcn": "initnw",
"name": "Hidden",
"netInputFcn": "netsum",
"netInputParam": {},
"positions": [],
"range": [
[
-1,
1
],
[
-1,
1
]
],
"size": 2,
"topologyFcn": "",
"transferFcn": "tansig",
"transferParam": {},
"userdata": {
"note": "Put your custom layer information here."
}
},
{
"dimensions": 1,
"distanceFcn": "",
"distanceParam": {},
"distances": [],
"initFcn": "initnw",
"name": "Output",
"netInputFcn": "netsum",
"netInputParam": {},
"positions": [],
"range": [
null,
null
],
"size": 1,
"topologyFcn": "",
"transferFcn": "purelin",
"transferParam": {},
"userdata": {
"note": "Put your custom layer information here."
}
}
],
"biases": [
{
"initFcn": "",
"learn": true,
"learnFcn": "learngdm",
"learnParam": {
"lr": 0.01,
"mc": 0.9
},
"size": 2,
"userdata": {
"note": "Put your custom layer information here."
}
},
{
"initFcn": "",
"learn": true,
"learnFcn": "learngdm",
"learnParam": {
"lr": 0.01,
"mc": 0.9
},
"size": 1,
"userdata": {
"note": "Put your custom layer information here."
}
}
],
"outputs": [
[],
{
"name": "Output",
"feedbackInput": [],
"feedbackDelay": 0,
"feedbackMode": "none",
"processFcns": [
"mapminmax"
],
"processParams": [
{
"ymin": -1,
"ymax": 1
}
],
"processSettings": [
{
"name": "mapminmax",
"xrows": 1,
"xmax": 33.9,
"xmin": 5.8,
"xrange": 28.099999999999998,
"yrows": 1,
"ymax": 1,
"ymin": -1,
"yrange": 2,
"gain": 0.0711743772241993,
"xoffset": 5.8,
"no_change": false
}
],
"processedRange": [
-1,
1
],
"processedSize": 1,
"range": [
5.8,
33.9
],
"size": 1,
"userdata": {
"note": "Put your custom output information here."
},
"exampleOutput": []
}
],
"inputWeights": [
{
"delays": 0,
"initFcn": "",
"initSettings": {
"range": [
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
],
[
-1,
1
]
]
},
"learn": 1,
"learnFcn": "learngdm",
"learnParam": {
"lr": 0.01,
"mc": 0.9
},
"size": [
2,
34
],
"userdata": {
"note": "Put your custom weight information here."
},
"weightFcn": "dotprod",
"weightParam": {}
},
[]
],
"layerWeights": [
[],
{
"delays": 0,
"initFcn": "",
"initSettings": {
"range": [
[
-1,
1
],
[
-1,
1
]
]
},
"learn": 1,
"learnFcn": "learngdm",
"learnParam": {
"lr": 0.01,
"mc": 0.9
},
"size": [
1,
2
],
"userdata": {
"note": "Put your custom weight information here."
},
"weightFcn": "dotprod",
"weightParam": {}
},
[],
[]
],
"adaptFcn": "adaptwb",
"adaptParam": {},
"divideFcn": "dividerand",
"divideParam": {
"trainRatio": 0.7,
"valRatio": 0.15,
"testRatio": 0.15
},
"divideMode": "sample",
"initFcn": "initlay",
"performFcn": "mse",
"performParam": {
"regularization": 0,
"normalization": "none"
},
"plotFcns": [
"plotperform",
"plottrainstate",
"ploterrhist",
"plotregression",
"plotfit"
],
"plotParams": [
{},
{},
{
"bins": 20
},
{},
{
"outputIndex": 1
}
],
"derivFcn": "defaultderiv",
"trainFcn": "trainlm",
"trainParam": {
"showWindow": true,
"showCommandLine": false,
"show": 25,
"epochs": 1000,
"time": null,
"goal": 0,
"min_grad": 1e-7,
"max_fail": 6,
"mu": 0.001,
"mu_dec": 0.1,
"mu_inc": 10,
"mu_max": 10000000000
},
"IW": [
[
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],
[
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]
],
[]
],
"LW": [
[],
[
0.09399700253836589,
-4.7353203198205005
],
[],
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Unexpanded neural network.json
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