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License: MIT License
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Feature Request: Add Mish activation
Mish is a new novel activation function proposed in this paper.
It has shown promising results so far and has been adopted in several packages including:
- TensorFlow-Addons
- SpaCy (Tok2Vec Layer)
- Thinc - SpaCy's official NLP based ML library
- Echo AI
- CNTKX - Extension of Microsoft's CNTK
- FastAI-Dev
- Darknet
- Yolov3
- BeeDNN - Library in C++
- Gen-EfficientNet-PyTorch
- dnet
All benchmarks, analysis and links to official package implementations can be found in this repository
It would be nice to have Mish as an option within the activation function group.
This is the comparison of Mish with other conventional activation functions in a SEResNet-50 for CIFAR-10:
[Help] Why output have 3 number?
When I try to predict:
//
trainer.Train(neural, data, data, 5000)
testData1 := []float64{13.05, 1.65, 2.55, 18, 98, 2.45, 2.43, .29, 1.44, 4.25, 1.12, 2.51, 1105}
testData2 := []float64{12.16, 1.61, 2.31, 22.8, 90, 1.78, 1.69, .43, 1.56, 2.45, 1.33, 2.26, 495}
testData3 := []float64{13.17, 2.59, 2.37, 20, 120, 1.65, .68, .53, 1.46, 9.3, .6, 1.62, 840}
result1 := neural.Predict(testData1)
result2 := neural.Predict(testData2)
result3 := neural.Predict(testData3)
t.Log(result1)
t.Log(result2)
t.Log(result3)
//
go-deep output :
godeep_wine_test.go:57: [0.9999999731472882 1.3716952393578034e-11 2.6838994748831026e-08]
godeep_wine_test.go:58: [1.0544492493308024e-06 0.9365886960309127 0.06341024951983812]
godeep_wine_test.go:59: [0.9999999731472882 1.3716952393578034e-11 2.6838994748831026e-08]
I noticed that wine simple data only have one tag. Why output have 3 number output [1.0544492493308024e-06 0.9365886960309127 0.06341024951983812]?
Question about sparse networks
Is connecting a sparse network similar to what is being done in NewNeural() supposed to work? I noticed the batch trainer uses NewNeural() internally.
How to save and load trained data?
When I load a trained Neural variable from a json file, it does not work.
How can I save and load trained model?
Question: what is different between deep.NewNeuron() and deep.NewNeural()
Question:
what is different between deep.NewNeuron() and deep.NewNeural()
wine data test results are not very accurate
wine data test results are not very accurate
Testcode：
func Test_wine_demo(t *testing.T) {
rand.Seed(time.Now().UnixNano())
data := training.Examples{
{Input: []float64{14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065}, Response: []float64{0, 0, 1}},
{Input: []float64{13.2, 1.78, 2.14, 11.2, 100, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.4, 1050}, Response: []float64{0, 0, 1}},
{Input: []float64{13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.68, 1.03, 3.17, 1185}, Response: []float64{0, 0, 1}},
{Input: []float64{14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480}, Response: []float64{0, 0, 1}},
{Input: []float64{13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735}, Response: []float64{0, 0, 1}},
{Input: []float64{14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450}, Response: []float64{0, 0, 1}},
{Input: []float64{14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290}, Response: []float64{0, 0, 1}},
{Input: []float64{14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295}, Response: []float64{0, 0, 1}},
{Input: []float64{14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045}, Response: []float64{0, 0, 1}},
{Input: []float64{13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.85, 7.22, 1.01, 3.55, 1045}, Response: []float64{0, 0, 1}},
{Input: []float64{14.1, 2.16, 2.3, 18, 105, 2.95, 3.32, 0.22, 2.38, 5.75, 1.25, 3.17, 1510}, Response: []float64{0, 0, 1}},
{Input: []float64{14.12, 1.48, 2.32, 16.8, 95, 2.2, 2.43, 0.26, 1.57, 5, 1.17, 2.82, 1280}, Response: []float64{0, 0, 1}},
{Input: []float64{13.75, 1.73, 2.41, 16, 89, 2.6, 2.76, 0.29, 1.81, 5.6, 1.15, 2.9, 1320}, Response: []float64{0, 0, 1}},
{Input: []float64{14.75, 1.73, 2.39, 11.4, 91, 3.1, 3.69, 0.43, 2.81, 5.4, 1.25, 2.73, 1150}, Response: []float64{0, 0, 1}},
{Input: []float64{14.38, 1.87, 2.38, 12, 102, 3.3, 3.64, 0.29, 2.96, 7.5, 1.2, 3, 1547}, Response: []float64{0, 0, 1}},
{Input: []float64{13.63, 1.81, 2.7, 17.2, 112, 2.85, 2.91, 0.3, 1.46, 7.3, 1.28, 2.88, 1310}, Response: []float64{0, 0, 1}},
{Input: []float64{14.3, 1.92, 2.72, 20, 120, 2.8, 3.14, 0.33, 1.97, 6.2, 1.07, 2.65, 1280}, Response: []float64{0, 0, 1}},
{Input: []float64{13.83, 1.57, 2.62, 20, 115, 2.95, 3.4, 0.4, 1.72, 6.6, 1.13, 2.57, 1130}, Response: []float64{0, 0, 1}},
{Input: []float64{14.19, 1.59, 2.48, 16.5, 108, 3.3, 3.93, 0.32, 1.86, 8.7, 1.23, 2.82, 1680}, Response: []float64{0, 0, 1}},
{Input: []float64{13.64, 3.1, 2.56, 15.2, 116, 2.7, 3.03, 0.17, 1.66, 5.1, 0.96, 3.36, 845}, Response: []float64{0, 0, 1}},
{Input: []float64{14.06, 1.63, 2.28, 16, 126, 3, 3.17, 0.24, 2.1, 5.65, 1.09, 3.71, 780}, Response: []float64{0, 0, 1}},
{Input: []float64{12.93, 3.8, 2.65, 18.6, 102, 2.41, 2.41, 0.25, 1.98, 4.5, 1.03, 3.52, 770}, Response: []float64{0, 0, 1}},
{Input: []float64{13.71, 1.86, 2.36, 16.6, 101, 2.61, 2.88, 0.27, 1.69, 3.8, 1.11, 4, 1035}, Response: []float64{0, 0, 1}},
{Input: []float64{12.85, 1.6, 2.52, 17.8, 95, 2.48, 2.37, 0.26, 1.46, 3.93, 1.09, 3.63, 1015}, Response: []float64{0, 0, 1}},
{Input: []float64{13.5, 1.81, 2.61, 20, 96, 2.53, 2.61, 0.28, 1.66, 3.52, 1.12, 3.82, 845}, Response: []float64{0, 0, 1}},
{Input: []float64{13.05, 2.05, 3.22, 25, 124, 2.63, 2.68, 0.47, 1.92, 3.58, 1.13, 3.2, 830}, Response: []float64{0, 0, 1}},
{Input: []float64{13.39, 1.77, 2.62, 16.1, 93, 2.85, 2.94, 0.34, 1.45, 4.8, 0.92, 3.22, 1195}, Response: []float64{0, 0, 1}},
{Input: []float64{13.3, 1.72, 2.14, 17, 94, 2.4, 2.19, 0.27, 1.35, 3.95, 1.02, 2.77, 1285}, Response: []float64{0, 0, 1}},
{Input: []float64{13.87, 1.9, 2.8, 19.4, 107, 2.95, 2.97, 0.37, 1.76, 4.5, 1.25, 3.4, 915}, Response: []float64{0, 0, 1}},
{Input: []float64{14.02, 1.68, 2.21, 16, 96, 2.65, 2.33, 0.26, 1.98, 4.7, 1.04, 3.59, 1035}, Response: []float64{0, 0, 1}},
{Input: []float64{13.73, 1.5, 2.7, 22.5, 101, 3, 3.25, 0.29, 2.38, 5.7, 1.19, 2.71, 1285}, Response: []float64{0, 0, 1}},
{Input: []float64{13.58, 1.66, 2.36, 19.1, 106, 2.86, 3.19, 0.22, 1.95, 6.9, 1.09, 2.88, 1515}, Response: []float64{0, 0, 1}},
{Input: []float64{13.68, 1.83, 2.36, 17.2, 104, 2.42, 2.69, 0.42, 1.97, 3.84, 1.23, 2.87, 990}, Response: []float64{0, 0, 1}},
{Input: []float64{13.76, 1.53, 2.7, 19.5, 132, 2.95, 2.74, 0.5, 1.35, 5.4, 1.25, 3, 1235}, Response: []float64{0, 0, 1}},
{Input: []float64{13.51, 1.8, 2.65, 19, 110, 2.35, 2.53, 0.29, 1.54, 4.2, 1.1, 2.87, 1095}, Response: []float64{0, 0, 1}},
{Input: []float64{13.48, 1.81, 2.41, 20.5, 100, 2.7, 2.98, 0.26, 1.86, 5.1, 1.04, 3.47, 920}, Response: []float64{0, 0, 1}},
{Input: []float64{13.28, 1.64, 2.84, 15.5, 110, 2.6, 2.68, 0.34, 1.36, 4.6, 1.09, 2.78, 880}, Response: []float64{0, 0, 1}},
{Input: []float64{13.05, 1.65, 2.55, 18, 98, 2.45, 2.43, 0.29, 1.44, 4.25, 1.12, 2.51, 1105}, Response: []float64{0, 0, 1}},
{Input: []float64{13.07, 1.5, 2.1, 15.5, 98, 2.4, 2.64, 0.28, 1.37, 3.7, 1.18, 2.69, 1020}, Response: []float64{0, 0, 1}},
{Input: []float64{14.22, 3.99, 2.51, 13.2, 128, 3, 3.04, 0.2, 2.08, 5.1, 0.89, 3.53, 760}, Response: []float64{0, 0, 1}},
{Input: []float64{13.56, 1.71, 2.31, 16.2, 117, 3.15, 3.29, 0.34, 2.34, 6.13, 0.95, 3.38, 795}, Response: []float64{0, 0, 1}},
{Input: []float64{13.41, 3.84, 2.12, 18.8, 90, 2.45, 2.68, 0.27, 1.48, 4.28, 0.91, 3, 1035}, Response: []float64{0, 0, 1}},
{Input: []float64{13.88, 1.89, 2.59, 15, 101, 3.25, 3.56, 0.17, 1.7, 5.43, 0.88, 3.56, 1095}, Response: []float64{0, 0, 1}},
{Input: []float64{13.24, 3.98, 2.29, 17.5, 103, 2.64, 2.63, 0.32, 1.66, 4.36, 0.82, 3, 680}, Response: []float64{0, 0, 1}},
{Input: []float64{13.05, 1.77, 2.1, 17, 107, 3, 3, 0.28, 2.03, 5.04, 0.88, 3.35, 885}, Response: []float64{0, 0, 1}},
{Input: []float64{14.21, 4.04, 2.44, 18.9, 111, 2.85, 2.65, 0.3, 1.25, 5.24, 0.87, 3.33, 1080}, Response: []float64{0, 0, 1}},
{Input: []float64{14.38, 3.59, 2.28, 16, 102, 3.25, 3.17, 0.27, 2.19, 4.9, 1.04, 3.44, 1065}, Response: []float64{0, 0, 1}},
{Input: []float64{13.9, 1.68, 2.12, 16, 101, 3.1, 3.39, 0.21, 2.14, 6.1, 0.91, 3.33, 985}, Response: []float64{0, 0, 1}},
{Input: []float64{14.1, 2.02, 2.4, 18.8, 103, 2.75, 2.92, 0.32, 2.38, 6.2, 1.07, 2.75, 1060}, Response: []float64{0, 0, 1}},
{Input: []float64{13.94, 1.73, 2.27, 17.4, 108, 2.88, 3.54, 0.32, 2.08, 8.9, 1.12, 3.1, 1260}, Response: []float64{0, 0, 1}},
{Input: []float64{13.05, 1.73, 2.04, 12.4, 92, 2.72, 3.27, 0.17, 2.91, 7.2, 1.12, 2.91, 1150}, Response: []float64{0, 0, 1}},
{Input: []float64{13.83, 1.65, 2.6, 17.2, 94, 2.45, 2.99, 0.22, 2.29, 5.6, 1.24, 3.37, 1265}, Response: []float64{0, 0, 1}},
{Input: []float64{13.82, 1.75, 2.42, 14, 111, 3.88, 3.74, 0.32, 1.87, 7.05, 1.01, 3.26, 1190}, Response: []float64{0, 0, 1}},
{Input: []float64{13.77, 1.9, 2.68, 17.1, 115, 3, 2.79, 0.39, 1.68, 6.3, 1.13, 2.93, 1375}, Response: []float64{0, 0, 1}},
{Input: []float64{13.74, 1.67, 2.25, 16.4, 118, 2.6, 2.9, 0.21, 1.62, 5.85, 0.92, 3.2, 1060}, Response: []float64{0, 0, 1}},
{Input: []float64{13.56, 1.73, 2.46, 20.5, 116, 2.96, 2.78, 0.2, 2.45, 6.25, 0.98, 3.03, 1120}, Response: []float64{0, 0, 1}},
{Input: []float64{14.22, 1.7, 2.3, 16.3, 118, 3.2, 3, 0.26, 2.03, 6.38, 0.94, 3.31, 970}, Response: []float64{0, 0, 1}},
{Input: []float64{13.29, 1.97, 2.68, 16.8, 102, 3, 3.23, 0.31, 1.66, 6, 1.07, 2.84, 1270}, Response: []float64{0, 0, 1}},
{Input: []float64{13.72, 1.43, 2.5, 16.7, 108, 3.4, 3.67, 0.19, 2.04, 6.8, 0.89, 2.87, 1285}, Response: []float64{0, 0, 1}},
{Input: []float64{12.37, 0.94, 1.36, 10.6, 88, 1.98, 0.57, 0.28, 0.42, 1.95, 1.05, 1.82, 520}, Response: []float64{0, 1, 0}},
{Input: []float64{12.33, 1.1, 2.28, 16, 101, 2.05, 1.09, 0.63, 0.41, 3.27, 1.25, 1.67, 680}, Response: []float64{0, 1, 0}},
{Input: []float64{12.64, 1.36, 2.02, 16.8, 100, 2.02, 1.41, 0.53, 0.62, 5.75, 0.98, 1.59, 450}, Response: []float64{0, 1, 0}},
{Input: []float64{13.67, 1.25, 1.92, 18, 94, 2.1, 1.79, 0.32, 0.73, 3.8, 1.23, 2.46, 630}, Response: []float64{0, 1, 0}},
{Input: []float64{12.37, 1.13, 2.16, 19, 87, 3.5, 3.1, 0.19, 1.87, 4.45, 1.22, 2.87, 420}, Response: []float64{0, 1, 0}},
{Input: []float64{12.17, 1.45, 2.53, 19, 104, 1.89, 1.75, 0.45, 1.03, 2.95, 1.45, 2.23, 355}, Response: []float64{0, 1, 0}},
{Input: []float64{12.37, 1.21, 2.56, 18.1, 98, 2.42, 2.65, 0.37, 2.08, 4.6, 1.19, 2.3, 678}, Response: []float64{0, 1, 0}},
{Input: []float64{13.11, 1.01, 1.7, 15, 78, 2.98, 3.18, 0.26, 2.28, 5.3, 1.12, 3.18, 502}, Response: []float64{0, 1, 0}},
{Input: []float64{12.37, 1.17, 1.92, 19.6, 78, 2.11, 2, 0.27, 1.04, 4.68, 1.12, 3.48, 510}, Response: []float64{0, 1, 0}},
{Input: []float64{13.34, 0.94, 2.36, 17, 110, 2.53, 1.3, 0.55, 0.42, 3.17, 1.02, 1.93, 750}, Response: []float64{0, 1, 0}},
{Input: []float64{12.21, 1.19, 1.75, 16.8, 151, 1.85, 1.28, 0.14, 2.5, 2.85, 1.28, 3.07, 718}, Response: []float64{0, 1, 0}},
{Input: []float64{12.29, 1.61, 2.21, 20.4, 103, 1.1, 1.02, 0.37, 1.46, 3.05, 0.906, 1.82, 870}, Response: []float64{0, 1, 0}},
{Input: []float64{13.86, 1.51, 2.67, 25, 86, 2.95, 2.86, 0.21, 1.87, 3.38, 1.36, 3.16, 410}, Response: []float64{0, 1, 0}},
{Input: []float64{13.49, 1.66, 2.24, 24, 87, 1.88, 1.84, 0.27, 1.03, 3.74, 0.98, 2.78, 472}, Response: []float64{0, 1, 0}},
{Input: []float64{12.99, 1.67, 2.6, 30, 139, 3.3, 2.89, 0.21, 1.96, 3.35, 1.31, 3.5, 985}, Response: []float64{0, 1, 0}},
{Input: []float64{11.96, 1.09, 2.3, 21, 101, 3.38, 2.14, 0.13, 1.65, 3.21, 0.99, 3.13, 886}, Response: []float64{0, 1, 0}},
{Input: []float64{11.66, 1.88, 1.92, 16, 97, 1.61, 1.57, 0.34, 1.15, 3.8, 1.23, 2.14, 428}, Response: []float64{0, 1, 0}},
{Input: []float64{13.03, 0.9, 1.71, 16, 86, 1.95, 2.03, 0.24, 1.46, 4.6, 1.19, 2.48, 392}, Response: []float64{0, 1, 0}},
{Input: []float64{11.84, 2.89, 2.23, 18, 112, 1.72, 1.32, 0.43, 0.95, 2.65, 0.96, 2.52, 500}, Response: []float64{0, 1, 0}},
{Input: []float64{12.33, 0.99, 1.95, 14.8, 136, 1.9, 1.85, 0.35, 2.76, 3.4, 1.06, 2.31, 750}, Response: []float64{0, 1, 0}},
{Input: []float64{12.7, 3.87, 2.4, 23, 101, 2.83, 2.55, 0.43, 1.95, 2.57, 1.19, 3.13, 463}, Response: []float64{0, 1, 0}},
{Input: []float64{12, 0.92, 2, 19, 86, 2.42, 2.26, 0.3, 1.43, 2.5, 1.38, 3.12, 278}, Response: []float64{0, 1, 0}},
{Input: []float64{12.72, 1.81, 2.2, 18.8, 86, 2.2, 2.53, 0.26, 1.77, 3.9, 1.16, 3.14, 714}, Response: []float64{0, 1, 0}},
{Input: []float64{12.08, 1.13, 2.51, 24, 78, 2, 1.58, 0.4, 1.4, 2.2, 1.31, 2.72, 630}, Response: []float64{0, 1, 0}},
{Input: []float64{13.05, 3.86, 2.32, 22.5, 85, 1.65, 1.59, 0.61, 1.62, 4.8, 0.84, 2.01, 515}, Response: []float64{0, 1, 0}},
{Input: []float64{11.84, 0.89, 2.58, 18, 94, 2.2, 2.21, 0.22, 2.35, 3.05, 0.79, 3.08, 520}, Response: []float64{0, 1, 0}},
{Input: []float64{12.67, 0.98, 2.24, 18, 99, 2.2, 1.94, 0.3, 1.46, 2.62, 1.23, 3.16, 450}, Response: []float64{0, 1, 0}},
{Input: []float64{12.16, 1.61, 2.31, 22.8, 90, 1.78, 1.69, 0.43, 1.56, 2.45, 1.33, 2.26, 495}, Response: []float64{0, 1, 0}},
{Input: []float64{11.65, 1.67, 2.62, 26, 88, 1.92, 1.61, 0.4, 1.34, 2.6, 1.36, 3.21, 562}, Response: []float64{0, 1, 0}},
{Input: []float64{11.64, 2.06, 2.46, 21.6, 84, 1.95, 1.69, 0.48, 1.35, 2.8, 1, 2.75, 680}, Response: []float64{0, 1, 0}},
{Input: []float64{12.08, 1.33, 2.3, 23.6, 70, 2.2, 1.59, 0.42, 1.38, 1.74, 1.07, 3.21, 625}, Response: []float64{0, 1, 0}},
{Input: []float64{12.08, 1.83, 2.32, 18.5, 81, 1.6, 1.5, 0.52, 1.64, 2.4, 1.08, 2.27, 480}, Response: []float64{0, 1, 0}},
{Input: []float64{12, 1.51, 2.42, 22, 86, 1.45, 1.25, 0.5, 1.63, 3.6, 1.05, 2.65, 450}, Response: []float64{0, 1, 0}},
{Input: []float64{12.69, 1.53, 2.26, 20.7, 80, 1.38, 1.46, 0.58, 1.62, 3.05, 0.96, 2.06, 495}, Response: []float64{0, 1, 0}},
{Input: []float64{12.29, 2.83, 2.22, 18, 88, 2.45, 2.25, 0.25, 1.99, 2.15, 1.15, 3.3, 290}, Response: []float64{0, 1, 0}},
{Input: []float64{11.62, 1.99, 2.28, 18, 98, 3.02, 2.26, 0.17, 1.35, 3.25, 1.16, 2.96, 345}, Response: []float64{0, 1, 0}},
{Input: []float64{12.47, 1.52, 2.2, 19, 162, 2.5, 2.27, 0.32, 3.28, 2.6, 1.16, 2.63, 937}, Response: []float64{0, 1, 0}},
{Input: []float64{11.81, 2.12, 2.74, 21.5, 134, 1.6, 0.99, 0.14, 1.56, 2.5, 0.95, 2.26, 625}, Response: []float64{0, 1, 0}},
{Input: []float64{12.29, 1.41, 1.98, 16, 85, 2.55, 2.5, 0.29, 1.77, 2.9, 1.23, 2.74, 428}, Response: []float64{0, 1, 0}},
{Input: []float64{12.37, 1.07, 2.1, 18.5, 88, 3.52, 3.75, 0.24, 1.95, 4.5, 1.04, 2.77, 660}, Response: []float64{0, 1, 0}},
{Input: []float64{12.29, 3.17, 2.21, 18, 88, 2.85, 2.99, 0.45, 2.81, 2.3, 1.42, 2.83, 406}, Response: []float64{0, 1, 0}},
{Input: []float64{12.08, 2.08, 1.7, 17.5, 97, 2.23, 2.17, 0.26, 1.4, 3.3, 1.27, 2.96, 710}, Response: []float64{0, 1, 0}},
{Input: []float64{12.6, 1.34, 1.9, 18.5, 88, 1.45, 1.36, 0.29, 1.35, 2.45, 1.04, 2.77, 562}, Response: []float64{0, 1, 0}},
{Input: []float64{12.34, 2.45, 2.46, 21, 98, 2.56, 2.11, 0.34, 1.31, 2.8, 0.8, 3.38, 438}, Response: []float64{0, 1, 0}},
{Input: []float64{11.82, 1.72, 1.88, 19.5, 86, 2.5, 1.64, 0.37, 1.42, 2.06, 0.94, 2.44, 415}, Response: []float64{0, 1, 0}},
{Input: []float64{12.51, 1.73, 1.98, 20.5, 85, 2.2, 1.92, 0.32, 1.48, 2.94, 1.04, 3.57, 672}, Response: []float64{0, 1, 0}},
{Input: []float64{12.42, 2.55, 2.27, 22, 90, 1.68, 1.84, 0.66, 1.42, 2.7, 0.86, 3.3, 315}, Response: []float64{0, 1, 0}},
{Input: []float64{12.25, 1.73, 2.12, 19, 80, 1.65, 2.03, 0.37, 1.63, 3.4, 1, 3.17, 510}, Response: []float64{0, 1, 0}},
{Input: []float64{12.72, 1.75, 2.28, 22.5, 84, 1.38, 1.76, 0.48, 1.63, 3.3, 0.88, 2.42, 488}, Response: []float64{0, 1, 0}},
{Input: []float64{12.22, 1.29, 1.94, 19, 92, 2.36, 2.04, 0.39, 2.08, 2.7, 0.86, 3.02, 312}, Response: []float64{0, 1, 0}},
{Input: []float64{11.61, 1.35, 2.7, 20, 94, 2.74, 2.92, 0.29, 2.49, 2.65, 0.96, 3.26, 680}, Response: []float64{0, 1, 0}},
{Input: []float64{11.46, 3.74, 1.82, 19.5, 107, 3.18, 2.58, 0.24, 3.58, 2.9, 0.75, 2.81, 562}, Response: []float64{0, 1, 0}},
{Input: []float64{12.52, 2.43, 2.17, 21, 88, 2.55, 2.27, 0.26, 1.22, 2, 0.9, 2.78, 325}, Response: []float64{0, 1, 0}},
{Input: []float64{11.76, 2.68, 2.92, 20, 103, 1.75, 2.03, 0.6, 1.05, 3.8, 1.23, 2.5, 607}, Response: []float64{0, 1, 0}},
{Input: []float64{11.41, 0.74, 2.5, 21, 88, 2.48, 2.01, 0.42, 1.44, 3.08, 1.1, 2.31, 434}, Response: []float64{0, 1, 0}},
{Input: []float64{12.08, 1.39, 2.5, 22.5, 84, 2.56, 2.29, 0.43, 1.04, 2.9, 0.93, 3.19, 385}, Response: []float64{0, 1, 0}},
{Input: []float64{11.03, 1.51, 2.2, 21.5, 85, 2.46, 2.17, 0.52, 2.01, 1.9, 1.71, 2.87, 407}, Response: []float64{0, 1, 0}},
{Input: []float64{11.82, 1.47, 1.99, 20.8, 86, 1.98, 1.6, 0.3, 1.53, 1.95, 0.95, 3.33, 495}, Response: []float64{0, 1, 0}},
{Input: []float64{12.42, 1.61, 2.19, 22.5, 108, 2, 2.09, 0.34, 1.61, 2.06, 1.06, 2.96, 345}, Response: []float64{0, 1, 0}},
{Input: []float64{12.77, 3.43, 1.98, 16, 80, 1.63, 1.25, 0.43, 0.83, 3.4, 0.7, 2.12, 372}, Response: []float64{0, 1, 0}},
{Input: []float64{12, 3.43, 2, 19, 87, 2, 1.64, 0.37, 1.87, 1.28, 0.93, 3.05, 564}, Response: []float64{0, 1, 0}},
{Input: []float64{11.45, 2.4, 2.42, 20, 96, 2.9, 2.79, 0.32, 1.83, 3.25, 0.8, 3.39, 625}, Response: []float64{0, 1, 0}},
{Input: []float64{11.56, 2.05, 3.23, 28.5, 119, 3.18, 5.08, 0.47, 1.87, 6, 0.93, 3.69, 465}, Response: []float64{0, 1, 0}},
{Input: []float64{12.42, 4.43, 2.73, 26.5, 102, 2.2, 2.13, 0.43, 1.71, 2.08, 0.92, 3.12, 365}, Response: []float64{0, 1, 0}},
{Input: []float64{13.05, 5.8, 2.13, 21.5, 86, 2.62, 2.65, 0.3, 2.01, 2.6, 0.73, 3.1, 380}, Response: []float64{0, 1, 0}},
{Input: []float64{11.87, 4.31, 2.39, 21, 82, 2.86, 3.03, 0.21, 2.91, 2.8, 0.75, 3.64, 380}, Response: []float64{0, 1, 0}},
{Input: []float64{12.07, 2.16, 2.17, 21, 85, 2.6, 2.65, 0.37, 1.35, 2.76, 0.86, 3.28, 378}, Response: []float64{0, 1, 0}},
{Input: []float64{12.43, 1.53, 2.29, 21.5, 86, 2.74, 3.15, 0.39, 1.77, 3.94, 0.69, 2.84, 352}, Response: []float64{0, 1, 0}},
{Input: []float64{11.79, 2.13, 2.78, 28.5, 92, 2.13, 2.24, 0.58, 1.76, 3, 0.97, 2.44, 466}, Response: []float64{0, 1, 0}},
{Input: []float64{12.37, 1.63, 2.3, 24.5, 88, 2.22, 2.45, 0.4, 1.9, 2.12, 0.89, 2.78, 342}, Response: []float64{0, 1, 0}},
{Input: []float64{12.04, 4.3, 2.38, 22, 80, 2.1, 1.75, 0.42, 1.35, 2.6, 0.79, 2.57, 580}, Response: []float64{0, 1, 0}},
{Input: []float64{12.86, 1.35, 2.32, 18, 122, 1.51, 1.25, 0.21, 0.94, 4.1, 0.76, 1.29, 630}, Response: []float64{1, 0, 0}},
{Input: []float64{12.88, 2.99, 2.4, 20, 104, 1.3, 1.22, 0.24, 0.83, 5.4, 0.74, 1.42, 530}, Response: []float64{1, 0, 0}},
{Input: []float64{12.81, 2.31, 2.4, 24, 98, 1.15, 1.09, 0.27, 0.83, 5.7, 0.66, 1.36, 560}, Response: []float64{1, 0, 0}},
{Input: []float64{12.7, 3.55, 2.36, 21.5, 106, 1.7, 1.2, 0.17, 0.84, 5, 0.78, 1.29, 600}, Response: []float64{1, 0, 0}},
{Input: []float64{12.51, 1.24, 2.25, 17.5, 85, 2, 0.58, 0.6, 1.25, 5.45, 0.75, 1.51, 650}, Response: []float64{1, 0, 0}},
{Input: []float64{12.6, 2.46, 2.2, 18.5, 94, 1.62, 0.66, 0.63, 0.94, 7.1, 0.73, 1.58, 695}, Response: []float64{1, 0, 0}},
{Input: []float64{12.25, 4.72, 2.54, 21, 89, 1.38, 0.47, 0.53, 0.8, 3.85, 0.75, 1.27, 720}, Response: []float64{1, 0, 0}},
{Input: []float64{12.53, 5.51, 2.64, 25, 96, 1.79, 0.6, 0.63, 1.1, 5, 0.82, 1.69, 515}, Response: []float64{1, 0, 0}},
{Input: []float64{13.49, 3.59, 2.19, 19.5, 88, 1.62, 0.48, 0.58, 0.88, 5.7, 0.81, 1.82, 580}, Response: []float64{1, 0, 0}},
{Input: []float64{12.84, 2.96, 2.61, 24, 101, 2.32, 0.6, 0.53, 0.81, 4.92, 0.89, 2.15, 590}, Response: []float64{1, 0, 0}},
{Input: []float64{12.93, 2.81, 2.7, 21, 96, 1.54, 0.5, 0.53, 0.75, 4.6, 0.77, 2.31, 600}, Response: []float64{1, 0, 0}},
{Input: []float64{13.36, 2.56, 2.35, 20, 89, 1.4, 0.5, 0.37, 0.64, 5.6, 0.7, 2.47, 780}, Response: []float64{1, 0, 0}},
{Input: []float64{13.52, 3.17, 2.72, 23.5, 97, 1.55, 0.52, 0.5, 0.55, 4.35, 0.89, 2.06, 520}, Response: []float64{1, 0, 0}},
{Input: []float64{13.62, 4.95, 2.35, 20, 92, 2, 0.8, 0.47, 1.02, 4.4, 0.91, 2.05, 550}, Response: []float64{1, 0, 0}},
{Input: []float64{12.25, 3.88, 2.2, 18.5, 112, 1.38, 0.78, 0.29, 1.14, 8.21, 0.65, 2, 855}, Response: []float64{1, 0, 0}},
{Input: []float64{13.16, 3.57, 2.15, 21, 102, 1.5, 0.55, 0.43, 1.3, 4, 0.6, 1.68, 830}, Response: []float64{1, 0, 0}},
{Input: []float64{13.88, 5.04, 2.23, 20, 80, 0.98, 0.34, 0.4, 0.68, 4.9, 0.58, 1.33, 415}, Response: []float64{1, 0, 0}},
{Input: []float64{12.87, 4.61, 2.48, 21.5, 86, 1.7, 0.65, 0.47, 0.86, 7.65, 0.54, 1.86, 625}, Response: []float64{1, 0, 0}},
{Input: []float64{13.32, 3.24, 2.38, 21.5, 92, 1.93, 0.76, 0.45, 1.25, 8.42, 0.55, 1.62, 650}, Response: []float64{1, 0, 0}},
{Input: []float64{13.08, 3.9, 2.36, 21.5, 113, 1.41, 1.39, 0.34, 1.14, 9.4, 0.57, 1.33, 550}, Response: []float64{1, 0, 0}},
{Input: []float64{13.5, 3.12, 2.62, 24, 123, 1.4, 1.57, 0.22, 1.25, 8.6, 0.59, 1.3, 500}, Response: []float64{1, 0, 0}},
{Input: []float64{12.79, 2.67, 2.48, 22, 112, 1.48, 1.36, 0.24, 1.26, 10.8, 0.48, 1.47, 480}, Response: []float64{1, 0, 0}},
{Input: []float64{13.11, 1.9, 2.75, 25.5, 116, 2.2, 1.28, 0.26, 1.56, 7.1, 0.61, 1.33, 425}, Response: []float64{1, 0, 0}},
{Input: []float64{13.23, 3.3, 2.28, 18.5, 98, 1.8, 0.83, 0.61, 1.87, 10.52, 0.56, 1.51, 675}, Response: []float64{1, 0, 0}},
{Input: []float64{12.58, 1.29, 2.1, 20, 103, 1.48, 0.58, 0.53, 1.4, 7.6, 0.58, 1.55, 640}, Response: []float64{1, 0, 0}},
{Input: []float64{13.17, 5.19, 2.32, 22, 93, 1.74, 0.63, 0.61, 1.55, 7.9, 0.6, 1.48, 725}, Response: []float64{1, 0, 0}},
{Input: []float64{13.84, 4.12, 2.38, 19.5, 89, 1.8, 0.83, 0.48, 1.56, 9.01, 0.57, 1.64, 480}, Response: []float64{1, 0, 0}},
{Input: []float64{12.45, 3.03, 2.64, 27, 97, 1.9, 0.58, 0.63, 1.14, 7.5, 0.67, 1.73, 880}, Response: []float64{1, 0, 0}},
{Input: []float64{14.34, 1.68, 2.7, 25, 98, 2.8, 1.31, 0.53, 2.7, 13, 0.57, 1.96, 660}, Response: []float64{1, 0, 0}},
{Input: []float64{13.48, 1.67, 2.64, 22.5, 89, 2.6, 1.1, 0.52, 2.29, 11.75, 0.57, 1.78, 620}, Response: []float64{1, 0, 0}},
{Input: []float64{12.36, 3.83, 2.38, 21, 88, 2.3, 0.92, 0.5, 1.04, 7.65, 0.56, 1.58, 520}, Response: []float64{1, 0, 0}},
{Input: []float64{13.69, 3.26, 2.54, 20, 107, 1.83, 0.56, 0.5, 0.8, 5.88, 0.96, 1.82, 680}, Response: []float64{1, 0, 0}},
{Input: []float64{12.85, 3.27, 2.58, 22, 106, 1.65, 0.6, 0.6, 0.96, 5.58, 0.87, 2.11, 570}, Response: []float64{1, 0, 0}},
{Input: []float64{12.96, 3.45, 2.35, 18.5, 106, 1.39, 0.7, 0.4, 0.94, 5.28, 0.68, 1.75, 675}, Response: []float64{1, 0, 0}},
{Input: []float64{13.78, 2.76, 2.3, 22, 90, 1.35, 0.68, 0.41, 1.03, 9.58, 0.7, 1.68, 615}, Response: []float64{1, 0, 0}},
{Input: []float64{13.73, 4.36, 2.26, 22.5, 88, 1.28, 0.47, 0.52, 1.15, 6.62, 0.78, 1.75, 520}, Response: []float64{1, 0, 0}},
{Input: []float64{13.45, 3.7, 2.6, 23, 111, 1.7, 0.92, 0.43, 1.46, 10.68, 0.85, 1.56, 695}, Response: []float64{1, 0, 0}},
{Input: []float64{12.82, 3.37, 2.3, 19.5, 88, 1.48, 0.66, 0.4, 0.97, 10.26, 0.72, 1.75, 685}, Response: []float64{1, 0, 0}},
{Input: []float64{13.58, 2.58, 2.69, 24.5, 105, 1.55, 0.84, 0.39, 1.54, 8.66, 0.74, 1.8, 750}, Response: []float64{1, 0, 0}},
{Input: []float64{13.4, 4.6, 2.86, 25, 112, 1.98, 0.96, 0.27, 1.11, 8.5, 0.67, 1.92, 630}, Response: []float64{1, 0, 0}},
{Input: []float64{12.2, 3.03, 2.32, 19, 96, 1.25, 0.49, 0.4, 0.73, 5.5, 0.66, 1.83, 510}, Response: []float64{1, 0, 0}},
{Input: []float64{12.77, 2.39, 2.28, 19.5, 86, 1.39, 0.51, 0.48, 0.64, 9.899999, 0.57, 1.63, 470}, Response: []float64{1, 0, 0}},
{Input: []float64{14.16, 2.51, 2.48, 20, 91, 1.68, 0.7, 0.44, 1.24, 9.7, 0.62, 1.71, 660}, Response: []float64{1, 0, 0}},
{Input: []float64{13.71, 5.65, 2.45, 20.5, 95, 1.68, 0.61, 0.52, 1.06, 7.7, 0.64, 1.74, 740}, Response: []float64{1, 0, 0}},
{Input: []float64{13.4, 3.91, 2.48, 23, 102, 1.8, 0.75, 0.43, 1.41, 7.3, 0.7, 1.56, 750}, Response: []float64{1, 0, 0}},
{Input: []float64{13.27, 4.28, 2.26, 20, 120, 1.59, 0.69, 0.43, 1.35, 10.2, 0.59, 1.56, 835}, Response: []float64{1, 0, 0}},
{Input: []float64{13.17, 2.59, 2.37, 20, 120, 1.65, 0.68, 0.53, 1.46, 9.3, 0.6, 1.62, 840}, Response: []float64{1, 0, 0}},
{Input: []float64{14.13, 4.1, 2.74, 24.5, 96, 2.05, 0.76, 0.56, 1.35, 9.2, 0.61, 1.6, 560}, Response: []float64{1, 0, 0}},
}
for i := range data {
deep.Standardize(data[i].Input)
// t.Log(data[i].Input[0], data[i].Response)
}
data.Shuffle()
fmt.Printf("have %d entries\n", len(data))
neural := deep.NewNeural(&deep.Config{
Inputs: len(data[0].Input),
Layout: []int{5, 3},
Activation: deep.ActivationSigmoid,
Mode: deep.ModeMultiClass,
Weight: deep.NewNormal(1, 0),
Bias: true,
})
// t.Log(neural.String())
//trainer := training.NewTrainer(training.NewSGD(0.005, 0.5, 1e-6, true), 50)
//trainer := training.NewBatchTrainer(training.NewSGD(0.005, 0.1, 0, true), 50, 300, 16)
//trainer := training.NewTrainer(training.NewAdam(0.3, 0), 50)
trainer := training.NewBatchTrainer(training.NewSGD(0.005, 0.1, 0, true), 50, 300, 16)
//data, heldout := data.Split(0.5)
trainer.Train(neural, data, data, 10000)
testData1 := []float64{13.48, 1.81, 2.41, 20.5, 100, 2.7, 2.98, .26, 1.86, 5.1, 1.04, 3.47, 920}
testData2 := []float64{12.37, 1.21, 2.56, 18.1, 98, 2.42, 2.65, .37, 2.08, 4.6, 1.19, 2.3, 678}
testData3 := []float64{14.13, 4.1, 2.74, 24.5, 96, 2.05, .76, .56, 1.35, 9.2, .61, 1.6, 560}
result1 := neural.Predict(testData1)
result2 := neural.Predict(testData2)
result3 := neural.Predict(testData3)
t.Log(result1)
t.Log(result2)
t.Log(result3)
}Output
- 1
godeep_wine_test.go:235: [0.04492230621063009 0.06510368089682408 0.8899740128925459]
godeep_wine_test.go:236: [0.04492230621063009 0.06510368089682408 0.8899740128925459]
godeep_wine_test.go:237: [0.5875463337465087 0.24358921339021378 0.1688644528632776]
- 2
godeep_wine_test.go:235: [6.005836072059106e-07 4.123962150664048e-06 0.9999952754542423]
godeep_wine_test.go:236: [0.7439214548586517 0.19263133364316964 0.06344721149817864]
godeep_wine_test.go:237: [0.7439214548588947 0.192631333643111 0.06344721149799419]
- 3
godeep_wine_test.go:235: [2.03594829215657e-06 6.759486831585679e-06 0.9999912045648762]
godeep_wine_test.go:236: [0.6203063056779012 0.31067966990984386 0.06901402441225492]
godeep_wine_test.go:237: [0.6203063056779012 0.31067966990984386 0.06901402441225492]
Support printer with interface
Would be great to support passing in an interface for the printer that the batch trainer uses, in order to implement custom stopping depending on loss. So loss would need to be part of the interface signature in numeric form.
Example showing errors
Hi, the example in the readme is giving errors:
// params: learning rate, momentum, alpha decay, nesterov
optimizer := training.NewSGD(0.05, 0.1, 1e-6, true)
// params: optimizer, verbosity (print stats at every 50th iteration)
trainer := training.NewTrainer(optimizer, 50)
Cannot use optimizer as (type *SGD) as type Solver
can we add a user defined loss functions to our model?
Is there any way to use the user-defined loss functions so that the model can be trained based on the loss from these defined functions? or am i missing something?
Using interfaces for activation and loss functions.
Currently there are a few Activation/Loss functions which are provided in go-deep. While internally these functions follow an interface, this is not completely exposed to the clients of this library. For example, If I would like to experiment with a new activation function I would not be able to without modifying the code in go-deep.
If we can expose the ability for clients of this library to provide their own activation functions by providing types that implement the Differentiable and Loss interfaces, I think the flexibility of this library will be greatly increased.
If this is something that sounds reasonable, I would be more than happy to take a crack at implementing this and submitting a merge request.
Train history feature
I'm using the library for some project, and I'm finding out how useful a training history feature is useful (as you can find in any soa library in python). Is there any plan for implement it ?
In any case, I'm starting a new one.
Save Neural struct in Disk
Hello,
I am trying to save the Neural structure to disk using "encoding / gob" but it seems that it does not work. Am I doing something wrong or should I use something else?
thank you
Nesterov flag
Why does the Nesterov flag not affect the result of calculations?
/training/solver.go:
// Update returns the update for a given weight
func (o *SGD) Update(value, gradient float64, iteration, idx int) float64 {
lr := o.lr / (1 + o.decay*float64(iteration))
o.moments[idx] = o.momentum*o.moments[idx] - lr*gradient
if o.nesterov {
o.moments[idx] = o.momentum*o.moments[idx] - lr*gradient
}
return o.moments[idx]
}ReLU is not correct.
Hi!
There is a code in the activation.go:
func (a ReLU) F(x float64) float64 { return math.Max(x, 0) }
func (a ReLU) Df(y float64) float64 {
if y > 0 {
return 1
}
return 0
}This is no correct!
The right option:
func (a ReLU) F(x float64) float64 { return math.Max(x, 0) }
func (a ReLU) Df(y float64) float64 {
if y < 0 {
return 1
}
return 0
}For example: https://github.com/FluxML/NNlib.jl/blob/master/src/activations.jl
Q
Do you plan to develop the library further?
Thanks!
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