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Add loading vanilla Tensorflow/Keras models
Add vanilla example Add logic to validate we have a genuine TFKG model in model.LoadMoodel Fix bug where inaccurate dataset len or an error were causing the callback.EventEnd not to fire on train/val/test
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13 files changed

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‎Makefile‎

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@@ -101,6 +101,20 @@ examples-transfer-raw:
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go generate ./...
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cd examples/transfer_learning && go run main.go
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examples-vanilla:
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go generate ./...
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docker-compose up -d tf-jupyter-golang
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docker-compose exec tf-jupyter-golang sh -c "cd /go/src/tfkg/examples/vanilla && python generate_vanilla_model.py && go run main.go"
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examples-vanilla-gpu:
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go generate ./...
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docker-compose up -d tf-jupyter-golang-gpu
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docker-compose exec tf-jupyter-golang-gpu sh -c "cd /go/src/tfkg/examples/vanilla && go run main.go"
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examples-vanilla-raw:
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go generate ./...
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cd examples/vanilla && go run main.go
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test-python:
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docker-compose up -d tf-jupyter-golang
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docker-compose exec tf-jupyter-golang sh -c "cd /go/src/tfkg && python test.py"

‎examples/iris/main.go‎

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@@ -168,6 +168,11 @@ func main() {
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},
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)
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m, e = model.LoadModel(errorHandler, logger, saveDir)
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if e != nil {
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return
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}
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logger.InfoF("main", "Finished training")
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// Create an inference provider, with a processor which will accept our input of [][]float32 and turn it into a tensor

‎examples/vanilla/data/iris.data‎

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5.1,3.5,1.4,0.2,0
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4.9,3.0,1.4,0.2,0
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4.7,3.2,1.3,0.2,0
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4.6,3.1,1.5,0.2,0
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5.0,3.6,1.4,0.2,0
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5.4,3.9,1.7,0.4,0
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4.6,3.4,1.4,0.3,0
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5.0,3.4,1.5,0.2,0
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4.4,2.9,1.4,0.2,0
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4.9,3.1,1.5,0.1,0
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5.4,3.7,1.5,0.2,0
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4.8,3.4,1.6,0.2,0
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4.8,3.0,1.4,0.1,0
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4.3,3.0,1.1,0.1,0
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5.8,4.0,1.2,0.2,0
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5.7,4.4,1.5,0.4,0
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5.4,3.9,1.3,0.4,0
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5.1,3.5,1.4,0.3,0
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5.7,3.8,1.7,0.3,0
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5.1,3.8,1.5,0.3,0
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5.4,3.4,1.7,0.2,0
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5.1,3.7,1.5,0.4,0
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4.6,3.6,1.0,0.2,0
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5.1,3.3,1.7,0.5,0
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4.8,3.4,1.9,0.2,0
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5.0,3.0,1.6,0.2,0
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5.0,3.4,1.6,0.4,0
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5.2,3.5,1.5,0.2,0
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5.2,3.4,1.4,0.2,0
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4.7,3.2,1.6,0.2,0
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4.8,3.1,1.6,0.2,0
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5.4,3.4,1.5,0.4,0
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5.2,4.1,1.5,0.1,0
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5.5,4.2,1.4,0.2,0
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4.9,3.1,1.5,0.1,0
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5.0,3.2,1.2,0.2,0
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5.5,3.5,1.3,0.2,0
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4.9,3.1,1.5,0.1,0
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4.4,3.0,1.3,0.2,0
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5.1,3.4,1.5,0.2,0
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5.0,3.5,1.3,0.3,0
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4.5,2.3,1.3,0.3,0
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4.4,3.2,1.3,0.2,0
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5.0,3.5,1.6,0.6,0
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5.1,3.8,1.9,0.4,0
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4.8,3.0,1.4,0.3,0
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5.1,3.8,1.6,0.2,0
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4.6,3.2,1.4,0.2,0
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5.3,3.7,1.5,0.2,0
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5.0,3.3,1.4,0.2,0
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7.0,3.2,4.7,1.4,1
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6.4,3.2,4.5,1.5,1
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6.9,3.1,4.9,1.5,1
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5.5,2.3,4.0,1.3,1
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6.5,2.8,4.6,1.5,1
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5.7,2.8,4.5,1.3,1
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6.3,3.3,4.7,1.6,1
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4.9,2.4,3.3,1.0,1
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6.6,2.9,4.6,1.3,1
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5.2,2.7,3.9,1.4,1
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5.0,2.0,3.5,1.0,1
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5.9,3.0,4.2,1.5,1
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6.0,2.2,4.0,1.0,1
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6.1,2.9,4.7,1.4,1
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5.6,2.9,3.6,1.3,1
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6.7,3.1,4.4,1.4,1
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5.6,3.0,4.5,1.5,1
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5.8,2.7,4.1,1.0,1
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6.2,2.2,4.5,1.5,1
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5.6,2.5,3.9,1.1,1
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5.9,3.2,4.8,1.8,1
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6.1,2.8,4.0,1.3,1
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6.3,2.5,4.9,1.5,1
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6.1,2.8,4.7,1.2,1
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6.4,2.9,4.3,1.3,1
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6.6,3.0,4.4,1.4,1
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6.8,2.8,4.8,1.4,1
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6.7,3.0,5.0,1.7,1
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6.0,2.9,4.5,1.5,1
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5.7,2.6,3.5,1.0,1
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5.5,2.4,3.8,1.1,1
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5.5,2.4,3.7,1.0,1
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5.8,2.7,3.9,1.2,1
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6.0,2.7,5.1,1.6,1
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5.4,3.0,4.5,1.5,1
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6.0,3.4,4.5,1.6,1
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6.7,3.1,4.7,1.5,1
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6.3,2.3,4.4,1.3,1
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5.6,3.0,4.1,1.3,1
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5.5,2.5,4.0,1.3,1
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5.5,2.6,4.4,1.2,1
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6.1,3.0,4.6,1.4,1
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5.8,2.6,4.0,1.2,1
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5.0,2.3,3.3,1.0,1
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5.6,2.7,4.2,1.3,1
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5.7,3.0,4.2,1.2,1
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5.7,2.9,4.2,1.3,1
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6.2,2.9,4.3,1.3,1
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5.1,2.5,3.0,1.1,1
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5.7,2.8,4.1,1.3,1
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6.3,3.3,6.0,2.5,2
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5.8,2.7,5.1,1.9,2
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7.1,3.0,5.9,2.1,2
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6.3,2.9,5.6,1.8,2
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6.5,3.0,5.8,2.2,2
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7.6,3.0,6.6,2.1,2
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4.9,2.5,4.5,1.7,2
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7.3,2.9,6.3,1.8,2
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6.7,2.5,5.8,1.8,2
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7.2,3.6,6.1,2.5,2
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6.5,3.2,5.1,2.0,2
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6.4,2.7,5.3,1.9,2
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6.8,3.0,5.5,2.1,2
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5.7,2.5,5.0,2.0,2
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5.8,2.8,5.1,2.4,2
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6.4,3.2,5.3,2.3,2
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6.5,3.0,5.5,1.8,2
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7.7,3.8,6.7,2.2,2
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7.7,2.6,6.9,2.3,2
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6.0,2.2,5.0,1.5,2
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6.9,3.2,5.7,2.3,2
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5.6,2.8,4.9,2.0,2
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7.7,2.8,6.7,2.0,2
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6.3,2.7,4.9,1.8,2
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6.7,3.3,5.7,2.1,2
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7.2,3.2,6.0,1.8,2
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6.2,2.8,4.8,1.8,2
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6.1,3.0,4.9,1.8,2
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6.4,2.8,5.6,2.1,2
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7.2,3.0,5.8,1.6,2
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7.4,2.8,6.1,1.9,2
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7.9,3.8,6.4,2.0,2
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6.4,2.8,5.6,2.2,2
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6.3,2.8,5.1,1.5,2
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6.1,2.6,5.6,1.4,2
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7.7,3.0,6.1,2.3,2
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6.3,3.4,5.6,2.4,2
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6.4,3.1,5.5,1.8,2
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6.0,3.0,4.8,1.8,2
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6.9,3.1,5.4,2.1,2
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6.7,3.1,5.6,2.4,2
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6.9,3.1,5.1,2.3,2
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5.8,2.7,5.1,1.9,2
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6.8,3.2,5.9,2.3,2
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6.7,3.3,5.7,2.5,2
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6.7,3.0,5.2,2.3,2
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6.3,2.5,5.0,1.9,2
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6.5,3.0,5.2,2.0,2
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6.2,3.4,5.4,2.3,2
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5.9,3.0,5.1,1.8,2
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‎examples/vanilla/data/iris.names‎

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1. Title: Iris Plants Database
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Updated Sept 21 by C.Blake - Added discrepency information
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2. Sources:
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(a) Creator: R.A. Fisher
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(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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(c) Date: July, 1988
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3. Past Usage:
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- Publications: too many to mention!!! Here are a few.
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1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
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to Mathematical Statistics" (John Wiley, NY, 1950).
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2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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-- Results:
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-- very low misclassification rates (0% for the setosa class)
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4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
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Transactions on Information Theory, May 1972, 431-433.
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-- Results:
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-- very low misclassification rates again
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5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
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conceptual clustering system finds 3 classes in the data.
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4. Relevant Information:
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--- This is perhaps the best known database to be found in the pattern
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recognition literature. Fisher's paper is a classic in the field
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and is referenced frequently to this day. (See Duda & Hart, for
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example.) The data set contains 3 classes of 50 instances each,
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where each class refers to a type of iris plant. One class is
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linearly separable from the other 2; the latter are NOT linearly
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separable from each other.
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--- Predicted attribute: class of iris plant.
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--- This is an exceedingly simple domain.
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--- This data differs from the data presented in Fishers article
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(identified by Steve Chadwick, spchadwick@espeedaz.net )
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The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
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where the error is in the fourth feature.
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The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
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where the errors are in the second and third features.
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5. Number of Instances: 150 (50 in each of three classes)
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6. Number of Attributes: 4 numeric, predictive attributes and the class
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7. Attribute Information:
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1. sepal length in cm
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2. sepal width in cm
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3. petal length in cm
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4. petal width in cm
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5. class:
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-- Iris Setosa
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-- Iris Versicolour
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-- Iris Virginica
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8. Missing Attribute Values: None
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Summary Statistics:
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Min Max Mean SD Class Correlation
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sepal length: 4.3 7.9 5.84 0.83 0.7826
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sepal width: 2.0 4.4 3.05 0.43 -0.4194
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petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
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petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
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9. Class Distribution: 33.3% for each of 3 classes.

‎examples/vanilla/generate_vanilla_model.py‎

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import os
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import tensorflow as tf
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import numpy as np
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csv_data = np.loadtxt('data/iris.data', delimiter=',')
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target_all = csv_data[:, -1]
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csv_data = csv_data[:, 0:-1]
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shuffled_indices = np.arange(csv_data.shape[0])
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np.random.shuffle(shuffled_indices)
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shuffled_inputs = csv_data[shuffled_indices]
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shuffled_targets = target_all[shuffled_indices]
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train_inputs = shuffled_inputs
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train_targets = shuffled_targets
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model = tf.keras.Sequential([
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tf.keras.layers.Input(shape=(4,), dtype=tf.float32),
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tf.keras.layers.Dense(10, activation="swish"),
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tf.keras.layers.Dense(10, activation="swish"),
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tf.keras.layers.Dense(3, activation="softmax")
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])
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model.compile(
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loss="sparse_categorical_crossentropy",
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optimizer="adam",
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metrics=['accuracy']
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)
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model.fit(
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train_inputs,
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train_targets,
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batch_size=3,
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epochs=10,
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verbose=0,
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)
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model.evaluate(
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train_inputs,
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train_targets,
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batch_size=3,
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verbose=1,
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)
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model.save("vanilla_model")

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