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The following commit(s) were added to refs/heads/asf-site by this push:
new 19f1bce draft blog post (cont'd)
19f1bce is described below
commit 19f1bcecbb86e46543dec033ca2201959dfe2a96
Author: Paul King <[email protected]>
AuthorDate: Thu Feb 27 17:30:36 2025 +1000
draft blog post (cont'd)
---
site/src/site/blog/wayang-tensorflow.adoc | 75 +++++++++++++++++--------------
1 file changed, 41 insertions(+), 34 deletions(-)
diff --git a/site/src/site/blog/wayang-tensorflow.adoc
b/site/src/site/blog/wayang-tensorflow.adoc
index 935debb..f41fe65 100644
--- a/site/src/site/blog/wayang-tensorflow.adoc
+++ b/site/src/site/blog/wayang-tensorflow.adoc
@@ -35,30 +35,30 @@ Now we can define a helper method to convert from our test
and training CSV file
----
def fileOperation(URI uri, boolean random) {
var textFileSource = new TextFileSource(uri.toString()) // <1>
- var mapOperator = new MapOperator<>(line -> line.split(",").with{ // <2>
+ var line2tupleOp = new MapOperator<>(line -> line.split(",").with{ // <2>
new Tuple(it[0..-2]*.toFloat() as float[], LABEL_MAP[it[-1]]) },
String, Tuple)
- var mapX = new MapOperator<>(tuple -> (float[]) tuple.field0, Tuple,
float[]) // <3>
- var mapY = new MapOperator<>(tuple -> (Integer) tuple.field1, Tuple,
Integer) // <3>
+ var mapData = new MapOperator<>(tuple -> (float[]) tuple.field0, Tuple,
float[]) // <3>
+ var mapLabel = new MapOperator<>(tuple -> (Integer) tuple.field1, Tuple,
Integer) // <3>
if (random) {
Random r = new Random()
- var randomOperator = new SortOperator<>(e -> r.nextInt(), String,
Integer) // <4>
- textFileSource.connectTo(0, randomOperator, 0)
- randomOperator.connectTo(0, mapOperator, 0)
+ var randomOp = new SortOperator<>(e -> r.nextInt(), String, Integer)
// <4>
+ textFileSource.connectTo(0, randomOp, 0)
+ randomOp.connectTo(0, line2tupleOp, 0)
} else {
- textFileSource.connectTo(0, mapOperator, 0)
+ textFileSource.connectTo(0, line2tupleOp, 0)
}
- mapOperator.connectTo(0, mapX, 0)
- mapOperator.connectTo(0, mapY, 0)
+ line2tupleOp.connectTo(0, mapData, 0)
+ line2tupleOp.connectTo(0, mapLabel, 0)
- new Tuple<>(mapX, mapY)
+ new Tuple<>(mapData, mapLabel)
}
----
<1> `TextFileSource` converts a text file into lines
-<2> `mapOperator` converts a line into a Tuple containing our float[] data and
an Integer label
-<3> We also have `mapX` and `mapY` operators for
+<2> `line2tupleOp` converts a line into a Tuple containing our `float[]` data
in `field0` and an `Integer` label in `field1`
+<3> We also have `mapData` and `mapLabe` operators for
getting the two parts from our Tuple
<4> We can optionally randomly sort the incoming dataset
@@ -73,25 +73,29 @@ var trainSource = fileOperation(TRAIN_PATH, true)
var testSource = fileOperation(TEST_PATH, false)
----
-We can now write the rest of our script:
+We can now write the rest of our script.
+First, we'll define features and labels:
[source,groovy]
----
-/* labels & features */
-Operator trainXSource = trainSource.field0
-Operator trainYSource = trainSource.field1
-Operator testXSource = testSource.field0
-Operator testYSource = testSource.field1
+Operator trainData = trainSource.field0
+Operator trainLabel = trainSource.field1
+Operator testData = testSource.field0
+Operator testLabel = testSource.field1
+----
+
+We'll define a model.
-/* model */
+[source,groovy]
+----
Op l1 = new Linear(4, 32, true)
-Op s1 = new Sigmoid()
-Op l2 = new Linear(32, 3, true)
-s1.with(l1.with(new Input(Input.Type.FEATURES)))
-l2.with(s1)
+Op s1 = new Sigmoid().with(l1.with(new Input(Input.Type.FEATURES)))
+Op l2 = new Linear(32, 3, true).with(s1)
DLModel model = new DLModel(l2)
+----
-/* training options */
+[source,groovy]
+----
Op criterion = new CrossEntropyLoss(3).with(
new Input(Input.Type.PREDICTED, Op.DType.FLOAT32),
new Input(Input.Type.LABEL, Op.DType.INT32)
@@ -104,12 +108,15 @@ option.setAccuracyCalculation(new Mean(0).with(new
Cast(Op.DType.FLOAT32).with(n
new ArgMax(1).with(new Input(Input.Type.PREDICTED, Op.DType.FLOAT32)),
new Input(Input.Type.LABEL, Op.DType.INT32)
))))
+var trainingOp = new DLTrainingOperator<>(model, option, float[], Integer)
+----
-var trainingOperator = new DLTrainingOperator<>(model, option, float[],
Integer)
-var predictOperator = new PredictOperator<>(float[], float[])
+[source,groovy]
+----
+var predictOp = new PredictOperator<>(float[], float[])
/* map to label */
-var mapOperator = new MapOperator<>(array -> array.toList().indexed().max{
it.value }.key, float[], Integer)
+var bestFitOp = new MapOperator<>(array -> array.toList().indexed().max{
it.value }.key, float[], Integer)
/* sink */
var predicted = []
@@ -118,13 +125,13 @@ var predictedSink = createCollectingSink(predicted,
Integer)
var groundTruth = []
var groundTruthSink = createCollectingSink(groundTruth, Integer)
-trainXSource.connectTo(0, trainingOperator, 0)
-trainYSource.connectTo(0, trainingOperator, 1)
-trainingOperator.connectTo(0, predictOperator, 0)
-testXSource.connectTo(0, predictOperator, 1)
-predictOperator.connectTo(0, mapOperator, 0)
-mapOperator.connectTo(0, predictedSink, 0)
-testYSource.connectTo(0, groundTruthSink, 0)
+trainData.connectTo(0, trainingOp, 0)
+trainLabel.connectTo(0, trainingOp, 1)
+trainingOp.connectTo(0, predictOp, 0)
+testData.connectTo(0, predictOp, 1)
+predictOp.connectTo(0, bestFitOp, 0)
+bestFitOp.connectTo(0, predictedSink, 0)
+testLabel.connectTo(0, groundTruthSink, 0)
var wayangPlan = new WayangPlan(predictedSink, groundTruthSink)
