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new b73846d update wayang tensorflow post to latest versions
b73846d is described below
commit b73846dde51cb1a90f92484c4cd35a4e9b4f4e10
Author: Paul King <[email protected]>
AuthorDate: Thu Aug 28 20:02:08 2025 +1000
update wayang tensorflow post to latest versions
---
site/src/site/blog/wayang-tensorflow.adoc | 36 +++++++++++++++++++++++--------
1 file changed, 27 insertions(+), 9 deletions(-)
diff --git a/site/src/site/blog/wayang-tensorflow.adoc
b/site/src/site/blog/wayang-tensorflow.adoc
index 61d7cb4..4818208 100644
--- a/site/src/site/blog/wayang-tensorflow.adoc
+++ b/site/src/site/blog/wayang-tensorflow.adoc
@@ -1,6 +1,7 @@
= Using TensorFlow from Apache Wayang
Paul King <paulk-asert|PMC_Member>
:revdate: 2025-02-28T09:30:00+00:00
+:updated: 2025-08-28T14:22:00+00:00
:keywords: groovy, wayang, iris, classification, apache wayang, tensorflow
:description: This post looks at using TensorFlow and Apache Wayang with
Apache Groovy.
@@ -98,6 +99,15 @@ The nodes can be activated by linear or non-linear functions.
image:img/deep_node.png[Neural net node,width=600]
+Let's first define our inputs:
+
+[source,groovy]
+----
+int[] noShape = null
+var features = new Input(noShape, Input.Type.FEATURES)
+var labels = new Input(noShape, Input.Type.LABEL, Op.DType.INT32)
+----
+
We'll have 4 inputs going to 32 hidden nodes to 3 outputs
with Sigmoid activation. These classes are all platform-agnostic.
Nowhere here do we mention TensorFlow or use any TensorFlow
@@ -106,7 +116,7 @@ classes.
[source,groovy]
----
Op l1 = new Linear(4, 32, true)
-Op s1 = new Sigmoid().with(l1.with(new Input(Input.Type.FEATURES)))
+Op s1 = new Sigmoid().with(l1.with(features))
Op l2 = new Linear(32, 3, true).with(s1)
DLModel model = new DLModel(l2)
----
@@ -116,20 +126,15 @@ providing some needed options, that will do our training.
[source,groovy]
----
-Op criterion = new CrossEntropyLoss(3).with(
- new Input(Input.Type.PREDICTED, Op.DType.FLOAT32),
- new Input(Input.Type.LABEL, Op.DType.INT32)
-)
+Op criterion = new CrossEntropyLoss(3).with(model.out, labels)
Optimizer optimizer = new Adam(0.1f) // optimizer with learning rate
int batchSize = 45
int epoch = 10
var option = new DLTrainingOperator.Option(criterion, optimizer, batchSize,
epoch)
option.setAccuracyCalculation(new Mean(0).with(
new Cast(Op.DType.FLOAT32).with(
- new Eq().with(new ArgMax(1).with(
- new Input(Input.Type.PREDICTED, Op.DType.FLOAT32)),
- new Input(Input.Type.LABEL, Op.DType.INT32)
-))))
+ new Eq().with(new ArgMax(1).with(model.out), labels)
+)))
var trainingOp = new DLTrainingOperator<>(model, option, float[], Integer)
----
@@ -174,6 +179,7 @@ new WayangContext().with {
execute(wayangPlan)
}
+println "labels: $LABEL_MAP"
println "predicted: $predicted"
println "ground truth: $groundTruth"
@@ -217,6 +223,7 @@ Start training:
[epoch 10, batch 3] loss: 0.318649 accuracy: 0.911111
Finish training.
+labels: [Iris-setosa:0, Iris-versicolor:1, Iris-virginica:2]
predicted: [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
ground truth: [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
test accuracy: 1
@@ -230,3 +237,14 @@ https://groovy.apache.org/[Apache Groovy]
and
https://wayang.apache.org/[Apache Wayang]!
Why not get involved!
+
+== More Information
+
+* Repo containing the source code: +
+https://github.com/paulk-asert/groovy-wayang-tensorflow
+
+.Update history
+****
+*28/Feb/2025*: Initial version. +
+*28/Aug/2025*: Updated for Apache Wayang 1.1.0 and Groovy 5.0.0.
+****
