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paulk pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/groovy-website.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 5b982cd minor tweaks
5b982cd is described below
commit 5b982cdd94928292ab4906ea565586511d683af2
Author: Paul King <[email protected]>
AuthorDate: Fri Feb 28 04:16:23 2025 +1000
minor tweaks
---
site/src/site/blog/wayang-tensorflow.adoc | 14 ++++++++------
1 file changed, 8 insertions(+), 6 deletions(-)
diff --git a/site/src/site/blog/wayang-tensorflow.adoc
b/site/src/site/blog/wayang-tensorflow.adoc
index 8cb1d7f..0322e1c 100644
--- a/site/src/site/blog/wayang-tensorflow.adoc
+++ b/site/src/site/blog/wayang-tensorflow.adoc
@@ -14,10 +14,10 @@
https://groovy.apache.org/blog/classifying-iris-flowers-with-deep[Deep Learning
* Classifying iris flowers using the
https://groovy.apache.org/blog/groovy-oracle23ai[Oracle 23ai Vector data type]
-> image:https://www.apache.org/logos/res/wayang/default.png[wayang
logo,100,float="right"]
+> image:https://www.apache.org/logos/res/wayang/default.png[wayang
logo,120,float="right"]
> [blue]_Let's look at classifying iris flowers using Apache Wayang
> and TensorFlow with Groovy_
->
+>
We'll look at an implementation heavily based on the
Java test in the Apache Wayang
@@ -37,7 +37,8 @@ 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 line2tupleOp = new MapOperator<>(line -> line.split(",").with{ // <2>
- new Tuple(it[0..-2]*.toFloat() as float[], LABEL_MAP[it[-1]]) },
String, Tuple)
+ new Tuple(it[0..-2]*.toFloat() as float[], LABEL_MAP[it[-1]])
+ }, String, Tuple)
var mapData = new MapOperator<>(tuple -> (float[]) tuple.field0, Tuple,
float[]) // <3>
var mapLabel = new MapOperator<>(tuple -> (Integer) tuple.field1, Tuple,
Integer) // <3>
@@ -120,9 +121,10 @@ 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 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)
))))
var trainingOp = new DLTrainingOperator<>(model, option, float[], Integer)
----
