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new be2f1ee 2023/02/17 02:58:40: Generated dev website from
groovy-website@667f3f0
be2f1ee is described below
commit be2f1ee630b5d6e3600b65212ea5043c17fa6b67
Author: jenkins <[email protected]>
AuthorDate: Fri Feb 17 02:58:40 2023 +0000
2023/02/17 02:58:40: Generated dev website from groovy-website@667f3f0
---
blog/classifying-iris-flowers-with-deep.html | 22 ++++++++++-----------
blog/detecting-objects-with-groovy-the.html | 26 +++++++++++++++----------
blog/encryption-and-decryption-with-groovy.html | 8 ++++----
3 files changed, 31 insertions(+), 25 deletions(-)
diff --git a/blog/classifying-iris-flowers-with-deep.html
b/blog/classifying-iris-flowers-with-deep.html
index 2526cf5..08c33bd 100644
--- a/blog/classifying-iris-flowers-with-deep.html
+++ b/blog/classifying-iris-flowers-with-deep.html
@@ -175,7 +175,7 @@ println "Validation error: " +
pretty(calculateRegressionError(bestMethod, model
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code><strong
class="lime">paulk@pop-os</strong>:<strong
class="blue">/extra/projects/iris_encog</strong>$ time groovy -cp "build/lib/*"
IrisEncog.groovy
+<pre><span class="lime">paulk@pop-os</span>:<span
class="blue">/extra/projects/iris_encog</span>$ time groovy -cp "build/lib/*"
IrisEncog.groovy
1/5 : Fold #1
1/5 : Fold #1/5: Iteration #1, Training Error: 1.43550735, Validation Error:
0.73302237
1/5 : Fold #1/5: Iteration #2, Training Error: 0.78845427, Validation Error:
0.73302237
@@ -193,7 +193,7 @@ Confusion matrix: Iris-setosa
Iris-versicolor Iris-virginica
real 0m3.073s
user 0m9.973s
-sys 0m0.367s</code></pre>
+sys 0m0.367s</pre>
</div>
</div>
<div class="paragraph">
@@ -254,7 +254,7 @@ println eval.stats()</code></pre>
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code><strong
class="lime">paulk@pop-os</strong>:<strong
class="blue">/extra/projects/iris_encog</strong>$ time groovy -cp "build/lib/*"
IrisDl4j.groovy
+<pre><span class="lime">paulk@pop-os</span>:<span
class="blue">/extra/projects/iris_encog</span>$ time groovy -cp "build/lib/*"
IrisDl4j.groovy
[main] INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded [CpuBackend] backend
[main] INFO org.nd4j.nativeblas.NativeOpsHolder - Number of threads used for
linear algebra: 4
[main] INFO org.nd4j.nativeblas.Nd4jBlas - Number of threads used for OpenMP
BLAS: 4
@@ -285,7 +285,7 @@ Confusion matrix format: Actual (rowClass) predicted as
(columnClass) N times
real 0m5.856s
user 0m25.638s
-sys 0m1.752s</code></pre>
+sys 0m1.752s</pre>
</div>
</div>
<div class="paragraph">
@@ -358,7 +358,7 @@ new ClassifierEvaluator().with {
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code><strong
class="lime">paulk@pop-os</strong>:<strong
class="blue">/extra/projects/iris_encog</strong>$ time groovy -cp "build/lib/*"
Iris.groovy
+<pre><span class="lime">paulk@pop-os</span>:<span
class="blue">/extra/projects/iris_encog</span>$ time groovy -cp "build/lib/*"
Iris.groovy
16:49:27.089 [main] INFO deepnetts.core.DeepNetts -
------------------------------------------------------------------------
16:49:27.091 [main] INFO deepnetts.core.DeepNetts - TRAINING NEURAL NETWORK
16:49:27.091 [main] INFO deepnetts.core.DeepNetts -
------------------------------------------------------------------------
@@ -384,7 +384,7 @@ Iris-versicolor 0 0
18 1
real 0m3.160s
user 0m10.156s
-sys 0m0.483s</code></pre>
+sys 0m0.483s</pre>
</div>
</div>
<div class="paragraph">
@@ -413,7 +413,7 @@ We’ll compile it up using static mode:</p>
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code><strong
class="lime">paulk@pop-os</strong>:<strong
class="blue">/extra/projects/iris_encog</strong>$ groovyc -cp "build/lib/*"
--compile-static Iris.groovy</code></pre>
+<pre><span class="lime">paulk@pop-os</span>:<span
class="blue">/extra/projects/iris_encog</span>$ groovyc -cp "build/lib/*"
--compile-static Iris.groovy</pre>
</div>
</div>
<div class="paragraph">
@@ -421,9 +421,9 @@ We’ll compile it up using static mode:</p>
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code><strong
class="lime">paulk@pop-os</strong>:<strong
class="blue">/extra/projects/iris_encog</strong>$ native-image
--report-unsupported-elements-at-runtime \
+<pre><span class="lime">paulk@pop-os</span>:<span
class="blue">/extra/projects/iris_encog</span>$ native-image
--report-unsupported-elements-at-runtime \
--initialize-at-run-time=groovy.grape.GrapeIvy,deepnetts.net.weights.RandomWeights
\
- --initialize-at-build-time --no-fallback
-H:ConfigurationFileDirectories=conf/ -cp ".:build/lib/*" Iris</code></pre>
+ --initialize-at-build-time --no-fallback
-H:ConfigurationFileDirectories=conf/ -cp ".:build/lib/*" Iris</pre>
</div>
</div>
<div class="paragraph">
@@ -436,7 +436,7 @@ We also did the same for the <code>RandomWeights</code>
class to avoid it being
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code><strong
class="lime">paulk@pop-os</strong>:<strong
class="blue">/extra/projects/iris_encog</strong>$ time ./iris
+<pre><span class="lime">paulk@pop-os</span>:<span
class="blue">/extra/projects/iris_encog</span>$ time ./iris
...
CLASSIFIER EVALUATION METRICS
Accuracy: 0.93460923 (How often is classifier correct in total)
@@ -453,7 +453,7 @@ Iris-versicolor 0 0
20 2
real 0m0.131s
user 0m0.096s
-sys 0m0.029s</code></pre>
+sys 0m0.029s</pre>
</div>
</div>
<div class="paragraph">
diff --git a/blog/detecting-objects-with-groovy-the.html
b/blog/detecting-objects-with-groovy-the.html
index f330f62..dbc2e52 100644
--- a/blog/detecting-objects-with-groovy-the.html
+++ b/blog/detecting-objects-with-groovy-the.html
@@ -202,19 +202,21 @@ dependencies {
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code
data-lang="sh">paulk@pop-os:/extra/projects/groovy-data-science$ ./gradlew
DLMXNet:run
-> Task :DeepLearningMxnet:run
-Downloading: 100% |████████████████████████████████████████| dog-ssd.jpg
-Loading: 100% |████████████████████████████████████████|
+<pre><span class="lime">paulk@pop-os</span>:<span
class="blue">/extra/projects/groovy-data-science</span>$ ./gradlew DLMXNet:run
+> Task :DeepLearningMxnet:run
+Downloading: 100% |<span
class="olive">████████████████████████████████████████</span>| dog-ssd.jpg
+Loading: 100% |<span
class="olive">████████████████████████████████████████</span>|
...
-class: "car", probability: 0.99991, bounds: [x=0.611, y=0.137, width=0.293,
height=0.160]
-class: "bicycle", probability: 0.95385, bounds: [x=0.162, y=0.207,
width=0.594, height=0.588]
-class: "dog", probability: 0.93752, bounds: [x=0.168, y=0.350, width=0.274,
height=0.593]</code></pre>
+class: <strong class="green">"car"</strong>, probability:
<strong>0.99991</strong>, bounds: [x=0.611, y=0.137, width=0.293, height=0.160]
+class: <strong class="green">"bicycle"</strong>, probability:
<strong>0.95385</strong>, bounds: [x=0.162, y=0.207, width=0.594, height=0.588]
+class: <strong class="green">"dog"</strong>, probability:
<strong>0.93752</strong>, bounds: [x=0.168, y=0.350, width=0.274,
height=0.593]</pre>
</div>
</div>
<div class="paragraph">
-<p>The displayed image looks like this:
-<span class="image"><img src="img/detected_objects.png" alt="Detected objects"
width="468"></span></p>
+<p>The displayed image looks like this:</p>
+</div>
+<div class="paragraph">
+<p><span class="image"><img src="img/detected_objects.png" alt="Detected
objects" width="468"></span></p>
</div>
</div>
</div>
@@ -231,7 +233,11 @@ class: "dog", probability: 0.93752, bounds: [x=0.168,
y=0.350, width=0.274, heig
<h2 id="_conclusion">Conclusion</h2>
<div class="sectionbody">
<div class="paragraph">
-<p>We have examined using Apache Groovy, DLJ and Apache MXNet to detect
objects within an image. We’ve used a model based on a rich deep learning
model, but we didn’t need to get into the details of the model or its
neural network layers. DLJ and Apache MXNet did the hard lifting for us. Groovy
provided a simple coding experience for building our application.</p>
+<p>We have examined using Apache Groovy, DLJ and Apache MXNet to detect objects
+within an image. We’ve used a model based on a rich deep learning model,
but we
+didn’t need to get into the details of the model or its neural network
layers.
+DLJ and Apache MXNet did the hard lifting for us. Groovy provided a simple
coding
+experience for building our application.</p>
</div>
</div>
</div></div></div></div></div><footer id='footer'>
diff --git a/blog/encryption-and-decryption-with-groovy.html
b/blog/encryption-and-decryption-with-groovy.html
index d4341ff..90ddede 100644
--- a/blog/encryption-and-decryption-with-groovy.html
+++ b/blog/encryption-and-decryption-with-groovy.html
@@ -68,8 +68,8 @@
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code data-lang="groovy">var text =
'Contrary to popular belief, Lorem Ipsum is not simply random text.\
- It has roots in a piece of classical Latin literature from 45 BC, making it
over 2000 years old.'</code></pre>
+<pre class="prettyprint highlight"><code data-lang="groovy">var text =
'Contrary to popular belief, Lorem Ipsum is not simply random text. It has \
+roots in a piece of classical Latin literature from 45 BC, making it over 2000
years old.'</code></pre>
</div>
</div>
<div class="paragraph">
@@ -167,7 +167,7 @@ println "Decrypted text : ${new
String(decrypted)}"</code></pre>
<div class="listingblock">
<div class="content">
<pre class="prettyprint highlight"><code>Encrypted bytes : [-117, 36, 18, 69,
-101, -8, 35, 93, -102, -49, -12, …, -19, -100]
-Encrypted text :
‹$E›ø#]šÏôæ”Á˜çp^µ³=L(Ö^_ŒC&gt;CIË„ö,1É8ÆŸ.Š?vßG,Èw‰å¼zÜf&gt;?µ›D¹éÆk€
°˜2êÔ}í©àhl$&gt;?¹¡Kå3ÔO?±&amp;…êî¶Ê–¾°®q®à—0ú‘ÔhO&lt;H¦ç®Ç”ÈhAëjó
QPyƒy6Ĥ*´un¼ï¯m¨´ÙjeJtëº\ó6 ƪKªœíœ
+Encrypted text :
‹$E›ø#]šÏôæ”Á˜çp^µ³=L(Ö^_ŒC>CIË„ö,1É8ÆŸ.Š?vßG,Èw‰å¼zÜf>?µ›D¹éÆk€
°˜2êÔ}í©àhl$>?¹¡Kå3ÔO?±&…êî¶Ê–¾°®q®à—0ú‘ÔhO<H¦ç®Ç”ÈhAëjó
QPyƒy6Ĥ*´un¼ï¯m¨´ÙjeJtëº\ó6 ƪKªœíœ
Decrypted bytes : [67, 111, 110, 116, 114, 97, 114, 121, 32, 116, 111, 32, …,
100, 46]
Decrypted text : Contrary to popular belief, Lorem Ipsum is not simply random
text. It has roots in a piece of classical Latin literature from 45 BC, making
it over 2000 years old.</code></pre>
</div>
@@ -227,7 +227,7 @@ println "Decrypted text : ${new
String(decrypted)}"</code></pre>
</div>
<div class="listingblock">
<div class="content">
-<pre class="prettyprint highlight"><code>Encrypted text :
Mªá?r?v9£÷~4µT'›ÙÝÁl¿Þg¾0ñŽ¡?Ü=³9 Q¬»3«ÖÁ¡µ
¾@4÷`FñÙŠfø7¥#›v¤Í–‰¼Ü¢ƒE6ô ŽTÙlæÏz&gt;o?àL›¡¢z1nÖo9]šOÔ¼SÔOÍ#Ý7LœÀî}ó5m%q•»l%
/AWT´¢zH#t솱l¶£—Œ «©wˆÃ®&gt;®Ü6ër-E
+<pre class="prettyprint highlight"><code>Encrypted text :
Mªá?r?v9£÷~4µT'›ÙÝÁl¿Þg¾0ñŽ¡?Ü=³9 Q¬»3«ÖÁ¡µ
¾@4÷`FñÙŠfø7¥#›v¤Í–‰¼Ü¢ƒE6ô ŽTÙlæÏz>o?àL›¡¢z1nÖo9]šOÔ¼SÔOÍ#Ý7LœÀî}ó5m%q•»l%
/AWT´¢zH#t솱l¶£—Œ «©wˆÃ®>®Ü6ër-E
Decrypted text : Contrary to popular belief, Lorem Ipsum is not simply random
text. It has roots in a piece of classical Latin literature from 45 BC, making
it over 2000 years old.</code></pre>
</div>
</div>
