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paulk pushed a commit to branch asf-site
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The following commit(s) were added to refs/heads/asf-site by this push:
new 3a023a3 general tidy up
3a023a3 is described below
commit 3a023a36449af2f1d51ba15743228478980ed4f8
Author: Paul King <[email protected]>
AuthorDate: Fri Feb 17 12:25:05 2023 +1000
general tidy up
---
.../blog/classifying-iris-flowers-with-deep.adoc | 24 ++++++++++++++++------
.../deep-learning-and-eclipse-collections.adoc | 20 ++++++++++--------
2 files changed, 30 insertions(+), 14 deletions(-)
diff --git a/site/src/site/blog/classifying-iris-flowers-with-deep.adoc
b/site/src/site/blog/classifying-iris-flowers-with-deep.adoc
index 1a4448c..51d05f5 100644
--- a/site/src/site/blog/classifying-iris-flowers-with-deep.adoc
+++ b/site/src/site/blog/classifying-iris-flowers-with-deep.adoc
@@ -340,12 +340,24 @@ user 0m0.096s
sys 0m0.029s
----
-We can see here that the speed has dramatically increased. This is great, but
we should note, that using GraalVM often involves some tricky investigation
especially for Groovy which by default has its dynamic nature. There are a few
features of Groovy which won't be available when using Groovy's static nature
and some libraries might be problematical. As an example, Deep Netts has log4j2
as one of its dependencies. At the time of writing, there are still issues
using log4j2 with GraalVM. [...]
-
-(*Update*: I put the Deep Netts GraalVM _iris_ application with some more
detailed instructions into its own
https://github.com/paulk-asert/groovy-data-science/tree/master/subprojects/IrisGraalVM[subproject].)
+We can see here that the speed has dramatically increased. This is great,
+but we should note, that using GraalVM often involves some tricky
+investigation especially for Groovy which by default has its dynamic
+nature. There are a few features of Groovy which won't be available
+when using Groovy's static nature and some libraries might be
+problematical. As an example, Deep Netts has log4j2 as one of its
+dependencies. At the time of writing, there are still issues using
+log4j2 with GraalVM. We excluded the `log4j-core` dependency and used
+`log4j-to-slf4j` backed by `logback-classic` to sidestep this problem.
== Conclusion
-We have seen a few different libraries for performing deep learning
classification using Groovy.
-Each has its own strengths and weaknesses.
-There are certainly options to cater for folks wanting blinding fast startup
speeds through to options which scale to massive computing farms in the cloud.
\ No newline at end of file
+We have seen a few different libraries for performing deep learning
classification
+using Groovy. Each has its own strengths and weaknesses.
+There are certainly options to cater for folks wanting blinding fast startup
speeds
+through to options which scale to massive computing farms in the cloud.
+
+.Update history
+****
+*27/Sep/2022*: I put the Deep Netts GraalVM _iris_ application with some more
detailed instructions into its own
https://github.com/paulk-asert/groovy-data-science/tree/master/subprojects/IrisGraalVM[subproject].
+****
diff --git a/site/src/site/blog/deep-learning-and-eclipse-collections.adoc
b/site/src/site/blog/deep-learning-and-eclipse-collections.adoc
index c0c149a..6a9d944 100644
--- a/site/src/site/blog/deep-learning-and-eclipse-collections.adoc
+++ b/site/src/site/blog/deep-learning-and-eclipse-collections.adoc
@@ -11,6 +11,8 @@ Recently, a couple of the highly recommended katas for
Eclipse Collections have
revamped to include "pet" and "fruit" emojis for a bit of extra fun. What
could be better
than _Learning_ Eclipse Collections?_Deep Learning_ and Eclipse Collections of
course!
+== Setting up our model and data
+
First, we create a `PetType` enum with the emoji as `toString`:
[source,groovy]
@@ -82,7 +84,9 @@ assert counts == expected
As we expect, it passes.
-Now, for a bit of fun, we will use a neural network trained to detect cat and
dog images and apply it to our emojis. We'll follow the process described
http://ramok.tech/2018/01/03/java-image-cat-vs-dog-recognizer-with-deep-neural-networks/[here].
It uses DeepLearning4J to train and then use a model. The images used to train
the model were real cat and dog images, not emojis, so we aren't expecting our
model to be super accurate.
+== Applying deep learning
+
+Now, for a bit of fun, we will use a neural network trained to detect cat and
dog images and apply it to our emojis. We'll follow the process described
http://ramok.tech/2018/01/03/java-image-cat-vs-dog-recognizer-with-deep-neural-networks/[here].
It uses https://deeplearning4j.konduit.ai/[Eclipse DeepLearning4j] to train
and then use a model. The images used to train the model were real cat and dog
images, not emojis, so we aren't expecting our model to be super accurate.
The first attempt was to write the emojis into swing JLabel components and
then save using a buffered image. This lead to poor looking images:
@@ -122,15 +126,15 @@ Note that the model exceeds the maximum allowable size
for normal GitHub repos,
When we run the script, we get the following output:
-[source]
+[subs="quotes"]
----
-[main] INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded [CpuBackend] backend
+[maroon]##[main] INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded
[CpuBackend] backend
...
[main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Blas
vendor: [OPENBLAS]
...
-==============================================================================================================
+==========================================================================================
VertexName (VertexType) nIn,nOut TotalParams
ParamsShape Vertex Inputs
-==============================================================================================================
+==========================================================================================
input_1 (InputVertex) -,- - -
-
block1_conv1 (Frozen ConvolutionLayer) 3,64 1792
b:{1,64}, W:{64,3,3,3} [input_1]
block1_conv2 (Frozen ConvolutionLayer) 64,64 36928
b:{1,64}, W:{64,64,3,3} [block1_conv1]
@@ -154,12 +158,12 @@ flatten (PreprocessorVertex) -,- -
-
fc1 (Frozen DenseLayer) 25088,4096 102764544
b:{1,4096}, W:{25088,4096} [flatten]
fc2 (Frozen DenseLayer) 4096,4096 16781312
b:{1,4096}, W:{4096,4096} [fc1]
predictions (OutputLayer) 4096,2 8194 b:{1,2},
W:{4096,2} [fc2]
---------------------------------------------------------------------------------------------------------------
+------------------------------------------------------------------------------------------
Total Parameters: 134268738
Trainable Parameters: 8194
Frozen Parameters: 134260544
-==============================================================================================================
-...
+==========================================================================================
+...##
Tabby is a cat
Dolly is a cat
Spot is a dog
