Artificial Intelligence for Crystal (based on https://github.com/SergioFierens/ai4r)

Add this to your application's shard.yml:

dependencies:
  ai4cr:
    github: drhuffman12/ai4cr
    branch: master

require "ai4cr"

So far, only Ai4cr::NeuralNetwork::Backpropagation and related tests have been ported.

NOTE: marshal_dump and marshal_load from ai4r have been replaced by to_json and from_json instead, e.g.:

# Create and save a net
net = Ai4cr::NeuralNetwork::Backpropagation.new(...)
File.write("../ai4cr_ui/db/seeds/BackpropagationNet.new.json",net.to_json)

# Train and save a net
net.train(some_input, expected_output)
File.write("../ai4cr_ui/db/seeds/BackpropagationNet.trained.json",net.to_json)

# Verify serialization in a spec
json = net.to_json
net2 = Ai4cr::NeuralNetwork::Backpropagation.from_json(json)
assert_approximate_equality_of_nested_list net.weights, net2.weights, 0.000000001

• Compiler

Use --release for more code optimizations during the compilation steps.

e.g.: time crystal spec --release

• Multithreading

Use the -Dpreview_mt (for crystal build or -D preview_mt for crystal spec) flag for multithreading.

e.g.:

# build:
time CRYSTAL_WORKERS=14 crystal build examples/rnn_simple_manager_example_relu.cr --release -D preview_mt
# run (and log to tmp folder):
time CRYSTAL_WORKERS=24 ./rnn_simple_manager_example_relu > tmp/log_relu.txt 2>&1

(Personally, as for how many CRYSTAL_WORKERS, I'd recommend keep it to less than the number of cores in your CPU, so that you leave at least one or two cores for the OS and apps.)

See also:

• https://crystal-lang.org/2019/09/23/crystal-0.31.0-released.html
• https://crystal-lang.org/2019/09/06/parallelism-in-crystal.html

• Shards:

rm -rf ~/.cache/shards/

• Compiler:

rm -rf ~/.cache/crystal/

REMINDER: Running Crystal in a Docker container (at least used to) runs slower than running Crystal on bare-metal. So, for more performance, run it outside of a Docker container.

To build and run them and see the debugging output (DEBUG=1):

DEBUG=1 crystal spec spec_bench

Example output:

To dig deeper into performance refinement:

crystal build --release src/mini_nets_vs_backprop
mkdir -p tmp/
valgrind --tool=callgrind --cache-sim=yes --branch-sim=yes --callgrind-out-file=tmp/mini_nets_vs_backprop.out ./mini_nets_vs_backprop

NOTE: Parsing to/from JSON can cause slight discrepancies in the float values. This difference shouldn't matter much, but in case you're interested:

Parsing Float64 values to/from JSON is sometimes slightly off, but only by about 0.0000000000000001%. This seems more likely when scientific notation gets involved, which could happen during the first training session, but becomes more likely the more times the net is trained.

For example, see below example code and note discrepancies after the "033410668390063" parts:

require "json"

# a = 6.033410668390063e-5
# a = 6.0334106683900631e-5
# a = 0.60334106683900634
a = 0.60334106683900631
      
b = a.to_json
c = JSON.parse(b)
puts "a,b,c == #{[a, b, c]}"
      
# GIVEN "a = 6.033410668390063e-5", puts => a == 6.033410668390063e-5; b == "6.033410668390063e-5"; c == 6.0334106683900634e-5 (off)
# GIVEN "a = 6.0334106683900631e-5", puts => a == 6.033410668390063e-5 (off); b == "6.033410668390063e-5" (off); c == 6.0334106683900634e-5 (off)
# GIVEN "a = 0.60334106683900634", puts => a == 0.6033410668390063 (off); b == "0.6033410668390063" (off); c == 0.6033410668390063 (off)
# GIVEN "a = 0.60334106683900631", puts => a == 0.6033410668390063 (off); b == "0.6033410668390063" (off); c == 0.6033410668390063 (off)

• Generate an error history plot using AsciiBarCharter and ErrorStats#history , e.g.:

plot: '▇▊▂_▅▅▅_▅_▅▅▅▅_▅▅__▅_▅____▅___'

(Run crystal spec spec_examples to see more error history plot examples. NOTE: These run short training sessions, so some tests are likely to fail some of the time.)

• Add Cmn ("Connectable Mini Networks") (WIP)

  • simple benchmark comparisons
  • Learning Styles
    • Pelu
    • Relu
    • Sigmoid
    • Tanh
  • MiniNet
    • MiniNetConcerns modules
    • JSON importable/exportable
    • can use various 'Learning Styles'
    • (?) move 'Learning Styles'-specific methods from MiniNet into Enum
  • misc Connectable Net Sets (WIP)
    • Chain
    • RNN
      • RnnSimple
      • Code to split and train on a 'sequence of input and output data'
    • (TBD)
    • ...
  • (TBD)
  • ...

• Port from ai4r:

  • classifiers
    • classifier.rb
    • hyperpipes.rb
    • ib1.rb
    • id3.rb
    • multilayer_perceptron.rb
    • naive_bayes.rb
    • one_r.rb
    • prism.rb
    • simple_linear_regression.rb
    • votes.rb
      • zero_r.rb
  • clusterers
    • average_linkage.rb
    • bisecting_k_means.rb
    • centroid_linkage.rb
    • clusterer.rb
    • complete_linkage.rb
    • diana.rb
    • k_means.rb
    • median_linkage.rb
    • single_linkage.rb
    • ward_linkage_hierarchical.rb
    • ward_linkage.rb
      • weighted_average_linkage.rb
  • data
    • data_set.rb
    • parameterizable.rb
    • proximity.rb
      • statistics.rb
  • experiment
    • classifier_evaluator.rb
  • genetic_algorithm
    • genetic_algorithm.rb
  • neural_network
    • backpropagation.rb
    • hopfield.rb
  • som
    • layer.rb
    • node.rb
    • som.rb
    • two_phase_layer.rb

If you'd like another class of Ai4r ported, feel free to submit a new issue.

1. Fork it ( https://github.com/drhuffman12/ai4cr/fork )
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

docker-compose run app scripts/reformat

# for a cleaner build:
docker-compose build --force-rm --no-cache --pull

# normally:
docker-compose build

docker-compose run app scripts/version_info

TODO: Find out why bin/ameba runs fine in CI but not locally.

docker-compose run app shards update

docker-compose run app crystal spec

docker-compose run app sh

NOTE: This isn't yet Crystal 1.0 compatible!

icr -r ./src/ai4cr

• The docs at: https://drhuffman12.github.io/ai4cr/

• The specs and https://github.com/SergioFierens/ai4r for more info.

• drhuffman12 Daniel Huffman - creator, maintainer

I included the triangle-square-cross training example as test cases.

As expected, the net sometimes correctly guesses all of the examples, more or less, depending on how many times it is trained and various other (random) factors.

Below is an example of the net successfully recognizing all nine test cases.

$ cd $MY_DEV_FOLDER

$ git clone https://github.com/drhuffman12/ai4cr.git

$ cd ai4cr

$ docker-compose build

For any tests that should (well, usually) NEVER fail (e.g.: in spite of sufficient training), put them into spec, and run them via:

$ docker-compose run app scripts/test_always
..............................

Finished in 4.01 milliseconds
30 examples, 0 failures, 0 errors, 0 pending
Execute: 00:00:00.010855717

For any tests that could fails sometimes (e.g.: if not trained enough), put them into spec_examples, and run them via:

$ docker-compose run app scripts/test_sometimes
.............

Finished in 6.76 seconds
16 examples, 0 failures, 0 errors, 0 pending
Execute: 00:00:06.769663359

NOTE: That time, it took less than a second to build. I did notice that it took about 10 seconds to build the first run and only less than a second each successive run.

TODO: Convert this to using the logger.

To see more debug info (all those extra puts commands) during testing, Set the DEBUG env variable to 1, e.g.

DEBUG=1 crystal spec