SHAInet

SHAInet - stands for Super Human Artificial Intelligence network a neural network in pure Crystal

At the Roadmap you can see what we plan to add to the network as the project will progress.

Installation

Add this to your application's shard.yml:

dependencies:
  shainet:
    github: NeuraLegion/shainet

Usage

Standard training on XOR example

require "shainet"

training_data = [
  [[0, 0], [0]],
  [[1, 0], [1]],
  [[0, 1], [1]],
  [[1, 1], [0]],
]
# Initialize a new network
xor = SHAInet::Network.new
# Add a new layer of the input type with 2 neurons and classic neuron type (memory)
xor.add_layer(:input, 2, "memory", SHAInet.sigmoid)
# Add a new layer of the hidden type with 2 neurons and classic neuron type (memory)
xor.add_layer(:hidden, 2, "memory", SHAInet.sigmoid)
# Add a new layer of the output type with 1 neurons and classic neuron type (memory)
xor.add_layer(:output, 1, "memory", SHAInet.sigmoid)
# Fully connect the network layers
xor.fully_connect

# data, training_type, cost_function, activation_function, epochs, error_threshold (sum of errors), learning_rate, momentum)
xor.train(training_data, :sgdm, :mse, 10000, 0.001)

# Run the trained network
xor.run([0, 0])

Batch training on the iris dataset using adam

# Configure label encoding
label = {
  "setosa"     => [0.to_f64, 0.to_f64, 1.to_f64],
  "versicolor" => [0.to_f64, 1.to_f64, 0.to_f64],
  "virginica"  => [1.to_f64, 0.to_f64, 0.to_f64],
}
# Initiate a new network
iris = SHAInet::Network.new
iris.add_layer(:input, 4, "memory", SHAInet.sigmoid)
iris.add_layer(:hidden, 5, "memory", SHAInet.sigmoid)
iris.add_layer(:output, 3, "memory", SHAInet.sigmoid)
iris.fully_connect

# load all relevant information from the iris.csv
outputs = Array(Array(Float64)).new
inputs = Array(Array(Float64)).new
CSV.each_row(File.read(__DIR__ + "/test_data/iris.csv")) do |row|
  row_arr = Array(Float64).new
  row[0..-2].each do |num|
    row_arr << num.to_f64
  end
  inputs << row_arr
  outputs << label[row[-1]]
end
# Normalize using min_max
normalized = SHAInet::TrainingData.new(inputs, outputs)
normalized.normalize_min_max
# Train using rprop
iris.train_batch(normalized.data, :adam, :mse, 20000, 0.01)
iris.run(normalized.normalized_inputs.first)

Development

Basic Features

• [x] Add sgd,minibatch-update.
• [x] Add more activation functions.
• [ ] Add more cost functions.
• [x] Add more gradient optimizers
  • [x] Add iRprop+
  • [x] ADAM

Advanced Features

• [ ] Bind and use CUDA (GPU acceleration)
• [ ] graphic printout of network architecture.
• [ ] Add LSTM.
  • [ ] RNN.
• [ ] Convolutional Neural Net.
• [ ] GNG (growing neural gas).
• [ ] SOM (self organizing maps).
• [ ] DBM (deep belief network).
• [ ] Add support for multiple neuron types.

Contributing

1. Fork it ( https://github.com/NeuraLegion/shainet/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

Contributors

• ArtLinkov - creator, maintainer
• bararchy - creator, maintainer