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.
Add this to your application's shard.yml:
dependencies:
shainet:
github: NeuraLegion/shainetStandard 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)- Add sgd,minibatch-update.
- Add more activation functions.
- Add more cost functions.
- Add more gradient optimizers
- Add iRprop+
- ADAM
- 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.
- Fork it ( https://github.com/NeuraLegion/shainet/fork )
- Create your feature branch (git checkout -b my-new-feature)
- Commit your changes (git commit -am 'Add some feature')
- Push to the branch (git push origin my-new-feature)
- Create a new Pull Request
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