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https://github.com/neuralegion/shainet

SHAInet - a pure Crystal machine learning library
https://github.com/neuralegion/shainet
convolutional-neural-networks crystal deep-learning deep-neural-networks machine-learning neural-network
Last synced: 8 days ago
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SHAInet - a pure Crystal machine learning library
Host: GitHub
URL: https://github.com/neuralegion/shainet
Owner: NeuraLegion
License: mit
Created: 2017-12-06T15:25:15.000Z (about 7 years ago)
Default Branch: master
Last Pushed: 2023-01-11T12:13:47.000Z (almost 2 years ago)
Last Synced: 2024-12-18T19:07:15.598Z (8 days ago)
Topics: convolutional-neural-networks, crystal, deep-learning, deep-neural-networks, machine-learning, neural-network
Language: Crystal
Homepage:
Size: 25.9 MB
Stars: 184
Watchers: 31
Forks: 19
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

        


[![Crystal CI](https://github.com/NeuraLegion/shainet/actions/workflows/crystal.yml/badge.svg)](https://github.com/NeuraLegion/shainet/actions/workflows/crystal.yml)

SHAInet - stands for Super Human Artificial Intelligence network

a neural network in pure [Crystal](https://crystal-lang.org/)

This is a free-time project, happily hosted by NeuraLegion that was created as part of some internal research. We started it with research in mind, rather than production, and just kept going, also thanks to members of the community.

We wanted to try and implement some inspiration from the biological world into this project. In addition to that, we wanted to try an approach for NNs using object-oriented modeling instead of matrices. The main reason behind that was, to try new types of neurons aiming for more robust learning (if possible) or at least have more fine-tuned control over the manipulation of each neuron (which is difficult using a matrix-driven approach).

At the [Roadmap](https://github.com/NeuraLegion/shainet#development) 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`:

```yaml

dependencies:

  shainet:

    github: NeuraLegion/shainet

```

## Usage

More usage examples can be found in the specs

### Standard training on XOR example  

```crystal

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

# Adjust network parameters

xor.learning_rate = 0.7

xor.momentum = 0.3

# data, training_type, cost_function, activation_function, epochs, error_threshold (sum of errors), learning_rate, momentum)

xor.train(

      data: training_data,

      training_type: :sgdm,

      cost_function: :mse,

      epochs: 5000,

      error_threshold: 0.000001,

      log_each: 1000)

# Run the trained network

xor.run([0, 0])

```

### Batch training on the iris dataset using adam

```crystal

# Create a new Data object based on a CSV

data = SHAInet::Data.new_with_csv_input_target("iris.csv", 0..3, 4)

# Split the data in a training set and a test set

training_set, test_set = data.split(0.67)

# Initiate a new network

iris = SHAInet::Network.new

# Add layers

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

# Adjust network parameters

xor.learning_rate = 0.7

xor.momentum = 0.3

# Train the network

iris.train_batch(

      data: normalized.data.shuffle,

      training_type: :adam,

      cost_function: :mse,

      epochs: 20000,

      error_threshold: 0.000001,

      log_each: 1000)

# Test the network's performance

iris.test(test_set)

```

### Using convolutional network

```crystal

# Load training data (partial dataset)

raw_data = Array(Array(Float64)).new

csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_train.csv"))

10000.times do

  # CSV.each_row(File.read(__DIR__ + "/test_data/mnist_train.csv")) do |row|

  csv.next

  new_row = Array(Float64).new

  csv.row.to_a.each { |value| new_row << value.to_f64 }

  raw_data << new_row

end

raw_input_data = Array(Array(Float64)).new

raw_output_data = Array(Array(Float64)).new

raw_data.each do |row|

  raw_input_data << row[1..-1]

  raw_output_data << [row[0]]

end

training_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)

training_data.for_mnist_conv

training_data.data_pairs.shuffle!

# Load test data (partial dataset)

raw_data = Array(Array(Float64)).new

csv = CSV.new(File.read(__DIR__ + "/test_data/mnist_test.csv"))

1000.times do

  csv.next

  new_row = Array(Float64).new

  csv.row.to_a.each { |value| new_row << value.to_f64 }

  raw_data << new_row

end

raw_input_data = Array(Array(Float64)).new

raw_output_data = Array(Array(Float64)).new

raw_data.each do |row|

  raw_input_data << row[1..-1]

  raw_output_data << [row[0]]

end

# Load data to a CNNData helper class

test_data = SHAInet::CNNData.new(raw_input_data, raw_output_data)

test_data.for_mnist_conv # Normalize and make labels into 'one-hot' vectors

# Initialize Covnolutional network

cnn = SHAInet::CNN.new

# Add layers to the model

cnn.add_input([height = 28, width = 28, channels = 1]) # Output shape = 28x28x1

cnn.add_conv(

  filters_num: 20,

  window_size: 5,

  stride: 1,

  padding: 2,

  activation_function: SHAInet.none)  # Output shape = 28x28x20

cnn.add_relu(0.01)                    # Output shape = 28x28x20

cnn.add_maxpool(pool: = 2, stride = 2) # Output shape = 14x14x20

cnn.add_conv(

  filters_num: 20,

  window_size: 5,

  stride: 1,

  padding: 2,

  activation_function: SHAInet.none)  # Output shape = 14x14x40

cnn.add_maxpool(pool:2, stride: 2)    # Output shape = 7x7x40

cnn.add_fconnect(l_size: 10, activation_function: SHAInet.sigmoid)

cnn.add_fconnect(l_size: 10, activation_function: SHAInet.sigmoid)

cnn.add_softmax

cnn.learning_rate = 0.005

cnn.momentum = 0.02

# Train the model on the training-set

cnn.train_batch(

  data: training_data.data_pairs,

  training_type: :sgdm,

  cost_function: :mse,

  epochs: 3,

  error_threshold: 0.0001,

  log_each: 1,

  mini_batch_size: 50)

# Evaluate accuracy on the test-set

correct_answers = 0

test_data.data_pairs.each do |data_point|

  result = cnn.run(data_point[:input], stealth: true)

  if (result.index(result.max) == data_point[:output].index(data_point[:output].max))

    correct_answers += 1

  end

end

# Print the layer activations

cnn.inspect("activations")

puts "We managed #{correct_answers} out of #{test_data.data_pairs.size} total"

puts "Cnn output: #{cnn.output}"

```

### Evolutionary optimizer example:

```crystal

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],

    }

    iris = SHAInet::Network.new

    iris.add_layer(:input, 4, :memory, SHAInet.sigmoid)

    iris.add_layer(:hidden, 4, :memory, SHAInet.sigmoid)

    iris.add_layer(:output, 3, :memory, SHAInet.sigmoid)

    iris.fully_connect

    # Get data from a local file

    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

    data = SHAInet::TrainingData.new(inputs, outputs)

    data.normalize_min_max

    training_data, test_data = data.split(0.9)

    iris.train_es(

      data: training_data,

      pool_size: 50,

      learning_rate: 0.5,

      sigma: 0.1,

      cost_function: :c_ent,

      epochs: 500,

      mini_batch_size: 15,

      error_threshold: 0.00000001,

      log_each: 100,

      show_slice: true)

    # Test the trained model

    correct = 0

    test_data.data.each do |data_point|

      result = iris.run(data_point[0], stealth: true)

      expected = data_point[1]

      # puts "result: \t#{result.map { |x| x.round(5) }}"

      # puts "expected: \t#{expected}"

      error_sum = 0.0

      result.size.times do |i|

        error_sum += (result[i] - expected[i]).abs

      end

      correct += 1 if error_sum < 0.3

    end

    puts "Correct answers: (#{correct} / #{test_data.size})"

    (correct > 10).should eq(true)

```

## Development

### Basic Features  

  - [x] Train network

  - [x] Save/load

  - [x] Activation functions:

    - [x] Sigmoid

    - [x] Bipolar sigmoid

    - [x] log-sigmoid

    - [x] Tanh

    - [x] ReLU

    - [x] Leaky ReLU

    - [x] Softmax

  - [x] Cost functions:

    - [x] Quadratic

    - [x] Cross-entropy

  - [x] Gradient optimizers

    - [x] SGD + momentum

    - [x] iRprop+  

    - [x] ADAM

    - [x] ES (evolutionary strategy, non-backprop)

  - [x] Autosave during training

### Advanced Features

  - [x] Support activation functions as Proc

  - [x] Support cost functions as Proc

  - [x] Convolutional Neural Net.  

  - [ ] Add support for multiple neuron types.  

  - [ ] Bind and use CUDA (GPU acceleration)  

  - [ ] graphic printout of network architecture.  

  

### Possible Future Features

  - [ ] RNN (recurant neural network)

  - [ ] LSTM (long-short term memory)

  - [ ] GNG (growing neural gas).  

  - [ ] SOM (self organizing maps).  

  - [ ] DBM (deep belief network).  

## 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](https://github.com/ArtLinkov) - creator, maintainer

- [bararchy](https://github.com/bararchy) - creator, maintainer

- [drujensen](https://github.com/drujensen) - contributor

- [hugoabonizio](https://github.com/hugoabonizio) - contributor

- [Rémy Marronnier](https://github.com/rmarronnier) - contributor

- [psikoz](https://github.com/psikoz) - logo desgin