🔥 TensorFlow - the end-to-end machine learning platform - for Ruby
This gem is currently experimental and only supports basic tensor operations at the moment. Check out Torch.rb for a more complete deep learning library.
To run a TensorFlow model in Ruby, convert it to ONNX and use ONNX Runtime. Check out this tutorial for a full example.
Install TensorFlow. For Homebrew, use:
brew install libtensorflowAdd this line to your application’s Gemfile:
gem "tensorflow"This library follows the TensorFlow 2 Python API. Many methods and options are missing at the moment. Here’s the current plan. Additional PRs welcome!
a = Tf.constant([1, 2, 3]) b = Tf.constant([4, 5, 6]) a + bv = Tf::Variable.new(0.0) w = v + 1Tf::Math.abs([-1, -2]) Tf::Math.sqrt([1.0, 4.0, 9.0])def fizzbuzz(max_num) max_num.times do |i| num = Tf.constant(i + 1) if (num % 3).to_i == 0 && (num % 5).to_i == 0 puts "FizzBuzz" elsif (num % 3).to_i == 0 puts "Fizz" elsif (num % 5).to_i == 0 puts "Buzz" else puts num.to_i end end end fizzbuzz(15)# load train_dataset = Tf::Data::Dataset.from_tensor_slices([train_examples, train_labels]) test_dataset = Tf::Data::Dataset.from_tensor_slices([test_examples, test_labels]) # shuffle and batch train_dataset = train_dataset.shuffle(100).batch(32) test_dataset = test_dataset.batch(32) # iterate train_dataset.each do |examples, labels| # ... endmnist = Tf::Keras::Datasets::MNIST (x_train, y_train), (x_test, y_test) = mnist.load_data x_train = x_train / 255.0 x_test = x_test / 255.0 model = Tf::Keras::Models::Sequential.new([ Tf::Keras::Layers::Flatten.new(input_shape: [28, 28]), Tf::Keras::Layers::Dense.new(128, activation: "relu"), Tf::Keras::Layers::Dropout.new(0.2), Tf::Keras::Layers::Dense.new(10, activation: "softmax") ]) model.compile(optimizer: "adam", loss: "sparse_categorical_crossentropy", metrics: ["accuracy"]) model.fit(x_train, y_train, epochs: 5) model.evaluate(x_test, y_test)Run:
brew install tensorflowAlternatively, download the shared library and move the files in lib to /usr/local/lib.
Download the shared library and move the files in lib to /usr/local/lib.
Download the shared library and move tensorflow.dll to C:\Windows\System32.
View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/ankane/tensorflow-ruby.git cd tensorflow-ruby bundle install bundle exec rake test