🔥 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 libtensorflow
Add 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 + b
v = Tf::Variable.new(0.0)
w = v + 1
Tf::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|
# ...
end
mnist = 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 tensorflow
Alternatively, 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