Rumale::Torch provides the learning and inference by the neural network defined in torch.rb with the same interface as Rumale.
torch.rb is a runtime dependent gem of Rumale::Torch. It requires to install LibTorch:
$ brew install automake libtorch
Here, automake is needed to install rice gem, which torch.rb depends on.
Add this line to your application's Gemfile:
gem 'rumale-torch'And then execute:
$ bundle install
Or install it yourself as:
$ gem install rumale-torch
We start by downloading the pendigits dataset from LIBSVM Data web site.
$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/pendigits
$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/pendigits.tTraining phase:
require 'rumale'
require 'rumale/torch'
Torch.manual_seed(1)
device = Torch.device('cpu')
# Loading pendigits dataset consisting of
# 16-dimensional data divided into 10 classes.
x, y = Rumale::Dataset.load_libsvm_file('pendigits')
# Define a neural network in torch.rb framework.
class MyNet < Torch::NN::Module
def initialize
super
@dropout = Torch::NN::Dropout.new(p: 0.5)
@fc1 = Torch::NN::Linear.new(16, 128)
@fc2 = Torch::NN::Linear.new(128, 10)
end
def forward(x)
x = @fc1.call(x)
x = Torch::NN::F.relu(x)
x = @dropout.call(x)
x = @fc2.call(x)
Torch::NN::F.softmax(x)
end
end
net = MyNet.new.to(device)
# Create a classifier with neural network model.
classifier = Rumale::Torch::NeuralNetClassifier.new(
model: net, device: device,
batch_size: 10, max_epoch: 50, validation_split: 0.1,
verbose: true
)
# Learning classifier.
classifier.fit(x, y)
# Saving model.
Torch.save(net.state_dict, 'pendigits.pth')
File.binwrite('pendigits.dat', Marshal.dump(classifier))Testing phase:
require 'rumale'
require 'rumale/torch'
# Loading neural network model.
class MyNet < Torch::NN::Module
def initialize
super
@dropout = Torch::NN::Dropout.new(p: 0.5)
@fc1 = Torch::NN::Linear.new(16, 128)
@fc2 = Torch::NN::Linear.new(128, 10)
end
def forward(x)
x = @fc1.call(x)
x = Torch::NN::F.relu(x)
# x = @dropout.call(x)
x = @fc2.call(x)
Torch::NN::F.softmax(x)
end
end
net = MyNet.new
net.load_state_dict(Torch.load('pendigits.pth'))
# Loading classifier.
classifier = Marshal.load(File.binread('pendigits.dat'))
classifier.model = net
# Loading test dataset.
x_test, y_test = Rumale::Dataset.load_libsvm_file('pendigits.t')
# Predict labels of test data.
p_test = classifier.predict(x_test)
# Evaluate predicted result.
accuracy = Rumale::EvaluationMeasure::Accuracy.new.score(y_test, p_test)
puts(format("Accuracy: %2.1f%%", accuracy * 100))The result of executing the above scripts is as follows:
$ ruby train.rb
epoch: 1/50 - loss: 0.2073 - accuracy: 0.3885 - val_loss: 0.2074 - val_accuracy: 0.3853
epoch: 2/50 - loss: 0.1973 - accuracy: 0.4883 - val_loss: 0.1970 - val_accuracy: 0.4893
epoch: 3/50 - loss: 0.1962 - accuracy: 0.4997 - val_loss: 0.1959 - val_accuracy: 0.5013
...
epoch: 50/50 - loss: 0.1542 - accuracy: 0.9199 - val_loss: 0.1531 - val_accuracy: 0.9293
$ ruby test.rb
Accuracy: 91.2%Perform 5-fold cross-validation for regression problem using the housing dataset.
$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/housingThe example script:
require 'rumale'
require 'rumale/torch'
Torch.manual_seed(1)
device = Torch.device('cpu')
# Loading pendigits dataset consisting of
# 13-dimensional data with single target variable.
x, y = Rumale::Dataset.load_libsvm_file('housing')
# Define a neural network in torch.rb framework.
class MyNet < Torch::NN::Module
def initialize
super
@fc1 = Torch::NN::Linear.new(13, 128)
@fc2 = Torch::NN::Linear.new(128, 1)
end
def forward(x)
x = @fc1.call(x)
x = Torch::NN::F.relu(x)
x = @fc2.call(x)
end
end
net = MyNet.new.to(device)
# Create a regressor with neural network model.
regressor = Rumale::Torch::NeuralNetRegressor.new(
model: net, device: device, batch_size: 10, max_epoch: 100
)
# Create evaluation measure, splitting strategy, and cross validation.
ev = Rumale::EvaluationMeasure::R2Score.new
kf = Rumale::ModelSelection::ShuffleSplit.new(n_splits: 5, test_size: 0.1, random_seed: 1)
cv = Rumale::ModelSelection::CrossValidation.new(estimator: regressor, splitter: kf, evaluator: ev)
# Perform 5-cross validation.
report = cv.perform(x, y)
# Output result.
mean_score = report[:test_score].sum / kf.n_splits
puts(format("5-CV R2-score: %.3f", mean_score))The execution result is as follows:
$ ruby cv.rb
5-CV R2-score: 0.755Bug reports and pull requests are welcome on GitHub at https://github.com/yoshoku/rumale-torch. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.
The gem is available as open source under the terms of the BSD-3-Clause License.
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