require "dnn" require "dnn/datasets/cifar10" # If you use numo/linalg then please uncomment out. # require "numo/linalg/autoloader" include DNN::Models include DNN::Layers include DNN::Optimizers include DNN::Losses x_train, y_train = DNN::CIFAR10.load_train x_test, y_test = DNN::CIFAR10.load_test x_train = Numo::SFloat.cast(x_train) / 255 x_test = Numo::SFloat.cast(x_test) / 255 y_train = DNN::Utils.to_categorical(y_train, 10, Numo::SFloat) y_test = DNN::Utils.to_categorical(y_test, 10, Numo::SFloat) model = Sequential.new model << InputLayer.new([32, 32, 3]) model << Conv2D.new(32, 3, padding: true) model << Dropout.new(0.25) model << ReLU.new model << Conv2D.new(32, 3, padding: true) model << BatchNormalization.new model << ReLU.new model << MaxPool2D.new(2) model << Conv2D.new(64, 3, padding: true) model << Dropout.new(0.25) model << ReLU.new model << Conv2D.new(64, 3, padding: true) model << BatchNormalization.new model << ReLU.new model << MaxPool2D.new(2) model << Conv2D.new(128, 3, padding: true) model << Dropout.new(0.25) model << ReLU.new model << Conv2D.new(128, 3, padding: true) model << BatchNormalization.new model << ReLU.new model << Flatten.new model << Dense.new(512) model << BatchNormalization.new model << ReLU.new model << Dense.new(10) model.setup(Adam.new, SoftmaxCrossEntropy.new) model.train(x_train, y_train, 10, batch_size: 128, test: [x_test, y_test]) accuracy, loss = model.evaluate(x_test, y_test) puts "accuracy: #{accuracy}" puts "loss: #{loss}"