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Contents
require "dnn" require "dnn/lib/cifar10" #require "numo/linalg/autoloader" include DNN::Layers include DNN::Activations include DNN::Optimizers Model = DNN::Model CIFAR10 = DNN::CIFAR10 x_train, y_train = CIFAR10.load_train x_test, y_test = CIFAR10.load_test x_train = Numo::SFloat.cast(x_train) x_test = Numo::SFloat.cast(x_test) x_train /= 255 x_test /= 255 y_train = DNN::Util.to_categorical(y_train, 10) y_test = DNN::Util.to_categorical(y_test, 10) model = Model.new model << InputLayer.new([32, 32, 3]) model << Conv2D.new(16, 5, padding: true) model << BatchNormalization.new model << ReLU.new model << Conv2D.new(16, 5, padding: true) model << BatchNormalization.new model << ReLU.new model << MaxPool2D.new(2) model << Conv2D.new(32, 5, padding: true) model << BatchNormalization.new model << ReLU.new model << Conv2D.new(32, 5, padding: true) model << BatchNormalization.new model << ReLU.new model << MaxPool2D.new(2) model << Conv2D.new(64, 5, padding: true) model << BatchNormalization.new model << ReLU.new model << Conv2D.new(64, 5, padding: true) model << BatchNormalization.new model << ReLU.new model << Flatten.new model << Dense.new(512) model << BatchNormalization.new model << ReLU.new model << Dropout.new(0.5) model << Dense.new(10) model << SoftmaxWithLoss.new model.compile(Adam.new) model.train(x_train, y_train, 10, batch_size: 100, test: [x_test, y_test])
Version data entries
25 entries across 25 versions & 1 rubygems