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Size: 1.54 KB
Versions: 5
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Stored size: 1.54 KB
Contents
require "dnn" require "dnn/datasets/cifar100" # If you use numo/linalg then please uncomment out. # require "numo/linalg/autoloader" include DNN::Layers include DNN::Activations include DNN::Optimizers include DNN::Losses include DNN::Models CIFAR100 = DNN::CIFAR100 x_train, y_train = CIFAR100.load_train x_test, y_test = CIFAR100.load_test x_train = Numo::SFloat.cast(x_train) x_test = Numo::SFloat.cast(x_test) x_train /= 255 x_test /= 255 y_train = y_train[true, 1] y_test = y_test[true, 1] y_train = DNN::Utils.to_categorical(y_train, 100, Numo::SFloat) y_test = DNN::Utils.to_categorical(y_test, 100, Numo::SFloat) model = Sequential.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(1024) model << BatchNormalization.new model << ReLU.new model << Dropout.new(0.5) model << Dense.new(100) model.setup(Adam.new, SoftmaxCrossEntropy.new) model.train(x_train, y_train, 10, batch_size: 100, test: [x_test, y_test])
Version data entries
5 entries across 5 versions & 1 rubygems