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, Numo::SFloat)
y_test = DNN::Util.to_categorical(y_test, 10, Numo::SFloat)

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

2 entries across 2 versions & 1 rubygems

Version	Path
ruby-dnn-0.8.8	examples/cifar10_example.rb
ruby-dnn-0.8.7	examples/cifar10_example.rb