Contents

require "dnn"
require "dnn/datasets/mnist"
# 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::MNIST.load_train
x_test, y_test = DNN::MNIST.load_test

x_train = x_train.reshape(x_train.shape[0], 784)
x_test = x_test.reshape(x_test.shape[0], 784)

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)

class MLP < Model
  def initialize
    super
    @d1 = Dense.new(256)
    @d2 = Dense.new(256)
    @d3 = Dense.new(10)
  end

  def forward(x)
    x = InputLayer.new(784).(x)
    x = @d1.(x)
    x = ReLU.(x)
    x = @d2.(x)
    x = ReLU.(x)
    x = @d3.(x)
    x
  end
end

model = MLP.new
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}"

Version data entries

7 entries across 7 versions & 1 rubygems

Version	Path
ruby-dnn-1.3.0	examples/mnist_define_by_run.rb
ruby-dnn-1.2.3	examples/mnist_define_by_run.rb
ruby-dnn-1.2.2	examples/mnist_define_by_run.rb
ruby-dnn-1.2.1	examples/mnist_define_by_run.rb
ruby-dnn-1.2.0	examples/mnist_define_by_run.rb
ruby-dnn-1.1.6	examples/mnist_define_by_run.rb
ruby-dnn-1.1.5	examples/mnist_define_by_run.rb