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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 include DNN::Callbacks MNIST = DNN::MNIST EPOCHS = 3 BATCH_SIZE = 128 x_train, y_train = MNIST.load_train x_test, y_test = MNIST.load_test x_train = Numo::SFloat.cast(x_train).reshape(x_train.shape[0], 784) x_test = Numo::SFloat.cast(x_test).reshape(x_test.shape[0], 784) x_train /= 255 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 @l1 = Dense.new(256) @l2 = Dense.new(256) @l3 = Dense.new(10) @bn1 = BatchNormalization.new @bn2 = BatchNormalization.new end def call(x) x = InputLayer.(x) x = @l1.(x) x = @bn1.(x) x = ReLU.(x) x = @l2.(x) x = @bn2.(x) x = ReLU.(x) x = @l3.(x) x end end model = MLP.new model.setup(Adam.new, SoftmaxCrossEntropy.new) # Add EarlyStopping callback for model. # This callback is stop the training when test accuracy is over 0.9. model.add_callback(EarlyStopping.new(:test_accuracy, 0.9)) model.train(x_train, y_train, EPOCHS, batch_size: BATCH_SIZE, test: [x_test, y_test])
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5 entries across 5 versions & 1 rubygems