examples/mnist_example.rb in ruby-dnn-0.10.1 vs examples/mnist_example.rb in ruby-dnn-0.10.2

@@ -1,39 +1,39 @@
-require "dnn"
-require "dnn/mnist"
-# 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
-Model = DNN::Model
-MNIST = DNN::MNIST
-
-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)
-
-model = Model.new
-
-model << InputLayer.new(784)
-
-model << Dense.new(256)
-model << ReLU.new
-
-model << Dense.new(256)
-model << ReLU.new
-
-model << Dense.new(10)
-
-model.compile(RMSProp.new, SoftmaxCrossEntropy.new)
-
-model.train(x_train, y_train, 10, batch_size: 100, test: [x_test, y_test])
+require "dnn"
+require "dnn/mnist"
+# 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
+Model = DNN::Model
+MNIST = DNN::MNIST
+
+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)
+
+model = Model.new
+
+model << InputLayer.new(784)
+
+model << Dense.new(256)
+model << ReLU.new
+
+model << Dense.new(256)
+model << ReLU.new
+
+model << Dense.new(10)
+
+model.compile(RMSProp.new, SoftmaxCrossEntropy.new)
+
+model.train(x_train, y_train, 10, batch_size: 100, test: [x_test, y_test])