iris_example.rb in ruby-dnn-0.10.2

- old
+ new
@@ -1,34 +1,34 @@
-require "dnn"
-require "dnn/iris"
-# 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
-Iris = DNN::Iris
-
-x, y = Iris.load(true)
-x_train, y_train = x[0...100, true], y[0...100]
-x_test, y_test = x[100...150, true], y[100...150]
-
-x_train /= 255
-x_test /= 255
-
-y_train = DNN::Utils.to_categorical(y_train, 3, Numo::SFloat)
-y_test = DNN::Utils.to_categorical(y_test, 3, Numo::SFloat)
-
-model = Model.new
-
-model << InputLayer.new(4)
-
-model << Dense.new(64)
-model << ReLU.new
-
-model << Dense.new(3)
-
-model.compile(Adam.new, SoftmaxCrossEntropy.new)
-
-model.train(x_train, y_train, 1000, batch_size: 10, test: [x_test, y_test])
+require "dnn"
+require "dnn/iris"
+# 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
+Iris = DNN::Iris
+
+x, y = Iris.load(true)
+x_train, y_train = x[0...100, true], y[0...100]
+x_test, y_test = x[100...150, true], y[100...150]
+
+x_train /= 255
+x_test /= 255
+
+y_train = DNN::Utils.to_categorical(y_train, 3, Numo::SFloat)
+y_test = DNN::Utils.to_categorical(y_test, 3, Numo::SFloat)
+
+model = Model.new
+
+model << InputLayer.new(4)
+
+model << Dense.new(64)
+model << ReLU.new
+
+model << Dense.new(3)
+
+model.compile(Adam.new, SoftmaxCrossEntropy.new)
+
+model.train(x_train, y_train, 1000, batch_size: 10, test: [x_test, y_test])