cifar100_example.rb in ruby-dnn-0.10.2

- old
+ new
@@ -1,71 +1,71 @@
-require "dnn"
-require "dnn/cifar100"
-# 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
-CIFAR100 = DNN::CIFAR100
-
-x_train, y_train = CIFAR100.load_train
-x_test, y_test = CIFAR100.load_test
-
-x_train = Numo::SFloat.cast(x_train)
-x_test = Numo::SFloat.cast(x_test)
-
-x_train /= 255
-x_test /= 255
-
-y_train = y_train[true, 1]
-y_test = y_test[true, 1]
-
-y_train = DNN::Utils.to_categorical(y_train, 100, Numo::SFloat)
-y_test = DNN::Utils.to_categorical(y_test, 100, 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(1024)
-model << BatchNormalization.new
-model << ReLU.new
-model << Dropout.new(0.5)
-
-model << Dense.new(100)
-
-model.compile(Adam.new, SoftmaxCrossEntropy.new)
-
-model.train(x_train, y_train, 10, batch_size: 100, test: [x_test, y_test])
+require "dnn"
+require "dnn/cifar100"
+# 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
+CIFAR100 = DNN::CIFAR100
+
+x_train, y_train = CIFAR100.load_train
+x_test, y_test = CIFAR100.load_test
+
+x_train = Numo::SFloat.cast(x_train)
+x_test = Numo::SFloat.cast(x_test)
+
+x_train /= 255
+x_test /= 255
+
+y_train = y_train[true, 1]
+y_test = y_test[true, 1]
+
+y_train = DNN::Utils.to_categorical(y_train, 100, Numo::SFloat)
+y_test = DNN::Utils.to_categorical(y_test, 100, 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(1024)
+model << BatchNormalization.new
+model << ReLU.new
+model << Dropout.new(0.5)
+
+model << Dense.new(100)
+
+model.compile(Adam.new, SoftmaxCrossEntropy.new)
+
+model.train(x_train, y_train, 10, batch_size: 100, test: [x_test, y_test])