examples/iris_example.rb in ruby-dnn-1.2.3 vs examples/iris_example.rb in ruby-dnn-1.3.0

@@ -1,10 +1,11 @@
 require "dnn"
 require "dnn/datasets/iris"
 # 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, y = DNN::Iris.load(true)
@@ -12,46 +13,21 @@
 x_test, y_test = x[100...150, true], y[100...150]
 
 y_train = DNN::Utils.to_categorical(y_train, 3, Numo::SFloat)
 y_test = DNN::Utils.to_categorical(y_test, 3, Numo::SFloat)
 
-epochs = 1000
-batch_size = 32
+model = Sequential.new
 
-opt = Adam.new
-lf = SoftmaxCrossEntropy.new
+model << InputLayer.new(4)
 
-train_iter = DNN::Iterator.new(x_train, y_train)
-test_iter = DNN::Iterator.new(x_test, y_test, random: false)
+model << Dense.new(16)
+model << Sigmoid.new
 
-w1 = DNN::Param.new(Numo::SFloat.new(4, 16).rand_norm)
-b1 = DNN::Param.new(Numo::SFloat.zeros(16))
-w2 = DNN::Param.new(Numo::SFloat.new(16, 3).rand_norm)
-b2 = DNN::Param.new(Numo::SFloat.zeros(3))
+model << Dense.new(3)
 
-net = -> x, y do
-  h = Dot.(x, w1) + b1
-  h = Sigmoid.(h)
-  out = Dot.(h, w2) + b2
-  out
-end
+model.setup(Adam.new, SoftmaxCrossEntropy.new)
 
-(1..epochs).each do |epoch|
-  train_iter.foreach(batch_size) do |x_batch, y_batch, step|
-    x = DNN::Tensor.convert(x_batch)
-    y = DNN::Tensor.convert(y_batch)
-    out = net.(x, y)
-    loss = lf.(out, y)
-    loss.link.backward
-    puts "epoch: #{epoch}, step: #{step}, loss = #{loss.data.to_f}"
-    opt.update([w1, b1, w2, b2])
-  end
-end
+model.train(x_train, y_train, 1000, batch_size: 32, test: [x_test, y_test])
 
-correct = 0
-test_iter.foreach(batch_size) do |x_batch, y_batch, step|
-  x = DNN::Tensor.convert(x_batch)
-  y = DNN::Tensor.convert(y_batch)
-  out = net.(x, y)
-  correct += out.data.max_index(axis: 1).eq(y_batch.max_index(axis: 1)).count
-end
-puts "correct = #{correct}"
+accuracy, loss = model.evaluate(x_test, y_test)
+puts "accuracy: #{accuracy}"
+puts "loss: #{loss}"