README.md in ruby-dnn-0.16.2 vs README.md in ruby-dnn-1.0.0

@@ -40,10 +40,15 @@
 model << Dense.new(10)
 
 model.setup(Adam.new, SoftmaxCrossEntropy.new)
 
 model.train(x_train, y_train, 10, batch_size: 128, test: [x_test, y_test])
+
+
+accuracy, loss = model.evaluate(x_test, y_test)
+puts "accuracy: #{accuracy}"
+puts "loss: #{loss}"
 ```
 
 When create a model with 'define by run' style:  
 
 ```ruby
@@ -69,10 +74,14 @@
 model = MLP.new
 
 model.setup(Adam.new, SoftmaxCrossEntropy.new)
 
 model.train(x_train, y_train, 10, batch_size: 128, test: [x_test, y_test])
+
+accuracy, loss = model.evaluate(x_test, y_test)
+puts "accuracy: #{accuracy}"
+puts "loss: #{loss}"
 ```
 
 Please refer to examples for basic usage.  
 If you want to know more detailed information, please refer to the source code.
 
@@ -83,9 +92,22 @@
 | Activations | Sigmoid, Tanh, Softsign, Softplus, Swish, ReLU, LeakyReLU, ELU, Mish |
 | Basic | Flatten, Reshape, Dropout, BatchNormalization |
 | Pooling | MaxPool2D, AvgPool2D, GlobalAvgPool2D, UnPool2D |
 | Optimizers | SGD, Nesterov, AdaGrad, RMSProp, AdaDelta, RMSPropGraves, Adam, AdaBound |
 | Losses | MeanSquaredError, MeanAbsoluteError, Hinge, HuberLoss, SoftmaxCrossEntropy, SigmoidCrossEntropy |
+
+## Datasets
+● Iris  
+● MNIST  
+● Fashion-MNIST  
+● CIFAR-10  
+● CIFAR-100  
+● STL-10
+
+## Examples
+● VAE  
+● DCGAN  
+● Pix2pix
 
 ## TODO
 ● Write a test.  
 ● Write a document.  
 ● Support to GPU.