correct assessor typo (#463)

correct assessor typos in several files.

README.md

@@ -104,7 +104,7 @@ You can use these commands to get more information about the experiment
 * [Run an experiment on multiple machines?](docs/tutorial_2_RemoteMachineMode.md)
 * [Run an experiment on OpenPAI?](docs/PAIMode.md)
 * [Run an experiment on Kubeflow?](docs/KubeflowMode.md)
-* [Try different tuners and assessors](docs/tutorial_3_tryTunersAndAccessors.md)
+* [Try different tuners and assessors](docs/tutorial_3_tryTunersAndAssessors.md)
 * [Implement a customized tuner](docs/howto_2_CustomizedTuner.md)
 * [Implement a customized assessor](examples/assessors/README.md)
 * [Use Genetic Algorithm to find good model architectures for Reading Comprehension task](examples/trials/ga_squad/README.md)

docs/RELEASE.md

@@ -86,10 +86,10 @@ Initial release of Neural Network Intelligence (NNI).
    * Installation and Deployment
       * Support pip install and source codes install
       * Support training services on local mode(including Multi-GPU mode) as well as multi-machines mode
-   * Tuners, Accessors and Trial
+   * Tuners, Assessors and Trial
       * Support AutoML algorithms including:  hyperopt_tpe, hyperopt_annealing, hyperopt_random, and evolution_tuner
       * Support assessor(early stop) algorithms including: medianstop algorithm
-      * Provide Python API for user defined tuners and accessors
+      * Provide Python API for user defined tuners and assessors
       * Provide Python API for user to wrap trial code as NNI deployable codes
    * Experiments
       * Provide a command line toolkit 'nnictl' for experiments management

docs/tutorial_3_tryTunersAndAccessors.md → docs/tutorial_3_tryTunersAndAssessors.md

-# Tutorial - Try different Tuners and Accessors
+# Tutorial - Try different Tuners and Assessors
 
-NNI provides an easy to adopt approach to set up parameter tuning algorithms as well as early stop policies, we call them **Tuners** and **Accessors**.
+NNI provides an easy to adopt approach to set up parameter tuning algorithms as well as early stop policies, we call them **Tuners** and **Assessors**.
   
 **Tuner** specifies the algorithm you use to generate hyperparameter sets for each trial. In NNI, we support two approaches to set the tuner. 
 1. Directly use tuner provided by nni sdk
@@ -18,17 +18,17 @@ NNI provides an easy to adopt approach to set up parameter tuning algorithms as
 
 
 **Assessor** specifies the algorithm you use to apply early stop policy. In NNI, there are two approaches to set theassessor. 
-1. Directly use accessor provided by nni sdk
+1. Directly use assessor provided by nni sdk
 
         required fields: builtinAssessorName and classArgs. 
 
-2. Customize your own tuner file
+2. Customize your own assessor file
 
         required fields: codeDirectory, classFileName, className and classArgs.
 
 ### **Learn More about assessor**
 * For detailed defintion and usage aobut the required field, please refer to [Config an experiment](ExperimentConfig.md)
-* Find more about the detailed instruction about [enable accessor](EnableAssessor.md)
+* Find more about the detailed instruction about [enable assessor](EnableAssessor.md)
 * [How to implement your own assessor](../examples/assessors/README.md)
 
 ## **Learn More**