Merge f27791b into 5ed6bb4

CONTRIBUTING.md

@@ -4,6 +4,10 @@ Contributions are welcome, and greatly appreciated! Every little bit helps, and
 We recommend you to check our [Developer Tools Guide](#developer-tools-guide) 
 to make the development process easier and standard.
 
+Notably, you can follow the tag of [call for contributors](https://github.com/jhfjhfj1/autokeras/labels/call%20for%20contributors) in the issues.
+Those issues are designed for the external contributors to solve.
+The pull requests solving these issues are most likely to be merged.
+
 There are many ways to contribute to Auto-Keras,
 including submit feedback, fix bugs, implement features, and write documentation.
 The guide for each type of contribution is as follows.

mkdocs/docs/start.md

@@ -18,32 +18,6 @@ You need to download the code from the GitHub repo and run the following command
     python setup.py install
 
 
-#### How to visualize the best selected architecture ?
-
-While trying to create a model, let's say an Image classifier on MNIST, there is a facility for the user to visualize a .PDF depiction of the best architecture that was chosen by autokeras, after model training is complete. 
-
-Prerequisites : 
-1) graphviz must be installed in your system. Refer [Installation Guide](https://graphviz.gitlab.io/download/)  
-2) Additionally, also install "graphviz" python package using pip / conda
-
-pip : pip install graphviz
-
-conda : conda install -c conda-forge python-graphviz
-
-If the above installations are complete, proceed with the following steps :
-
-Step 1 : Specify a *path* before starting your model training
-
-    clf = ImageClassifier(path="~/automodels/",verbose=True, augment=False) # Give a custom path of your choice
-    clf.fit(x_train, y_train, time_limit=30 * 60)
-    clf.final_fit(x_train, y_train, x_test, y_test, retrain=True)
-
-Step 2 : After the model training is complete, run *examples/visualize.py*, whilst passing the same *path* as parameter
-
-    if __name__ == '__main__':
-        visualize('~/automodels/')
-
-
 ## Example
 
 We show an example of image classification on the MNIST dataset, which is a famous benchmark image dataset for hand-written digits classification. Auto-Keras supports different types of data inputs. 
@@ -160,7 +134,6 @@ Step 2 : After the model training is complete, run *examples/visualize.py*, whil
         visualize('~/automodels/')
 
 
-
         
 # CnnModule tutorial