From f27791b3f89c508dbd26e10549db94841d951bec Mon Sep 17 00:00:00 2001 From: Haifeng Jin Date: Fri, 4 Jan 2019 13:20:33 -0600 Subject: [PATCH] docs --- CONTRIBUTING.md | 4 ++++ mkdocs/docs/start.md | 27 --------------------------- 2 files changed, 4 insertions(+), 27 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 671443fd0..28f792860 100644 --- a/CONTRIBUTING.md +++ b/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. diff --git a/mkdocs/docs/start.md b/mkdocs/docs/start.md index a571ebbf3..0f475411d 100644 --- a/mkdocs/docs/start.md +++ b/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