Documentation listing supported layers for Keras, Caffe and TensorFlow

[Azamlynny]

[thatbrguy]

@Azamlynny That's a very neat documentation, good job! I have a few suggestions before we can approve this:

• Place the documentation in docs/source. That's where most of our documentation is.
• Link this documentation to our README.md file.
• Please open a new PR with the graph reset functionality that you added for your previous task. Looks like you closed the old one and did not create another PR for graph reset.

[Ram81]

@Azamlynny minor changes

[yashdusing]

Also, could you please specify how you've tested the layers and the procedure used. We will need to cross verify it once on our end as well before approving the PR. A code template would be helpful as well .

[thatbrguy]

@Azamlynny Please add a section for Data layers. I've just pasted the layers here for your reference:

[thatbrguy]

@Azamlynny Excellent work so far! That's a very neat documentation.

I've posted a review with some changes. @yashdusing Can you just verify if I have not missed something?

[yashdusing]

Hello @Azamlynny . Pretty good work so far. Few changes though (I've attached a screenshot completing the core layers) :

• follow the format as show in picture i.e. if layer is keras only or caffe only then mention it in bracket. If layer is keras only, then even tensorflow would support it through keras API so mention keras in tensorflow column. If layer is supported in all of the frameworks (for eg. dropout), keep it as blank (i.e. don't add in brackets). Also, please refer the documentations for the frameworks before marking it as done. While caffe does have activation layers, it does not have a separate activation layer like keras does and has a separate layer for each of them which are mentioned layer (relu, sigmoid, tanH, etc) so activation layer should be crossed in caffe column.
• Reference :

1. For caffe : http://caffe.berkeleyvision.org/tutorial/layers.html
2. For keras : https://keras.io/
3. For tensorflow 1.4 : https://github.com/tensorflow/docs/blob/r1.4/site/en/api_docs/api_docs/python/index.md
4. For Fabrik caffe : data.js
5. For Fabrik Keras : Fabrik's keras app's import, export files
6. For Fabrik Tensorflow : import_graphdef.py
   

(EDIT:

1. Also for layers having different names in caffe and keras, like inner product, mention Keras's layer name in Keras section (dense in this case)
2. ActivityRegularization is a keras only layer so caffe column should be crossed as well contrary to what is shown in image. )

[yashdusing]

@Ram81 @thatbrguy Please look into the format of the screenshot I posted and see if I missed something out or any changes are needed.

[thatbrguy]

@yashdusing I think we'll just mention the different names across platforms in brackets. (Inner-Product vs Dense). Let's not mention keras in the TensorFlow section for now. We'll have a separate task to deal with supported tensorflow APIs.

@Azamlynny Also, looks like inner-product is crossed out for Caffe. It's available on caffe so please modify that

[Azamlynny]

@yashdusing I think we'll just mention the different names across platforms in brackets. (Inner-Product vs Dense). Let's not mention keras in the TensorFlow section for now. We'll have a separate task to deal with supported tensorflow APIs.

I am not sure by what you mean by mention the different names across platforms in brackets. Where would you want this? In the Layer column, or in the actual Caffe and Keras columns and if in these locations what would the layer column be called?

[thatbrguy]

@Azamlynny Actually the table looks neater with just ticks. So, if you notice any layer available in Caffe as well as Keras or Tensorflow, with just different names (but mapped to the same Fabrik layer name) you can add it to the notes section after the table