Developer guide

Developer install

Clone the repository on your local machine:

```shell script $ git clone https://github.com/tonio73/dnnviewer.git

## Run

For local development, the entry point is the file `dnnviewer.py` in the root directory.

Model ZOO is in 

```shell script
$ dnnviewer.py --model-keras dnnviewer-data/models/MNIST_dense128.h5 --test-dataset mnist

Software architecture

The application is based on Dash for the user interface management (which is generating ReactJs components), and Plotly for the data visualization.

Code is separated in two branches:

• The Graphical representation
• Adapters to read existing models, currently only supporting Keras

Graphical representation

Top level class it the dnnviewer.Grapher. It holds a collection of layers based on the base class dnnviewer.layers.AbstractLayer.AbstractLayer

Bridge - adapters to load neural network representation

Within dnnviewer.bridge is the code to load a DNN model in Keras and extract the DNN topology, weights, compute the gradients and activations

Launching the application while developping

Preselect model and data

CIFAR-10 Convolutional neural network (at the beginning of training, single model)

$ dnnviewer --model-keras dnnviewer-data/models/CIFAR-10_CNN5-Reg.tf --test-dataset cifar10

MNIST Convolutional neural network based on LeNet5 (single model)

$ dnnviewer --model-keras dnnviewer-data/models/MNIST_LeNet60.h5 --test-dataset mnist

Fashion MNIST convolutionnal network (sequence of models)

$ dnnviewer --sequence-keras "dnnviewer-data/models/FashionMNIST_checkpoints/model1_{epoch}" --test-dataset fashion-mnist

Debug options

--debug is launching the Dash application in debug mode:

• The application is automatically reloaded when the code is saved
• The Dash debug tools are shown on screen (errors, callback dep graph)

--log-level DEBUG to get the debug messages displayed in the console log

Tooling

Code quality

• Code is PEP8 compliant, thanks to flake8 and Intellij for watching
• There are a few pytest unit tests in unittests/

Logging and exceptions

Logging is done using the standard Python logs

import logging

#...
def my_smart_function():
    logger = logging.getLogger(__name__)

    logger.error('Not smart enough')

Exceptions shall be used whenever the code execution shall be interrupted

Exceptions within the bridge package

Exceptions within the bridge package are based on the exception class bridge.AbstractModelSequence.ModelError

Exceptions within Dash callbacks

Dash callbacks are safeguarded, no need to add the try-except around the callback code.

To prevent update, the exception to raise is dash.exceptions.PreventUpdate

Run unit test

From project root directory:

```shell script $ python -m pytest

or, simpler:
```shell script
$ make tests

Code linting

```shell script $ make lint

### Create and upload package

First increase the version number in `setup.py`

Then:

```shell script
$ make package
$ make package_upload

Pypi.org credentials required

To force reinstall of the package (in case of minor version increase), do not forget the –no-deps: shell script $ pip install --force-reinstall --no-deps dnnviewer