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A library to build and train neural networks in with TensorFlow for Computer Vision

Project Description

[![Code Issues](](

A simple toolkit to easily apply image classification networks.

## Usage

Train a model:

$ python

Evaluate a model:

$ python

* --config=Examples/myconfig
* --name=myname

## Requirements

The following python moduls need to be installed:

* tensorflow 0.71
* numpy
* scipy
* PIL / pillow

You can do that by

$ pip install -r requirements.txt

## Development

First of all, you should install the additional development requirements:

$ pip install -r requirements-dev.txt

For development, you can avoid reinstalling the TensorVision package by adding
TensorVision to your PYTHONPATH. In my case, that is:

$ export PYTHONPATH="${PYTHONPATH}:/home/moose/GitHub/TensorVision/"

You can run the tests by

$ python test

## Workflow

### One-time stuff

* Create a for general configuration

### Each time

Each time you get a new task

#### Create JSON file

Create a json file (e.g. `cifar10_cnn.json`). It has at least the following

"model": {
"input_file": "examples/inputs/",
"architecture_file" : "examples/networks/",
"optimizer_file" : "examples/optimizer/"

#### Adjust input file

The `input_file` contains the path to a Python file. This Python file has to
have a function `inputs(hypes, q, phase, data_dir)`.

#### Adjust architecture file

The `architecture_file` contains the architecture of the network. It has to
have the following functions:

* `loss(H, logits, labels)`
* `inference(H, images, train=True)`
* `evaluation(H, logits, labels)`

#### Adjust the solver file

The `optimizer_file` contains the path to a Python file. This Python file has
to have a function `training(H, loss, global_step)`. It defines how one tries
to find a minimum of the loss function.

## Changelog

Release History

This version
History Node


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