A library to build and train neural networks in with TensorFlow for Computer Vision
Project description
[](https://www.quantifiedcode.com/app/project/3ef49e94e03a42b0bf896f5377c8e741)
[](https://github.com/
TensorVision/TensorVision/blob/master/LICENSE)
A simple toolkit to easily apply image classification networks.
## Usage
Train a model:
```bash
$ python train.py
```
Evaluate a model:
```bash
$ python eval.py
```
Flags:
* --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
```bash
$ pip install -r requirements.txt
```
## Development
First of all, you should install the additional development requirements:
```bash
$ 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:
```bash
$ export PYTHONPATH="${PYTHONPATH}:/home/moose/GitHub/TensorVision/"
```
You can run the tests by
```bash
$ python setup.py test
```
## Workflow
### One-time stuff
* Create a config.py 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
content:
```json
{
"model": {
"input_file": "examples/inputs/cifar10_input.py",
"architecture_file" : "examples/networks/cifar_net.py",
"optimizer_file" : "examples/optimizer/exp_decay.py"
}
}
```
#### 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
[](https://github.com/
TensorVision/TensorVision/blob/master/LICENSE)
A simple toolkit to easily apply image classification networks.
## Usage
Train a model:
```bash
$ python train.py
```
Evaluate a model:
```bash
$ python eval.py
```
Flags:
* --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
```bash
$ pip install -r requirements.txt
```
## Development
First of all, you should install the additional development requirements:
```bash
$ 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:
```bash
$ export PYTHONPATH="${PYTHONPATH}:/home/moose/GitHub/TensorVision/"
```
You can run the tests by
```bash
$ python setup.py test
```
## Workflow
### One-time stuff
* Create a config.py 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
content:
```json
{
"model": {
"input_file": "examples/inputs/cifar10_input.py",
"architecture_file" : "examples/networks/cifar_net.py",
"optimizer_file" : "examples/optimizer/exp_decay.py"
}
}
```
#### 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
