cifar-10
Requirements
These softwares must be installed on your local machine:
- Docker (with nvidia-docker2)
OR
- Conda virtual environment
Installation
- Clone this repository and change directory to it
- Copy
env.example
as.env
and edit environment variables - Build custom image:
docker build -t tensorflow/tensorflow:custom-gpu-py3 .
Usage
- To start tensorflow container:
nvidia-docker run -it -u $(id -u):$(id -g) -v "$(realpath ./)":/tf/ -p 6006:6006 --name tf tensorflow/tensorflow:custom-gpu-py3 bash
From the container bash you can execute the train script:
cd tf
python train.py
# All available hyperparameters will be prompted.
Available models are linear
, mlp
, cnn
, resnet
, lstm
Example
python train.py --model mlp --hidden-layers 2 6 8 --units 10 64 512 --activation relu sigmoid tanh --output-activation sigmoid softmax --lr 0.1 0.01 --epochs 10 50 100 --momentum 0.9 0.7 --batch-size 32 1024
It will train all model combinaison using Talos grid search. It will generate a csv file at the end.
Optional
- You can execute
tensorboard
:
docker exec -d tf tensorboard --logdir=./logs
- Browse http://localhost:6006