https://github.com/naetherm/karpathy_tensorflow
https://github.com/naetherm/karpathy_tensorflow
Last synced: 9 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/naetherm/karpathy_tensorflow
- Owner: naetherm
- License: mit
- Created: 2017-06-21T05:53:56.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-06-29T10:56:16.000Z (almost 9 years ago)
- Last Synced: 2025-03-27T18:19:00.771Z (about 1 year ago)
- Language: Python
- Size: 4.79 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# karpathy_tensorflow
Simple implementation of the kaparthy code (http://karpathy.github.io/2015/05/21/rnn-effectiveness/) in Tensorflow.
This implementation was mainly done for learning purposes and further deepening my understanding of recurrent neura networks with tensorflow.
## Installing Tensorflow
For installing Tensorflow just follow the instruction of the official homepage [1]. For this implementation the latest release of Tensorflow was used (version 1.2.0).
[1] https://www.tensorflow.org/install/
## Usage
Start training:
```bash
python train.py --datapath=shakespeare/
```
The default model to use is ```bash rnn``` but can be changed by the command line parameters.
```bash
python train.py --data_path=shakespeare/ --model=lstm
```
Available models are: ```bash rnn, lstm, gru and nas```.
After training a model you can run a sample on it:
```bash
python sample.py --data_path=shakespeare/
```
The length of the sample that should be created can be changed by the parameter ```bash --n```:
```bash
python sample.py --data_path=shakespeare/ --n=1000
```
## Further samples
The wikipedia ```train.txt``` can be obtained [here](https://mega.nz/#!Qa4z0DiL!BBNviuoNUN-awop3inFs_MyilijwLSy6O36-feGUMr8), and the linux ```train.txt``` can be obtained [here](https://mega.nz/#!9S5xhaIY!4_grlN6D3yPJYBCfiT_uR0N-tXkFlmHXWuafyL7KwaQ).