https://github.com/udacity/CarND-TensorFlow-Lab
TensorFlow Lab for Self-Driving Car ND
https://github.com/udacity/CarND-TensorFlow-Lab
Last synced: 11 months ago
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TensorFlow Lab for Self-Driving Car ND
- Host: GitHub
- URL: https://github.com/udacity/CarND-TensorFlow-Lab
- Owner: udacity
- License: mit
- Created: 2016-10-08T21:24:45.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2022-07-06T21:25:49.000Z (almost 4 years ago)
- Last Synced: 2024-08-08T23:19:59.793Z (almost 2 years ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 1.75 MB
- Stars: 125
- Watchers: 48
- Forks: 399
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
- License: LICENSE
- Codeowners: CODEOWNERS
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README
# TensorFlow Neural Network Lab
[](http://www.udacity.com/drive)
[
](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html)
We've prepared a Jupyter notebook that will guide you through the process of creating a single layer neural network in TensorFlow.
## Environment Set-up
Follow the set-up instruction for the [Term 1 Starter Kit](https://github.com/udacity/CarND-Term1-Starter-Kit). Contained within are instructions for utilizing a Jupyter notebook within Docker; if you use Anaconda make sure to activate the environment first with
```
source activate {environment_name}
```
and then open the notebook with
```
jupyter notebook
```
#### The Lab
The notebook has 3 problems for you to solve:
- Problem 1: Normalize the features
- Problem 2: Use TensorFlow operations to create features, labels, weight, and biases tensors
- Problem 3: Tune the learning rate, number of steps, and batch size for the best accuracy
This is a self-assessed lab. Compare your answers to the solutions [here](https://github.com/udacity/CarND-TensorFlow-Lab/blob/master/solutions.ipynb). If you have any difficulty completing the lab, Udacity provides a few services to answer any questions you might have.
## Help
Remember that you can get assistance from mentors and fellow students in Student Hub or in Knowledge. You can also review the concepts from the previous lessons.