https://github.com/fqaiser94/carnd-tensorflow-lab
https://github.com/fqaiser94/carnd-tensorflow-lab
Last synced: 12 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/fqaiser94/carnd-tensorflow-lab
- Owner: fqaiser94
- Created: 2017-10-29T19:19:14.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-10-29T19:20:39.000Z (over 8 years ago)
- Last Synced: 2025-04-07T15:54:30.293Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 1.75 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
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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.
## Windows Instructions
#### Install Docker
If you don't have Docker already, download and install Docker from [here](https://docs.docker.com/engine/installation/windows/).
#### Clone the Repository
Run the command below to clone the Lab Repository:
```sh
$ git clone https://github.com/udacity/CarND-TensorFlow-Lab.git
```
#### Run the Notebook using Docker
Run the following command from the same directory as the command above.
```sh
$ docker run -it -p 8888:8888 -v `pwd`:/notebooks udacity/carnd-tensorflow-lab
```
#### View The Notebook
Open a browser window and go [here](http://localhost:8888/notebooks/CarND-TensorFlow-Lab/lab.ipynb). This is the notebook you'll be working on. 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.
## OS X and Linux Instructions
#### Install Anaconda
This lab requires [Anaconda](https://www.continuum.io/downloads) and [Python 3.4](https://www.python.org/downloads/) or higher. If you don't meet all of these requirements, install the appropriate package(s).
#### Run the Anaconda Environment
Run these commands in your terminal to install all the requirements:
```sh
$ git clone https://github.com/udacity/CarND-TensorFlow-Lab.git
$ conda env create -f CarND-TensorFlow-Lab/environment.yml
$ conda install --name CarND-TensorFlow-Lab -c conda-forge tensorflow
```
#### Run the Notebook
Run the following commands from the same directory as the commands above.
```sh
$ source activate CarND-TensorFlow-Lab
$ jupyter notebook
```
#### View The Notebook
Open a browser window and go [here](http://localhost:8888/notebooks/CarND-TensorFlow-Lab/lab.ipynb). This is the notebook you'll be working on. 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 your mentor, the Forums (click the link on the left side of the classroom), or the [Slack channel](https://carnd-slack.udacity.com). You can also review the concepts from the previous lessons.