Implement the LeNet-5 deep neural network model.

Download the data from THE MNIST DATABASE of handwritten digits

This lab requires:

• CarND Term1 Starter Kit

The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.

A precompiled image with all dependencies required for the first term is available on Docker Hub.

Once you have docker working, pull the image using the following command:

docker pull udacity/carnd-term1-starter-kit

In your shell, navigate to the directory of a project, e.g.

$ cd ~/src/CarND-LeNet-Lab

From within this directory, you are going to run a Jupyter server. In order to do this you must attach to the correct port and share a local volume.

The easiest way to share a local volume is via the pwd command, a shell command that prints the working directory. This command will be used differently based on your shell.

If you're using bash or Docker Quickstart Terminal:

docker run -it --rm --entrypoint "/run.sh" -p 8888:8888 -v `pwd`:/src udacity/carnd-term1-starter-kit

Let's break this down.

docker run is the command a startup and run a Docker container.

-it forces the container to run in the foreground (interactive mode) and provides an I/O to the container.

--rm removes the container once it stops running. It prevents the buildup of stale containers once you stop them from running.

--entrypoint "/run.sh" tells the container to run run.sh when it opens. In our case, this file activates the conda environment and opens a jupyter notebook in the background. This is also included in the dockerfile, but sometimes does not appropriately run without this flag.

-p 8888:8888 maps port 8888 on our local machine to port 8888 in the Docker container, this allows us to access port 8888 in the container by visiting localhost:8888.

-v ${pwd}:/src mounts the pwd (present working directory) to the /src directory in the container. Basically, this lets us access files from our local machine on the docker container.

udacity/carnd-term1-starter-kit is the name of the container to run.

To learn more about Docker visit the docs.