You've done all the hard work! No, really, you have. It's a fairly simple endeavor to run the code if you don't have any translation errors in the code. Essentially, you follow a few simple steps to run your GAN:

1. Run the following command in the root folder of your repository:

sudo ./run.sh 

2. You should see the following output in the Terminal once everything is working correctly:

username@username-comp:~/full-gan$ sudo ./run.sh 
[sudo] password for username: 
Sending build context to Docker daemon 3.998MB
Step 1/3 : FROM base_image
 ---> c398836f2b23
Step 2/3 : RUN apt install -y python3-pydot python-pydot-ng graphviz
 ---> Using cache
 ---> 37424cd81385
Step 3/3 : ADD . /
 ---> c91a0189d9c1
Successfully built c91a0189d9c1
Successfully tagged ch3:latest
access control disabled, clients can connect from any host
################# Model Summaries
################# Download MNIST
################# Tensorflow connecting to the GPU
Epoch: 0, [Discriminator :: Loss: 0.7186179], [ Generator :: Loss: 
                                                0.7297293]
Epoch: 1, [Discriminator :: Loss: 0.39331502], [ Generator :: Loss: 
                                                 0.7450044]
Epoch: 2, [Discriminator :: Loss: 0.3295707], [ Generator :: Loss: 
                                                0.8133272]
Epoch: 3, [Discriminator :: Loss: 0.29371032], [ Generator :: Loss: 
                                                 0.8316293]
Epoch: 4, [Discriminator :: Loss: 0.29231048], [ Generator :: Loss: 
                                                 1.032237]
Epoch: 5, [Discriminator :: Loss: 0.30067348], [ Generator :: Loss: 
                                                 1.07507]
Epoch: 6, [Discriminator :: Loss: 0.23213515], [ Generator :: Loss: 
                                                 1.2063006]
...

3. A few key points here:
   • Anything with ############ has been reduced to a simple summary line—there's too many lines for a book to show them.
   • This code could be improved with a graphical look at the Discriminator and Generator losses.
   • If you want to check to make sure that your docker image has been built, check it using the docker images command in another Terminal window. You should see that a ch3 image was recently built.
   • If you aren't seeing PNGs in your data directory, make sure that the data directory is located at $HOME/full-gan/data. If the directory is not there, feel free to modify your run.sh file to change the mapped volume.

Now, let's talk about the results of this GAN!