As we did for project 2 create and submit models for the 
flower and fruits datasets available at /home/u/usman/cs_677_datasets/.

Your goal is to achieve above 90% accuracy on the test datasets.

You may train your own model from scratch but it may take long
to train and achieve above 90% accuracy. You may also fine tune
the weights of the pre-trained model.

Submit your project as two files train.py and test.py. Your
train.py takes two inputs: the input training directory
and a model file name to save the model to.

python train.py traindir <model file>

Your test.py take two inputs: the test directory
and a model file name to load the model.

python test.py testdir <model file>

The output of test.py is the test error of the data which is
the number of misclassifications divided by size of the test set.

Submit your models by copying into the directory
/afs/cad/courses/ccs/s21/cs/677/002/<ucid>.
For example if your ucid is abc12 then copy your solution into 
/afs/cad/courses/ccs/s21/cs/677/002/abc12.

Your project is due May 9th 2021.
-----------------------------------------------------------------

To use Keras first setup your miniconda tensorflow environment by
following the steps here https://wiki.hpc.arcs.njit.edu/index.php/MinicondaUserMaintainedEnvs.

1. Login directly to a datasci node with

"srun -p datasci --gres=gpu:1 --mem=32GB --pty bash"

2. After logging into a node activate your tensorflow-gpu miniconda environment
and run your python program within the environment.

3. The manual login gives you more control over development. In case
your model needs to run for longer you can submit your command via a 
script. Use the script template below if needed.

#!/bin/bash
#SBATCH --job-name=cnn_job
#SBATCH --output=cnn_job.%j.out # %j expands to slurm JobID
#SBATCH --nodes=1
#SBATCH --tasks-per-node=1
#SBATCH --partition=datasci
#SBATCH --gres=gpu:1
#SBATCH --mem=32GB

#purge and load the correct modules
module purge > /dev/null 2>&1

#if you tensorflow miniconda environment is called tf then
#activate it as shown below
conda activate tf

#now run your python program
srun python train.py newmodel