Need python 3.6 or any.

$ git clone https://github.com/anizzzzzzzz/Neural-Network.git
$ cd Neural-Network/

To create a virtual environment, go to your project's directory and run virtualenv.

python3 -m virtualenv venv

py -m virtualenv venv

Note : The second argument is the location to create the virtualenv. Generally, you can just create this in your project and call it venv. [ virtualenv will create a virtual Python installation in the venv folder.]

Before you can start installing or using packages in your virtualenv, you'll need to activate it.

source venv/bin/activate

.\venv\Scripts\activate

which python

Output : .../venv/bin/python

where python

Output : .../venv/bin/python.exe

pip install -r requirements.txt

The link, MNIST Handwritten Digits Data, points you to the MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples.

Download all four zipped data and unzip them and place it into

[path_of_project]/Neural-Network/src/MultilayerAnn/data

• Run LoadData.py to load the handwritting data and save it into numpy zip files for efficient loading later. It will create 'mnist_scaled.npz' file inside directory.
• Train the model by executing TrainData.py file. After training completion, model will be saved in model directory along with images of cost and training/validation accuracy.
• Run PredictData.py for testing the test-data.numpy zip files