simple_neural_networks
The code was tested with Python 3.6.8 (Anaconda distribution). It requires the following packages (use pip install in your virtual environment):
- dataclasses (this should not be required with Python 3.7 and up)
- pickleshare
- numpy
- Pillow
There are two notebooks in this repository:
-
neural_network.ipynb -- implements the network from Chapters 1 and 2
-
one-fell-swoop.ipynb -- implements the same network, but with the fully matrix-based approach (there's no looping over the mini-batch). This was given as a problem in Chapter 2.
However, I only saw about 10-20% performance increase with the fully matrix-based approach, not 100% as Michael Nielsen stated in the book. So if you find a problem with my implementation, make a pull request!
There are two sets of images, one is the canonical MNIST digits in mnist.pkl.gz, and the other is in the non-MNIST-digits directory.
The latter ones are my own handwriting scanned and scaled to 28x28 pixel size. They are used for "real-life" tests in addition to the validation set from MNIST.