Convolutional Neural Network trained for mnist dataset

Small Neural Network trained on mnsit dataset with Keras (Tensorflow backend).

How to use

I have created a Dockerfile with all the dependencies. To pass the display and be able to use Matplotlib to display images with the Docker container, I also used XQuartz.

To use the Docker image with Matplotlib:

1. Build the Docker image from the Dockerfile docker build -t pythonml .
2. Start XQuartz open -a XQuartz
3. In a bash session, add localhost to xhost and export the display xhost + 127.0.0.1 and then export DISPLAY=host.docker.internal
4. Start a bash session in the Docker container docker run -it -e DISPLAY=host.docker.internal:0 -v $PWD:/t -w /t pythonml bash

Train the network within the Docker container: python3 model.py
Predict on a single input: IMG_PATH=<img_path> python3 predict.py

Accuracy, val_loss and val_acc after 13 epochs:

Achieved 98% on validation set.

Architecture of Network

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
lambda_1 (Lambda)            (None, 28, 28, 1)         0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 26, 26, 64)        640
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 64)        0
_________________________________________________________________
flatten_1 (Flatten)          (None, 10816)             0
_________________________________________________________________
dense_1 (Dense)              (None, 128)               1384576
_________________________________________________________________
dense_2 (Dense)              (None, 10)                1290
=================================================================
Total params: 1,386,506
Trainable params: 1,386,506
Non-trainable params: 0

Notes

• You may need to install tkinter. For that, just issue apt-get install python3-tk within the bash session of the Docker container.

Errors along the way

• I was training with white numbers on black background and testing input with inverse colors, which gives me wrong results.
• I obtained way better accuracy by not using a Dropout layer in the network.
• It is much slower to use fit_generator() than fit(), hence fit() has been used. fa