MNIST classification example

This script shows a simple example how to do image classification with Gluon. The model is trained on MNIST digits image dataset and the goal is to classify the digits 0-9. The model has the following layout:

net = nn.Sequential()
net.add(nn.Dense(128, activation='relu'))
net.add(nn.Dense(64, activation='relu'))
net.add(nn.Dense(10))

The script provides the following commandline arguments:

MXNet Gluon MNIST Example

optional arguments:
  -h, --help            show this help message and exit
  --batch-size BATCH_SIZE
                        batch size for training and testing (default: 100)
  --epochs EPOCHS       number of epochs to train (default: 10)
  --lr LR               learning rate (default: 0.1)
  --momentum MOMENTUM   SGD momentum (default: 0.9)
  --cuda                Train on GPU with CUDA
  --log-interval N      how many batches to wait before logging training
                        status

After one epoch we get the following output vector for the given test image:

[-5.461655 -4.745 -1.8203478 -0.5705207 8.923972 -2.2358544 -3.3020825 -2.409004 4.0074944 10.362008]

As we can see the highest activation is 10.362 which corresponds to label 9.