example/gluon/mnist/README.md

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# 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:

<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/gluon/mnist/test_image.png" width="250" height="250">

[-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`.