Defining networks using simple and efficient code with Gluon

The newest addition to the broad range of deep learning frameworks is Gluon. Gluon is recently launched by AWS and Microsoft to provide an API with simple, easy-to-understand code without the loss of performance. Gluon is already included in the latest release of MXNet and will be available in future releases of CNTK (and other frameworks). Just like Keras, Gluon is a wrapper around other deep learning frameworks. The main difference between Keras and Gluon, is that Gluon will (at first) focus on imperative frameworks. 

How to do it...

1. At the moment, gluon is included in the latest release of MXNet (follow the steps in Building efficient models with MXNet to install MXNet). 
2. After installing, we can directly import gluon as follows:

from mxnet import gluon

3. Next, we create some dummy data. For this we need the data to be in MXNet's NDArray or Symbol:

import mxnet as mx
import numpy as np
x_input = mx.nd.empty((1, 5), mx.gpu())
x_input[:] = np.array([[1,2,3,4,5]], np.float32)

y_input = mx.nd.empty((1, 5), mx.gpu())
y_input[:] = np.array([[10, 15, 20, 22.5, 25]], np.float32)

4. With Gluon, it's really straightforward to build a neural network by stacking layers:

net = gluon.nn.Sequential()
with net.name_scope():
    net.add(gluon.nn.Dense(16, activation="relu"))
    net.add(gluon.nn.Dense(len(y_input)))

5. Next, we initialize the parameters and we store these on our GPU as follows:

net.collect_params().initialize(mx.init.Normal(), ctx=mx.gpu())

6. With the following code we set the loss function and the optimizer:

softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .1})

7. We're ready to start training or model:

n_epochs = 10

for e in range(n_epochs):
    for i in range(len(x_input)):
        input = x_input[i]
        target = y_input[i]
        with mx.autograd.record():
            output = net(input)
            loss = softmax_cross_entropy(output, target)
            loss.backward()
        trainer.step(input.shape[0])

We've shortly demonstrated how to implement a neural network architecture with Gluon. Gluon is a powerful extension that can be used to implement deep learning architectures with clean code. At the same time, there is almost no performance loss when using Gluon.