The newest addition to the range of deep learning frameworks is Gluon. Gluon is recently launched by AWS and Microsoft to provide an API 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.

- At the moment,
`gluon`

is included in the latest release of MXNet (follow the steps in*Building efficient models with MXNet*to install MXNet). - After installing, we can directly import
`gluon`

as follows:

from mxnet import gluon

- 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)

- 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)))

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

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

- 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})

- 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])

### Note

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.