mxnet.gluon¶
The Gluon library in Apache MXNet provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.
Example¶
The following example shows how you might create a simple neural network with three layers: one input layer, one hidden layer, and one output layer.
net = gluon.nn.Sequential()
# When instantiated, Sequential stores a chain of neural network layers.
# Once presented with data, Sequential executes each layer in turn, using
# the output of one layer as the input for the next
with net.name_scope():
net.add(gluon.nn.Dense(256, activation="relu")) # 1st layer (256 nodes)
net.add(gluon.nn.Dense(256, activation="relu")) # 2nd hidden layer
net.add(gluon.nn.Dense(num_outputs))
Tutorials¶
The Gluon guide. Start here!
A Gluon add-on module for computer vision.
A Gluon add-on module for natural language processing.
APIs and Packages¶
Core Modules¶
Neural network components.
Recurrent neural network components.
Training¶
Loss functions for training neural networks.
Parameter getting and setting functions.
Functions for applying an optimizer on a set of parameters.
Data¶
Dataset utilities.
Image dataset utilities.
A module for loading pre-trained neural network models.