Gluon Package

Warning

This package is currently experimental and may change in the near future.

Overview

Gluon package is a high-level interface for MXNet designed to be easy to use while keeping most of the flexibility of low level API. Gluon supports both imperative and symbolic programming, making it easy to train complex models imperatively in Python and then deploy with symbolic graph in C++ and Scala.

Parameter

Parameter
ParameterDict

Containers

Block
HybridBlock
SymbolBlock

Neural Network Layers

Containers

Sequential
HybridSequential

Basic Layers

Dense
Activation
Dropout
BatchNorm
LeakyReLU
Embedding

Convolutional Layers

Conv1D
Conv2D
Conv3D
Conv1DTranspose
Conv2DTranspose
Conv3DTranspose

Pooling Layers

MaxPool1D
MaxPool2D
MaxPool3D
AvgPool1D
AvgPool2D
AvgPool3D
GlobalMaxPool1D
GlobalMaxPool2D
GlobalMaxPool3D
GlobalAvgPool1D
GlobalAvgPool2D
GlobalAvgPool3D

Recurrent Layers

RecurrentCell
RNN
LSTM
GRU
RNNCell
LSTMCell
GRUCell
SequentialRNNCell
BidirectionalCell
DropoutCell
ZoneoutCell
ResidualCell

Trainer

Trainer

Loss functions

L2Loss
L1Loss
SoftmaxCrossEntropyLoss
KLDivLoss

Utilities

split_data
split_and_load
clip_global_norm

Data

Dataset
ArrayDataset
RecordFileDataset
ImageRecordDataset
Sampler
SequentialSampler
RandomSampler
BatchSampler
DataLoader

Vision

MNIST
CIFAR10

Model Zoo

Model zoo provides pre-defined and pre-trained models to help bootstrap machine learning applications.

Vision

get_model

ResNet

resnet18_v1
resnet34_v1
resnet50_v1
resnet101_v1
resnet152_v1
resnet18_v2
resnet34_v2
resnet50_v2
resnet101_v2
resnet152_v2
ResNetV1
ResNetV2
BasicBlockV1
BasicBlockV2
BottleneckV1
BottleneckV2
get_resnet

VGG

vgg11
vgg13
vgg16
vgg19
vgg11_bn
vgg13_bn
vgg16_bn
vgg19_bn
VGG
get_vgg

Alexnet

alexnet
AlexNet

DenseNet

densenet121
densenet161
densenet169
densenet201
DenseNet

SqueezeNet

squeezenet1_0
squeezenet1_1
SqueezeNet

Inception

inception_v3
Inception3

API Reference