AWS DeepLens
Developer Guide

Supporting MXNet Layers

You can use the following Apache MXNet modeling layers to train deep learning model for AWS DeepLens.

Supported MXNet Modeling Layers
Layer Description

Activation

Applies an activation function to the input

BatchNorm

Applies batch normalization

Concat

Joins input arrays along a given axis

_contrib_MultiBoxDetection

Converts a multibox detection prediction

_contrib_MultiBoxPrior

Generates prior boxes from data, sizes, and ratios

Convolution

Applies a convolution layer on input

Deconvolution

Applies a transposed convolution on input

elemwise_add

Applies element-wise addition of arguments

Flatten

Collapses the higher dimensions of an input into an 2-dimensional array

FullyConnected

Applies a linear transformation of Y = WX + b on input X

InputLayer

Specifies the input to a neural network

L2Norm

Applies L2 normalization to the input array

LRN

( Local Response Normalization )

Applies local response normalization to the input array

Pooling

Performs pooling on the input

Reshape

Reshapes the input array with a different view without changing the data

ScaleShift

Applies scale and shift operations on input elements

SoftmaxActivation

Applies Softmax activation to the input

SoftmaxOutput

Computes the gradient of cross-entropy loss with respect to Softmax output

transpose

Permutes the dimensions of an array

UpSampling

Performs nearest-neighbor or bilinear upsampling to input

_mul

Performs multiplication

_Plus

Performs an element-wise sum of the input arrays with broadcasting

For more information about MXNet layers, see MXNet Gluon Neural Network Layers.