AWS DeepLens
Developer Guide

Supporting Caffe Layers

You can use the following Caffe layers to train deep learning models supported by AWS DeepLens.

Supported Caffe Layers
Layer Description

BatchNorm

Normalizes the input to have 0-mean and/or unit variance across the batch

Concat

Concatenates input blobs

Convolution

Convolves the input with a bank of learned filters

Deconvolution

Performs in the opposite sensor of the Convolution layer

Dropout

Performs dropout

Eltwise

Performs element-wise operations, such as product and sum, along multiple input blobs

Flatten

Reshapes the input blob into flat vectors

InnerProduct

Computes an inner product

Input

Provides input data to the model

LRN (Local Response Normalization)

Normalizes the input in a local region across or within feature maps

Permute

Permutes the dimensions of a blob

Pooling

Pools the input image by taking the max, average, etc.,. within regions

Power

Computes the output as (shift + scale * x) ^ power for each input element x

ReLU

Computes rectified linear activations

Reshape

Changes the dimensions of the input blob, without changing its data

ROIPooling

Applies pooling for each region of interest

Scale

Computes the element-wise product of two input blobs

Slice

Slices an input layer to multiple output layers along a given dimension

Softmax

Computes the Softmax activations

Tile

Copies a blob along specified dimensions

For more information about Caffe layers, see Caffe Layers .