AWS DeepLens
Developer Guide

Supporting TensorFlow Layers

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

Supported TensorFlow Layers
Layer Description

Add

Computes element-wise addition

AvgPool

Performs average pooling on the input

BatchToSpaceND

Rearranges data from batch into blocks of spatial data

BiasAdd

Adds bias

Const

Creates a constant tensor

Conv2D

Computes a 2-D convolution

Conv2DBackpropInput

Computes the gradients of convolution with respect to the input

Identity

Returns a tensor with the same shape and contents as input

Maximum

Computes element-wise maximization.

MaxPool

Performs the max pooling on the input

Mean

Computes the mean of elements across dimensions of a tensor

Mul

Computes element-wise multiplication

Neg

Computes numerical negative value element-wise

Pad

Pads a tensor

Placeholder

Inserts a placeholder for a tensor that will be always fed

Prod

Computes the product of elements across dimensions of a tensor

RandomUniform

Outputs random values from a uniform distribution

Range

Creates a sequence of numbers

Relu

Computes rectified linear activations

Reshape

Reshapes a tensor

Rsqrt

Computes reciprocal of square root

Shape

Returns the shape of a tensor

Softmax

Computes Softmax activations

SpaceToBatchND

Zero-pads and then rearranges blocks of spatial data into batch

Square

Computes element-wise square

Squeeze

Removes dimensions of size 1 from the shape of a tensor

StopGradient

Stops gradient computation

Sub

Computes element-wise subtraction

Sum

Computes the sum of elements across dimensions of a tensor

Tile

Constructs a tensor by tiling a given tensor

For more information about TensorFlow layers, see TensorFlow Layers .