org.deeplearning4j.zoo.model

Class AlexNet

java.lang.Object

org.deeplearning4j.zoo.ZooModel

org.deeplearning4j.zoo.model.AlexNet

All Implemented Interfaces:

InstantiableModel

public class AlexNet
extends ZooModel

AlexNet Dl4j's AlexNet model interpretation based on the original paper ImageNet Classification with Deep Convolutional Neural Networks and the imagenetExample code referenced.
References:
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/train_val.prototxt Model is built in dl4j based on available functionality and notes indicate where there are gaps waiting for enhancements. Bias initialization in the paper is 1 in certain layers but 0.1 in the imagenetExample code Weight distribution uses 0.1 std for all layers in the paper but 0.005 in the dense layers in the imagenetExample code

Method Summary

Modifier and Type	Method and Description
MultiLayerConfiguration	conf()
MultiLayerNetwork	init()
ModelMetaData	metaData()
Class<? extends Model>	modelType()
long	pretrainedChecksum(PretrainedType pretrainedType)
String	pretrainedUrl(PretrainedType pretrainedType)
void	setInputShape(int[][] inputShape)

Methods inherited from class org.deeplearning4j.zoo.ZooModel

initPretrained, initPretrained, modelName, pretrainedAvailable

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

pretrainedUrl

public String pretrainedUrl(PretrainedType pretrainedType)

pretrainedChecksum

public long pretrainedChecksum(PretrainedType pretrainedType)

modelType

public Class<? extends Model> modelType()

conf

public MultiLayerConfiguration conf()

init

public MultiLayerNetwork init()

metaData

public ModelMetaData metaData()

setInputShape

public void setInputShape(int[][] inputShape)