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DL4J实战之四:经典卷积实例(GPU版本),进阶和基础哪一个难

作者:互联网

org.deeplearning4j

deeplearning4j-core

1.0.0-beta6

org.nd4j

nd4j-native

1.0.0-beta6

如果您用GPU做训练,且CUDA版本是9.2,则依赖库和版本如下:

org.deeplearning4j

deeplearning4j-core

1.0.0-beta6

org.deeplearning4j

deeplearning4j-cuda-9.2

1.0.0-beta6

org.nd4j

nd4j-cuda-9.2-platform

1.0.0-beta6

内存设置

在这里插入图片描述

在这里插入图片描述

CPU版本

在这里插入图片描述

=Confusion Matrix=

0 1 2 3 4 5 6 7 8 9


973 1 0 0 0 0 2 2 1 1 | 0 = 0

0 1132 0 2 0 0 1 0 0 0 | 1 = 1

1 5 1018 1 1 0 0 4 2 0 | 2 = 2

0 0 2 1003 0 3 0 1 1 0 | 3 = 3

0 0 1 0 975 0 2 0 0 4 | 4 = 4

2 0 0 6 0 880 2 1 1 0 | 5 = 5

6 1 0 0 3 4 944 0 0 0 | 6 = 6

0 3 6 1 0 0 0 1012 2 4 | 7 = 7

3 0 1 1 0 1 1 2 964 1 | 8 = 8

0 0 0 2 6 2 0 2 0 997 | 9 = 9

Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times

==================================================================

13:24:31.616 [main] INFO com.bolingcavalry.convolution.LeNetMNISTReLu - 完成训练和测试,耗时[158739]毫秒

13:24:32.116 [main] INFO com.bolingcavalry.convolution.LeNetMNISTReLu - 最新的MINIST模型保存在[/home/will/temp/202106/26/minist-model.zip]

GPU版本

13:27:08.277 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Backend used: [CUDA]; OS: [Linux]

13:27:08.277 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Cores: [4]; Memory: [7.7GB];

13:27:08.277 [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Blas vendor: [CUBLAS]

13:27:08.300 [main] INFO org.nd4j.linalg.jcublas.JCublasBackend - ND4J CUDA build version: 9.2.148

13:27:08.301 [main] INFO org.nd4j.linalg.jcublas.JCublasBackend - CUDA device 0: [GeForce GTX 950M]; cc: [5.0]; Total memory: [4242604032]

![在这里插入图片描述](https://img-blog.csdn

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【docs.qq.com/doc/DSmxTbFJ1cmN1R2dB】 完整内容开源分享

img.cn/20210627103449840.png?,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70#pic_center)

=Confusion Matrix=

0 1 2 3 4 5 6 7 8 9


973 1 0 0 0 0 2 2 1 1 | 0 = 0

0 1129 0 2 0 0 2 2 0 0 | 1 = 1

1 3 1021 0 1 0 0 4 2 0 | 2 = 2

0 0 1 1003 0 3 0 1 2 0 | 3 = 3

0 0 1 0 973 0 3 0 0 5 | 4 = 4

1 0 0 6 0 882 2 1 0 0 | 5 = 5

标签:INFO,13,进阶,DL4J,nd4j,GPU,之四,org,main
来源: https://blog.csdn.net/m0_64384202/article/details/122153501