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https://issues.apache.org/jira/browse/TIKA-2672?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16536503#comment-16536503
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ASF GitHub Bot commented on TIKA-2672:
--------------------------------------
chrismattmann commented on issue #241: Fix for TIKA-2672
URL: https://github.com/apache/tika/pull/241#issuecomment-403346420
I'm unable to build this locally, @ThejanW. I keep getting:
```
[ERROR] Failed to execute goal on project tika-dl: Could not resolve
dependencies for project org.apache.tika:tika-dl:jar:2.0.0-SNAPSHOT: Could not
find artifact
org.bytedeco.javacpp-presets:openblas:jar:linux-x86:0.3.0-1.4.2-SNAPSHOT in
oss-sonatype (https://oss.sonatype.org/content/repositories/snapshots/) ->
[Help 1]
[ERROR]
[ERROR] To see the full stack trace of the errors, re-run Maven with the -e
switch.
[ERROR] Re-run Maven using the -X switch to enable full debug logging.
[ERROR]
[ERROR] For more information about the errors and possible solutions, please
read the following articles:
[ERROR] [Help 1]
http://cwiki.apache.org/confluence/display/MAVEN/DependencyResolutionException
nonas:tika-dl mattmann$
```
Any ideas @thammegowda or @agibsonccc?
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> Upgrade dl4j to 1.0.0-beta
> --------------------------
>
> Key: TIKA-2672
> URL: https://issues.apache.org/jira/browse/TIKA-2672
> Project: Tika
> Issue Type: Task
> Reporter: Tim Allison
> Priority: Major
> Attachments: TIKA-2672.patch
>
>
> Let's try to upgrade dl4j. I think I got us most of the way there, but I got
> this error when reading the json config file. Can someone with more
> knowledge of layer specs help ([~thammegowda], perhaps :))?
> {noformat}
> org.deeplearning4j.exception.DL4JInvalidConfigException: Invalid
> configuration for layer (idx=-1, name=convolution2d_2, type=ConvolutionLayer)
> for width dimension: Invalid input configuration for kernel width. Require 0
> < kW <= inWidth + 2*padW; got (kW=3, inWidth=1, padW=0)
> Input type = InputTypeConvolutional(h=149,w=1,c=32), kernel = [3, 3], strides
> = [1, 1], padding = [0, 0], layer size (output channels) = 32, convolution
> mode = Truncate
> {noformat}
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