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[OPS] Saver fixes. (compare)
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[CORE] Fix to the Session memor… (compare)
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[CORE] No more session locking. (compare)
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got unknown format starting with '?PNG\r\n\032\n\000\000\000\rIHDR'
@oranda I implemented my entire PhD research in Scala, loved using Scala for ML! I work in industry now and one thing I learned is that Scala has a very bad reputation. My CTO told me that he feels that one needs a PhD to understand Scala code. The Scala community and its emphasis on a lot of the advanced syntactic features of the language have not helped one bit.
Sometimes its better to hide those capabilities and emphasize how easy it is to get started with a language if we want to attract new talent. But the community has a bias for Monads/DSLs/Type Theory etc.
I've put out the second video in a series on tensorflow for scala: https://www.youtube.com/watch?v=NwZ5WviQJVQ
Third video in the series: https://www.youtube.com/watch?v=WpqH9aqR3Mo
Tho I don't actually talk about tensorflow but ZIO library design shrug
0.5.5
release that hopefully resolves this. Sorry about that but including support for windows has been a bit of a painful process because I don't have a good way to cross-compile.
tf.createWith(graph)
for constructed nodes to be owned by the given graph. In which case only a session.close()
is required to release all resources. Sound about right?
.close()
on anything as TF Scala will try to free native objects when references to them are garbage collected. Of course this is too ideal and in practice you might run into memory issues if your sessions hold large objects and you set the JVM memory limit high. However, I’d say you can ignore that until you notice memory becoming an issue for your use case.
session.close()
will not release tensors that are returned from calls to session.run
.
val batchSize = 100
val accMetric = tf.metrics.MapMetric(
(v: (Output[Float], (Output[Float], Output[Float]))) => {
val positives = v._1 > 0.5f
val binary = tf
.select(
positives,
tf.fill(Shape(batchSize, positives.shape(1)))(1f),
tf.fill(Shape(batchSize, positives.shape(1)))(0f)
)
.toFloat
(binary, v._2._2.toFloat)
},
tf.metrics.Accuracy("Accuracy")
)