@moloneymb I found ...CheckpointSaver(summariesDir, StepHookTrigger(1000))),.......
http://platanios.org/tensorflow_scala/
There is 'tf.saver(....)' method. I couldn't find an actual example. If this doesn't help, please shout for other's help :)
@eaplatanios
I found that there is a way to retrieve updated weight values.
https://www.quora.com/How-do-I-print-weights-of-a-fully-connected-neural-network-in-tensorflow
This was for Python tensorflow but I tested and I saw that it worked with the Scala version too.
//train your network heresess.run(optimizer, feed_dict={x:data, y:labels}print("layer 1 weights:", sess.run(W1))print("layer 2 weights:", sess.run(W2))
I was trying to persist training information (using akka persistence) and it seems only 'session' contains relevant information.
https://github.com/cosmir17/Scala-akka-tensorflow-sharetrade-helper/blob/1313169c97c2d6d716911e03ba66d9cea7aed4ab/src/main/scala/QDecisionPolicyActor.scala#L68
I don't want to serialise a file created locally by Tensorflow. Once I get these training information in runtime. I can do something like, https://doc.akka.io/docs/akka/2.5.4/java/distributed-data.html (akka cluster)
To note that my personal project is for fun but it will be so interesting to experiment Tensorflow and Akka together in distributed environment.
Exception in thread "main" org.platanios.tensorflow.jni.FailedPreconditionException: Input 1 ('Transpose') for 'Conv2D' was not previously added to ShapeRefiner.
at org.platanios.tensorflow.jni.Op$.finish(Native Method)
at org.platanios.tensorflow.api.ops.Op$Builder.$anonfun$build$1(Op.scala:2646)
at org.platanios.tensorflow.api.utilities.package$.using(package.scala:31)
at org.platanios.tensorflow.api.ops.Op$Builder.build(Op.scala:2589)
at org.platanios.tensorflow.api.ops.NN.conv2D(NN.scala:1240)
at org.platanios.tensorflow.api.ops.NN.conv2D$(NN.scala:1219)
at org.platanios.tensorflow.api.package$tf$.conv2D(package.scala:294)
at co.mumbler.models.layers.ConvLayer.apply(Layers.scala:47)
at co.mumbler.main.Style$.$anonfun$net_preloaded$1(Style.scala:176)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:237)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike.map(TraversableLike.scala:237)
at scala.collection.TraversableLike.map$(TraversableLike.scala:230)
at scala.collection.immutable.List.map(List.scala:298)
at co.mumbler.main.Style$.net_preloaded(Style.scala:174)
at co.mumbler.main.Style$.$anonfun$apply$1(Style.scala:35)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:62)
at org.platanios.tensorflow.api.ops.Op$.createWith(Op.scala:2043)
at org.platanios.tensorflow.api.ops.package$API.createWith(package.scala:120)
at org.platanios.tensorflow.api.ops.package$API.createWith$(package.scala:114)
at org.platanios.tensorflow.api.package$tf$.createWith(package.scala:294)
at co.mumbler.main.Style$.apply(Style.scala:32)
at co.mumbler.main.Main$.main(Main.scala:193)
at co.mumbler.main.Main.main(Main.scala)
Iām sometimes developing on linux and sometimes on OSX. I currently manage this by manually changing the classifier in build.sbt:
// "org.platanios" %% "tensorflow" % "0.4.1" classifier "linux-gpu-x86_64",
"org.platanios" %% "tensorflow" % "0.4.1" classifier ādarwin-cpu-x86_64ā,but this is not ideal. Can I make a āuniversalā build.sbt? On a related note is possible to assemble a univeral jar that will run on both systems?
I need to make my code generic so I need to demonstrate to the API that the types Iām supporting are supported by the API. So I find myself doing things like:
def apply[T: TF: IsDecimal: IsReal](m: MLStructure): Layer[T]So first a basic scala question (sorry). I can find the syntax def f[A: B]() documented. In particuar, the syntax Class1: Class2. Does it mean the Class1 must implement Class2? How does it differ to A <: B? Itās hard to google for it.
Secondly, is this how the API should be used? The name TF strikes me as being private. If not, how should make my code generic?
I have a simple question that anyone can answer. How can I save Session to file(s), one or two lines of codes, in the standard way?
The only examples I found are the following. It appears that they alone actually don't save training data to files, and working on their own. They have to be declared in some API (e.g. tf.learn.InMemoryEstimator(...))
SummarySaver(summariesDir, tf.learn.StepHookTrigger(100))
CheckpointSaver(summariesDir, tf.learn.StepHookTrigger(1000))Once I figure out how to save 'session', I am going to do the following, paths are going to be substituted from the file paths created during training process. Is it the right way? or is there a simpler and more standard way?
val savePath = Paths.get(".../model-store-python")
val metaPath = Paths.get(".../model-store-python/my-model.meta")
val metaGraphDef = MetaGraphDef.parseFrom(new BufferedInputStream(new FileInputStream(metaPath.toFile)))
val saver = tf.Saver.fromMetaGraphDef(metaGraphDef)
saver.restore(sess, tf.Saver.latestCheckpoint(savePath, "my-model.meta").get)
import org.platanios.tensorflow.api.types.DataType
import org.platanios.tensorflow.api.{tf, _} Can someone please help showing some small demo that how it can be done in Gradle. Thanks
tf.data.datasetFromOutputSlices() method for creating a TF Dataset. I just reported issue #165 which details problems with using this method in a practical example. Has any one faced something similar?
[warn] ::::::::::::::::::::::::::::::::::::::::::::::
[warn] :: UNRESOLVED DEPENDENCIES ::
[warn] ::::::::::::::::::::::::::::::::::::::::::::::
[warn] :: org.platanios#tensorflow;0.4.0: not found
https://aiconf.com.ua/en/home/ - September 15;
https://conference.data-science.com.ua/ - October 19;
https://devoxx.com.ua/ - November 1-2;
https://aibooster.com.ua/cv2019/ - November 9;
https://www.ukraine.buildstuff.events/ - November 18-19
tf.whileLoop. The package is private to force users into using the tf namespace for all ops. Not sure if this is the best approach but it's parallel to the Python API of TensorFlow and I was hoping it would allow for a smoother transition.
