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##### Activity
Gili Tzabari
@cowwoc
I've actually also got a second case of integers... I've got inputs that are enums, so I converted their ordinal value to an int. There I can obviously just cast it to a float. It's the timestamps where things get more complicated.
@Craigacp
I wouldn't pass in a timestamp to an ML system as a monotonically increasing integer. It's probably better to split it out into categoricals which represent months, days, possibly the season, along with the hour of the day. If you pass in the timestamp directly then the model has to expend capacity learning the cyclic behaviour and parsing the timestamp.
Gili Tzabari
@cowwoc
Okay. So if later on I want a model that also predicts the timestamp of an event (e.g. it is currently 20 degrees, predict what time we will hit 23 degrees) should the output again contain the timestamp broken down into time categoricals?
@Craigacp
Yeah I think that's probably easiest. Otherwise it's hard to parse the signal.
Plus if the loss is split out into different chunks you can reward the model for predicting the correct hour & day even if it gets the number of minutes wrong.
Whereas with a timestamp it's harder to design the loss function to do that.
Gili Tzabari
@cowwoc
Hmm, I found an interesting tutorial at https://www.tensorflow.org/tutorials/structured_data/time_series#time ... they break down timestamps into sin/cos components which I would have never thought to do.
So, what's the point of Tensorflow having integer, boolean, etc types if only float is really usable? Are they there to just let you convert integers to float on the graph (late binding)? And you always need to convert to float before feeding the values into an Input node?
@Craigacp
Tensorflow is a computation graph and an autodiff system. The autodiff only applies to floats as gradients are harder to define on non-continuous spaces. But you can use the computational graph on other types just fine. For example if you're doing object detection that's going to return a bounding box on an image which needs to be integers to line up with the pixels, so the natural return type is an integer tensor. Also boolean is useful for controlling graph elements with tf.cond (i.e. if statements).
You can compute functions of integer tensors without any trouble, but if you want to differentiate those functions to perform gradient descent that's where you hit the issue.
Gili Tzabari
@cowwoc
Don't you have to performance gradient descent on all nodes in the graph for backprop to work?
I mean, what's the point of having nodes in the graph that autodiff does not run on? When would that be fine?
@Craigacp
All nodes between your inputs and outputs.
Gili Tzabari
@cowwoc
Sorry, what? You're saying that you do or do not need all nodes between your inputs and output to be differentiable?
@Craigacp
You need all the nodes that connect your outputs to the parameters you want to learn to be differentiable.
Gili Tzabari
@cowwoc
Right. So when would you want to use non-differentiable nodes in Tensorflow? What lives outside the path between the input and output nodes?
@Craigacp
You can add nodes which trigger printouts or saving based on specific computation conditions, you can construct the paths that you want to load data from, you can perform operations on the outputs of your ML model (e.g. the bounding box example, you might want to colour the boxes based on the probability of correct classification)
All these things you can add into the computational graph.
Gili Tzabari
@cowwoc
I see. Are there any online examples I could look at that would show this in action?
@Craigacp
Also as I mentioned above you might have nodes which compute the learning rate, or control the presence or absence of dropout on some layers. These would be integer or boolean inputs and compute some function which might have a boolean output.
Erm, well we build them under the covers in a bunch of bits of TF-Java (as does Keras in Python). I'm not sure I've seen explicit examples of this functionality in TF 2 examples, but to be honest I've not looked at much TF 2 example code.
Gili Tzabari
@cowwoc
@Craigacp Looking at https://www.tensorflow.org/tutorials/structured_data/time_series#time they calculate both cos and sin of the timestamp. Is there a point to passing both to the model as input? Or do they only use one of them?
@Craigacp
It looks like they use both. I agree that computing one from the other is possible and so there is redundant information in there, but it might make it easier for the network to learn. Feature engineering is an art more than a science.
Gili Tzabari
@cowwoc
:thumbsup:
Gili Tzabari
@cowwoc
Hi again. How do I deal with optional input data? For example, each training example may contain 2 to 4 input values (e.g. some weather stations have 4 temperature sensors, others 3, and so on). I tried passing math.nan in place of sensor data but this broke training (the loss function returned nan). What should I do in this case? Set the values to zero? Set them to random values? Ideally I want the model to skip them and not use them for training.
@Craigacp
There's no one way of doing that with a neural net. Usually you set the missing value to something moderately sensible and have it learn around it, but in general it is model and task specific.
Gili Tzabari
@cowwoc
Okay. Suppose normal reading range between -50 and 50. Am I better off assigning missing values some average value (e.g. 0) or maybe I'm better off assigning a value that would never occur in real life (e.g. -1000) and hope that the model will learn to suppress/mask such inputs?
@Craigacp
It depends why it's missing.
jxtps
@jxtps
You may want to have a separate piece of input that’s all 1s for where the data is valid, and 0s where it’s not - i.e. an input mask. You’d then need to incorporate that into your network architecture in some sensible way (very task/network specific). If this is actually on the output you can usually provide some weight to the sample so it gets de-weighted compared to the rest of the batch.
(The point of the input mask being to make it easier for your network to learn to ignore that part of the data)
Gili Tzabari
@cowwoc
Interesting. I think I will experiment with 3 different options: (1) a separate input mask per sensor (2) a single input indicating the number of sensors present (3) an (inline) invalid sensor value implying that masking should take place.
Jakob Sultan Ericsson
@jakeri

Hello again,
We have gone from TF Java 0.2.0 to 0.3.1 on our linux hosts.
And basically just changed so that it compiles. We load and unload a bunch of different savedmodels.
We are now experiencing OutOfMemory on bytedeco as if the memory is not reclaimed. We try to calculate the size of the incoming savedmodel and only keep models to roughly half of the available memory of the host (we have a guava cache that get approximate weight of the model and only hold models up to half of the available memory).
We are closing all tensors, savedmodel-bundles etc (not really changing from 0.2.0).

-Dorg.bytedeco.javacpp.maxbytes=25G
-Dorg.bytedeco.javacpp.maxphysicalbytes=25G

We are about to try 0.4.0-SNAPSHOT and also do some kind of more specific test case.

23 replies
Jakob Sultan Ericsson
@jakeri

We have continued to try to nail down our problem with memory. We believe it is something strange with Linux version of 0.3.1 (and 0.4.0-SNAPSHOT). Some memory is not deallocated and it is quite visible when you run large models.

I’ve tried to build a “test case” for this. Maven project with a Dockerfile. The Dockerfile will download a somewhat large model from tfhub (efficientnet_b7_classification_1).

The test is basically:

            for (int q = 0; q < 10; q++) {
//and unpack inside src/main/resources
try (SavedModelBundle savedModelBundle = SavedModelBundle.load(savedModelSourcePath.toString(), "serve")) {
}
Pointer.deallocateReferences(); //trying to force
System.gc(); //trying to force
}

When running on Linux, memory will increase and usually crash after 3-4 iterations (depending on bytedeco mem flags or host memory) but running same test on OSX test will usually pass and memory decreases from time to time.
Do you think this is related to tensorflow/java#304 or something else?
We are trying to do some more with Tensorflow debug-logging with allocation and deallocation to try to find some pointers where this could be.

@Craigacp
You can run the whole thing inside valgrind on Linux which will show where the allocated memory is lost. There are issues in the C API's model load code where it leaks memory, though when I looked at it it wasn't enough of a leak to go pop in a few iterations (though I was testing a much smaller model). The response from Google who maintain the C API was not promising - tensorflow/tensorflow#48802.
Gili Tzabari
@cowwoc
@Craigacp I suspect tensorflow/tensorflow#48802 might get more traction if someone were to attach a self-contained testcase, ideally in pure C. Maybe it doesn't matter, but the way the issue is now is not super easy for a newbie contributor to pick it up.
Samuel Otter
@samuelotter

Hi, I'm also investigating this memory leak together with @jakeri.

I'm not sure i completely grok all the interactions between tensorflow, javacpp and the java wrappers but it looks like the TF_Session handle isn't destroyed when closing the Session. This can be confirmed by running with tensorflow VLOG set to 2 which will log allocations and deallocations. Some things are never deallocated (memory allocated restore ops for example).

If I understand things correctly Session::delete will be invoked when closing the session, which should release the reference and ultimately the Pointer should be deallocated, which should trigger the the DeleteDeallocator in AbstractTF_Session. This works when calling TF_Session::newSession since that will set the deallocator on the pointer before returning, but, when loading a saved model the TF_Session object is created internally in native code in TF_LoadSessionFromSavedModel and returned, which means the deallocator is never set on the Pointer, which means the DeleteDeallocator is never invoked.
When manually calling TF_CloseSession and TF_DeleteSession on the native handles it leaks much less memory (still a little it I think, but significantly less).

Samuel Audet
@saudet
You're right, that's an issue. TF_Session.loadSessionFromSavedModel() needs to be called instead.
@Craigacp
The call to TF_Graph above that also returns a pointer without a deallocator right? It doesn't look like the session takes ownership of it such that it'll be deleted on session close, so we should probably change that over to TF_Graph.newGraph() too.
@Craigacp
Then we'll need to make sure the session and the graph live past the pointer scope.
@Craigacp
@samuelotter could you see if this branch fixes your issue - https://github.com/Craigacp/tensorflow-java/tree/saved-model-leak
Samuel Audet
@saudet
Yes, it's also calling close() on the graph, so we have to change that as well.
@Craigacp
SavedModelBundle.close closes the graph, shouldn't that be sufficient?
Samuel Audet
@saudet
Looks like it:
// Destroy an options object.  Graph will be deleted once no more
// TFSession's are referencing it.
public static native void TF_DeleteGraph(TF_Graph arg0);
Not sure if that means it will crash if we try to deallocate it explicitly anyway. The C API is very user unfriendly.
@Craigacp
The unit tests all passed with this change, so presumably we'd have hit a double free already if it was going to happen.
The wording of TF_LoadSesssionFromSavedModel suggests that it doesn't care about the graph so I'd expect that to mean that it's our job to manage it.
Jakob Sultan Ericsson
@jakeri
Hey, me and @samuelotter will do some tests during the day with branch above. Is it isolated enough to be backported to 0.3-branch?
4 replies
@Craigacp
Yes we will be able to backport this if it fixes the issue.
James Piggott
@JamesPiggott
Hello, I want to run a Tensorflow model I found with a Java app, but I am having difficulty with getting the input just right. Below you can see the result from the layer analysis. I found a few examples for one-dimensional input (mnist) and I got another model working that required integers, but creating Tensor<TFloat32> with dimensions {batch, height, width, channels} is a difficult task. I would like some help.
input_image=name: "serving_default_input_image:0"
dtype: DT_FLOAT
tensor_shape {
dim {
size: -1
}
dim {
size: -1
}
dim {
size: -1
}
dim {
size: 3
}
}
5 replies
Karl Lessard
@karllessard

@JamesPiggott , I don’t know if you are subscribed to the TensorFlow discussion forum but it is the new TF platform for starting new discussion and we’ll slowly migrate the different topics started on this Gitter channel to it.

That being said, I’ve replied and posted an example of converting BufferedImage instances to a float tensor (TFloat32) in this post, if you can please take a look and maybe continue the discussion from there?

(BTW sorry but my original snippet was in Kotlin, let me know if you need help to convert it to Java)