tensorflow | Go binding for Tensorflow | Machine Learning library

 by   danieldk Go Version: Current License: No License

kandi X-RAY | tensorflow Summary

kandi X-RAY | tensorflow Summary

tensorflow is a Go library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. tensorflow has no bugs and it has low support. However tensorflow has 20 vulnerabilities. You can download it from GitHub.

Go binding for Tensorflow
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              tensorflow has a low active ecosystem.
              It has 131 star(s) with 11 fork(s). There are 10 watchers for this library.
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              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 4 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of tensorflow is current.

            kandi-Quality Quality

              tensorflow has 0 bugs and 0 code smells.

            kandi-Security Security

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              tensorflow has 20 vulnerability issues reported (0 critical, 7 high, 13 medium, 0 low).
              tensorflow code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tensorflow does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              tensorflow releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

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            Community Discussions

            QUESTION

            What is XlaBuilder for?
            Asked 2022-Mar-20 at 18:41

            What's the XLA class XlaBuilder for? The docs describe its interface but don't provide a motivation.

            The presentation in the docs, and indeed the comment above XlaBuilder in the source code

            ...

            ANSWER

            Answered 2021-Dec-15 at 01:32

            XlaBuilder is the C++ API for building up XLA computations -- conceptually this is like building up a function, full of various operations, that you could execute over and over again on different input data.

            Some background, XLA serves as an abstraction layer for creating executable blobs that run on various target accelerators (CPU, GPU, TPU, IPU, ...), conceptually kind of an "accelerator virtual machine" with conceptual similarities to earlier systems like PeakStream or the line of work that led to ArBB.

            The XlaBuilder is a way to enqueue operations into a "computation" (similar to a function) that you want to run against the various set of accelerators that XLA can target. The operations at this level are often referred to as "High Level Operations" (HLOs).

            The returned XlaOp represents the result of the operation you've just enqueued. (Aside/nerdery: this is a classic technique used in "builder" APIs that represent the program in "Static Single Assignment" form under the hood, the operation itself and the result of the operation can be unified as one concept!)

            XLA computations are very similar to functions, so you can think of what you're doing with an XlaBuilder like building up a function. (Aside: they're called "computations" because they do a little bit more than a straightforward function -- conceptually they are coroutines that can talk to an external "host" world and also talk to each other via networking facilities.)

            So the fact XlaOps can't be used across XlaBuilders may make more sense with that context -- in the same way that when building up a function you can't grab intermediate results in the internals of other functions, you have to compose them with function calls / parameters. In XlaBuilder you can Call another built computation, which is a reason you might use multiple builders.

            As you note, you can choose to inline everything into one "mega builder", but often programs are structured as functions that get composed together, and ultimately get called from a few different "entry points". XLA currently aggressively specializes for the entry points it sees API users using, but this is a design artifact similar to inlining decisions, XLA can conceptually reuse computations built up / invoked from multiple callers if it thought that was the right thing to do. Usually it's most natural to enqueue things into XLA however is convenient for your description from the "outside world", and allow XLA to inline and aggressively specialize the "entry point" computations you've built up as you execute them, in Just-in-Time compilation fashion.

            Source https://stackoverflow.com/questions/70339753

            QUESTION

            WebSocket not working when trying to send generated answer by keras
            Asked 2022-Feb-17 at 12:52

            I am implementing a simple chatbot using keras and WebSockets. I now have a model that can make a prediction about the user input and send the according answer.

            When I do it through command line it works fine, however when I try to send the answer through my WebSocket, the WebSocket doesn't even start anymore.

            Here is my working WebSocket code:

            ...

            ANSWER

            Answered 2022-Feb-16 at 19:53

            There is no problem with your websocket route. Could you please share how you are triggering this route? Websocket is a different protocol and I'm suspecting that you are using a HTTP client to test websocket. For example in Postman:

            Postman New Screen

            HTTP requests are different than websocket requests. So, you should use appropriate client to test websocket.

            Source https://stackoverflow.com/questions/71099818

            QUESTION

            Could not resolve com.google.guava:guava:30.1-jre - Gradle project sync failed. Basic functionality will not work properly - in kotlin project
            Asked 2022-Feb-14 at 19:47

            It was a project that used to work well in the past, but after updating, the following errors appear.

            ...

            ANSWER

            Answered 2021-Sep-17 at 11:03

            Add mavenCentral() in Build Script

            Source https://stackoverflow.com/questions/69205327

            QUESTION

            Tensorflow setup on RStudio/ R | CentOS
            Asked 2022-Feb-11 at 09:36

            For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used conda, miniconda, virtualenv but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?

            Following are the steps I am using to install tensorflow in RStudio.

            Since RStudio simply crashes each time I run tensorflow::tf_config() I have no way to check what is going wrong.

            ...

            ANSWER

            Answered 2022-Jan-16 at 00:08

            Perhaps my failed attempts will help someone else solve this problem; my approach:

            • boot up a clean CentOS 7 vm
            • install R and some dependencies

            Source https://stackoverflow.com/questions/70645074

            QUESTION

            Saving model on Tensorflow 2.7.0 with data augmentation layer
            Asked 2022-Feb-04 at 17:25

            I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.

            Here is the code of data augmentation:

            ...

            ANSWER

            Answered 2022-Feb-04 at 17:25

            This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

            Source https://stackoverflow.com/questions/69955838

            QUESTION

            Is it possible to use a collection of hyperspectral 1x1 pixels in a CNN model purposed for more conventional datasets (CIFAR-10/MNIST)?
            Asked 2021-Dec-17 at 09:08

            I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:

            ...

            ANSWER

            Answered 2021-Dec-16 at 10:18

            If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

            I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

            Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

            That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

            Some further ideas:

            • If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
            • If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

            Source https://stackoverflow.com/questions/70226626

            QUESTION

            ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'
            Asked 2021-Nov-13 at 07:14

            i have an import problem when executing my code:

            ...

            ANSWER

            Answered 2021-Oct-06 at 20:27

            You're using outdated imports for tf.keras. Layers can now be imported directly from tensorflow.keras.layers:

            Source https://stackoverflow.com/questions/69471749

            QUESTION

            Accuracy in Calculating Fourth Derivative using Finite Differences in Tensorflow
            Asked 2021-Sep-16 at 13:01

            I am writing a small code to calculate the fourth derivative using the method of finite differences in tensorflow. This is as follows:

            ...

            ANSWER

            Answered 2021-Sep-16 at 13:01

            The issue is related to the choice of floating-point types.

            • tf.linspace automatically selects tf.float32 as its type, while
            • np.linspace creates a float64 array, which has much more precision.

            Making the following modification:

            Source https://stackoverflow.com/questions/69125173

            QUESTION

            AssertionError: Tried to export a function which references untracked resource
            Asked 2021-Sep-07 at 11:23

            I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training.

            ...

            ANSWER

            Answered 2021-Sep-06 at 13:25

            Your issue is not related to 'transformer_transducer/transducer_encoder/inputs_embedding/ convolution_stack/conv2d/kernel:0'.
            The error code tells you this element is referring to a non trackable element. It seems the non-trackable object is not directly assigned to an attribute of this conv2d/kernel:0.

            To solve your issue, we need to localize Tensor("77040:0", shape=(), dtype=resource) from this error code:

            Source https://stackoverflow.com/questions/69040420

            QUESTION

            Stopping and starting a deep learning google cloud VM instance causes tensorflow to stop recognizing GPU
            Asked 2021-Jul-18 at 15:05

            I am using the pre-built deep learning VM instances offered by google cloud, with an Nvidia tesla K80 GPU attached. I choose to have Tensorflow 2.5 and CUDA 11.0 automatically installed. When I start the instance, everything works great - I can run:

            ...

            ANSWER

            Answered 2021-Jun-25 at 09:11

            Some people (sadly not me) are able to resolve this by setting the following at the beginning of their script/main:

            Source https://stackoverflow.com/questions/68119561

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            Vulnerabilities

            No vulnerabilities reported

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