tensorflow | An Open Source Machine Learning Framework for Everyone | Machine Learning library

 by   tensorflow C++ Version: 2.14.0rc1 License: Apache-2.0

kandi X-RAY | tensorflow Summary

kandi X-RAY | tensorflow Summary

tensorflow is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow has a Permissive License and it has medium support. However tensorflow has 210 bugs and it has 5 vulnerabilities. You can download it from GitHub.

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well. TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages. Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

            kandi-support Support

              tensorflow has a medium active ecosystem.
              It has 175562 star(s) with 88446 fork(s). There are 7705 watchers for this library.
              There were 9 major release(s) in the last 6 months.
              There are 1930 open issues and 35459 have been closed. On average issues are closed in 294 days. There are 193 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tensorflow is 2.14.0rc1

            kandi-Quality Quality

              tensorflow has 210 bugs (14 blocker, 3 critical, 121 major, 72 minor) and 7277 code smells.

            kandi-Security Security

              tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tensorflow code analysis shows 5 unresolved vulnerabilities (0 blocker, 3 critical, 2 major, 0 minor).
              There are 300 security hotspots that need review.

            kandi-License License

              tensorflow is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tensorflow releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tensorflow and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow implemented functionality, and help decide if they suit your requirements.
            • Run a single model iteration .
            • Splits the given computation into multiple tensors .
            • Decorate a function .
            • Compute an RNN layer .
            • Compute the eigenvalues of a Hermitagonal matrix .
            • Decorator for functions .
            • Produce an RNN layer .
            • Return the gradient of an einsum operator .
            • Creates a csv dataset .
            • Extracts inputs and attrs .
            Get all kandi verified functions for this library.

            tensorflow Key Features

            No Key Features are available at this moment for tensorflow.

            tensorflow Examples and Code Snippets

            expected shape=(None, 784), found shape=(None, 28, 28)
            Pythondot img1Lines of Code : 2dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            img_final = np.reshape(img, (1,784))
            Keras: AttributeError: 'Adam' object has no attribute '_name'
            Pythondot img2Lines of Code : 34dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # !pip install keras-rl2
            import tensorflow as tf
            from keras.layers import Dense, Flatten
            import gym
            from rl.agents.dqn import DQNAgent
            from rl.policy import BoltzmannQPolicy
            from rl.memory import SequentialMemory
            env = gym.make('CartPole-
            Using pretrained models for mnist dataset
            Pythondot img3Lines of Code : 10dot img3License : Strong Copyleft (CC BY-SA 4.0)
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            (x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data()
            print(x_trian.shape) # (60000, 28, 28)
            # train set / data 
            x_train = np.expand_dims(x_train, axis=-1)
            x_train = tf.image.resize(x_train, [224,224]) 
            print(x_train.shape) #
            The size of the image input to the neural network is abnormal
            Pythondot img4Lines of Code : 3dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            predict = model.predict(np.array([image]))[0]
            Executing model.fit multiple times
            Pythondot img5Lines of Code : 4dot img5License : Strong Copyleft (CC BY-SA 4.0)
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            modelWeights = model.get_weights()
            copy iconCopy
            import tensorflow as tf
            from tensorflow.python.ops import resource_variable_ops
            class MyModule(tf.Module):
              def __init__(self):
                                            tf.TensorSpec(shape=[None], dtype=
            Proper way of resizing image for Deep Learning models
            Pythondot img7Lines of Code : 4dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            x_train = np.array([cv2.resize(img, dim) for img in x_train[:,:,:]])
            x_train = np.array([cv2.resize(img, dim) for img in x_train])
            Proper way of resizing image for Deep Learning models
            Pythondot img8Lines of Code : 28dot img8License : Strong Copyleft (CC BY-SA 4.0)
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            import tensorflow as tf
            import matplotlib.pyplot as plt
            (X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()
            train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
            test_dataset  = tf.data.Dataset.f
            tf.keras.callbacks.ModelCheckpoint ignores the montior parameter and always use loss
            Pythondot img9Lines of Code : 11dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            callbacks = [
            copy iconCopy
            ├── circle
            │   ├── shared
            │   └── unshared
            ├── square
            │   ├── shared
            │   └── unshared
            └── triangle
                ├── shared
                └── unshared
            import pathlib
            # Get project root depending on your project structure.

            Community Discussions


            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



            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


            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:



            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


            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.



            Answered 2021-Sep-17 at 11:03

            Add mavenCentral() in Build Script

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


            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.



            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


            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:



            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


            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:



            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


            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:



            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


            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:



            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


            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.



            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


            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:



            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

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install tensorflow

            See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.
            You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.


            The TensorFlow project strives to abide by generally accepted best practices in open-source software development:.
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