tf | TensorFlow samples | Machine Learning library

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kandi X-RAY | tf Summary

kandi X-RAY | tf Summary

tf is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. tf has no bugs, it has no vulnerabilities and it has low support. However tf build file is not available. You can download it from GitHub.

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            kandi-support Support

              tf has a low active ecosystem.
              It has 15 star(s) with 9 fork(s). There are 4 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 5 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of tf is current.

            kandi-Quality Quality

              tf has 0 bugs and 0 code smells.

            kandi-Security Security

              tf has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tf code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              tf 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.

            kandi-Reuse Reuse

              tf releases are not available. You will need to build from source code and install.
              tf has no build file. You will be need to create the build yourself to build the component from source.
              tf saves you 68 person hours of effort in developing the same functionality from scratch.
              It has 176 lines of code, 5 functions and 4 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tf and discovered the below as its top functions. This is intended to give you an instant insight into tf implemented functionality, and help decide if they suit your requirements.
            • Load data and labels .
            • Pads the contents to the specified number of characters .
            • Split word into words .
            • Return one - hot vector corresponding to the given index .
            • Log content to stdout .
            Get all kandi verified functions for this library.

            tf Key Features

            No Key Features are available at this moment for tf.

            tf Examples and Code Snippets

            No Code Snippets are available at this moment for tf.

            Community Discussions

            QUESTION

            Not able to get reasonable results from DenseVariational
            Asked 2021-Jun-15 at 16:05

            I am trying a regression problem with the following dataset (sinusoidal curve) of size 500

            First, I tried with 2 dense layer with 10 units each

            ...

            ANSWER

            Answered 2021-Mar-18 at 15:40

            QUESTION

            Model.evaluate returns 0 loss when using custom model
            Asked 2021-Jun-15 at 15:52

            I am trying to use my own train step in with Keras by creating a class that inherits from Model. It seems that the training works correctly but the evaluate function always returns 0 on the loss even if I send to it the train data, which have a big loss value during the training. I can't share my code but was able to reproduce using the example form the Keras api in https://keras.io/guides/customizing_what_happens_in_fit/ I changed the Dense layer to have 2 units instead of one, and made its activation to sigmoid.

            The code:

            ...

            ANSWER

            Answered 2021-Jun-12 at 17:27

            As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.

            Source https://stackoverflow.com/questions/67951244

            QUESTION

            How can I find to access to GPUs via Tensorflow in PyCharm?
            Asked 2021-Jun-15 at 14:43

            I have a problem about not accessing GPU in PyCharm and I use NVIDIA as GPU.

            I installed tensorflow-gpu in Python Interpreter of Setting part in Pycharm and then I run the code but I still cannot access it.

            I wonder if I should use CUDA library? How can I fix it?

            Here is my code snippet which is shown below.

            ...

            ANSWER

            Answered 2021-Jun-14 at 11:14

            I fixed my issue.

            Here are the steps of solving that issue.

            1 ) Download CUDA from https://developer.nvidia.com/cuda-downloads

            2 ) Download CUDNN from https://developer.nvidia.com/rdp/cudnn-download

            3 ) Copy bin,include and lastly lib from CUDNN zip file and paste it C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA{version}

            4 ) Then run the .py code in PyCharm and it perceives GPU at last.

            Source https://stackoverflow.com/questions/67952801

            QUESTION

            Apache Beam SIGKILL
            Asked 2021-Jun-15 at 13:51

            The Question

            How do I best execute memory-intensive pipelines in Apache Beam?

            Background

            I've written a pipeline that takes the Naemura Bird dataset and converts the images and annotations to TF Records with TF Examples of the required format for the TF object detection API.

            I tested the pipeline using DirectRunner with a small subset of images (4 or 5) and it worked fine.

            The Problem

            When running the pipeline with a bigger data set (day 1 of 3, ~21GB) it crashes after a while with a non-descriptive SIGKILL. I do see a memory peak before the crash and assume that the process is killed because of a too high memory load.

            I ran the pipeline through strace. These are the last lines in the trace:

            ...

            ANSWER

            Answered 2021-Jun-15 at 13:51

            Multiple things could cause this behaviour, because the pipeline runs fine with less Data, analysing what has changed could lead us to a resolution.

            Option 1 : clean your input data

            The third line of the logs you provide might indicate that you're processing unclean data in your bigger pipeline mmap(NULL, could mean that | "Get Content" >> beam.Map(lambda x: x.read_utf8()) is trying to read a null value.

            Is there an empty file somewhere ? Are your files utf8 encoded ?

            Option 2 : use smaller files as input

            I'm guessing using the fileio.ReadMatches() will try to load into memory the whole file, if your file is bigger than your memory, this could lead to errors. Can you split your data into smaller files ?

            Option 3 : use a bigger infrastructure

            If files are too big for your current machine with a DirectRunner you could try to use an on-demand infrastructure using another runner on the Cloud such as DataflowRunner

            Source https://stackoverflow.com/questions/67684186

            QUESTION

            Recommended way of measuring execution time in Tensorflow Federated
            Asked 2021-Jun-15 at 13:49

            I would like to know whether there is a recommended way of measuring execution time in Tensorflow Federated. To be more specific, if one would like to extract the execution time for each client in a certain round, e.g., for each client involved in a FedAvg round, saving the time stamp before the local training starts and the time stamp just before sending back the updates, what is the best (or just correct) strategy to do this? Furthermore, since the clients' code run in parallel, are such a time stamps untruthful (especially considering the hypothesis that different clients may be using differently sized models for local training)?

            To be very practical, using tf.timestamp() at the beginning and at the end of @tf.function client_update(model, dataset, server_message, client_optimizer) -- this is probably a simplified signature -- and then subtracting such time stamps is appropriate?

            I have the feeling that this is not the right way to do this given that clients run in parallel on the same machine.

            Thanks to anyone can help me on that.

            ...

            ANSWER

            Answered 2021-Jun-15 at 12:01

            There are multiple potential places to measure execution time, first might be defining very specifically what is the intended measurement.

            1. Measuring the training time of each client as proposed is a great way to get a sense of the variability among clients. This could help identify whether rounds frequently have stragglers. Using tf.timestamp() at the beginning and end of the client_update function seems reasonable. The question correctly notes that this happens in parallel, summing all of these times would be akin to CPU time.

            2. Measuring the time it takes to complete all client training in a round would generally be the maximum of the values above. This might not be true when simulating FL in TFF, as TFF maybe decided to run some number of clients sequentially due to system resources constraints. In practice all of these clients would run in parallel.

            3. Measuring the time it takes to complete a full round (the maximum time it takes to run a client, plus the time it takes for the server to update) could be done by moving the tf.timestamp calls to the outer training loop. This would be wrapping the call to trainer.next() in the snippet on https://www.tensorflow.org/federated. This would be most similar to elapsed real time (wall clock time).

            Source https://stackoverflow.com/questions/67982276

            QUESTION

            Terraform Optional Parameter for List of String
            Asked 2021-Jun-15 at 10:40

            Trying to implement Azure WAF policy and associate with http listener the code was working fine until I try to include a new optional parameter called http_listener_ids

            Tf code:

            ...

            ANSWER

            Answered 2021-Jun-15 at 10:40

            QUESTION

            How to add several binary classifiers at the end of a MLP with Keras?
            Asked 2021-Jun-15 at 02:43

            Say I have an MLP that looks like:

            ...

            ANSWER

            Answered 2021-Jun-15 at 02:43

            In your problem you are trying to use Sequential API to create the Model. There are Limitations to Sequential API, you can just create a layer by layer model. It can't handle multiple inputs/outputs. It can't be used for Branching also.

            Below is the text from Keras official website: https://keras.io/guides/functional_api/

            The functional API makes it easy to manipulate multiple inputs and outputs. This cannot be handled with the Sequential API.

            Also this stack link will be useful for you: Keras' Sequential vs Functional API for Multi-Task Learning Neural Network

            Now you can create a Model using Functional API or Model Sub Classing.

            In case of functional API Your Model will be

            Assuming Output_1 is classification with 17 classes Output_2 is calssification with 2 classes and Output_3 is regression

            Source https://stackoverflow.com/questions/67977986

            QUESTION

            How to use hcl write to set expressions with ${}?
            Asked 2021-Jun-14 at 19:21

            I am trying to use hclwrite to generate .tf files.

            According to the example in hclwrite Example, I can generate variables like foo = env.PATH, but I don't know how to generate more forms of expressions. For example, the following.

            ...

            ANSWER

            Answered 2021-Jun-14 at 19:21

            The hclwrite tool currently has no facility to automatically generate arbitrary expressions. Its helper functions are limited only to generating plain references and literal values. SetAttributeValue is the one for literal values, and so that's why the library correctly escaped the ${ sequence in your string, to ensure that it will be interpreted literally.

            If you want to construct a more elaborate expression then you'll need to do so manually by assembling the tokens that form the expression and then calling SetAttributeRaw instead.

            In the case of your example, it looks like you'd need to generate the following eight tokens:

            • TokenOQuote with the bytes "
            • TokenQuotedLit with the bytes hello
            • TokenTemplateInterp with the bytes ${
            • TokenIdent with the bytes var
            • TokenDot with the bytes .
            • TokenIdent with the bytes stage
            • TokenTemplateSeqEnd with the bytes }
            • TokenCQuote with the bytes "

            The SetAttributeValue function is automating the simpler case of generating three tokens: TokenOQuote, TokenQuotedLit, TokenCQuote.

            You can potentially automate the creation of tokens for the var.stage portion of this expression by using TokensForTraversal, which is what SetAttributeTraversal does internally. However, unless you already have a parsed hcl.Traversal representing var.stage, or unless you need things to be more dynamic than you've shown in practice, I expect that it would take more code to construct that input traversal than to just write out the three tokens literally as I showed above.

            Source https://stackoverflow.com/questions/67945463

            QUESTION

            Tensorflow ValueError: Dimensions must be equal: LSTM+MDN
            Asked 2021-Jun-14 at 19:07

            I am trying to make a next-word prediction model with LSTM + Mixture Density Network Based on this implementation(https://www.katnoria.com/mdn/).

            Input: 300-dimensional word vectors*window size(5) and 21-dimensional array(c) representing topic distribution of the document, used to train hidden initial states.

            Output: mixing coefficient*num_gaussians, variance*num_gaussians, mean*num_gaussians*300(vector size)

            x.shape, y.shape, c.shape with an experimental 161 obserbations gives me such:

            (TensorShape([161, 5, 300]), TensorShape([161, 300]), TensorShape([161, 21]))

            ...

            ANSWER

            Answered 2021-Jun-14 at 19:07

            for MDN model , the likelihood for each sample has to be calculated with all the Gaussians pdf , to do that I think you have to reshape your matrices ( y_true and mu) and take advantage of the broadcasting operation by adding 1 as the last dimension . e.g:

            Source https://stackoverflow.com/questions/67965364

            QUESTION

            SHAP DeepExplainer with TensorFlow 2.4+ error
            Asked 2021-Jun-14 at 14:52

            I'm trying to compute shap values using DeepExplainer, but I get the following error:

            keras is no longer supported, please use tf.keras instead

            Even though i'm using tf.keras?

            ...

            ANSWER

            Answered 2021-Jun-14 at 14:52

            TL;DR

            • Add tf.compat.v1.disable_v2_behavior() at the top for TF 2.4+
            • calculate shap values on numpy array, not on df

            Full reproducible example:

            Source https://stackoverflow.com/questions/66814523

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

            Vulnerabilities

            No vulnerabilities reported

            Install tf

            You can download it from GitHub.
            You can use tf like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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