ano_pred_cvpr2018 | Official implementation of Paper Future Frame | Predictive Analytics library

 by   StevenLiuWen Python Version: Current License: No License

kandi X-RAY | ano_pred_cvpr2018 Summary

kandi X-RAY | ano_pred_cvpr2018 Summary

ano_pred_cvpr2018 is a Python library typically used in Analytics, Predictive Analytics, Deep Learning, Pytorch applications. ano_pred_cvpr2018 has no bugs, it has no vulnerabilities and it has low support. However ano_pred_cvpr2018 build file is not available. You can download it from GitHub.

Official implementation of Paper Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018
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              ano_pred_cvpr2018 has a low active ecosystem.
              It has 325 star(s) with 112 fork(s). There are 17 watchers for this library.
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              It had no major release in the last 6 months.
              There are 20 open issues and 36 have been closed. On average issues are closed in 104 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of ano_pred_cvpr2018 is current.

            kandi-Quality Quality

              ano_pred_cvpr2018 has 0 bugs and 42 code smells.

            kandi-Security Security

              ano_pred_cvpr2018 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              ano_pred_cvpr2018 code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              ano_pred_cvpr2018 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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              ano_pred_cvpr2018 releases are not available. You will need to build from source code and install.
              ano_pred_cvpr2018 has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              ano_pred_cvpr2018 saves you 1285 person hours of effort in developing the same functionality from scratch.
              It has 2886 lines of code, 126 functions and 46 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed ano_pred_cvpr2018 and discovered the below as its top functions. This is intended to give you an instant insight into ano_pred_cvpr2018 implemented functionality, and help decide if they suit your requirements.
            • Builds a network of inputs
            • Compute the correlation coefficient
            • Leaky ReLU
            • Return the antipads of a tensor
            • Load a single batch
            • Generate the coefficients for a given parameter
            • Get a tf dataset
            • Convert a config dictionary to a dictionary
            • Evaluate the model
            • Convert flow to image
            • Calculate the mean PSNR for a loss file
            • Compute the ROC curve
            • Compute pSNR error
            • Calculate precision recall curve
            • Read from a flo file
            • Calculate the average psnr file
            • Calculate the score for a given loss file
            • Test the test function
            • Compute the model
            • Generator for a pix2pix 2d image
            • Argument parser
            • Train the model
            • Visualize the flow
            • Calculate the L2 loss between predictions and predictions
            • Convert flow to image
            • Convert a tensorflow tensorflow flow into an image
            • Calculate the gradient loss for each channel
            Get all kandi verified functions for this library.

            ano_pred_cvpr2018 Key Features

            No Key Features are available at this moment for ano_pred_cvpr2018.

            ano_pred_cvpr2018 Examples and Code Snippets

            No Code Snippets are available at this moment for ano_pred_cvpr2018.

            Community Discussions

            QUESTION

            will TensorFlow utilize GPU for predictive Analysis?
            Asked 2020-Nov-21 at 21:35

            GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

            note: predictive Analysis requires no graphics processing.

            ...

            ANSWER

            Answered 2020-Nov-21 at 21:35
            The short answer: yes, it will! The slightly longer answer:

            The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

            The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

            If you want to know more about this, I'd recommend you start here or here.

            some things to consider:
            • how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
            • Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
            • Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.

            Source https://stackoverflow.com/questions/64948197

            QUESTION

            Restructuring Pandas Dataframe for large number of columns
            Asked 2020-Nov-01 at 19:39

            I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.

            The current structure is as below.

            What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):

            Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.

            ...

            ANSWER

            Answered 2020-Nov-01 at 19:39

            You might want try pivoting your table:

            Source https://stackoverflow.com/questions/64630691

            QUESTION

            Display data from two json files in react native
            Asked 2020-May-17 at 23:55

            I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files

            ...

            ANSWER

            Answered 2020-May-17 at 23:55

            The new object to get params in React Navigation 5 is:

            Source https://stackoverflow.com/questions/61859411

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

            Vulnerabilities

            No vulnerabilities reported

            Install ano_pred_cvpr2018

            Install 3rd-package dependencies of python (listed in requirements.txt)
            Other libraries
            cd into Data folder of project and run the shell scripts (ped1.sh, ped2.sh, avenue.sh, shanghaitech.sh) under the Data folder. Please manually download all datasets from ped1.tar.gz, ped2.tar.gz, avenue.tar.gz and shanghaitech.tar.gz and tar each tar.gz file, and move them in to Data folder. You can also download data from BaiduYun(https://pan.baidu.com/s/1j0TEt-2Dw3kcfdX-LCF0YQ) i9b3.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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