five-video-classification-methods | Code that accompanies my blog post outlining five video | Machine Learning library

 by   harvitronix Python Version: v1.0 License: MIT

kandi X-RAY | five-video-classification-methods Summary

kandi X-RAY | five-video-classification-methods Summary

five-video-classification-methods is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. five-video-classification-methods has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

The five video classification methods:. See the accompanying blog post for full details:
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            kandi-support Support

              five-video-classification-methods has a medium active ecosystem.
              It has 1145 star(s) with 480 fork(s). There are 54 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 47 open issues and 75 have been closed. On average issues are closed in 29 days. There are 8 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of five-video-classification-methods is v1.0

            kandi-Quality Quality

              five-video-classification-methods has 0 bugs and 0 code smells.

            kandi-Security Security

              five-video-classification-methods has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              five-video-classification-methods code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              five-video-classification-methods is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              five-video-classification-methods releases are available to install and integrate.
              Build file is available. You can build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed five-video-classification-methods and discovered the below as its top functions. This is intended to give you an instant insight into five-video-classification-methods implemented functionality, and help decide if they suit your requirements.
            • Train the model .
            • 3d Convolutional Convectors
            • Extract video files .
            • Create a frame generator .
            • Move files .
            • Validate the trained model .
            • Creates and returns training and validation generator .
            • Get test lists .
            • Predict a model .
            • Extract features from an image .
            Get all kandi verified functions for this library.

            five-video-classification-methods Key Features

            No Key Features are available at this moment for five-video-classification-methods.

            five-video-classification-methods Examples and Code Snippets

            Train the model .
            pythondot img1Lines of Code : 72dot img1License : Permissive (MIT License)
            copy iconCopy
            def train(data_type, seq_length, model, saved_model=None,
                      class_limit=None, image_shape=None,
                      load_to_memory=False, batch_size=32, nb_epoch=100):
                # Helper: Save the model.
                checkpointer = ModelCheckpoint(
                    filepath=o  
            3d Convolutional Convex Network
            pythondot img2Lines of Code : 61dot img2License : Permissive (MIT License)
            copy iconCopy
            def c3d(self):
                    """
                    Build a 3D convolutional network, aka C3D.
                        https://arxiv.org/pdf/1412.0767.pdf
            
                    With thanks:
                        https://gist.github.com/albertomontesg/d8b21a179c1e6cca0480ebdf292c34d2
                    """
                    
            Initialize the model .
            pythondot img3Lines of Code : 60dot img3License : Permissive (MIT License)
            copy iconCopy
            def __init__(self, nb_classes, model, seq_length,
                             saved_model=None, features_length=2048):
                    """
                    `model` = one of:
                        lstm
                        lrcn
                        mlp
                        conv_3d
                        c3d
                    `nb_classe  

            Community Discussions

            Trending Discussions on five-video-classification-methods

            QUESTION

            Keras custom data generator - Error: 'int' object has no attribute 'shape'
            Asked 2021-Apr-29 at 15:38

            I am using tensorflow.keras with Tensorflow version 2.4.1. I have written a custom generator but during startig of first epoch it gives error: 'int' object has no attribute 'shape'

            ...

            ANSWER

            Answered 2021-Apr-29 at 15:38

            You're returning i as your target, which is an integer. You will need to transform i into a NumPy array. You know the drill:

            Source https://stackoverflow.com/questions/67317332

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

            Vulnerabilities

            No vulnerabilities reported

            Install five-video-classification-methods

            You can download it from GitHub.
            You can use five-video-classification-methods 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.

            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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