3DCNN | 3D convolutional neural network for video classification | Machine Learning library

by kcct-fujimotolab Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | 3DCNN Summary

3DCNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. 3DCNN has no bugs, it has no vulnerabilities and it has low support. However 3DCNN build file is not available. You can download it from GitHub.

This code requires UCF-101 dataset. This code generates graphs of accuracy and loss, plot of model, result and class names as txt file and model as hd5 and json. You can use visualize_input.py to make an input image which will maximize the specific output. This code is able to maximize a layer's output of any classification model. (Only dense layer convolutional layer(2D/3D) and pooling layer(2D/3D) are allowed.).

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3DCNN has a low active ecosystem.

It has 246 star(s) with 105 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 17 open issues and 6 have been closed. On average issues are closed in 130 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of 3DCNN is current.

Quality

3DCNN has no bugs reported.

Security

3DCNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

3DCNN does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

3DCNN releases are not available. You will need to build from source code and install.

3DCNN has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed 3DCNN and discovered the below as its top functions. This is intended to give you an instant insight into 3DCNN implemented functionality, and help decide if they suit your requirements.

Generate an input image
Deprocessing image
Calculate the Adam algorithm
Save a matplotlib figure
Takes an image and prints its maximum value
Load data from files
Create a 3D video from a file
Return the UCF classname from the given filename
Create 3D CNN
Create a 3D video from a file
Plot the model accuracy
Saves the result
Return the uF classname from the given filename

Get all kandi verified functions for this library.

3DCNN Key Features

No Key Features are available at this moment for 3DCNN.

3DCNN Examples and Code Snippets

No Code Snippets are available at this moment for 3DCNN.

Community Discussions

Trending Discussions on 3DCNN

3D CNN model throwing a Negative dimension error-- dimension issue

Cannot convert tf.keras.layers.ConvLSTM2D layer to open vino intermediate representation

How can I resolve ValueError: Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata).?

QUESTION

3D CNN model throwing a Negative dimension error-- dimension issue

Asked 2021-Jun-21 at 13:46

I am creating a 3D CNN model with Height = 128, Width = 128, Channels = 3.The code for 3D CNN-

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ANSWER

Answered 2021-Jun-21 at 13:46

Without specifying data_format argument, a Conv3D layer considers the input shape as:

batch_shape + (conv_dim1, conv_dim2, conv_dim3, channels)

Which you have specified as:

batch_shape + (width=128, height=128, depth=3, channels=1)

Therefore you have a data which its' shape is (128,128,3) and has 1 channel.

As the convolution operation applies to the first 3 dimensions which are (128,128,3), after first convolution by kernel_size=3 the 3rd dimension (the one you specified as depth=3), shrinks to 1. Then in the next layer (MaxPooling3D) it can not get pooling by 2, because the shape does not fit. So, consider to change the depth dimension by larger numbers or change kernel_size parameter. For example input shape could be (128,128,128,1) or the kernel_size should change to something else like (3,3,1).

P.S: If you have a RGB image, then number of channels is 3 and the last dimension should be set to 3. In 3D images there is another concept named depth (another dimension) which is different from channel. So:

3D Image RGB: (width, height, depth, 3)
3D Image Grayscale: (width, height, depth, 1)
2D Image RGB: (width, height, 3)
2D Image Grayscale: (width, height, 1)

Source https://stackoverflow.com/questions/68068140

QUESTION

Cannot convert tf.keras.layers.ConvLSTM2D layer to open vino intermediate representation

Asked 2020-Mar-21 at 20:45

I am trying to convert a trained model in tensorflow to Open VINO Intermediate Representation.

I have a model of the form given below

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ANSWER

Answered 2020-Mar-21 at 20:45

Actually the script to convert from h5 to .pb suggested by intel was not good enough. Always use the code from here to convert your keras model to .pb.

Once you obtain your .pb file now convert your model to IR using

Source https://stackoverflow.com/questions/60606297

QUESTION

How can I resolve ValueError: Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata).?

Asked 2020-Feb-10 at 14:38

I'm trying to implement a 3Dcnn model in keras, but I have a problem with how my model is called. Running the following code:

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ANSWER

Answered 2020-Feb-10 at 14:38

You are not correctly using your layer with the Functional API, as you are not giving inputs to layers. This is the correct way:

Source https://stackoverflow.com/questions/60152154

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

Vulnerabilities

No vulnerabilities reported

Install 3DCNN

You can download it from GitHub.
You can use 3DCNN 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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