MVCNN-TensorFlow | An Multi-View CNN implementation with TensorFlow | Machine Learning library

by WeiTang114 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | MVCNN-TensorFlow Summary

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

An Multi-View CNN (MVCNN) implementation with TensorFlow.

Support

Quality

Security

License

Reuse

Support

MVCNN-TensorFlow has a low active ecosystem.

It has 104 star(s) with 67 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 16 open issues and 10 have been closed. On average issues are closed in 87 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of MVCNN-TensorFlow is current.

Quality

MVCNN-TensorFlow has no bugs reported.

Security

MVCNN-TensorFlow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

MVCNN-TensorFlow is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

MVCNN-TensorFlow releases are not available. You will need to build from source code and install.

MVCNN-TensorFlow has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Train the model .
Embed inference multiview .
Load data from listfiles .
2d convolution layer .
Adds summaries for each loss .
Creates a variable with weight decay .
Compute the forward layer .
Summarize activations .
Load a single parameter .
Load data .

Get all kandi verified functions for this library.

MVCNN-TensorFlow Key Features

No Key Features are available at this moment for MVCNN-TensorFlow.

MVCNN-TensorFlow Examples and Code Snippets

No Code Snippets are available at this moment for MVCNN-TensorFlow.

Community Discussions

Trending Discussions on MVCNN-TensorFlow

Tensorflow 2.0 How make share parameters among convolutional layers?

QUESTION

Tensorflow 2.0 How make share parameters among convolutional layers?

Asked 2019-Jul-04 at 17:36

I am trying to re-implement Multi-View CNN (MVCNN) in Tensorflow 2.0. However, from what I see, keras layers do not have the options reuse=True|False like in tf.layers. Is there any way that I can define my layers which share parameters using the new API? Or I need to build my model in a TFv1 fashion?

Thank you very much!

...

ANSWER

Answered 2019-Jul-04 at 17:36

To share the parameters of a model you just have to use the same model. This is the new paradigm introduced in TensorFlow 2.0; In TF 1.xt we were using a graph-oriented approach, where we need to re-use the same graph to share the variables, but now we can just re-use the same tf.keras.Model object with different inputs.

Is the object that carries its own variables.

Using a Keras model and tf.GradientTape you can train a model sharing the variables easily as shown in the example below.

Source https://stackoverflow.com/questions/56889038

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

Vulnerabilities

No vulnerabilities reported

Install MVCNN-TensorFlow

You can download it from GitHub.
You can use MVCNN-TensorFlow 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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