cuda-convnet | started from Alex 's code on google code | Code Editor library

by mavenlin C Version: Current License: No License

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kandi X-RAY | cuda-convnet Summary

cuda-convnet is a C library typically used in Editor, Code Editor, Visual Studio Code applications. cuda-convnet has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

started from Alex's code on google code

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cuda-convnet has a low active ecosystem.

It has 42 star(s) with 24 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cuda-convnet is current.

Quality

cuda-convnet has 0 bugs and 0 code smells.

Security

cuda-convnet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cuda-convnet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cuda-convnet 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.

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

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

It has 2722 lines of code, 312 functions and 13 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

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Community Discussions

Trending Discussions on cuda-convnet

AttributeError: module 'tensorflow' has no attribute 'batch_matrix_band_part'

TensorFlow CNN Tutorial: How to edit top layer to be locally connected?

QUESTION

AttributeError: module 'tensorflow' has no attribute 'batch_matrix_band_part'

Asked 2017-Apr-11 at 19:19

I tried to solve the exercise in this website Convolutional Neural Networks

the exercise is:

The model architecture in inference() differs slightly from the CIFAR-10 model specified in cuda-convnet. In particular, the top layers of Alex's original model are locally connected and not fully connected. Try editing the architecture to exactly reproduce the locally connected architecture in the top layer.

I tried to add (batch_matrix_band_part) function in the cifar10.py in last part of inference()::

...

ANSWER

Answered 2017-Apr-11 at 18:46

It's exactly as the error is saying - tensorflow does not have a method called batch_matrix_band_part. Instead, use tf.matrix_band_part

Source https://stackoverflow.com/questions/43353862

QUESTION

TensorFlow CNN Tutorial: How to edit top layer to be locally connected?

Asked 2017-Apr-07 at 14:16

I have some background in machine learning and python, but I am just learning TensorFlow. I am going through the tutorial on deep convolutional neural nets to teach myself how to use it for image classification. Along the way there is an exercise, which I am having trouble completing.

EXERCISE: The model architecture in inference() differs slightly from the CIFAR-10 model specified in cuda-convnet. In particular, the top layers of Alex's original model are locally connected and not fully connected. Try editing the architecture to exactly reproduce the locally connected architecture in the top layer.

The exercise refers to the inference() function in the cifar10.py model. The 2nd to last layer (called local4) has a shape=[384, 192], and the top layer has a shape=[192, NUM_CLASSES], where NUM_CLASSES=10 of course. I think the code that we are asked to edit is somewhere in the code defining the top layer:

...

ANSWER

Answered 2017-Feb-14 at 17:56

I'll try to answer your question although I'm not 100% I got it right as well.

Looking at the cuda-convnet architecture we can see that the TensorFlow and cuda-convnet implementations start to differ after the second pooling layer.

TensorFlow implementation implements two fully connected layers and softmax classifier.

cuda-convnet implements two locally connected layers, one fully connected layer and softmax classifier.

The code snippet you included refers only to the softmax classifier and is in fact shared between the two implementations. To reproduce the cuda-convnet implementation using TensorFlow we have to replace the existing fully connected layers with two locally connected layers and a fully connected one.

Since Tensor doesn't have locally connected layers as part of the SDK we have to figure out a way to implement it using the existing tools. Here is my attempt to implement the first locally connected layers:

Source https://stackoverflow.com/questions/41734584

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