ePooling | Expectation pooling : An effective and interpretable method | Machine Learning library

by gao-lab Python Version: Current License: No License

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

kandi X-RAY | ePooling Summary

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

Expectation pooling: An effective and interpretable method of pooling for predicting DNA-protein binding

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Support

ePooling has a low active ecosystem.

It has 8 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

ePooling has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ePooling is current.

Quality

ePooling has 0 bugs and 0 code smells.

Security

ePooling has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ePooling code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ePooling 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

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

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

Generate and save and save data
Stores the training set
Generate a random matrix
Load motif list
Train a CNN model
Build a CNN
Create a directory
Generate the matrix of motifs
Generate a matrix for training
Convert sequence to matrix
Read results from a result file
Extract label from a record
Compute the ROCAUC
Load test dataset
Load data from a dataset
Plot the maximum loss loss loss loss loss
Plot the loss loss loss loss for soft pooling
Plots the average learning rate loss loss loss
Write data to pwm file
Get all data info for a directory
Load motifs
Return a list of all training experiments

Get all kandi verified functions for this library.

ePooling Key Features

No Key Features are available at this moment for ePooling.

ePooling Examples and Code Snippets

No Code Snippets are available at this moment for ePooling.

Community Discussions

Trending Discussions on ePooling

Extract rows and columns of a tensor using indices

QUESTION

Extract rows and columns of a tensor using indices

Asked 2019-Sep-06 at 09:56

I have a 2-D tensor x, which is a placeholder x = tf.placeholder(tf.int32, shape=[None, 100]). Now in the graph, I want to extract randomly 50% rows of x. Here is what I tried:

...

ANSWER

Answered 2019-Sep-06 at 09:56

That's almost right, but:

You have to cast to tf.shape(x)[0] to float before multiplying by 0.5.
You cannot use int to cast a tensor data type, you need to use tf.cast.

Source https://stackoverflow.com/questions/57819633

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

Vulnerabilities

No vulnerabilities reported

Install ePooling

You can download it from GitHub.
You can use ePooling 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.