keras-training | jet classification and regression training in keras | Machine Learning library

by fastmachinelearning Python Version: Current License: GPL-3.0

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kandi X-RAY | keras-training Summary

keras-training is a Python library typically used in Artificial Intelligence, Machine Learning, Keras applications. keras-training has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However keras-training build file is not available. You can download it from GitHub.

jet classification and regression training in keras

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keras-training has a low active ecosystem.

It has 6 star(s) with 16 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 25 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-training is current.

Quality

keras-training has no bugs reported.

Security

keras-training has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

keras-training is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Add leaves to the tree
Generate a tree structure
Compute the delta_phi between two angles
Rotate a set of pixel vectors
Quantized Leakemaxrelu
Clip the input using clip_gradient
Round a value to x
Call the convolutional function
Quantize a tensor
Compute the input tensor
Write a keras model to a JSON file
Quantized tanh
Call the convolutional layer
Compute the Tensor
Implementation of binary sigmoid
Call the Tensor
Calculate the network
Performs a single step
Quantized KL divergence
Quantized maxrelu
Plot a confusion matrix
2D convolutional layer
Get the weights for each layer
Make the ROC curve
Conv1D convolutional layer
Get features from a tree

Get all kandi verified functions for this library.

keras-training Key Features

No Key Features are available at this moment for keras-training.

keras-training Examples and Code Snippets

No Code Snippets are available at this moment for keras-training.

Community Discussions

Trending Discussions on keras-training

Tensorflow InvalidArgumentError: Incompatible shapes after slicing input by Lambda Layer

QUESTION

Tensorflow InvalidArgumentError: Incompatible shapes after slicing input by Lambda Layer

Asked 2018-Jul-03 at 14:52

In my Convolution Network, I recently add a Lambda Layer as the input layer for select specific channels of the input images following the answer from this question

...

ANSWER

Answered 2018-Jul-03 at 14:52

The problem lies the way I slice the input using the Lambda Layer.

The input shape has 4 properties in this order: batch_size, width, height, channels.

For selecting multiple array of the input data, because Tensorflow is not supporting the advance indexing method from numpy, we should slice the input tensor first, use dim expanding to add the color depth, and then concatenate them later.

Source https://stackoverflow.com/questions/51132991

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

Vulnerabilities

No vulnerabilities reported

Install keras-training

Install miniconda2 by sourcing install_miniconda.sh in your home directory. Log out and log back in after this.

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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