numpy-ml | Machine learning | Machine Learning library
kandi X-RAY | numpy-ml Summary
kandi X-RAY | numpy-ml Summary
numpy-ml is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Neural Network applications. numpy-ml has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has medium support. You can install using 'pip install numpy-ml' or download it from GitHub, PyPI.
Machine learning, in numpy
Machine learning, in numpy
Support
Quality
Security
License
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Support
numpy-ml has a medium active ecosystem.
It has 11766 star(s) with 3214 fork(s). There are 442 watchers for this library.
It had no major release in the last 12 months.
There are 16 open issues and 26 have been closed. On average issues are closed in 34 days. There are 12 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of numpy-ml is 0.1.2
Quality
numpy-ml has 0 bugs and 0 code smells.
Security
numpy-ml has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
numpy-ml code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
numpy-ml 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.
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numpy-ml releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
numpy-ml saves you 7712 person hours of effort in developing the same functionality from scratch.
It has 16321 lines of code, 1204 functions and 99 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed numpy-ml and discovered the below as its top functions. This is intended to give you an instant insight into numpy-ml implemented functionality, and help decide if they suit your requirements.
- Plots Bayes
- Fit the covariance matrix
- Fit the posterior distribution
- Predict the posterior of the Gaussian distribution
- Fit the hyperparameters
- Drops low frequency tokens
- Keep the top - N tokens in the corpus
- Plot the scheduler scheduler
- Calculate King loss
- Plot the GP distribution
- Fits the corpus
- Test the taxi agent
- Run a single episode
- Predict the log - likelihood for each sample
- Compute the perplexity of the given words
- Plot the logistic regression
- Computes the similarity of the input file
- Generate sentences
- Plot a KNN problem
- Backward
- Backward - naive attention
- Plot a regression problem
- Greedy policy
- Tile the state space
- Calculate the output dimensions for convolution
- Test the HMM
Get all kandi verified functions for this library.
numpy-ml Key Features
No Key Features are available at this moment for numpy-ml.
numpy-ml Examples and Code Snippets
No Code Snippets are available at this moment for numpy-ml.
Community Discussions
Trending Discussions on numpy-ml
QUESTION
How do I export a graph to Tensorflow Serving so that the input is b64?
Asked 2019-Apr-29 at 16:09
I have a Keras graph with a float32 tensor of shape (?, 224, 224, 3) that I want to export to Tensorflow Serving, in order to make predictions with RESTful. Problem is that I cannot input tensors, but encoded b64 strings, as that is a limitation of the REST API. That means that when exporting the graph, the input needs to be a string that needs to be decoded.
How can I "inject" the new input to be converted to the old tensor, without retraining the graph itself? I have tried several examples [1][2].
I currently have the following code for exporting:
...ANSWER
Answered 2018-Aug-07 at 14:51You can use tf.decode_base64:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install numpy-ml
You can install using 'pip install numpy-ml' or download it from GitHub, PyPI.
You can use numpy-ml 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.
You can use numpy-ml 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 more details on the available models, see the project documentation.
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