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numpy-ml | Machine learning, in numpy | Machine Learning library

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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

kandi-support Support

  • numpy-ml has a medium active ecosystem.
  • It has 10270 star(s) with 2889 fork(s). There are 414 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 13 open issues and 25 have been closed. On average issues are closed in 30 days. There are 8 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of numpy-ml is current.

quality kandi Quality

  • numpy-ml has 0 bugs and 0 code smells.


  • 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 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.


  • 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.

  • plot the model
  • Plot the scheduler
  • Decode the model .
  • Plots Bayes .
  • Compute the MFCC of a time series .
  • Compute the CD - kernel .
  • Calculate the confidence interval of the GP .
  • Update the objective function .
  • Run minibatcher on a corpus .
  • Generate a mel spectrogram

numpy-ml Key Features

Machine learning, in numpy

numpy-ml Examples and Code Snippets

  • For rapid experimentation
  • How do I export a graph to Tensorflow Serving so that the input is b64?

For rapid experimentation

$ git clone https://github.com/ddbourgin/numpy-ml.git
$ cd numpy-ml && virtualenv npml && source npml/bin/activate
$ pip3 install -r requirements-dev.txt

Community Discussions

Trending Discussions on numpy-ml
  • How do I export a graph to Tensorflow Serving so that the input is b64?
Trending Discussions on numpy-ml


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:

image = tf.placeholder(dtype=tf.string, shape=[None], name='source')

signature = predict_signature_def(inputs={'image_bytes': image},
                                 outputs={'output': model.output})

I somehow need to find a way to convert image to model.input, or a way to get the model output to connect to image.

Any help would be greatly appreciated!


Answered 2018-Aug-07 at 14:51

You can use tf.decode_base64:

image = tf.placeholder(dtype=tf.string, shape=[None], name='source')
image_b64decoded = tf.decode_base64(image)
signature = predict_signature_def(inputs={'image_bytes': image_b64decoded},
                                 outputs={'output': model.output})


If you need to use tf.image.decode_image, you can get it to work with multiple inputs using tf.map_fn:

image = tf.placeholder(dtype=tf.string, shape=[None], name='source')
image_b64decoded = tf.decode_base64(image)
image_decoded = tf.map_fn(tf.image.decode_image, image_b64decoded, dtype=tf.uint8)

This will work as long as the images have all the same dimensions, of course. However, the result is a tensor with completely unknown shape, because tf.image.decode_image can output a different number of dimensions depending on the type of image. You can either reshape it or use another tf.image.decode_* call so at least you have a known number of dimensions in the tensor.

Source https://stackoverflow.com/questions/51728513

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


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.


For more details on the available models, see the project documentation.

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