skflow | Simplified interface for TensorFlow ( mimicking Scikit Learn | Machine Learning library

by tensorflow Python Version: 0.1.0 License: Apache-2.0

X-Ray Key Features Code Snippets(1)Community Discussions(7)Vulnerabilities Install Support

kandi X-RAY | skflow Summary

skflow is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. skflow has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install skflow' or download it from GitHub, PyPI.

Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

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Quality

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Support

skflow has a highly active ecosystem.

It has 3187 star(s) with 466 fork(s). There are 165 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 114 have been closed. On average issues are closed in 93 days. There are 1 open pull requests and 0 closed requests.

It has a negative sentiment in the developer community.

The latest version of skflow is 0.1.0

Quality

skflow has 0 bugs and 0 code smells.

Security

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

skflow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

skflow is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

skflow releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

skflow saves you 1231 person hours of effort in developing the same functionality from scratch.

It has 2770 lines of code, 216 functions and 67 files.

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

Top functions reviewed by kandi - BETA

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

Write the doc to f
Write member to file
Return imported modules
Format a docstring
Write a class to a file
Generates the signature for a function
Removes indentation from docstring
Get the members of a class
Writes a module doc to f
Write docstring to f
Prints a function
Determine if a member should be included
Write libraries to directory
Write other members to f
Write the documentation to a file
Return the fully qualified anchor name
Return a list of all library files
Collect all members of a module
Raise a RuntimeError if there are no leftover members
Return the name of the module to name

Get all kandi verified functions for this library.

skflow Key Features

No Key Features are available at this moment for skflow.

skflow Examples and Code Snippets

Tensorflow Dataset API doubles graph protobuff filesize

Python

Lines of Code : 14

License : Strong Copyleft (CC BY-SA 4.0)

Copy

class IteratorInitializerHook(tf.train.SessionRunHook):
    def __init__(self):
        super(IteratorInitializerHook, self).__init__()
        self.iterator_initiliser_func = None

    def after_create_session(self, session, coord):

Community Discussions

Trending Discussions on skflow

Tensorflow typeError 'feature_columns' with linear classifier

How To Use tf.estimator.DNNClassifier (Scikit Flow?)

tf.train.get.global_step error during Tensorflow classification example

Tensorflow-example with flowers

low training score in MLPClassifier (and other classifiers) of scikit

contrib.learn.estimator() for tensorflow0.12

which higher layer abstraction to use for tensorflow

QUESTION

Tensorflow typeError 'feature_columns' with linear classifier

Asked 2019-Oct-14 at 08:58

Im just tying to get some code to work but i keep getting errors.

The code is

...

ANSWER

Answered 2017-Oct-09 at 13:11

You have to define your feature_columns - here is the complete script:

Source https://stackoverflow.com/questions/46628087

QUESTION

How To Use tf.estimator.DNNClassifier (Scikit Flow?)

Asked 2019-Apr-06 at 16:31

Could someone point me to a basic working example for tf.estimator.DNNClassifier (originally skflow)?

Since I'm familiar with Sklearn, I was excited to read about Scikit Flow on this blog. Especially the api looked pretty much the same as SK-Learn.

However, I was having a problem getting the code from the blog to work.

Then I read from Scikit Flow Github that it moved to tensorflow/tensorflow/contrib/learn/python/learn.

Upon further investigation, I found tf.contrib.learn.DNNClassifier moved to tf.estimator.DNNClassifier.

However, now api for estimator seems pretty different than sklearn classifier.

I would appreciate if someone could point me to a basic working example.

Here's the code from the blog above.

...

ANSWER

Answered 2019-Apr-06 at 16:31

The API was changed very much, so now you can do something like this (an official example is available here):

Source https://stackoverflow.com/questions/55550396

QUESTION

tf.train.get.global_step error during Tensorflow classification example

Asked 2017-Nov-05 at 14:05

I am following Siraj Raval's YouTube video for building a simple classifier for the iris flower data set. The video was dated May of 2016 so I am sure that there are some areas of Tensorflow that have been updated. I am getting an error that says "please switch to tf.train.get.global_step. I am working off of the older dated Tensorflow library and I have tried figuring out the new one by researching feature_columns. I thought this would fix it but the error persists. Any help is greatly appreciated and any advice on becoming an educated Tensorflow user is openly welcome.

Here is my code

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ANSWER

Answered 2017-Nov-05 at 04:01

You need to specify the number of training steps in the classifier.fit method in your code. I have edited your code and given comments where its necessary.

Source https://stackoverflow.com/questions/47115957

QUESTION

Tensorflow-example with flowers

Asked 2017-Aug-18 at 13:54

I have tried out this most basic example with the very nice flowers. According to this older question (https://stackoverflow.com/a/41380178/6444605) there have been some changes. But is this example up-to-date now too? I ask, because I get this error:

classifier = skflow.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3) TypeError: _ _ init _ _() takes at least 3 arguments (3 given)

That's the code:

...

ANSWER

Answered 2017-Aug-16 at 15:53

I think you need to tell where the features are:

Source https://stackoverflow.com/questions/45716666

QUESTION

low training score in MLPClassifier (and other classifiers) of scikit

Asked 2017-Jul-17 at 20:20

(Update: Posted final findings as a separate answer)

I am getting started with trying to understand how to use scikit models for training. I've experimented with well known datasets like iris, MNIST etc - they are all well structured data, ready to be used. This is the first time I am trying to build a model out of raw data on my own and the results are less than desirable.

The data I chose to use is NHSTA's crash data for the last 3 years.

Here is a snapshot of the data, to give you an idea of the fields without having to download the data.

My first experiment is simple - try and build a model that given "License State Code" and "Age", try and predict the gender (M or F).

...

ANSWER

Answered 2017-Jul-17 at 14:00

The problem is not the model, it's the data. 'Age' and "License State Code' aren't the best parameters to determine 'Sex' .

Try using the same models for predicting 'Injury Severity' with 'Safety Equipment' and you should get better results.

Source https://stackoverflow.com/questions/45145991

QUESTION

contrib.learn.estimator() for tensorflow0.12

Asked 2017-Apr-28 at 05:02

I am using contrib.learn.estimator to predict in tensorflow0.12 environment.

...

ANSWER

Answered 2017-Apr-28 at 05:02

Your model_fn argument shall not be the return of ModelFnOps(). A mamual model_fn name is required:

Source https://stackoverflow.com/questions/42404724

QUESTION

which higher layer abstraction to use for tensorflow

Asked 2017-Jan-12 at 13:59

I am looking for higher layer abstractions for my deep learning project.

Few doubts lately.

I am really confused about which is more actively maintained tflearn(docs), or tensorflow.contrib.learn. But projects are different and actively contributed on Github. I did not find why are people working this way, same goal, same name, but working differently.
That was not enough, we also have skflow, why do we have this project separately, this aims to mimic scikit-learn like functionality for deep learning(just like tflearn do).
There are more and more coming, which one choose, and which one will be maintained in future?

Any ideas?

PS: I know this might get closed. but I would definitely want some answers first. Those wanting it closed, please care to drop a reason/hint/link in comments

...

ANSWER

Answered 2017-Jan-12 at 13:59

What about keras (https://keras.io/)? It is easy to use. However you can do pretty much everything you want with it. It uses either theano or tensorflow as its backend. Kaggle contests are often solved using keras (e.g. https://github.com/EdwardTyantov/ultrasound-nerve-segmentation).

Edit:

Because you did not specify python I would also recommend matconvnet if you look for more abstraction.

Source https://stackoverflow.com/questions/41535845

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

Vulnerabilities

No vulnerabilities reported

Install skflow

You can install using 'pip install skflow' or download it from GitHub, PyPI.
You can use skflow 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 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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