nolearn | Combines the ease use scikit-learn with the power | Machine Learning library
kandi X-RAY | nolearn Summary
kandi X-RAY | nolearn Summary
nolearn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Neural Network applications. nolearn 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 nolearn' or download it from GitHub, PyPI.
Combines the ease of use of scikit-learn with the power of Theano/Lasagne
Combines the ease of use of scikit-learn with the power of Theano/Lasagne
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Quality
Security
License
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Support
nolearn has a highly active ecosystem.
It has 941 star(s) with 268 fork(s). There are 54 watchers for this library.
It had no major release in the last 12 months.
There are 33 open issues and 173 have been closed. On average issues are closed in 73 days. There are 7 open pull requests and 0 closed requests.
It has a positive sentiment in the developer community.
The latest version of nolearn is 0.6.1
Quality
nolearn has 0 bugs and 231 code smells.
Security
nolearn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
nolearn code analysis shows 0 unresolved vulnerabilities.
There are 4 security hotspots that need review.
License
nolearn is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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nolearn 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.
nolearn saves you 1670 person hours of effort in developing the same functionality from scratch.
It has 3705 lines of code, 316 functions and 28 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed nolearn and discovered the below as its top functions. This is intended to give you an instant insight into nolearn implemented functionality, and help decide if they suit your requirements.
- Fit the model
- Build the network
- Configures the network
- Fill missing layer sizes
- Create function for iterating over the layers
- Returns a dictionary of parameters for the given name
- Get the model s params
- Get all params of all layers
- Compute the features
- Yield successive n - sized chunks from l
- Compute features
- Call overfeat
- Compute features from a list of images
- Prepare image
- Load weights from source
- Load parameters from source
- Returns a dict of all parameter values
- Check that all keyword arguments are used
- Computes the accuracy of the loss function
- Predict probabilities
- Predict probabilities for X
- Compute the cache key for the given data
- Number of samples
- Save weights to a file
- Save all parameters to a file
Get all kandi verified functions for this library.
nolearn Key Features
No Key Features are available at this moment for nolearn.
nolearn Examples and Code Snippets
No Code Snippets are available at this moment for nolearn.
Community Discussions
Trending Discussions on nolearn
QUESTION
Convolutional Neural Net-Keras-val_acc Keyerror 'acc'
Asked 2019-Nov-01 at 00:16
ANSWER
Answered 2017-Mar-09 at 08:38from keras source :
QUESTION
Convolutional Neural Network - Visualizing weights
Asked 2017-Jan-12 at 21:44
Main Problem
I cannot understand the Plot of the weights of a specific layer.
I used a method from no-learn : plot_conv_weights(layer, figsize=(6, 6))
Im using lasagne as my neural-network library.
The plot comes out fine, but I dont know how i should interpret it.
Neural Network Structure
The structure im using :
...ANSWER
Answered 2017-Jan-12 at 06:21Normally when you visualize the weights you want to check 2 things:
- That they are smooth and cover a wide range of values, i.e. it's not a bunch of 1's and 0's. That would mean the non-linearity is being saturated.
- That they have some kind of structure. Normally you tend to see oriented edges although this is more difficult to see when you have small filters like 3x3.
That being said, your weights do not appear to be saturated, but they indeed seem to be too random. During training, did the network converge correctly? I am also surprised at how big your filters are (30x30). Not sure what you are trying to accomplish with that.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install nolearn
You can install using 'pip install nolearn' or download it from GitHub, PyPI.
You can use nolearn 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 nolearn 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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