Metadata-Version: 1.1
Name: theanets
Version: 0.1.0
Summary: A library of neural nets in theano
Home-page: http://github.com/lmjohns3/theano-nets/
Author: Leif Johnson
Author-email: leif@leifjohnson.net
License: MIT
Description: # theano-nets
        
        This package contains implementations of several common neural network
        structures, using the amazing [Theano][] package for optimization.
        
        [Theano]: http://deeplearning.net/software/theano/
        
        ## Installation
        
        Install the latest published code using pip:
        
            pip install theanets
        
        Or download the current source and run it from there:
        
            git clone http://github.com/lmjohns3/theano-nets
            cd theano-nets
            python setup.py develop
        
        ## Getting started
        
        There are a few examples in the `examples/` directory. Run an example with the
        `--help` flag to get a list of all the command-line arguments ; there are many
        of them, but some of the notable ones are :
        
            -n or --layers N1 N2 N3 N4
        
        Build a network with `N1` inputs, two hidden layers with `N2` and `N3` units,
        and `N4` outputs. (Note that this argument is fixed in the code for the
        examples, since it needs to correspond to the shape of the data being
        processed.)
        
            -g or --activation logistic|relu|linear|norm:mean+logistic|...
        
        Use the given activation function for hidden layer units. (All output layer
        units have a linear activation function.) Several activation functions can be
        pipelined together using `+`.
        
            -O or --optimize sgd|hf|sgd+hf|...
        
        Use the given optimization method to train network parameters. Like the
        activations, several training methods can be used in sequence by concatenating
        their names with `+`.
        
        ## Using the library
        
        Probably the easiest way to start with the library is to copy one of the
        examples and modify it to perform your tasks. The usual workflow involves
        instantiating `theanets.Experiment` with a subclass of `theanets.Network`, then
        adding some data by calling `add_dataset(...)`, and finally calling `train()` to
        learn a good set of parameters for your data. You can then `save()` the trained
        model to a pickle, or call the trained `network` directly with new data to
        compute a feedforward pass.
        
        The documentation is relatively sparse, so please file bugs if you find that
        there's a particularly hard-to-understand area in the code.
        
Keywords: deep-belief-network neural-network theano machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
