Metadata-Version: 1.1
Name: theanets
Version: 0.5.2
Summary: A library of neural nets in theano
Home-page: http://github.com/lmjohns3/theanets
Author: Leif Johnson
Author-email: leif@lmjohns3.com
License: MIT
Description: theanets
        ========
        
        The ``theanets`` package provides tools for defining and optimizing several
        common types of neural network models. It uses Python for rapid development, and
        under the hood Theano_ provides graph optimization and fast computations on the
        GPU.
        
        The package defines models for classification_, autoencoding_, regression_, and
        prediction_. Models can easily be created with any number of feedforward_ or
        recurrent_  layers_ and combined with different regularizers:
        
        - L1/L2 weight decay
        - L1/L2 hidden activation penalties (e.g., sparse autoencoders)
        - Dropout/gaussian noise (e.g., denoising autoencoders)
        
        Several optimization algorithms are also included:
        
        - SGD_ and variants: NAG_, Rprop_, RmsProp_, ADADELTA_
        - Many algorithms from ``scipy.optimize.minimize``
        - Greedy layerwise_ pre-training
        
        At present there are no RBMs, convolutions, or maxout in ``theanets`` -- for
        those, you might want to look at Morb_, Lasagne_, or pylearn2_. There are many
        other neural networks toolkits out there as well, in many other languages; see
        `this stackoverflow question`_ for a few additional pointers, or just search for
        them.
        
        .. _Theano: http://deeplearning.net/software/theano/
        
        .. _classification: http://theanets.readthedocs.org/en/latest/generated/theanets.feedforward.Classifier.html
        .. _autoencoding: http://theanets.readthedocs.org/en/latest/generated/theanets.feedforward.Autoencoder.html
        .. _regression: http://theanets.readthedocs.org/en/latest/generated/theanets.feedforward.Regressor.html
        .. _prediction: http://theanets.readthedocs.org/en/latest/generated/theanets.recurrent.Predictor.html
        
        .. _feedforward: http://theanets.readthedocs.org/en/latest/generated/theanets.layers.Feedforward.html
        .. _recurrent: http://theanets.readthedocs.org/en/latest/generated/theanets.layers.Recurrent.html
        .. _layers: http://theanets.readthedocs.org/en/latest/reference.html#module-theanets.layers
        
        .. _SGD: http://theanets.readthedocs.org/en/latest/generated/theanets.trainer.SGD.html
        .. _NAG: http://theanets.readthedocs.org/en/latest/generated/theanets.trainer.NAG.html
        .. _Rprop: http://theanets.readthedocs.org/en/latest/generated/theanets.trainer.Rprop.html
        .. _RmsProp: http://theanets.readthedocs.org/en/latest/generated/theanets.trainer.RmsProp.html
        .. _ADADELTA: http://theanets.readthedocs.org/en/latest/generated/theanets.trainer.ADADELTA.html
        .. _layerwise: http://theanets.readthedocs.org/en/latest/generated/theanets.trainer.Layerwise.html
        
        .. _Morb: https://github.com/benanne/morb
        .. _Lasagne: https://github.com/benanne/Lasagne
        .. _pylearn2: http://deeplearning.net/software/pylearn2
        .. _this stackoverflow question: http://stackoverflow.com/questions/11477145/open-source-neural-network-library
        
        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/theanets
            cd theanets
            python setup.py develop
        
        Example
        -------
        
        Let's say you wanted to create a classifier and train it on some 100-dimensional
        data points that you've classified into 10 categories. You can define your model
        and train it using a few lines of code::
        
          import climate
          import theanets
          import my_data_set
        
          climate.enable_default_logging()
        
          exp = theanets.Experiment(
              theanets.Classifier,
              layers=(100, 200, 100, 10),
              hidden_l1=0.1,
          )
        
          exp.train(
              my_data_set.training_data,
              my_data_set.validation_data,
              optimize='sgd',
              learning_rate=0.01,
              momentum=0.5,
          )
        
          exp.network.predict(my_data_set.test_data)
        
        The trained ``network`` lets you make predictions on unseen test data.
        
        More Information
        ----------------
        
        Source: https://github.com/lmjohns3/theanets
        
        Documentation: http://theanets.readthedocs.org
        
        Mailing list: https://groups.google.com/forum/#!forum/theanets
        
Keywords: machine-learning neural-network deep-neural-network recurrent-neural-network autoencoder sparse-autoencoder classifier theano
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
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
