Metadata-Version: 1.1
Name: theanets
Version: 0.7.2
Summary: Feedforward and recurrent neural nets using Theano
Home-page: http://github.com/lmjohns3/theanets
Author: Leif Johnson
Author-email: leif@lmjohns3.com
License: MIT
Description: .. image:: https://travis-ci.org/lmjohns3/theanets.svg?branch=master
        .. image:: https://coveralls.io/repos/lmjohns3/theanets/badge.svg?branch=master
           :target: https://coveralls.io/r/lmjohns3/theanets?branch=master
        
        ============
        ``THEANETS``
        ============
        
        The ``theanets`` package is a deep learning and neural network toolkit. It is
        written in Python to interoperate with excellent tools like ``numpy`` and
        ``scikit-learn``, and it uses Theano_ to accelerate computations when possible
        using your GPU. The package aims to provide:
        
        - a simple API for building and training common types of neural network models;
        - thorough documentation;
        - easy-to-read code;
        - and, under the hood, a fully expressive graph computation framework.
        
        The package strives to "make the easy things easy and the difficult things
        possible." Please try it out, and let us know what you think!
        
        .. _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/theanets
            cd theanets
            python setup.py develop
        
        Note that the contents of this README are linked to the version of ``theanets``
        that you are using. If you're looking at the `README on GitHub`_, you'll need
        the current GitHub code; if you're looking at the `README for a pypi release`_,
        you'll need the code associated with that release.
        
        .. _README on GitHub: https://github.com/lmjohns3/theanets
        .. _README for a pypi release: https://pypi.python.org/pypi/theanets
        
        Quick Start: Classification
        ===========================
        
        Suppose you want to create a classifier and train it on some 100-dimensional
        data points that you've classified into 10 categories. No problem! With just a
        few lines you can (a) provide some data, (b) build and (c) train a model,
        and (d) evaluate the model:
        
        .. code:: python
        
          import climate
          import theanets
          from sklearn.datasets import make_classification
          from sklearn.metrics import confusion_matrix
        
          climate.enable_default_logging()
        
          # Create a classification dataset.
          X, y = make_classification(
              n_samples=3000, n_features=100, n_classes=10, n_informative=10)
          X = X.astype('f')
          y = y.astype('i')
          cut = int(len(X) * 0.8)  # training / validation split
          train = X[:cut], y[:cut]
          valid = X[cut:], y[cut:]
        
          # Build a classifier model with 100 inputs and 10 outputs.
          net = theanets.Classifier(layers=[100, 10])
        
          # Train the model using SGD with momentum.
          net.train(train, valid, algo='sgd', learning_rate=1e-4, momentum=0.9)
        
          # Show confusion matrices on the training/validation splits.
          for label, (X, y) in (('training:', train), ('validation:', valid)):
              print(label)
              print(confusion_matrix(y, net.predict(X)))
        
        Layers
        ------
        
        The model above is quite simplistic! Make it a bit more sophisticated by adding
        a hidden layer:
        
        .. code:: python
        
          net = theanets.Classifier([100, 1000, 10])
        
        In fact, you can just as easily create 3 (or any number of) hidden layers:
        
        .. code:: python
        
          net = theanets.Classifier([100, 1000, 1000, 1000, 10])
        
        By default, hidden layers use the relu transfer function. By passing a tuple
        instead of just an integer, you can change some of these layers to use different
        activations_:
        
        .. code:: python
        
          maxout = (1000, 'maxout:4')  # maxout with 4 pieces.
          net = theanets.Classifier([
              100, 1000, maxout, (1000, 'tanh'), 10])
        
        .. _activations: http://theanets.readthedocs.org/en/latest/api/activations.html
        
        By passing a dictionary instead, you can specify even more attributes of each
        layer_, like how its parameters are initialized:
        
        .. code:: python
        
          # Sparsely-initialized layer with large nonzero weights.
          foo = dict(name='foo', size=1000, std=1, sparsity=0.9)
          net = theanets.Classifier([
              100, foo, (1000, 'maxout:4'), (1000, 'tanh'), 10])
        
        .. _layer: http://theanets.readthedocs.org/en/latest/api/layers.html
        
        Specifying layers is the heart of building models in ``theanets``. Read more
        about this in `Specifying Layers`_.
        
        .. _Specifying Layers: http://localhost:8080/guide.html#guide-creating-specifying-layers
        
        Regularization
        --------------
        
        Adding regularizers is easy, too! Just pass them to the training method. For
        instance, you can train up a sparse classification model with weight decay:
        
        .. code:: python
        
          # Penalize hidden-unit activity (L1 norm) and weights (L2 norm).
          net.train(train, valid, hidden_l1=0.001, weight_l2=0.001)
        
        In ``theanets`` dropout is treated as a regularizer and can be set on many
        layers at once:
        
        .. code:: python
        
          net.train(train, valid, hidden_dropout=0.5)
        
        or just on a specific layer:
        
        .. code:: python
        
          net.train(train, valid, dropout={'foo:out': 0.5})
        
        Similarly, you can add Gaussian noise to any of the layers (here, just to the
        input layer):
        
        .. code:: python
        
          net.train(train, valid, input_noise=0.3)
        
        Optimization Algorithms
        -----------------------
        
        You can optimize your model using any of the algorithms provided by downhill_
        (SGD, NAG, RMSProp, ADADELTA, etc.), or additionally using a couple of
        `pretraining methods`_ specific to neural networks.
        
        .. _downhill: http://downhill.readthedocs.org/
        .. _pretraining methods: http://theanets.readthedocs.org/en/latest/api/trainers.html
        
        You can also make as many successive calls to train() as you like. Each call can
        include different training algorithms:
        
        .. code:: python
        
          net.train(train, valid, algo='rmsprop')
          net.train(train, valid, algo='nag')
        
        different learning hyperparameters:
        
        .. code:: python
        
          net.train(train, valid, algo='rmsprop', learning_rate=0.1)
          net.train(train, valid, algo='rmsprop', learning_rate=0.01)
        
        and different regularization hyperparameters:
        
        .. code:: python
        
          net.train(train, valid, input_noise=0.7)
          net.train(train, valid, input_noise=0.3)
        
        Training models is a bit more art than science, but ``theanets`` tries to make
        it easy to evaluate different training approaches. Read more about this in
        `Training a Model`_.
        
        .. _Training a Model: http://theanets.readthedocs.org/en/latest/guide.html#guide-training
        
        Quick Start: Recurrent Models
        =============================
        
        Recurrent neural networks are becoming quite important for many sequence-based
        tasks in machine learning; one popular toy example for recurrent models is to
        generate text that's similar to some body of training text.
        
        In these models, a recurrent classifier is set up to predict the identity of the
        next character in a sequence of text, given all of the preceding characters. The
        inputs to the model are the one-hot encodings of a sequence of characters from
        the text, and the corresponding outputs are the class labels of the subsequent
        character. The ``theanets`` code has a Text_ helper class that provides easy
        encoding and decoding of text to and from integer classes; using the helper
        makes the top-level code look like:
        
        .. code:: python
        
          import numpy as np, re, theanets
        
          chars = re.sub(r'\s+', ' ', open('corpus.txt').read().lower())
          txt = theanets.recurrent.Text(chars, min_count=10)
          A = 1 + len(txt.alpha)  # of letter classes
        
          # create a model to train: input -> gru -> relu -> softmax.
          net = theanets.recurrent.Classifier([A, (100, 'gru'), (1000, 'relu'), A])
        
          # train the model iteratively; draw a sample after every epoch.
          seed = txt.encode(txt.text[300017:300050])
          for tm, _ in net.itertrain(txt.classifier_batches(100, 32), momentum=0.9):
              print('{}|{} ({:.1f}%)'.format(
                  txt.decode(seed),
                  txt.decode(net.predict_sequence(seed, 40)),
                  100 * tm['acc']))
        
        This example uses several features of ``theanets`` that make modeling neural
        networks fun and interesting. The model uses a layer of `Gated Recurrent Units`_
        to capture the temporal dependencies in the data. It also `uses a callable`_ to
        provide data to the model, and takes advantage of `iterative training`_ to
        sample an output from the model after each training epoch.
        
        .. _Text: http://theanets.readthedocs.org/en/latest/api/generated/theanets.recurrent.Text.html
        .. _Gated Recurrent Units: http://theanets.readthedocs.org/en/latest/api/generated/theanets.layers.recurrent.GRU.html
        .. _uses a callable: http://downhill.readthedocs.org/en/stable/guide.html#data-using-callables
        .. _iterative training: http://downhill.readthedocs.org/en/stable/guide.html#iterative-optimization
        
        To run this example, download a text you'd like to model (e.g., Herman
        Melville's *Moby Dick*) and save it in ``corpus.txt``::
        
          curl http://www.gutenberg.org/cache/epub/2701/pg2701.txt > corpus.txt
        
        Then when you run the script, the output might look something like this
        (abbreviated to show patterns)::
        
          used for light, but only as an oi|pr vgti ki nliiariiets-a, o t.;to niy  , (16.6%)
          used for light, but only as an oi|s bafsvim-te i"eg nadg tiaraiatlrekls tv (20.2%)
          used for light, but only as an oi|vetr uob bsyeatit is-ad. agtat girirole, (28.5%)
          used for light, but only as an oi|siy thinle wonl'th, in the begme sr"hey  (29.9%)
          used for light, but only as an oi|nr. bonthe the tuout honils ohe thib th  (30.5%)
          used for light, but only as an oi|kg that mand sons an, of,rtopit bale thu (31.0%)
          used for light, but only as an oi|nsm blasc yan, ang theate thor wille han (32.1%)
          used for light, but only as an oi|b thea mevind, int amat ars sif istuad p (33.3%)
          used for light, but only as an oi|msenge bie therale hing, aik asmeatked s (34.1%)
          used for light, but only as an oi|ge," rrermondy ghe e comasnig that urle  (35.5%)
          used for light, but only as an oi|s or thartich comase surt thant seaiceng (36.1%)
          used for light, but only as an oi|s lot fircennor, unding dald bots trre i (37.1%)
          used for light, but only as an oi|st onderass noptand. "peles, suiondes is (38.2%)
          used for light, but only as an oi|gnith. s. lited, anca! stobbease so las, (39.3%)
          used for light, but only as an oi|chics fleet dong berieribus armor has or (40.1%)
          used for light, but only as an oi|cs and quirbout detom tis glome dold pco (41.1%)
          used for light, but only as an oi|nht shome wand, the your at movernife lo (42.0%)
          used for light, but only as an oi|r a reald hind the, with of the from sti (43.0%)
          used for light, but only as an oi|t beftect. how shapellatgen the fortower (44.0%)
          used for light, but only as an oi|rtucated fanns dountetter from fom to wi (45.2%)
          used for light, but only as an oi|r the sea priised tay queequings hearhou (46.8%)
          used for light, but only as an oi|ld, wode, i long ben! but the gentived.  (48.0%)
          used for light, but only as an oi|r wide-no nate was him. "a king to had o (49.1%)
          used for light, but only as an oi|l erol min't defositanable paring our. 4 (50.0%)
          used for light, but only as an oi|l the motion ahab, too, and relay in aha (51.0%)
          used for light, but only as an oi|n dago, and contantly used the coil; but (52.3%)
          used for light, but only as an oi|l starbuckably happoss of the fullies ti (52.4%)
          used for light, but only as an oi|led-bubble most disinuan into the mate-- (53.3%)
          used for light, but only as an oi|len. ye?' 'tis though moby starbuck, and (53.6%)
          used for light, but only as an oi|l, and the pequodeers. but was all this: (53.9%)
          used for light, but only as an oi|ling his first repore to the pequod, sym (54.4%)
          used for light, but only as an oi|led escried; we they like potants--old s (54.3%)
          used for light, but only as an oi|l-ginqueg! i save started her supplain h (54.3%)
          used for light, but only as an oi|l is, the captain all this mildly bounde (54.9%)
        
        Here, the seed text is shown left of the pipe character, and the randomly
        sampled sequence follows. In parantheses are the per-character accuracy values
        on the training set while training the model. The pattern of learning proceeds
        from almost-random character generation, to producing groups of letters
        separated by spaces, to generating words that seem like they might belong in
        *Moby Dick*, things like "captain," "ahab, too," and "constantly used the coil."
        
        Much amusement can be derived from a temporal model extending itself forward in
        this way. After all, how else would we ever think of "Pequodeers,"
        "Starbuckably," or "Ginqueg"?!
        
        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
