A library of neural nets in theano
This package contains implementations of several common neural network structures, using the amazing Theano package for optimization.
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
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 held constant in the example code, 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 whitespace.
- -O or –optimize sgd|hf|sgd hf|layerwise hf|…
Use the given optimization method(s) to train network parameters. Several training methods can be used in sequence by separating their names with spaces on the command line.
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:
exp = theanets.Experiment(theanets.Classifier) exp.add_dataset('train', my_dataset[:1000]) exp.add_dataset('valid', my_dataset[1000:]) exp.train()
You can save() the trained model to a pickle, or use the trained network directly to predict() the outputs on a new dataset:
The documentation is relatively sparse, so please file bugs if you find that there’s a particularly hard-to-understand area in the code.
This package is distributed under an MIT license.
Copyright (c) 2013 Leif Johnson <email@example.com>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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