Lightweight library to build and train neural networks in Theano
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Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are:
Its design is governed by six principles <http://lasagne.readthedocs.org/en/latest/user/development.html#philosophy>
_:
In short, you can install a known compatible version of Theano and the latest Lasagne development version via:
.. code-block:: bash
pip install -r https://raw.githubusercontent.com/Lasagne/Lasagne/master/requirements.txt pip install https://github.com/Lasagne/Lasagne/archive/master.zip
For more details and alternatives, please see the Installation instructions <http://lasagne.readthedocs.org/en/latest/user/installation.html>
_.
Documentation is available online: http://lasagne.readthedocs.org/
For support, please refer to the lasagne-users mailing list <https://groups.google.com/forum/#!forum/lasagne-users>
_.
.. code-block:: python
import lasagne import theano import theano.tensor as T
input_var = T.tensor4('X') target_var = T.ivector('y')
from lasagne.nonlinearities import leaky_rectify, softmax network = lasagne.layers.InputLayer((None, 3, 32, 32), input_var) network = lasagne.layers.Conv2DLayer(network, 64, (3, 3), nonlinearity=leaky_rectify) network = lasagne.layers.Conv2DLayer(network, 32, (3, 3), nonlinearity=leaky_rectify) network = lasagne.layers.Pool2DLayer(network, (3, 3), stride=2, mode='max') network = lasagne.layers.DenseLayer(lasagne.layers.dropout(network, 0.5), 128, nonlinearity=leaky_rectify, W=lasagne.init.Orthogonal()) network = lasagne.layers.DenseLayer(lasagne.layers.dropout(network, 0.5), 10, nonlinearity=softmax)
prediction = lasagne.layers.get_output(network) loss = lasagne.objectives.categorical_crossentropy(prediction, target_var) loss = loss.mean() + 1e-4 * lasagne.regularization.regularize_network_params( network, lasagne.regularization.l2)
params = lasagne.layers.get_all_params(network, trainable=True) updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)
train_fn = theano.function([input_var, target_var], loss, updates=updates)
for epoch in range(100): loss = 0 for input_batch, target_batch in training_data: loss += train_fn(input_batch, target_batch) print("Epoch %d: Loss %g" % (epoch + 1, loss / len(training_data)))
test_prediction = lasagne.layers.get_output(network, deterministic=True) predict_fn = theano.function([input_var], T.argmax(test_prediction, axis=1)) print("Predicted class for first test input: %r" % predict_fn(test_data[0]))
For a fully-functional example, see examples/mnist.py <examples/mnist.py>
,
and check the Tutorial <http://lasagne.readthedocs.org/en/latest/user/tutorial.html>
for in-depth
explanations of the same. More examples, code snippets and reproductions of
recent research papers are maintained in the separate Lasagne Recipes <https://github.com/Lasagne/Recipes>
_ repository.
If you find Lasagne useful for your scientific work, please consider citing it
in resulting publications. We provide a ready-to-use BibTeX entry for citing Lasagne <https://github.com/Lasagne/Lasagne/wiki/Lasagne-Citation-(BibTeX)>
_.
Lasagne is a work in progress, input is welcome.
Please see the Contribution instructions <http://lasagne.readthedocs.org/en/latest/user/development.html>
_ for details
on how you can contribute!