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Lasagne

Lasagne is a lightweight library to build and train neural networks in Theano.
Its main features are:

• Supports feed-forward networks such as Convolutional Neural Networks (CNNs),
  recurrent networks including Long Short-Term Memory (LSTM), and any
  combination thereof
• Allows architectures of multiple inputs and multiple outputs, including
  auxiliary classifiers
• Many optimization methods including Nesterov momentum, RMSprop and ADAM
• Freely definable cost function and no need to derive gradients due to
  Theano's symbolic differentiation
• Transparent support of CPUs and GPUs due to Theano's expression compiler

Its design is governed by six principles <http://lasagne.readthedocs.org/en/latest/user/development.html#philosophy>_:

• Simplicity: Be easy to use, easy to understand and easy to extend, to
  facilitate use in research
• Transparency: Do not hide Theano behind abstractions, directly process and
  return Theano expressions or Python / numpy data types
• Modularity: Allow all parts (layers, regularizers, optimizers, ...) to be
  used independently of Lasagne
• Pragmatism: Make common use cases easy, do not overrate uncommon cases
• Restraint: Do not obstruct users with features they decide not to use
• Focus: "Do one thing and do it well"

Installation

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

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>_.

Example

.. code-block:: python

import lasagne
import theano
import theano.tensor as T

create Theano variables for input and target minibatch

input_var = T.tensor4('X')
target_var = T.ivector('y')

create a small convolutional neural network

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)

create loss function

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)

create parameter update expressions

params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01,
momentum=0.9)

compile training function that updates parameters and returns training loss

train_fn = theano.function([input_var, target_var], loss, updates=updates)

train network (assuming you've got some training data in numpy arrays)

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)))

use trained network for predictions

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.

Citation

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)>_.

Development

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!