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:
- 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”
In short, you can install a known compatible version of Theano and the latest Lasagne development version via:
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.
Documentation is available online: http://lasagne.readthedocs.org/
For support, please refer to the lasagne-users mailing list.
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))
For a fully-functional example, see examples/mnist.py, and check the Tutorial for in-depth explanations of the same. More examples, code snippets and reproductions of recent research papers are maintained in the separate 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.
Lasagne is a work in progress, input is welcome.
Please see the Contribution instructions for details on how you can contribute!