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.
Lasagne is a work in progress, input is welcome.
Please see the Contribution instructions for details on how you can contribute!