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:
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[0]))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!