This is a fork of Lasagne. The main branch contains some basic changes to the library (e.g. allowing multiple inputs and outputs for a layer), while our implementations of the Kronecker factored approximations to the Hessian of neural networks [1,2,3,4], which you are probably here for, are in the kf branch.

[1] Aleksandar Botev, Hippolyt Ritter, and David Barber. Practical Gauss-Newton Optimisation for Deep Learning. In ICML, 2017.

[2] James Martens and Roger Grosse. Optimizing Neural Networks with Kronecker-factored Approximate Curvature. In ICML, 2015.

[3] Roger Grosse and James Martens. A Kronecker-factored Approximate Fisher Matrix for Convolution Layers. In ICML, 2016.

[4] Hippolyt Ritter, Aleksandar Botev, and David Barber. A Scalable Laplace Approximation for Neural Networks. In ICLR, 2018.

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:

• 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:

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

Documentation is available online: http://lasagne.readthedocs.org/

For support, please refer to the lasagne-users mailing list.

Example

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.

Development

Lasagne is a work in progress, input is welcome.

Please see the Contribution instructions for details on how you can contribute!