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https://github.com/Lasagne/Lasagne

Lightweight library to build and train neural networks in Theano
https://github.com/Lasagne/Lasagne

deep-learning-library neural-networks python theano

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Lightweight library to build and train neural networks in Theano

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README 

        
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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

`_:



* 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

`_.



Documentation

-------------



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



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

`_.



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 `_,

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.



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 `_.



Development

-----------



Lasagne is a work in progress, input is welcome.



Please see the `Contribution instructions

`_ for details

on how you can contribute!