Use three functions to hopefully make eaxmple easier to grasp.
Merge pull request #35 from dnouri/mnist-example-refactor

clean up MNIST example
First set of unit tests; for nntools.objectives

Found a bug in nntools.objectives.crossentropy (no return value).

To run tests, first install necessary requirements with:

  python setup.py dev

Then run:

  py.test

(These are assuming you've activated your virtualenv.)
Woops, I meant to remove this as part of #28; it's not needed anymore.
Remove Bunch fanciness; make 'train' a generator over epochs.
T.signal.downsample raises AttributeError on the T module for me.
Structure this example in the same way as mnist.py
Merge pull request #37 from dnouri/mnist-example-refactor

mnist_conv.py example refactor
Add unit tests for nntools.init.
Add experimental Travis configuration
MultipleInputsLayer bugfix, ConcatLayer, FlattenLayer
Cosmetics
Merge pull request #36 from dnouri/test-objectives

Test nntools.objectives + nntools.init + Travis configuration
Uniform (Glorot) is probably a better default initialization + simplified flattening code
cuda-convnet based convolution and max-pooling layers + utilities. The layers can be used as drop-in replacements for the default Conv2DLayer and MaxPool2DLayer. This means that they shuffle the inputs from bc01 to c01b and the outputs from c01b to bc01 as needed. This behavior can be turned off with the 'dimshuffle' keyword argument. If it is turned off, the user is responsible for appropriately shuffling the layer input and output. nntools.layers.cuda_convnet.bc01_to_c01b and .c01b_to_bc01 are provided for this purpose.
defer pylearn2 imports to the classes that need them, so that nntools.layers.cuda_convnet can always be imported, even if pylearn2 is not available.
Merge pull request #39 from benanne/cuda_convnet

cuda-convnet based convolution and max-pooling layers + utilities.
added NINLayer, might need some tweaking
NINLayer_c01b, optimized for c01b layout (to use with the cuda-convnet based layers with dimshuffle=False)
added GlobalPoolLayer and a utility function to count the number of parameters of a model