Mariana: Deep Neural Networks should be Easy to Write

Mariana’s goal is to create a powerful language through which complex deep neural networks can be meaningfully expressed and easily manipulated. It’s here to empower researchers, teachers and students, while greatly facilitating AI knowledge transfer into other domains

Mariana is a Python Machine Learning Framework built on top of Theano. She Mariana lives on github.

More Documentation:

  • More examples and presentation material available here.
  • YouTube presentation (english).
  • YouTube presentation (french).

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Why is it cool?

If you can draw it, you can write it.

Mariana provides an interface so simple and intuitive that writing models becomes a breeze. Networks are graphs of connected layers and that allows for the craziest deepest architectures you can think of, as well as for a super light and clean interface. The paradigm is simple create layers and connect them using ‘>’. Plugging per layer regularizations, costs and things such as dropout and custom initilisations is also super easy.

Here’s a snippet for an instant MLP with dropout, ReLU units and L1 regularization:

import Mariana.activations as MA
import Mariana.decorators as MD
import Mariana.layers as ML
import Mariana.costs as MC
import Mariana.regularizations as MR
import Mariana.scenari as MS

ls = MS.GradientDescent(lr = 0.01)
cost = MC.NegativeLogLikelihood()

i = ML.Input(28*28, name = "inputLayer")
h = ML.Hidden(300, activation = MA.ReLU(), decorators = [MD.BinomialDropout(0.2)], regularizations = [ MR.L1(0.0001) ])
o = ML.SoftmaxClassifier(9, learningScenario = ls, costObject = cost, regularizations = [ MR.L1(0.0001) ])

MLP = i > h > o

Here are some full fledged examples.

Mariana also supports trainers that encapsulate the whole training to make things even easier.

So in short:

  • no YAML
  • completely modular and extendable
  • use the trainer to encapsulate your training in a safe environement
  • write your models super fast
  • save your models and resume training
  • export your models into DOT format to obtain clean and easy to communicate graphs
  • free your imagination and experiment
  • no requirements concerning the format of the datasets

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