I am looking for gratis software that is GPU accelerated that can work with neural networks. I have seen many pieces of software that I can use for neural networks but many of them do not have GPU acceleration. Here are my requirements

Support for the backpropagation algorithm.

I would like support for a genetic algorithm such as NEAT but it is not required

A good nice to use interface

I would like it to work on windows but I could also use it on Linux

That is it for my requirements. In case you are wondering what GPUs I use I use Nvidia GPUs. So the application would have to support CUDA

  • 1
    Is coding directly like in Theano ok? Sep 26, 2015 at 5:12
  • There is quite a range of choices here. Are you interested in specific uses of neural networks? Interest in NEAT might imply networks for AI, A-life, reinforcement learning, robotics. Whilst machine vision networks are currently monster CNNs that almost require GPU acceleration in order to train them. Sep 26, 2015 at 7:24

2 Answers 2


You can use Caffe:

  • open source (BSD 2-Clause license)
  • gratis
  • Easy to compile on Linux/OS X, harder on Windows
  • GPU support for Nvidia
  • on CPU, makes use of multithreading
  • use the backpropagation algorithm
  • allows the user to define neural network using config files
  • MLP, CNN but no RNN yet
  • written in C++ but Python and Matlab binds available



You can use the Python package theanets:

  • open source (MIT License)
  • gratis
  • it's a wrapper around Theano (the latter can use your GPU if Nvidia, AMD is not fully supported yet).
  • on CPU, does not make use of multithreading
  • Easy to compile on Linux/OS X, harder on Windows
  • MLP, RNN

The following code shows how easy it is to specify an ANN and train it:

import climate
import theanets
from sklearn.datasets import make_classification
from sklearn.metrics import confusion_matrix


# Create a classification dataset.
X, y = make_classification(
    n_samples=3000, n_features=100, n_classes=10, n_informative=10)
X = X.astype('f')
y = y.astype('i')
cut = int(len(X) * 0.8)  # training / validation split
train = X[:cut], y[:cut]
valid = X[cut:], y[cut:]

# Build a classifier model with 100 inputs and 10 outputs.
exp = theanets.Experiment(theanets.Classifier, layers=(100, 10))

# Train the model using SGD with momentum.
exp.train(train, valid, algorithm='sgd', learning_rate=1e-4, momentum=0.9)

# Show confusion matrices on the training/validation splits.
for label, (X, y) in (('training:', train), ('validation:', valid)):
    print(confusion_matrix(y, exp.network.predict(X)))

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