5

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? – Franck Dernoncourt Sep 26 '15 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. – Neil Slater Sep 26 '15 at 7:24
3

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

FYI:

1

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

climate.enable_default_logging()

# 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(label)
    print(confusion_matrix(y, exp.network.predict(X)))

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.