Hi Andre I've found keras to be very easy to use, have a lot of features, and has extensive documentation. However, I usually use mxnet because it seems faster and allows multi-GPU training very easily.
Jacob On Tue, Apr 5, 2016 at 1:10 PM, André Cruz <an...@cabine.org> wrote: > Thank you Andy for the pointers. Is any one of those 3 better supported or > recommended in any way for this task? So as to know where to start. > > André > > > On Tuesday, 5 April 2016, Andreas Mueller <t3k...@gmail.com> wrote: > >> Hi Andre >> There are no pre-trained neural nets (and no convolutional neural nets >> at all) in scikit-learn. >> Check out sklearn-theano, nolearn or keras. >> >> The knn is pretty straight-forward from the docs. >> >> Cheers, >> Andy >> >> On 04/05/2016 10:54 AM, André Cruz wrote: >> > Hello all. >> > >> > I've been using GraphLab while doing a machine learning specialization >> in Coursera (https://www.coursera.org/specializations/machine-learning). >> However, I would also like to try scikit-learn for comparison and maybe use >> it since it is open source. Could someone look at this GraphLab snippet and >> help me convert it to scikit-learn? Basically I'm loading the AlexNet model >> pre-trained on the imagenet dataset, extracting features from images stored >> locally, and then using k-nearest neighbour to find similar images based on >> some queries: >> > >> > >> > ------------- >> > import graphlab >> > >> > # build model >> > image_data = graphlab.image_analysis.load_images('images/') # load >> images from dir >> > deep_learning_model = graphlab.load_model(' >> http://s3.amazonaws.com/GraphLab-Datasets/deeplearning/imagenet_model_iter45') >> # load trained imagenet model >> > image_data['deep_features'] = >> deep_learning_model.extract_features(image_data) >> > knn_model = >> graphlab.nearest_neighbors.create(image_data,features=['deep_features']) >> > >> > # query model >> > query_sframe = graphlab.image_analysis.load_images('query') >> > query_sframe['deep_features'] = >> deep_learning_model.extract_features(query_sframe) >> > results = knn_model.query(query_frame) >> > ------------- >> > >> > Thanks for the help and best regards, >> > André Cruz >> > >> ------------------------------------------------------------------------------ >> > _______________________________________________ >> > Scikit-learn-general mailing list >> > Scikit-learn-general@lists.sourceforge.net >> > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> >> ------------------------------------------------------------------------------ >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> > > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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