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
> >
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