Perhaps you can help me sort out an error. Taking it line-by-line in the example you linked, I get failures. In the first instance, This line:
extractor = gl.feature_engineering.DeepFeatureExtractor(features = 'image')
results in an error:
TypeError: __init__() got an unexpected keyword argument 'features'
which seems consistent with the result from
help(gl.feature_engineering.DeepFeatureExtractor). However, when I simply pass 'image' as an argument (not as a keyword), I get an error which suggests that for the MNIST data the feature extractor is looking for a non-existent, pickled imagenet model. I get an identical error if I supply data in an SFrame built from my own images scaled to 256x256:
IOError Traceback (most recent call last)
<ipython-input-25-d91f27947128> in <module>()
11 # extractor = gl.featureengineering.DeepFeatureExtractor(features = 'image')
---> 12 extractor = gl.featureengineering.DeepFeatureExtractor('image')
14 # Fit the encoder for a given dataset.
/usr/lib/python2.7/site-packages/graphlab/toolkits/featureengineering/deepfeatureextractor.pyc in init(self, feature, model, outputcolumnname)
151 import graphlab as gl
--> 152 self.state['model'] = gl.loadmodel(modelpath)
153 if type(self.state['model']) is not _NeuralNetClassifier:
154 raise ValueError("Model parameters must be of type NeuralNetClassifier " +
/usr/lib/python2.7/site-packages/graphlab/toolkits/model.pyc in loadmodel(location)
69 # Not a ToolkitError so try unpickling the model.
---> 70 unpickler = gl_pickle.GLUnpickler(location)
72 # Get the version
/usr/lib/python2.7/site-packages/graphlab/glpickle.pyc in init(self, filename)
452 if not _os.path.exists(filename):
--> 453 raise IOError('%s is not a valid file name.' % filename)
455 # GLC 1.3 Pickle file
IOError: http://s3.amazonaws.com/dato-datasets/deeplearning/imagenetmodeliter45 is not a valid file name.