The ImageFeaturizer
relies on a CNTK model to do the featurization, one can set
this model using the modelLocation
parameter.
Converts the representation of an m X n pixel image to an m * n vector of Doubles
Converts the representation of an m X n pixel image to an m * n vector of Doubles
The input column name is assumed to be "image", the output column name is "<uid>_output"
Converts the representation of an m X n pixel image to an m * n vector of Doubles
Converts the representation of an m X n pixel image to an m * n vector of Doubles
The input column name is assumed to be "image", the output column name is "<uid>_output"
The
ImageFeaturizer
relies on a CNTK model to do the featurization, one can set this model using themodelLocation
parameter. To map the nodes of the CNTK model onto the standard "layers" structure of a feed forward neural net, one needs to supply a list of node names that range from the output node, back towards the input node of the CNTK Function. This list does not need to be exhaustive, and is provided to you if you use a model downloaded from theModelDownloader
, one can find this layer list in the schema of the downloaded model.The
ImageFeaturizer
takes an input column of images (the type returned by theImageReader
), and automatically resizes them to fit the CMTKModel's inputs. It then feeds them through a pre-trained CNTK model. One can truncate the model using thecutOutputLayers
parameter that determines how many layers to truncate from the output of the network. For example, layer=0 means that no layers are removed, layer=2 means that the image featurizer returns the activations of the layer that is two layers from the output layer.