Resource retrieval

Get the pre-trained net:

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Basic usage

Classify an image:

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The prediction is an Entity object, which can be queried:

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Get a list of available properties of the predicted Entity:

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Obtain the probabilities of the ten most likely entities predicted by the net:

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An object outside the list of the ImageIdentify dataset will be misidentified:

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Obtain the list of names of all available classes:

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Feature extraction

Remove the last two layers of the trained net so that the net produces a vector representation of an image:

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Get a set of images:

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Visualize the features of a set of images:

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Visualize convolutional weights

Extract the weights of the first convolutional layer in the trained net:

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Visualize the weights as a list of 64 images of size 7x7:

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Transfer learning

Use the pre-trained model to build a classifier for telling apart images of dogs and cats. Create a test set and a training set:

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Remove the linear layer from the pre-trained net:

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Create a new net composed of the pre-trained net followed by a linear layer and a softmax layer:

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Train on the dataset, freezing all the weights except for those in the "linearNew" layer (use TargetDevice -> "GPU" for training on a GPU):

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Perfect accuracy is obtained on the test set:

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Net information

Inspect the number of parameters of all arrays in the net:

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Obtain the total number of parameters:

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Obtain the layer type counts:

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Display the summary graphic:

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Export to MXNet

Export the net into a format that can be opened in MXNet :

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Export also creates a net.params file containing parameters:

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Get the size of the parameter file:

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The size is similar to the byte count of the resource object:

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