Resource retrieval

Retrieve the pre-trained net:

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Basic usage

Apply the trained net to a set of inputs:

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Give class probabilities for a single input:

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Feature extraction

Create a subset of the MNIST dataset:

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Remove the last linear layer of the net, which will be used as a feature extractor:

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Visualize the features of a subset of the MNIST dataset:

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Visualization of net operation

Extract the convolutional features from the first layer:

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Visualize the features:

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Training the uninitialized architecture

Retrieve the uninitialized architecture:

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Retrieve the MNIST dataset:

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Use the training dataset provided:

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Use the test dataset provided:

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Train the net:

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Generate a ClassifierMeasurementsObject of the net with the test set:

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Evaluate the accuracy on the validation set:

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Visualize the confusion matrix:

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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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Represent the MXNet net as a graph:

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