Create and initialize a net with three layers, but train only the last layer:

Evaluate the trained net on an input:

The first layer of the initial net started with zero biases:

The biases of the first layer remain zero in the trained net:

The biases of the third layer have been trained:

Create a frozen layer with given array values:

Nest this layer inside a bigger net:

Get the learning rate multipliers that will be used by default in NetTrain, for all arrays of the net:

Train with the net:

Check the learning rate multipliers that were used to train:

The arrays of the frozen layer were unchanged during training:

Train an existing network to solve a new task. Obtain a pre-trained convolutional model that was trained on handwritten digits:

Remove the final two layers, and attach two new layers, in order to classify images into 3 classes:

Generate training data by rasterizing the characters "x", "y", and "z" with a variety of fonts, sizes, and cases:

Train the modified network on the new task:

Classify an unseen letter:

Measure the performance on the original training data, which includes the training and validation set:

Train LeNet on the MNIST dataset with specific learning rate multipliers, returning a NetTrainResultsObject:

Obtain the actual learning rate multipliers used on individual weight arrays: