Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
Layers & models have three weight attributes:
weights
is the list of all weights variables of the layer.trainable_weights
is the list of those that are meant to be updated (via gradient descent) to minimize the loss during training.non_trainable_weights
is the list of those that aren't meant to be trained. Typically they are updated by the model during the forward pass.7
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