pytensor_ml.model.Model.initialize#
- Model.initialize(seed=None, initializers=None)#
Redraw every trainable weight from its own initializer, in place, and return self.
Each parameter was already built holding a draw, so this is what makes a run reproducible rather than what makes it trainable: one seed regenerates all of them. Every layer declares how each parameter it builds is drawn, so a batch norm layer redraws to its identity transform and a bias to zero.
- Parameters:
- seed
intornumpyGenerator, optional Seed for reproducible initialization.
- initializers
dictmappingparametertoInitializer, optional How to draw specific parameters, in place of what they declare. Keyed by the parameter object, reached through the layer that owns it –
{block.ff.fc_in.b: ...}– so a parameter no constructor keyword exposes is still addressable.
- seed