class_docstr: MaxNorm weight constraint.
  
  Constrains the weights incident to each hidden unit
  to have a norm less than or equal to a desired value.
  
  Any hidden unit vector with a norm less than the max norm
  constaint will not be altered.
class_docstr_tok: ["MaxNorm","weight","constraint",".","Constrains","the","weights","incident","to","each","hidden","unit","to","have","a","norm","less","than","or","equal","to","a","desired","value",".","Any","hidden","unit","vector","with","a","norm","less","than","the","max","norm","constaint","will","not","be","altered","."]
code: def __init__(
      self, value, axis=0, frequency=1, unit="batch", module_filter="*"
  ):
      self.value = float(value)
      self.axis = axis
  
      self.frequency = frequency
      self.unit = unit
      self.module_filter = module_filter
  
imports: from fnmatch import fnmatch
  import torch
  from .callbacks import Callback
label_desc: Use image node_id 1 to create a new MaxNorm object from inherited base classes: Constraint with example: obj = MaxNorm(value, axis, frequency, unit, module_filter)