parameters: (self, value)
docstring: Turns AMSGrad mode on. May be called only before training starts.
          AMSGrad helps against divergence and rapid vanishing of previous states
          memory, which may become a problem for the optimizers that use 
          the moving mean for squared gradient history (Adam, NAdam, RMSprop).
          See https://openreview.net/pdf?id=ryQu7f-RZ.
docstring_summary: Turns AMSGrad mode on. May be called only before training starts.
          AMSGrad helps against divergence and rapid vanishing of previous states
          memory, which may become a problem for the optimizers that use 
          the moving mean for squared gradient history (Adam, NAdam, RMSprop).
          See https://openreview.net/pdf?id=ryQu7f-RZ.
docstring_tokens: Turns AMSGrad mode on . May be called only before training starts . AMSGrad helps against divergence and rapid vanishing of previous states memory which may become a problem for the optimizers that use the moving mean for squared gradient history ( Adam NAdam RMSprop ) . See https : // openreview . net / pdf?id = ryQu7f - RZ .
function: def ams_grad(self, value):
          """Turns AMSGrad mode on. May be called only before training starts.
          AMSGrad helps against divergence and rapid vanishing of previous states
          memory, which may become a problem for the optimizers that use 
          the moving mean for squared gradient history (Adam, NAdam, RMSprop).
          See https://openreview.net/pdf?id=ryQu7f-RZ.
          """
          self._internal.set_ams_grad(value)
function_tokens: def ams_grad ( self , value ) : self . _internal . set_ams_grad ( value )