optimize.py 文件源码

def compile(
        self,s_inputs_, s_loss_, v_params_, s_grads_=None, s_reg_=0,
        fetches_=None, updates_=None, givens_=None,
        trunc_grad_=None, profile_=False
    ):
        '''
        compile optimizer against specific model

        Args:
            s_inputs_: list of symbolic input tensors, including label
            s_loss_: optimization loss, symbolic scalar
            v_params_: list of shared parameters to optimize
            s_grads: list of gradients to apply, must be same order as v_params_, default is None (use autodiff).
            s_reg_: symbolic regularization term, default 0 (no regularization)
            updates: update operation for shared values after a step of optimization,
                usually RNN states. Takes form [(v_var, s_new_var), ...]

        Returns: None
        '''
        self.s_loss = s_loss_
        self.s_reg = s_reg_
        if s_grads_ is None:
            s_grads_ = T.grad(
                self.s_loss + self.s_reg, list(v_params_), disconnected_inputs='warn')

        if type(trunc_grad_)==float:
            self.s_grads = [T.clip(g,-trunc_grad_,trunc_grad_) for g in s_grads_]
        else:
            self.s_grads = s_grads_