optimizer.py 文件源码

def compute_gradients(self, loss, *args, **kwargs):
        train_vars = None
        if self.trainable_names is not None:
            log.info('All trainable vars:\n'+str([var.name for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)]))
            train_vars = []
            for scope_name in self.trainable_names:
                new_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope_name)
                if len(new_vars) == 0:
                    raise ValueError('The scope name, {}, you specified does not contain any trainable variables.'.format(scope_name))
                train_vars.extend(new_vars)
            log.info('Variables to be trained:\n'+str([var.name for var in train_vars]))
        if train_vars is not None:
            self.var_list = train_vars

        gvs = self._optimizer.compute_gradients(loss,
                                                var_list=train_vars,
                                                *args, **kwargs)
        if self.clip:
            # gradient clipping. Some gradients returned are 'None' because
            # no relation between the variable and loss; so we skip those.
            gvs = [(tf.clip_by_value(grad, -1., 1.), var)
                   for grad, var in gvs if grad is not None]
        return gvs