learning_seg.py 文件源码

def _setup_model_loss(self, keep_moving_averages=False, num_classes=10):
        self.learning_rate = tf.placeholder(
            tf.float32, shape=[], name="learning_rate_placeholder")
        optimizer = self._optimizer(self.learning_rate, optname=self.cnf.get(
            'optname', 'momentum'), **self.cnf.get('opt_kwargs', {'decay': 0.9}))
        self.grads_and_vars, self.training_loss = self._process_towers_grads(
            optimizer, self.model, is_classification=self.classification, loss_type=self.loss_type)

        if self.clip_norm and not self.clip_by_global_norm:
            self.grads_and_vars = self._clip_grad_norms(
                self.grads_and_vars, max_norm=self.norm_threshold)
        apply_gradients_op = optimizer.apply_gradients(self.grads_and_vars)
        if keep_moving_averages:
            variables_averages_op = self._moving_averages_op()
            with tf.control_dependencies([apply_gradients_op, variables_averages_op]):
                self.train_op = tf.no_op(name='train_op')
        else:
            self.train_op = apply_gradients_op