net.py 文件源码

def classify(model_range, seg_range, feature_lr, classifier_lr):
        feat_opt = tf.train.AdamOptimizer(feature_lr)
        clas_opt = tf.train.AdamOptimizer(classifier_lr)
        for model in model_range:
            for seg in seg_range:
                with tf.variable_scope('classifier-{}-{}'.format(model, seg)):
                    self.preds[(model, seg)] = slim.conv2d(self.feature, 500, [1, 1])
                    self.clas_vars[(model, seg)] = slim.get_model_variables()[-2:]

                with tf.variable_scope('losses-{}-{}'.format(model, seg)):
                    self.losses[(model, seg)] = self.loss(self.labels, self.preds[(model, seg)])
                    grad = tf.gradients(self.losses[(model, seg)], self.feat_vars + self.clas_vars[(model, seg)])
                    train_op_feat = feat_opt.apply_gradients(zip(grad[:-2], self.feat_vars))
                    train_op_clas = clas_opt.apply_gradients(zip(grad[-2:], self.clas_vars[(model, seg)]))
                    self.train_ops[(model, seg)] = tf.group(train_op_feat, train_op_clas)
        return self.losses, self.train_ops