📅 最后修改于: 2020-12-10 06:05:18 🧑 作者: Mango
在这里，我们将重点介绍TensorFlow中的MetaGraph形成。这将帮助我们了解TensorFlow中的导出模块。 MetaGraph包含基本信息，这是训练，执行评估或对先前训练过的图进行推理所必需的。
以下是相同的代码片段-
def export_meta_graph(filename = None, collection_list = None, as_text = False):
"""this code writes `MetaGraphDef` to save_path/filename.
Arguments:
filename: Optional meta_graph filename including the path. collection_list:
List of string keys to collect. as_text: If `True`,
writes the meta_graph as an ASCII proto.
Returns:
A `MetaGraphDef` proto. """
以下是其中一种典型的使用模式-
# Build the model ...
with tf.Session() as sess:
# Use the model ...
# Export the model to /tmp/my-model.meta.
meta_graph_def = tf.train.export_meta_graph(filename = '/tmp/my-model.meta')