在这里,我们将关注TensorFlow中的MetaGraph形成.这有助于我们了解TensorFlow中的导出模块. MetaGraph包含基本信息,这些信息是对先前训练过的图表进行训练,执行评估或运行推理所必需的.
以下是相同和减号的代码片段;
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. """
其中一个典型的使用模型在下面提到 :
# 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')