Here, we will focus on MetaGraph formation in TensorFlow. This will help us understand export module in TensorFlow. The MetaGraph contains the basic information, which is required to train, perform evaluation, or run inference on a previously trained graph.
Following is the code snippet for the same −
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. """
One of the typical usage model for the same is mentioned below −
# 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')