如何从tensorflow的ckpt和pb模型中提取节点名称，以及实现ckpt模型向pb模型的转换？

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pythonimport tensorflow as tfcheckpoint_path='model.ckpt-8000'reader=tf.train.load_checkpoint(checkpoint_path)var_to_shape_map=reader.get_variable_to_shape_map()for key in var_to_shape_map: print(key)

ckpt

from tensorflow.python import pywrap_tensorflow checkpoint_path = 'model.ckpt-8000' reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path) var_to_shape_map = reader.get_variable_to_shape_map() for key in var_to_shape_map: print("tensor_name: ", key)

pb

import tensorflow as tf import os model_name = './mobilenet_v2_140_inf_graph.pb' def create_graph(): with tf.gfile.FastGFile(model_name, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name='') create_graph() tensor_name_list = [tensor.name for tensor in tf.get_default_graph().as_graph_def().node] for tensor_name in tensor_name_list: print(tensor_name,'\n')

ckpt转pb

def freeze_graph(input_checkpoint,output_graph): ''' :param input_checkpoint: :param output_graph: PB模型保存路径 :return: ''' output_node_names = "xxx" saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True) graph = tf.get_default_graph() input_graph_def = graph.as_graph_def() with tf.Session() as sess: saver.restore(sess, input_checkpoint) output_graph_def = graph_util.convert_variables_to_constants( sess=sess, input_graph_def=input_graph_def,# 等于:sess.graph_def output_node_names=output_node_names.split(",")) with tf.gfile.GFile(output_graph, "wb") as f: f.write(output_graph_def.SerializeToString()) print("%d ops in the final graph." % len(output_graph_def.node)) for op in graph.get_operations(): print(op.name, op.values())

以上这篇tensorflow ckpt模型和pb模型获取节点名称,及ckpt转pb模型实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持易盾网络。