这篇文章主要介绍tensorflow中ckpt模型和pb模型如何获取节点名称，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！
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())以上是“tensorflow中ckpt模型和pb模型如何获取节点名称”这篇文章的所有内容，感谢各位的阅读！希望分享的内容对大家有帮助，更多相关知识，欢迎关注创新互联行业资讯频道！
