import tensorflow as tf
from tensorflow.python.framework import graph_util
tf.reset_default_graph() # 重置计算图
output_graph_path = 'model/model_tfnew.pb'
with tf.Session() as sess:

tf.global_variables_initializer().run()
output_graph_def = tf.GraphDef()
# 获得默认的图
graph = tf.get_default_graph()
with open(output_graph_path, "rb") as f:
output_graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(output_graph_def, name="")
# 得到当前图有几个操作节点
print("%d ops in the final graph." % len(output_graph_def.node))

tensor_name = [tensor.name for tensor in output_graph_def.node]
print(tensor_name)
print('---------------------------')
# 在log_graph文件夹下生产日志文件，可以在tensorboard中可视化模型
summaryWriter = tf.summary.FileWriter('log_graph/', graph)


for op in graph.get_operations():
# print出tensor的name和值