环境：keras 2.5
TensorFlow2.5
python3.7
在用keras迁移学习后，得到了.h5模型文件，想将它转换成.pd格式，使用了网上的这个代码
#*-coding:utf-8-*
"""
将keras的.h5的模型文件，转换成TensorFlow的pb文件
"""
# ==========================================================
from keras.models import load_model
import tensorflow as tf
import os
from keras import backend
def h5_to_pb(h5_model, output_dir, model_name, out_prefix="output_", log_tensorboard=True):
""".h5模型文件转换成pb模型文件
Argument:
h5_model: str
.h5模型文件
output_dir: str
pb模型文件保存路径
model_name: str
pb模型文件名称
out_prefix: str
根据训练，需要修改
log_tensorboard: bool
是否生成日志文件
Return:
pb模型文件
"""
if os.path.exists(output_dir) == False:
os.mkdir(output_dir)
out_nodes = []
for i in range(len(h5_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(h5_model.output[i], out_prefix + str(i + 1))
sess = backend.get_session()
from tensorflow.python.framework import graph_util, graph_io
# 写入pb模型文件
init_graph = sess.graph.as_graph_def()
main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes)
graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False)
# 输出日志文件
if log_tensorboard:
from tensorflow.python.tools import import_pb_to_tensorboard
import_pb_to_tensorboard.import_to_tensorboard(os.path.join(output_dir, model_name), output_dir)
if __name__ == '__main__':
# .h模型文件路径参数
input_path = 'G:/model/'
weight_file = 'AItree0301am13.h5'
weight_file_path = os.path.join(input_path, weight_file)
output_graph_name = weight_file[:-3] + '.pb'
# pb模型文件输出输出路径
output_dir = os.path.join(os.getcwd(), "G:/model/")
# 加载模型
h5_model = load_model(weight_file_path)
h5_to_pb(h5_model, output_dir=output_dir, model_name=output_graph_name)
print('Finished')
from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K
#路径参数
input_path = 'G:/model/'
weight_file = 'AItree0301am13.h5'
weight_file_path = osp.join(input_path,weight_file)
output_graph_name = weight_file[:-3] + '.pb'
#转换函数
def h5_to_pb(h5_model,output_dir,model_name,out_prefix = "output_",log_tensorboard = True):
if osp.exists(output_dir) == False:
os.mkdir(output_dir)
out_nodes = []
for i in range(len(h5_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(h5_model.output[i],out_prefix + str(i + 1))
sess = K.get_session()
from tensorflow.python.framework import graph_util,graph_io
init_graph = sess.graph.as_graph_def()
main_graph = graph_util.convert_variables_to_constants(sess,init_graph,out_nodes)
graph_io.write_graph(main_graph,output_dir,name = model_name,as_text = False)
if log_tensorboard:
from tensorflow.python.tools import import_pb_to_tensorboard
import_pb_to_tensorboard.import_to_tensorboard(osp.join(output_dir,model_name),output_dir)
#输出路径
output_dir = osp.join(os.getcwd(),"G:/model/")
#加载模型
h5_model = load_model(weight_file_path)
h5_to_pb(h5_model,output_dir = output_dir,model_name = output_graph_name)
print('model saved')
，最后显示的为错误
assert d in name_to_node, "%s is not in graph" % d
AssertionError: output_1 is not in graph