Keras模型.h5转成tensorflow的.pb

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我是靠谱客的博主迅速嚓茶，最近开发中收集的这篇文章主要介绍Keras模型.h5转成tensorflow的.pb，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

在keras中，保存.h5模型可能出现两种保存方法：

即（1）保存模型权重和网络结构，以及（2）只保存模型权重。

.h5转.pb的两种方式：

方法一：

Keras的.h5模型转成tensorflow的.pb格式模型，方便后期的前端部署。直接上代码

from keras.models import Model
from keras.layers import Dense, Dropout
from keras.applications.mobilenet import MobileNet
from keras.applications.mobilenet import preprocess_input
from keras.preprocessing.image import load_img, img_to_array
import tensorflow as tf
from keras import backend as K
import os
base_model = MobileNet((None, None, 3), alpha=1, include_top=False, pooling='avg', weights=None)
x = Dropout(0.75)(base_model.output)
x = Dense(10, activation='softmax')(x)
model = Model(base_model.input, x)
model.load_weights('mobilenet_weights.h5')
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
from tensorflow.python.framework.graph_util import convert_variables_to_constants
graph = session.graph
with graph.as_default():
freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
output_names += [v.op.name for v in tf.global_variables()]
input_graph_def = graph.as_graph_def()
if clear_devices:
for node in input_graph_def.node:
node.device = ""
frozen_graph = convert_variables_to_constants(session, input_graph_def,
output_names, freeze_var_names)
return frozen_graph
output_graph_name = 'NIMA.pb'
output_fld = ''
#K.set_learning_phase(0)
print('input is :', model.input.name)
print ('output is:', model.output.name)
sess = K.get_session()
frozen_graph = freeze_session(K.get_session(), output_names=[model.output.op.name])
from tensorflow.python.framework import graph_io
graph_io.write_graph(frozen_graph, output_fld, output_graph_name, as_text=False)
print('saved the constant graph (ready for inference) at: ', os.path.join(output_fld, output_graph_name)

方法二：

先建立好网络结构model，然后调用load_weights函数加载权重参数，然后再开始转：

本文主要记录Keras训练得到的.h5模型文件转换成TensorFlow的.pb文件

#*-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 = 'satellite/train_dir/models/'
weight_file = 'satellite_iv3_ft.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(), "satellite/train_dir/models/")
#
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')

其中，load_model()这个函数不是从keras中加载的，功能就是自己创建的网络结构，创建好网络结构后加载模型，最后才得到h5_model。然后才开始调用h5_to_pb转化。