在运行并且训练出一个模型后获得了模型的结构与许多参数，为了防止再次训练以及需要更好地去使用，我们需要保存当前状态

基本保存方式 h5

# 此处假设model为一个已经训练好的模型类
model.save('my_model.h5')

转换为json格式存储基本参数

# 此处假设model为一个已经训练好的模型类
json_string = model.to_json()
open('my_model_architecture.json','w').write(json_string)

转换为二进制pb格式

以下代码为我从网络中寻找到的，可以将模型中的内容转换为pb格式，但需要更改其中的h5为你的模型的h5

import sys \\
from keras.models import load_model \\
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K

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

input_fld = sys.path[0] 
weight_file = 'my_model.h5'
output_graph_name = 'tensor_model.pb'

output_fld = input_fld + '/tensorflow_model/'
if not os.path.isdir(output_fld):
    os.mkdir(output_fld) 
    weight_file_path = osp.join(input_fld, weight_file)
K.set_learning_phase(0) 
net_model = load_model(weight_file_path) 
print('input is :', net_model.input.name) 
print ('output is:', net_model.output.name) 
sess = K.get_session() 
frozen_graph = freeze_session(K.get_session(), output_names=[net_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: ', osp.join(output_fld, output_graph_name))