save——model模块保存和载入使用简单例子

 

 

https://www.w3xue.com/exp/article/201812/10995.html

 

=====1====实践模型存入
import tensorflow as tf
from tensorflow import saved_model as sm
# 首先定义一个极其简单的计算图
X = tf.placeholder(tf.float32, shape=(3, ))
scale = tf.Variable([10, 11, 12], dtype=tf.float32)
y = tf.multiply(X, scale)
# 在会话中运行
with tf.Session() as sess:
    sess.run(tf.initializers.global_variables())
    value = sess.run(y, feed_dict={X: [1., 2., 3.]})
    print(value)
    
    # 准备存储模型
    path = '/home/×××/tf_model/model_1'
    builder = sm.builder.SavedModelBuilder(path)
    
    # 构建需要在新会话中恢复的变量的 TensorInfo protobuf
    X_TensorInfo = sm.utils.build_tensor_info(X)
    scale_TensorInfo = sm.utils.build_tensor_info(scale)
    y_TensorInfo = sm.utils.build_tensor_info(y)
    # 构建 SignatureDef protobuf
    SignatureDef = sm.signature_def_utils.build_signature_def(
                                inputs={'input_1': X_TensorInfo, 'input_2': scale_TensorInfo},
                                outputs={'output': y_TensorInfo},
                                method_name='what'
    )
    # 将 graph 和变量等信息写入 MetaGraphDef protobuf
    # 这里的 tags 里面的参数和 signature_def_map 字典里面的键都可以是自定义字符串，TensorFlow 为了方便使用，不在新地方将自定义的字符串忘记，可以使用预定义的这些值
    builder.add_meta_graph_and_variables(sess, tags=[sm.tag_constants.TRAINING], 
                                         signature_def_map={sm.signature_constants.CLASSIFY_INPUTS: SignatureDef}
  ) 
　# 将 MetaGraphDef 写入磁盘
    builder.save()



=====222===模型导入

import tensorflow as tf
from tensorflow import saved_model as sm
# 需要建立一个会话对象，将模型恢复到其中
with tf.Session() as sess:
    path = '/home/×××/tf_model/model_1'
    MetaGraphDef = sm.loader.load(sess, tags=[sm.tag_constants.TRAINING], export_dir=path)
    # 解析得到 SignatureDef protobuf
    SignatureDef_d = MetaGraphDef.signature_def
    SignatureDef = SignatureDef_d[sm.signature_constants.CLASSIFY_INPUTS]
    # 解析得到 3 个变量对应的 TensorInfo protobuf
    X_TensorInfo = SignatureDef.inputs['input_1']
    scale_TensorInfo = SignatureDef.inputs['input_2']
    y_TensorInfo = SignatureDef.outputs['output']
    # 解析得到具体 Tensor
    # .get_tensor_from_tensor_info() 函数中可以不传入 graph 参数，TensorFlow 自动使用默认图
    X = sm.utils.get_tensor_from_tensor_info(X_TensorInfo, sess.graph)
    scale = sm.utils.get_tensor_from_tensor_info(scale_TensorInfo, sess.graph)
    y = sm.utils.get_tensor_from_tensor_info(y_TensorInfo, sess.graph)
    print(sess.run(scale))
    print(sess.run(y, feed_dict={X: [3., 2., 1.]}))
# 输出
[10. 11. 12.]
[30. 22. 12.]

　　

========11111======实践模型存入（无格式，代码没对齐）

import tensorflow as tf
from tensorflow import saved_model as sm

# 首先定义一个极其简单的计算图
X = tf.placeholder(tf.float32, shape=(3, ))
scale = tf.Variable([10, 11, 12], dtype=tf.float32)
y = tf.multiply(X, scale)

# 在会话中运行
with tf.Session() as sess:
sess.run(tf.initializers.global_variables())
value = sess.run(y, feed_dict={X: [1., 2., 3.]})
print(value)

# 准备存储模型
path = '/home/×××/tf_model/model_1'
builder = sm.builder.SavedModelBuilder(path)

# 构建需要在新会话中恢复的变量的 TensorInfo protobuf
X_TensorInfo = sm.utils.build_tensor_info(X)
scale_TensorInfo = sm.utils.build_tensor_info(scale)

y_TensorInfo = sm.utils.build_tensor_info(y)

# 构建 SignatureDef protobuf
SignatureDef = sm.signature_def_utils.build_signature_def(
inputs={'input_1': X_TensorInfo, 'input_2': scale_TensorInfo},
outputs={'output': y_TensorInfo},
method_name='what'
)

# 将 graph 和变量等信息写入 MetaGraphDef protobuf
# 这里的 tags 里面的参数和 signature_def_map 字典里面的键都可以是自定义字符串，TensorFlow 为了方便使用，不在新地方将自定义的字符串忘记，可以使用预定义的这些值
builder.add_meta_graph_and_variables(sess, tags=[sm.tag_constants.TRAINING],
signature_def_map={sm.signature_constants.CLASSIFY_INPUTS: SignatureDef}
)

　# 将 MetaGraphDef 写入磁盘
builder.save()

 

==================222222===========================================

=========模型导入

这样我们就把模型整体存储到了磁盘中，而且我们将三个变量 X, scale, y 全部序列化后存储到了其中，所以恢复模型时便可以将他们完全解析出来：

import tensorflow as tf
from tensorflow import saved_model as sm

# 需要建立一个会话对象，将模型恢复到其中
with tf.Session() as sess:
path = '/home/×××/tf_model/model_1'
MetaGraphDef = sm.loader.load(sess, tags=[sm.tag_constants.TRAINING], export_dir=path)

# 解析得到 SignatureDef protobuf
SignatureDef_d = MetaGraphDef.signature_def
SignatureDef = SignatureDef_d[sm.signature_constants.CLASSIFY_INPUTS]

# 解析得到 3 个变量对应的 TensorInfo protobuf
X_TensorInfo = SignatureDef.inputs['input_1']
scale_TensorInfo = SignatureDef.inputs['input_2']
y_TensorInfo = SignatureDef.outputs['output']

# 解析得到具体 Tensor
# .get_tensor_from_tensor_info() 函数中可以不传入 graph 参数，TensorFlow 自动使用默认图
X = sm.utils.get_tensor_from_tensor_info(X_TensorInfo, sess.graph)
scale = sm.utils.get_tensor_from_tensor_info(scale_TensorInfo, sess.graph)
y = sm.utils.get_tensor_from_tensor_info(y_TensorInfo, sess.graph)

print(sess.run(scale))
print(sess.run(y, feed_dict={X: [3., 2., 1.]}))

# 输出
[10. 11. 12.]
[30. 22. 12.]

 

 

 

============333333讲解=

https://github.com/Jerryzhangzhao/DL_tensorflow/blob/master/save%20and%20restore%20model/use%20saved%20model/save_and_restore_by_savedmodelbuilder.py