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