问题描述
我想采用我在线培训的tensorflow模型,并使用我分发的python程序在本地运行它.
I'd like to take the tensorflow model i've trained online and run it locally with a python program I distribute.
经过培训,我得到了一个包含两个文件/saved_model.pb和一个文件夹/variables的目录/model.在本地部署此方法最简单的方法是什么?
After training, I get a directory /model with two files /saved_model.pb and a folder /variables. What is the simplest way to deploy this locally?
我正在此处部署冻结的模型,但我不太了解.pb.我直接将save_model.pb下载到我的工作文件并尝试
I was following here for deploying frozen models, but I can't quite read in the .pb. I downloaded saved_model.pb to my working directly and tried
with tf.gfile.GFile("saved_model.pb", "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
google.protobuf.message.DecodeError: Truncated message.
根据此处,他们建议一条不同的路线.
Looking on SO here, they suggested a different route.
with tf.gfile.GFile("saved_model.pb", "rb") as f:
proto_b=f.read()
graph_def = tf.GraphDef()
text_format.Merge(proto_b, graph_def)
builtins.TypeError: a bytes-like object is required, not 'str'
我觉得这很混乱
type(proto_b)
<class 'bytes'>
type(graph_def)
<class 'tensorflow.core.framework.graph_pb2.GraphDef'>
为什么会出错,字符串也不都是?
Why the error, neither are strings?
部署云训练模型的最佳方法是什么?
What's the best way to deploy a cloud trained model?
完整代码
import tensorflow as tf
import sys
from google.protobuf import text_format
# change this as you see fit
#image_path = sys.argv[1]
image_path="test.jpg"
# Read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line
in tf.gfile.GFile("dict.txt")]
# Unpersists graph from file
with tf.gfile.GFile("saved_model.pb", "rb") as f:
proto_b=f.read()
graph_def = tf.GraphDef()
text_format.Merge(proto_b, graph_def)
with tf.Session() as sess:
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('conv1/weights:0')
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0': image_data})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))
推荐答案
您部署到CloudML Engine服务的模型的格式为 SavedModel
.使用 loader
SavedModel非常简单>模块:
The format of the model you deployed to the CloudML Engine service is a SavedModel
. Loading a SavedModel
in Python is fairly simple using the loader
module:
import tensorflow as tf
with tf.Session(graph=tf.Graph()) as sess:
tf.saved_model.loader.load(
sess,
[tf.saved_model.tag_constants.SERVING],
path_to_model)
要进行推断,您的代码几乎是正确的;您将需要确保将批次添加到session.run
,因此只需将image_data
包装在列表中:
To perform inference, you're code is almost correct; you will need to make sure that you are feeding a batch to session.run
, so just wrap image_data
in a list:
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('conv1/weights:0')
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0': [image_data]})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))
(请注意,根据您的图形,将input_data包裹在列表中可能会增加预测张量的等级,并且您需要相应地调整代码).
(Note that, depending on your graph, wrapping your input_data in a list may increase the rank of your predictions tensor, and you would need to adjust the code accordingly).
这篇关于从Google Cloud Machine Learning Engine本地加载保存的tensorflow模型.pb的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!