我想采用我在线训练的 tensorflow 模型,并使用我分发的 python 程序在本地运行它。
训练后,我得到一个目录/model,其中包含两个文件/saved_model.pb 和一个文件夹/variables。本地部署的最简单方法是什么?
我正在关注here用于部署卡住模型,但我无法完全阅读 .pb。我直接下载了saved_model.pb到我的工作中并尝试了
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.
正在寻找here ,他们建议了一条不同的路线。
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'
我觉得这很令人困惑
type(proto_b)
<class 'bytes'>
type(graph_def)
<class 'tensorflow.core.framework.graph_pb2.GraphDef'>
为什么会出现错误,字符串也不是?
部署云训练模型的最佳方法是什么?
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))
最佳答案
您部署到 CloudML Engine 服务的模型的格式是 SavedModel
。使用 loader
在 Python 中加载 SavedModel
相当简单模块:
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)
为了执行推理,您的代码几乎是正确的;您需要确保向 session.run
提供一批数据,因此只需将 image_data
包装在列表中即可:
# 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,根据您的图表,将 input_data 包装在列表中可能会增加您的预测张量的排名,并且您需要相应地调整代码)。
关于python - 从谷歌云机器学习引擎本地加载保存的 tensorflow 模型.pb,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/43667018/