无法使用训练好的Tensorflow模型 [英] unable to use Trained Tensorflow model

问题描述

我是深度学习和Tensorflow的新手。我将预训练的张量流 inceptionv3 模型重新训练为 saved_model.pb ，以识别不同类型的图像，但是当我尝试将fie与以下代码一起使用时。

I am new to Deep Learning and Tensorflow. I retrained a pretrained tensorflow inceptionv3 model as saved_model.pb to recognize different type of images but when I tried to use the fie with below code.

with tf.Session() as sess:
    with tf.gfile.FastGFile("tensorflow/trained/saved_model.pb",'rb') as  f:
        graph_def = tf.GraphDef()
        tf.Graph.as_graph_def()
        graph_def.ParseFromString(f.read())
        g_in=tf.import_graph_def(graph_def)
        LOGDIR='/log'
        train_writer=tf.summary.FileWriter(LOGDIR)
        train_writer.add_graph(sess.graph)

它给了我这个错误-

 File "testing.py", line 7, in <module>
graph_def.ParseFromString(f.read())
google.protobuf.message.DecodeError: Error parsing message

$ b $时出错b

我尝试了许多解决方案，可以在 tensorflow / python / tools 中找到此问题和模块，该模块使用 graph_def.ParseFromString（f.read（））函数给我同样的错误。请告诉我如何解决此问题或告诉我可以避免 ParseFromString（f.read（））函数的方式。任何帮助，将不胜感激。谢谢！

I tried many solution I can find for this problem and modules in tensorflow/python/tools which uses the graph_def.ParseFromString(f.read()) function are giving me same error. Please tell me how to solve this or tell me the way in which I can avoid ParseFromString(f.read()) function. Any help would be appreciated. Thank you!

推荐答案

我假设您使用 tf.saved_model.Builder TensorFlow ，在这种情况下，您可以执行以下操作：

I am assuming that you saved your trained model using tf.saved_model.Builder provided by TensorFlow, in which case you could possibly do something like:

export_path = './path/to/saved_model.pb'

# We start a session using a temporary fresh Graph
with tf.Session(graph=tf.Graph()) as sess:
    '''
    You can provide 'tags' when saving a model,
    in my case I provided, 'serve' tag 
    '''

    tf.saved_model.loader.load(sess, ['serve'], export_path)
    graph = tf.get_default_graph()

    # print your graph's ops, if needed
    print(graph.get_operations())

    '''
    In my case, I named my input and output tensors as
    input:0 and output:0 respectively
    ''' 
    y_pred = sess.run('output:0', feed_dict={'input:0': X_test})

在这里提供更多背景信息，这就是我保存可按上述方式加载的模型的方式。

To give some more context here, this is how I saved my model which can be loaded as above.

x = tf.get_default_graph().get_tensor_by_name('input:0')
y = tf.get_default_graph().get_tensor_by_name('output:0')

export_path = './models/'
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
signature = tf.saved_model.predict_signature_def(
                inputs={'input': x}, outputs={'output': y}
                )

# using custom tag instead of: tags=[tf.saved_model.tag_constants.SERVING]
builder.add_meta_graph_and_variables(sess=obj.sess,
                                     tags=['serve'],
                                     signature_def_map={'predict': signature})
builder.save()

此会将您的protobuf（'saved_model.pb'）保存在上述文件夹（此处为 models）中，然后可以按照上述说明进行加载。

This will save your protobuf ('saved_model.pb') in the said folder ('models' here) which can then be loaded as stated above.

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