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I am using a custom image set to train a neural network using Tensorflow API. After successful training process I get these checkpoint files containing values of different training var. I now want to get an inference model from these checkpoint files, I found this script which does that, which I can then use to generate deepdream images as explained in this tutorial. The problem is when I load my model using:

import tensorflow as tf
model_fn = 'export'

graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
  graph_def = tf.GraphDef()
  graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input')
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input-imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input':t_preprocessed})

I get this error:

graph_def.ParseFromString(f.read())

self.MergeFromString(serialized)

raise message_mod.DecodeError('Unexpected end-group tag.') google.protobuf.message.DecodeError: Unexpected end-group tag.

The script expect a protocol buffer file, I am not sure the script I am using to generate inference models is giving me proto buffer files or not.

Can someone please suggest what am I doing wrong, or is there a better way to achieve this. I simply want to convert checkpoint files generated by tensor to proto buffer.

Thanks

Community
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Umer
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1 Answers1

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The link to the script you ran is broken, but in any case the recommended thing is not to try to generate an inference model from a checkpoint, but rather to embed code at the end of your training program that will emit a "SavedModel" export (which is not the same thing as a checkpoint).

Please see [1], and in particular the heading "Building a Saved Model". Note that a Saved Model constitutes multiple files, one of which is indeed a protocol buffer (which directly answers your question I hope); the others are variable files and (optional) asset files.

[1] https://www.tensorflow.org/programmers_guide/saved_model

Christopher Olston
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