title: How to compile a embedding extractor on Edge TPU Model Compiler?
body: <h1>Error when compiling embedded extractor on coral</h1>
  <p>I am trying to retrain an image classifier on my coral edgeTPU device. Therefore I followed the steps explained on the <a href="https://coral.withgoogle.com/docs/edgetpu/retrain-classification-ondevice/" rel="nofollow noreferrer">Coral's &quot;Retrain an image classification model on-device&quot;  tutorial</a>:</p>
  <h2>embedded extractor creation</h2>
  <p>As a matter of fact, I created an embedding extractor tflite file according to the given example:</p>
  <pre><code>tflite_convert \
  --output_file=mobilenet_v1_embedding_extractor.tflite \
  --graph_def_file=mobilenet_v1_1.0_224_quant_frozen.pb \
  --input_arrays=input \
  --output_arrays=MobilenetV1/Logits/AvgPool_1a/AvgPool
  </code></pre>
  <h2>Edge TPU Model Compiler upload</h2>
  <p>I got the file <code>mobilenet_v1_embedding_extractor.tflite</code> and uploaded it into the <code>Edge TPU Model Compiler</code> . Unfortunately the compiling process does fail and I get the following error message:</p>
  <pre><code>
  ERROR: Something went wrong. Couldn't compile model.
  
  More details
  --------------
  Start Time     2019-05-02T14:14:53.309219Z
  State          FAILED
  Duration       5.963912978s
  Type           type.googleapis.com/google.cloud.iot.edgeml.v1beta1.CompileOperationMetadata
  Name           operations/compile/16259636989695619987
  
  </code></pre>
  <p>From my point of understanding the above mentioned procedure has to be accomplished before the on-device learning with the <code>classification_transfer_learning.py</code> script is executed on the raspberryPi + edgeTPU/ devBoard.</p>
  <p><em>I hope you can give me a hint to solve the problem and thanks in advance.</em></p>
  <h1>Update May 3 2019</h1>
  <p>The compling works without any erros, when i use the unmodified <code>mobilenet_v1_1.0_224_quant.tflite</code> model.</p>
  <p>I used the <a href="http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz" rel="nofollow noreferrer">quantized model</a> from <a href="https://www.tensorflow.org/lite/guide/hosted_models" rel="nofollow noreferrer">tensorflow</a>.</p>