 some others his age moved on to Sr. Apprentice, he remained behind as a Junior. When he finally snapped out of it, he hit a growth spurt that shot him up a good hand, maybe hand and a half, and did a complete one hundred and eighty degree turn. He set about with an effort that was almost double of his peers, and earned Sr. Apprentice in next to no time.
In his seventeenth turn, he finally took his Journeyman sail, a long trip meant to test apprentices to put what they’ve learned to the test. His trip was interrupted by one of the random tidal waves that have been coming more frequently in the last turns. The wave lifted the boat and dropped it on a rock outcropping, leaving it high and dry when the sea returned to normal. The watch dragon sent out in the wake of the reported wave spotted them and sent back to the weyr for assistance in flying the stranded seacrafters to safety. Having never been to the weyr himself, Firmin accepted the offer to go back there first and have the weyrhealer attend to a couple deep gashes to make sure they didn’t fester.
A surprise meeting at the weyr prompted Firmin to ask permission to stay on for awhile, putting his ship building skill to use helping the woodcarvers of the weyr.
<issue_start><issue_comment>Title: Error during quantization of  mnist network
username_0: Hi,

I have created the mnist armnn model and then tried to quantize it for a given raw tensor. I ma facing below errors during the quantization.

```
terminate called after throwing an instance of 'armnn::Exception'
  what():  Have no entry for layer GUID [7]

Thread 1 "ArmnnQuantizer" received signal SIGABRT, Aborted.
0x0000007fbbaf3528 in __GI_raise (sig=sig@entry=6) at../sysdeps/unix/sysv/linux/raise.c:54
54	../sysdeps/unix/sysv/linux/raise.c: No such file or directory.
(gdb) bt
#0  0x0000007fbbaf3528 in __GI_raise (sig=sig@entry=6) at../sysdeps/unix/sysv/linux/raise.c:54
#1  0x0000007fbbaf49e0 in __GI_abort () at abort.c:89
#2  0x0000007fbbd7a254 in __gnu_cxx::__verbose_terminate_handler() () from /usr/lib/aarch64-linux-gnu/libstdc++.so.6
#3  0x0000007fbbd77dc4 in?? () from /usr/lib/aarch64-linux-gnu/libstdc++.so.6
#4  0x0000007fbbd77e10 in std::terminate() () from /usr/lib/aarch64-linux-gnu/libstdc++.so.6
#5  0x0000007fbbd780d4 in __cxa_throw () from /usr/lib/aarch64-linux-gnu/libstdc++.so.6
#6  0x0000007fbe9d8d40 in armnn::RangeTracker::GetRange(unsigned int, unsigned int) const () from /home/arm01/armnn-devenv/Praveen/armnn1908/build/libarmnn.so
#7  0x0000007fbe9d0f48 in armnn::QuantizerVisitor::CreateQuantizedBias(armnn::IConnectableLayer const*, armnn::ConstTensor const&, armnn::Optional<armnn::ConstTensor> const&, std::vector<int, std::allocator<int> >&) ()
   from /home/arm01/armnn-devenv/Praveen/armnn1908/build/libarmnn.so
#8  0x0000007fbe9d1f08 in armnn::QuantizerVisitor::VisitFullyConnectedLayer(armnn::IConnectableLayer const*, armnn::FullyConnectedDescriptor const&, armnn::ConstTensor const&, armnn::Optional<armnn::ConstTensor> const&, char const*) ()
   from /home/arm01/armnn-devenv/Praveen/armnn1908/build/libarmnn.so
#9  0x0000007fbe946f68 in armnn::FullyConnectedLayer::Accept(armnn::ILayerVisitor&) const () from /home/arm01/armnn-devenv/Praveen/armnn1908/build/libarmnn.so
#10 0x0000007fbe9c3c90 in armnn::NetworkQuantizer::ExportNetwork() () from /home/arm01/armnn-devenv/Praveen/armnn1908/build/libarmnn.so
#11 0x00000055555573f4 in main ()

```
Below is the  command that I have used
For Tensor Generation
`./ImageTensorGenerator -i Images/007573-num7.png -f caffe -o ImageTensors/007573-num7.raw --new-width 28 --new-height 28 -l NCHW
`
For CSV file generation
`./ImageCSVFileGenerator -i../Mnist/ImageTensors/ -o../Mnist/MnistTraining2.csv`

For Quantization
`./ArmnnQuantizer -f mnist.armnn -c MnistTraining2.csv -d. -o mnistQuant.armnn`

Attached zip file contains all the required file mnist.cpp(which is used for generating mnist.armnn).raw and.csv file.

Please let me know where did I go wrong.
[mnist.zip](https://github.com/ARM-software/armnn/files/3967725/mnist.zip)

Thank you,
Praveen.
<issue_comment>username_1: Hi @username_0, Could you please let me know how did you generate the armnn graph (mnist.armnn). Did you used ArmnnConverter to do so? I was able to quantize the model by converting the model using ArmnnConverter and using armnn 19.11. Could you please try do the same at your end. In the meantime I will see if I can reproduce the issue in armnn 19.08 at my end.

Thanks
<issue_comment>username_0: Hi @username_1,

I have used the SerializeNetwork function present in the mnist.cpp which creates an armnn model.I have tried it in armnn 19.08. The same serializer worked for me previously for single layer(input->conv->output) and I was able to quantize it as well.

While using the ArmnnConverter we need to pass the mode file path. I don't have existing model file to convert to armnn model. Instead I have a network pointer which I have to serialize. 

So I am bit unclear on the usage of the ArmnnConverter here. Could you elaborate more on this?

Thank you,
Praveen.
<issue_comment>username_1: Hi @username_0,

I have tried using ArmnnConverter and ArmNN 19.08 and I didn't get any errors. This one has multiple layers so that could be a problem with the SerializeNetwork, i suspect.

Yes, you need to pass in a model file to ArmnnConverter.

Thanks
<issue_comment>username_0: Hi @username_1,

**Yes, you need to pass in a model file to ArmnnConverter.**
I have created a network from scratch and I donot have any model file that is derived from caffe,onnx,tensorflow,tflite. ArmnnConverter requires one of the said models  if I am correct. 


```
arm01@hikey960-hyd-2:~/armnn-devenv/Praveen/armnn1908/build$./ArmnnConverter 
Convert a neural network model from provided file to ArmNN format.

Options:
  --help                          Display usage information
  -f [ --model-format ] arg       Format of the model file, caffe-binary, 
                                  caffe-text.
  **-m [ --model-path ] arg         Path to model file.**
  -i [ --input-name ] arg         Identifier of the input tensors in the 
                                  network, separated by whitespace.
  -s [ --input-tensor-shape ] arg The shape of the input tensor in the network 
                                  as a flat array of integers, separated by 
                                  comma. Multiple shapes are separated by 
                                  whitespace. This parameter is optional, 
                                  depending