Adding ReadMe for Layer by Layer Debug Targets

debugging_output.md

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+# How to debug invalid output
+
+The TFLM debugging output tools allow TFLM users to easily debug their models
+by providing a tool that will compare the intermediate  values(output of each OP/Kernel)
+from a model post invoke between the TFLM and TfLite. As well as a way to
+compare intermediate values between TFLM x86 implementations and Optimized 
+Implementations.
+
+## How to debug TFLM Interpreter output on embedded targets
+
+First you call a C++ binary that takes a TfLite model and returns a file that has
+random inputs and their corresponding output values for each layer of the model
+it was provided.
+
+The second is you provide a TfLite model and file outputted by C++ binary above
+to a  python script. The script runs TFLM x86 inference comparison to the 
+expected output.
+
+## How to debug TFLM Python Interpreter output
+
+Using a python script mentioned in the section above when only a TfLite model is
+provided as input, the script generates random input and compares TFLM vs TfLite
+inference outputs for each layer of the model.
+
+## C++ Expected Layer by Layer Output Tool on TFLite Micro
+
+This C++ binary allows you to pass in a TfLite model and returns a flatbuffer
+file with input and the corresponding output values appended into it that can be
+passed into a python debugging tool which can compare those golden values vs
+the x86 TFLM reference kernel implementation.
+
+The C++ Tool/binary will write a debugging file to the path provide in
+2nd arg using the tflite_model provided in the 1st arg.
+
+##### Command bazel/blaze:
+
+```
+ bazel run tensorflow/lite/micro/tools:layer_cc -- \
+    </path/to/input_model.tflite>
+   </path/to/output.file_name>
+```
+
+##### How to Build using Makefile :
+
+```
+make -f tensorflow/lite/micro/tools/make/Makefile layer_by_layer_output_tool -j24
+```
+
+## Python Layer by Layer Debugging Tool 
+
+The Python Tool/Script can first be used to compare TFLM vs Tflite outputs for
+random inputs by only providing a TfLite file.
+
+#### TfLite vs TFLM command:
+``` 
+ bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+    --input_tflite_file=</path/to/my_model.tflite>
+```
+
+The Python Tool/Script can also be used to compare TFLM's python x86 output
+vs expected output provided by the C++ Tool/binary.
+
+#### TFLM vs Expected Command:
+``` 
+  bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
+    --input_tflite_file=</path/to/my_model.tflite> \
+    --layer_by_layer_data_file=</path/to/my_debug_flatbuffer_file>
+```
+
+#### Optional Flags:
+ ` --print_dump  `
+When this flag is set, it will print the TFLM output for each layer that is
+compared.
+
+ ` --rng`
+Integer random number seed for generating input data for comparisons against TFLite. (Default: 42)