Converted(quantized) model of simple dense neural network returns same repeated output
@sinban04 Could you please try to rerun the model using the latest TF version as you're using an older one?
Have you gone through [this](https://www.tensorflow.org/lite/performance/post_training_quantization#full_integer_quantization) part already?
Thank you! @sushreebarsa 
Sure, I already did try those in the link you gave.  I wrote the code with the content in the link in the first place. 

>  Could you please try to rerun the model using the latest TF version as you're using an older one?

In addition, I tried with the latest version as i said above (sorry, the content text is too long)

> I tried other tensorflow versions (2.13.0, or nightly: 2.14.0-dev20230706) but still same as before.

 @sinban04 Thank you for the kind response!
@pjpratik Could you please have a look at this?
Thank you! Hi @sinban04 

I have tried to reproduce the issue with a toy model similar to the one provided and observed that model outputs different results when inputs change  for full integer quantization and dynamic range quantization. Am I missing something here?

Please find the [gist](https://colab.research.google.com/gist/pjpratik/e969599feef00d0a7f00240a39caf0bb/61319.ipynb) and let us know if it helps.

Thanks. @pjpratik 
Okay, at first, thank you for your attention. 
I saw your gist code and even on your example, we can see the output is converging to a single value around 55~57.
To be clear, I wrote the code on the colab and the same problem is reproduced. 
Plz check this out: [mygist](https://colab.research.google.com/drive/1A-y90n8MLFqS71BGmPoZuEWIR7gBbB71?usp=sharing)
You would grasp the problem much better :)

FYI, my original dataset is 501 line, and 24KB Hi @sinban04 

Thanks for the gist. 

I did observe similar output for the full integer quantization but for float32 input/output I did observe different outputs in your gist as well.
>I saw your gist code and even on your example, we can see the output is converging to a single value around 55~57.

Yes, the reason is the quantization effect on the input/output. Please check this [Quantization scheme](https://www.tensorflow.org/lite/performance/quantization_spec) used by TFLite for 8 bit quantization.

 @pjpratik 
 
> but for float32 input/output I did observe different outputs in your gist as well.

I tried as well after your comment. It's making different out for now, 
but with all my dataset, it returns same floating value as i summarized the issue above
And as you see, with integer only mode, it returns same output regardless of the input.

From my experience, I assume that 
when the model is too complex (I suppose my model is not that complex though)
during quantization, the model weights are kinda quantized even not to function the proper prediction task.

So the simpler model you made returned some outputs (slightly different) but my model (more complex) did not.
For the same reason, with float16 quantization, 
when the model weights kinda "burst", it becomes impotent not to make proper predictions. 
(Y = Wx+b and it seems the weights are converging to 0, and the same output comes from bias)

Do you think my theory is valid ? And there's no way to fix this problem for now ?
Furthermore, i'm not sure what kind of model is appropriate for quantizaton and not (model compatibility for quantization)
You guys, Do you have any information about this quantization problem ? Hi @sinban04 

Thanks for the information.

The 8-bit quantization approximates floating point values using the following formula.

$$real value = (int8 value - zero point) \times scale$$

And as per https://arxiv.org/abs/1712.05877, TFLite uses a single set of
quantization parameters(scale and zero point)for all values within each activations array and within each weights array; separate arrays use separate quantization parameters.

The quantization scales are computed offline such that relative accuracy is maintained (Sec 2.2 of https://arxiv.org/abs/1712.05877), the Table 4.1 provides results of ResNet on ImageNet: Floating-point vs quantized network accuracy for various network depths.

Thanks.


 @pjpratik 
Thank you for the paper reference :) 
I have one more question about my case. 
As you can see in my first comment (main content at the top), i printed each layer of quantized model for debugging.
I expected the values of weight are all in range between [-128, 127] or [0, 256] as it's quantized in 8bit. (unsigned, or signed)
But, some values out of range exist at some layer. 

```
[ 2110  2151 -1352  1999  1340  -833 -1006  -657 -1454  -641  2383  2034
  2358  -577 -1184  2455  2270  2831  -626 -1301  2957  2355  -665 -1340
...
 -1140  2348  2233  2103  2280  2138  2450  2377 -1008  2026  1958  1892
 -1075 -1108  1946 -1078  2522  1899  1416  1882]
```
is it normal ? or is there some exceptional case ?
Or i got something wrong ?
 Hi @sinban04 

I see the weights are converted into int8 as intended and are in range [-128,127] when checked with netron.

I have attached the weights and also the [gist](https://colab.research.google.com/gist/pjpratik/baa2c163df1bf564d126b69a815a7467/61319.ipynb).

[FC3.txt](https://github.com/tensorflow/tensorflow/files/12247682/FC3.txt)
[FC2.txt](https://github.com/tensorflow/tensorflow/files/12247683/FC2.txt)
[FC1.txt](https://github.com/tensorflow/tensorflow/files/12247684/FC1.txt)

Thanks. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61319">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61319">No</a>

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 22.04.2 LTS
- TensorFlow installation (pip package or built from source):
```bash
export condaPip=`which -a pip | grep $condaEnvName`


$condaPip install tensorflow==2.7.0
$condaPip install numpy
$condaPip install pandas
$condaPip install scikit-learn
$condaPip install statsmodels 
$condaPip install matplotlib
$condaPip install future
$condaPip install onnx
$condaPip install torchviz
$condaPip install mpi
$condaPip install torch
$condaPip install tqdm
$condaPip install pydot
$condaPip install ipympl
$condaPip install seaborn
$condaPip install tabulate
$condaPip install xgboost
$condaPip install catboost
$condaPip install bokeh
```
Installed using conda
- TensorFlow library (version, if pip package or github SHA, if built from source):
Listed above

### 2. Code

Provide code to help us reproduce your issues using one of the following options:
```python
    dnn_model = keras.Sequential([
        normalizer,
        layers.Dense(512, activation='relu', input_dim=x_train.shape[1]),
        layers.Dense(512, activation='relu'),
        layers.Dense(1, activation='sigmoid')
    ])
```
https://www.tensorflow.org/lite/performance/post_training_quantization
My model code is simple as above
And I used **Post Training Quantization using TF.Lite.Converter** (32-float to 8-int)
Integer only post training quantization (https://www.tensorflow.org/lite/performance/post_training_quantization#integer_only)

(Input dim: 8)


### 3. Failure after conversion
#### Reset input shape
After the conversion, the model has been distorted and i solved the problem with resetting input format
```python
interpreter.resize_tensor_input(input_details['index'], (1,8))
```
And it works well but it returns same output all the time from different inputs
Like this,
```
[array([[76]], dtype=uint8), array([[76]], dtype=uint8), array([[76]], dtype=uint8), array([[76]], ...
```

I googled the cases same as mine, but some pointed out learning epochs, or Model structure.
Nothing was problematic with learning epochs and I suppose there are some model compatibility for quantization
I tried other tensorflow versions (2.13.0, or nightly: 2.14.0-dev20230706) but still same as before.

#### Quantization w/ floating value
https://www.tensorflow.org/lite/performance/post_training_quantization#integer_with_float_fallback_using_default_float_inputoutput
I tried quantization using float32 input/output, but it's same with float value
```
[array([[0.36328125]], dtype=float32), array([[0.36328125]], dtype=float32), array([[0.36328125]], dtype=float32), array([[0.36328125]], dtype=float32), array([[0.36328125]], dtype=float32), ...
```

#### Quantized Model Weights
I printed out model weights, but not sure why it happens

I'm afriad the most of model weights are going 0 during conversion, and it became useless.
So the identical output is returned due to Model Bias.
(You can see the weights of quantized model below)

Do you have any idea why it happens or any tips ? 

### 5. (optional) Any other info / logs

#### The predicted output of original model (Expected)
```
[0.04892164 0.05425507 0.34756148 0.12509596 0.05663526 0.05041704
 0.01185179 0.33582878 0.0572255  0.07754967 0.20722428 0.208343
 0.48504433 0.10513872 0.05141798 0.03368005 0.09833255 0.2034252
 0.109099   0.48486084 0.23009542 0.08055633 0.06971437 0.14443058
 0.12913615 0.03327829 0.06535947 0.4671367  0.20984942 0.35980904
...
 0.23956934 0.23102319 0.10548833 0.06846321 0.16444126 0.04730341
 0.47125074 0.1931791  0.16075167 0.06618178 0.06408405 0.50926745
 0.12504464 0.73206306 0.22954968 0.00386357 0.19742006 0.09496576
 0.04637471 0.06708863 0.37460512 0.17932612 0.10157627 0.04863232
 0.04412654 0.50302374 0.06885636 0.21528003 0.09171575 0.0635072
 0.08805934 0.3634451  0.57593703 0.09678486]
```

#### Quantized model weights

<details> 
<summary> Click to watch </summary>

```
Quantized weights
[[  0 161   1 199 181 127   0   0]]
[[-128  127 -126 -126 -126 -127 -127 -127]]
[[ 127 -128 -128 -128 -128 -128 -128 -128]]
[[  80   23  -77 ...    0    7  115]
 [  74   55  -27 ...   70   56  103]
 [ 112  -89   43 ...  -18 -112  -91]
 ...
 [  45  104   -4 ...  -57   91   47]
 [  43   97  -94 ...   19  113  -43]
 [ 110   50  110 ...   34   18    3]]
[  57  -20  195  271  152  140  164  230  186  199 -129   -9  -25  204
  192  -11  183  -83  210  137  166   13  -21   36  248  243 -118  128
 -114  224  221   85  221  134 -142  -60  108  194  118  222  183   39
  201  104 -130   51    7  -39   16  246  224  103 -103  -43  -36  198
  156  -87  224  175   76 -105  -54   97  -69   62  -26 -169  238   63
   99  235   32 -130  -98  152  247  -56  145  191 -122  161   88 -119
   -2  -17  104  158  147  123  221  189  236  170  266  191  210  162
  -43  193  -94  208  -94   71 -130   97  -95  -29  181  244  183 -120
   72  -40 -106 -151   41  125  210  -92   84  -14  187    1  141  -94
   43  141  195  201   -7  227  178    8   67  243   97  229  200   38
  195  197  203   26  186   94  170 -138  210  209   49 -150 -146  202
  187  199  -91  196   95  216  222  203  213  237  167  -39  187  180
  183  -80  -28  170  -62  243   69   48  -57  178  241  213  247   42
  207  131 -111 -117  149 -114 -131  152  143  -36 -124  -81 -141  126
  259  127   89   -4  124  -63  229  192  191   -4  -79  -46   35  -99
  195   77  -62  185  194   30  -75  192  130   35  179 -155  -96   -7
  178   29  177 -105  197   39  237  176  171  201   24  199  -70 -149
  236  220  211 -165  165  248 -100  -71 -152 -123   77  183  159  -72
  186   37 -127  159  183  211  171 -120   38   -7  183  208  -28 -101
  174  176  234  204  182  165  238  145  232  159  256   50  -59   77
  -31  109   -3 -117   41   48  140  131  203  235  117  106   80  197
   32  166  141  183  203  -62   59  183  -36  -54  188   28  221 -108
 -136  -79  190  161  199  111   62  153  -12  139  205  247  233  154
  209  222 -130   27  209 -103  -83  240  130  210  184  138 -104   72
  173   28   14  206  -80  119  177  124  158  200  242  230   83  156
  242    7  190  210  -94  114  210  224  225  -42  232  -34  -67  -39
  230  165  199  224 -134  163  156   -7  136  134  176    2  209   73
   40  192 -109  216  227  201   -6  157  250   81  142   19  218  187
  -52   13  236   29  -24  243   90  135  143  174  189  218  146  239
  173  230  211  211  139  248  178  217  210  206  -86  136  -65  198
  201  218  238  192  223 -125 -178  203   16 -146 -162 -116  198  220
  232  179  105  -51  137 -111  -35 -154  266  -38  -88 -123  189  -95
  -98  237  159  -63  186   72   71  -25  182  168  239  216  171  168
  253  -33  223 -148  109  186  198 -157  229  198  210  100  155 -130
  112  210  111 -114   59  190  224   50  177 -119  144  -95 -110  182
  225  183   56  223  119  171  155  200  220  156  -73  178   44  200
  194   -6  -65  220   18   81  -20  -43]
[[  23   24  -26 ...   36  -24   37]
 [  48   40  -87 ...   44  -32  -21]
 [  -2   16  -38 ... -101  -28  104]
 ...
 [  64   89    2 ...  -37   95  -48]
 [  47  -14  107 ...  -85  -23 -106]
 [  12   31  -60 ...   63  -71  -85]]
[ 2110  2151 -1352  1999  1340  -833 -1006  -657 -1454  -641  2383  2034
  2358  -577 -1184  2455  2270  2831  -626 -1301  2957  2355  -665 -1340
  2138  -948  1411  -747  -690 -1268  2206  -575  2340  2376    70  2465
  -530  -668 -1109   951  2015  2042  -825  -976  2430  -682 -1268  -174
 -1176  1994  -440  -954  -727 -1087 -1017 -1290  -968  2100  1938  -783
  1915  -175  2582  -340  1964 -1330  2090  1963  -688 -1356   -22  1897
  2157  2447  1994  2207  -593 -1213 -1291  2120  2198 -1067  -918  2017
  1983  2399  2494  2201  1857 -1121  2406  2163   -48  -895 -1257 -1288
  2175  2481  2058  2170  3646  -714 -1171  1959  2715  2042  2605  -928
  -473  2099  2286  2298  1995  1968  2472  -356  -901   971  2610  -796
  2199  2135  1871  2212  1927  -972  2225  -975  -820  -932 -1070  2459
  -755  -721  1895  2021  -967  1987  -689 -1477  2076 -1138  2072 -1029
  -413  1856  -649  2216  2087  2194  1955  1996  2144  1895  3004  -780
  1739 -1404  -586 -1151  1895  -940 -1537  3030  -282   832  2043  1985
  2074  1936  2167 -1049  2218  -531 -1049  -843 -1189  -541  2679  -911
  2163  1518  -842 -1254  -966  1975 -1307  -983 -1360  -761  2085 -1137
  2005 -1255  2499  2207  -797 -1105  2200  1923  1895  -885  -633  1901
  1926 -1021  2195  1965 -1151  -952  -502 -1146  2204 -1306  2188  1859
 -1117  1888 -1144  1930  2082  -522 -1029   326  2102 -1007  2078  2298
  2038  1992 -1017  1646 -1298  1881 -1000  1916  2775  -558  2738 -1283
  -715 -1176 -1282  2161  2398  1922  -746 -1193  2281 -1019  2200  2021
  2053  1871 -1253  -621 -1009 -1205  2219  -925 -1342     0  2216  -885
 -1417 -1225 -1295  2147  -984  2133  1986  2767 -1077  1961  2159 -1142
 -1275   259  2182 -1005  -987 -1213  2103  2014  2043 -1087 -1509 -1196
 -1394  -303 -1050  -995  2462  2664  2005 -1157  -931  2737 -1013 -1521
  -751  -934  2335  2780 -1111  2000  1935  2166  2061  2373  2191  -682
 -1478  1882  -505  2093  1916 -1253  -304  2630  1891 -1145 -1167  1923
  1887  1956  2147  2966  -332  -270   336 -1002 -1138  -676  2056  -930
  -520  2277  1894  -970 -1346  -768  1858  2171  2364  2105  -795  2191
  -999  2027 -1141  -381 -1358  -732  1929  -469 -1206 -1283  1889  2446
 -1072 -1082  2227  2127 -1278  2183 -1148  1875  2052  2225 -1003  -741
 -1108 -1066 -1231  2373  1970  2116  2023  -234  1028  2289  1832 -1344
  -649 -1129  1993   505   625  -932 -1135 -1160  -355 -1130  2307 -1242
 -1087  2216  1848  -836  -150  2590  1926  2043  -896  -810  1922 -1031
  -869  2524  -476  -228    76 -1179  -469  2511  2012 -1650  1855  2989
  2400  2430  -155 -1171  -961 -1158  1823 -1036 -1244  2838  2554  2139
  1925  -972  -403   364 -1263  2226  2255  2024  2200  2205 -1223  1982
  1809  -921  2116  2142  -843  2195  -212 -1501  2415  -676  -607  2274
  2172  2157 -1212 -1000  2007  2753 -1065 -1048 -1231  1937  2695 -1240
  -953 -1246  -608  1932  1148 -1232  2039  2225 -1069  2009  2291  2003
  2003 -1369  2226 -1174  2264  -890  2128  1888  3043  -734  -719 -1385
 -1140  2348  2233  2103  2280  2138  2450  2377 -1008  2026  1958  1892
 -1075 -1108  1946 -1078  2522  1899  1416  1882]
[[ -67  -52   50  -61  -66   54  107   16   83   32  -22  -59  -30  113
    95  -30 -123  -93    1   55   -5  -24   28   58  -77   60  -67   22
    59   21  -99   15  -18  -33   60  -62  111   22   10  -51  -74  -54
    36    1  -27   16  103  108   59  -37   53   80    9  100   62   78
   112  -94 -100   93  -96   23  -87   19  -86   88  -91 -102   97   20
    32  -59  -88  -98  -11 -106   90   18    5  -41  -67   16    9  -54
   -38  -70 -119 -102  -89  110  -16  -75   88    5    5    8  -36  -55
  -108  -49  -12   31   45 -115  -70 -120  -71   39   43 -124  -44  -27
   -21  -62 -127   77  105  -52  -30   46  -79  -46  -83  -64  -28   91
   -80  116   99   52   99   -9   38  116  -87  -63   64  -93   71   82
  -108   70  -48  111   31 -109   40  -62  -45  -92 -115  -73 -112 -118
    -7   48  -11   27   85   86  -59   11   40  -31   25  -88  -83 -116
   -99  -39  -37   36  -12  112   65   79   60    1  -51   73  -34  -55
    28   53   98 -122   16   85   59  111  -60   40 -125   19  -78  -35
    55   12  -26  -87  -96   85   16 -100  -45   94 -109   -9   18    5
    61   64  -93   17   -5 -124  110  -41   77  -67  -74   69  105  -68
   -28   32  -20  -18  -81  -57   38  -84   14  -53   48 -105  -52   54
   -92   78   40   89   24  -49 -106  -90   33   34 -111   74   -9  -72
   -93  -87   55   49   10   23  -93   31    4  -60  -41   57   18   34
    93  -96    3  -79  -47  -21  114  -36  -13   97   66   72   -7   89
   103   61  -36 -120  -57  111    7   83   38   10   37   17  -96  -12
   -57   48    7  -75    8   31   16   34 -122  -12   62 -118  -71  -85
   -47 -121 -108   34   78  -86   79  -56  -90   60   73  -10  -31   30
    92  -99  -80 -119  -34  -55   63   81   69   19   78   70  -47  113
    16  -97  -61   16   26   66 -103 -110  -31  -43  108  -78   81 -123
    62   61   66   71 -113   34   29   57 -117  -22   93   21  -59 -118
   102  -81   35  -61  -98  -58   74   44   16   38   70  -36  -66  -49
   -34   18   -2  -48 -103   23   23   32  -35  -94  -68   64   24  114
    58   83  -30   22    6  -72 -116   58   44  -32  -31  -89   69   62
   -70   47   48 -106   66   73  117   16  108 -101 -114   97  -75  -15
   -27  -95   39   58   23   44  -74   77   79  -12   -8  -30  -65   76
    49  -98   68  -36  -31  -26 -120  -30   17 -100  -56   89  -43  -49
    59  -99   83   36  -37   10  100  -18 -119  -64  111   42  -96   -6
    30   87   20  -86  -48    5   92   13   96  -31  -36  112 -112 -112
    71  -81 -109 -101  -36   72  -37   54  -89   69  -55  -67  -14    7
    89  113   52  -24  -25  -51  -57  -13  -24  -73   91 -104  -49  -51
   112  110  -60   25  -46  -77  -72   -9]]
[-960]
[[   0 -110   -2   74  -75  127    0    0]]
[[  80 -116    4   33  -75  127    0    0]]
[[-64  -8  50   9   0   0   0   0]]
[[   0 -110   -2   74  -75  127    0    0   96   33   50    9    0    0
     0    0  105   98   47  112  121  116  104  111  110   51   46   56
    47  115  105  116  -80   -2   96    9    0    0    0    0  112   40
    55    9    0    0    0    0    0    0    0    0    0    0    0    0
    98   55   -4   74  -75  127    0    0   80 -116    4   33  -75  127
     0    0    0 -110  124   40   -2  127    0    0    0    0    0    0
     0    0    0    0  -30   27   -5   74  -75  127    0    0  -64   -8
    50    9    0    0    0    0  -64   -8   50    9    0    0    0    0
     0    0    0    0    0    0    0    0  -14   81   -4   74  -75  127
     0    0  -64  -87   58   45  -75  127    0    0    0    0    0    0
     0    0    0    0    0    0    0    0    0    0    0    0  -14   27
    -5   74  -75  127    0    0    0  -34  106   42  -75  127    0    0
     0    0    0    0    0    0    0    0    0    0    0    0    0    0
     0    0  106   55   -4   74  -75  127    0    0  -80   35   19   41
   -75  127    0    0  120  126   -2   74  -75  127    0    0    0    0
     0    0    0    0    0    0  -22   27   -5   74  -75  127    0    0
   -32   73 -109    8    0    0    0    0    2    0    0    0    0    0
     0    0  116  102   46   69  110  113  117  101  117  101   84   80
    85   69  109   98  101  100  100  105  110  103   73  110  116  101
   103  101  114   66   97  116   99  104    0  103  101  115   47  107
  -127    3    0    0    0    0    0    0   48  -86   50    9    0    0
     0    0  -48   54   29    9    0    0    0    0    0    0    0    0
     0    0    0    0   97    3    0    0    0    0    0    0  -48   48
    50    9    0    0    0    0  -32 -100    1  -57  -75  127    0    0
     0    0    0    0    0    0    0    0   65    3    0    0    0    0
     0    0 -128   88   62    9    0    0    0    0    0   87   27    9
     0    0    0    0   16 -120 -109    8    0    0    0    0   88    1
     1   75  -75  127    0    0   80   50  -73    7    0    0    0    0
     2    0    0    0    3    0    0    0  120  126   -2   74  -75  127
     0    0  -32   49  -73    7    0    0    0    0    0 -110   -2   74
   -75  127    0    0    0   50  -73    7    0    0    0    0  109  101
    47   99   99   47   97  110   97   99  111  110  100   97   51   47
   -80   -2   96    9    0    0    0    0  112   40   55    9    0    0
     0    0    0    0    0    0    0    0    0    0   82   55   -4   74
   -75  127    0    0   80 -116    4   33  -75  127    0    0    0 -110
   124   40   -2  127    0    0    0    0    0    0    0    0    0    0
   -30   27   -5   74  -75  127    0    0]]
[[ -64   -8   50    9    0    0    0    0  -64   -8   50    9    0    0
     0    0    0    0    0    0    0    0    0    0  -22   81   -4   74
   -75  127    0    0  -64  -87   58   45  -75  127    0    0    0    0
     0    0    0    0    0    0    0    0    0    0    0    0    0    0
   -14   27   -5   74  -75  127    0    0    0  -34  106   42  -75  127
     0    0    0    0    0    0    0    0    0    0    0    0    0    0
     0    0    0    0   90   55   -4   74  -75  127    0    0   16   36
    19   41  -75  127    0    0  120  126   -2   74  -75  127    0    0
     0    0    0    0    0    0    0    0  -22   27   -5   74  -75  127
     0    0   40   74 -109    8    0    0    0    0    9    0    0    0
     0    0    0    0  116  102   46   69  110  113  117  101  117  101
    84   80   85   69  109   98  101  100  100  105  110  103   82   97
   103  103  101  100   84  101  110  115  111  114   66   97  116   99
   104    0  -31    1    0    0    0    0    0    0  -64   36   33    9
     0    0    0    0  -64   17   48    9    0    0    0    0    0    0
     0    0    0    0    0    0 -127    1    0    0    0    0    0    0
  -128   25   50    9    0    0    0    0  -32 -100    1  -57  -75  127
     0    0    0    0    0    0    0    0    0    0   97    1    0    0
     0    0    0    0   96    6   31    9    0    0    0    0  -80  -49
    86    9    0    0    0    0   16 -120 -109    8    0    0    0    0
    80    1    1   75  -75  127    0    0  -16   51  -73    7    0    0
     0    0    2    0    0    0    3    0    0    0  120  126   -2   74
   -75  127    0    0 -128   51  -73    7    0    0    0    0    0 -110
    -2   74  -75  127    0    0  -96   51  -73    7    0    0    0    0
    98   47  112  121  116  104  111  110   51   46   56   47  115  105
   116  101  -80   -2   96    9    0    0    0    0  112   40   55    9
     0    0    0    0    0    0    0    0    0    0    0    0   66   55
    -4   74  -75  127    0    0   80 -116    4   33  -75  127    0    0
     0 -110  124   40   -2  127    0    0    0    0    0    0    0    0
     0    0  -30   27   -5   74  -75  127    0    0  -64   -8   50    9
     0    0    0    0  -64   -8   50    9    0    0    0    0    0    0
     0    0    0    0    0    0  -30   81   -4   74  -75  127    0    0
   -64  -87   58   45  -75  127    0    0    0    0    0    0    0    0
     0    0    0    0    0    0    0    0    0    0  -14   27   -5   74
   -75  127    0    0    0  -34  106   42  -75  127    0    0    0    0
     0    0    0    0    0    0    0    0    0    0    0    0    0    0
    74   55   -4   74  -75  127    0    0]]
[[0]]
[[80]]
```

</details>


#### Debugger
- I used debugger
  - https://www.tensorflow.org/lite/performance/quantization_debugger

![image](https://github.com/tensorflow/tensorflow/assets/15901475/a67571a4-e017-4bcd-a6a0-80fdfbc8a62f)
