cancel
Showing results for 
Search instead for 
Did you mean: 

Network analyze result is only on final layer

pjb
Visitor

Hi - I'm attempting to run my first NN on an STM32H5. This is a low frame rate, low resolution vision application.

When loading NN into CubeMX, the Analyze results and graph are always the same - just the final dense layer. In case it matters, I train the network using tensorflow/keras and have tried multiple variations with similar results. Also, I have updated all tools and libraries within the last few weeks.
Below is my network_generated_report.txt. It shows the param/size and macc for all layers but the summary is just the final layer.

Why might this be and how do I get the complete results? Perhaps I am deeply misunderstanding something!

 

ST Edge AI Core v2.1.0-20194 329b0e98d
Created date          : 2025-06-05 12:55:47
Parameters            : generate --target stm32h5 --name network -m C:/AI/line_intensity_model.h5 --compression none --verbosity 1 --workspace C:/Users/me/AppData/Local/Temp/mxAI_workspace1431250019060016128019384163178723 --output C:/Users/me/.stm32cubemx/network_output

Exec/report summary (generate)
---------------------------------------------------------------------------------------------------------------
model file         :   C:\AI\line_intensity_model.h5                                                           
type               :   keras                                                                                   
c_name             :   network                                                                                 
compression        :   none                                                                                    
options            :   allocate-inputs, allocate-outputs                                                       
optimization       :   balanced                                                                                
target/series      :   stm32h5                                                                                 
workspace dir      :   C:\Users\me\AppData\Local\Temp\mxAI_workspace1431250019060016128019384163178723   
output dir         :   C:\Users\me\.stm32cubemx\network_output                                           
model_fmt          :   float                                                                                   
model_name         :   line_intensity_model                                                                    
model_hash         :   0xb124f1a71b6e4dc396d0dc1318a94841                                                      
params #           :   69,505 items (271.50 KiB)                                                               
---------------------------------------------------------------------------------------------------------------
input 1/1          :   'input_0', f32(1x24x24x2), 4.50 KBytes, activations                                     
output 1/1         :   'dense_17', f32(1x1), 4 Bytes, activations                                              
macc               :   25                                                                                      
weights (ro)       :   100 B (100 B) (1 segment) / -277,920(-100.0%) vs float model                            
activations (rw)   :   4,708 B (4.60 KiB) (1 segment) *                                                        
ram (total)        :   4,708 B (4.60 KiB) = 4,708 + 0 + 0                                                      
---------------------------------------------------------------------------------------------------------------
(*) 'input'/'output' buffers can be used from the activations buffer

Model name - line_intensity_model
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
m_id   layer (type,original)               oshape                 param/size              macc   connected to   | c_size              c_macc                c_type         
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
0      input_0 (Input, None)               [b:1,h:24,w:24,c:2]                                                  |                                           
       conv2d_10_conv2d (Conv2D, Conv2D)   [b:1,h:22,w:22,c:32]   608/2,432            278,816        input_0   | -2,432(-100.0%)     -278,816(-100.0%)     
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
1      input_1 (Input, None)               [b:1,h:22,w:22,c:32]                                                 |                                           
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
2      input_2 (Input, None)               [b:1,h:22,w:22,c:32]                                                 |                                           
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
3      input_3 (Input, None)               [b:1,h:11,w:11,c:32]                                                 |                                           
       conv2d_11_conv2d (Conv2D, Conv2D)   [b:1,h:9,w:9,c:64]     18,496/73,984      1,493,056        input_3   | -73,984(-100.0%)    -1,493,056(-100.0%)   
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
4      input_4 (Input, None)               [b:1,h:9,w:9,c:64]                                                   |                                           
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
5      input_5 (Input, None)               [b:1,h:9,w:9,c:64]                                                   |                                           
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
6      input_6 (Input, None)               [b:1,h:4,w:4,c:64]                                                   |                                           
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
7      input_7 (Input, None)               [b:1,c:1024]                                                         |                                           
       dense_15_dense (Dense, Dense)       [b:1,c:48]             49,200/196,800        49,200        input_7   | -196,800(-100.0%)   -49,200(-100.0%)      
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
8      input_8 (Input, None)               [b:1,c:48]                                                           |                                           
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
9      input_9 (Input, None)               [b:1,c:48]                                                           |                                           
       dense_16_dense (Dense, Dense)       [b:1,c:24]             1,176/4,704            1,176        input_9   | -4,704(-100.0%)     -1,176(-100.0%)       
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
10     input_10 (Input, None)              [b:1,c:24]                                                           |                                           
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
11     input_11 (Input, None)              [b:1,c:24]                                                           | +100(+100.0%)       +25(+100.0%)          Dense_[o][0]   
       dense_17 (Dense, Dense)             [b:1,c:1]              25/100                    25       input_11   | -100(-100.0%)       -25(-100.0%)          
------ ----------------------------------- ---------------------- ---------------- ----------- -------------- --- ------------------- --------------------- -------------- 
model/c-model: macc=1,822,273/25 -1,822,248(-100.0%) weights=278,020/100 -277,920(-100.0%) activations=--/4,708 io=--/0



Generated C-graph summary
------------------------------------------------------------------------------------------------------------------------
model name            : line_intensity_model
c-name                : network
c-node #              : 1
c-array #             : 5
activations size      : 4708 (1 segment)
weights size          : 100 (1 segment)
macc                  : 25
inputs                : ['input_0_output']
outputs               : ['dense_17_output']

C-Arrays (5)
------ ------------------ ----------- ------------------------- ------------- --------- 
c_id   name (*_array)     item/size   domain/mem-pool           c-type        comment   
------ ------------------ ----------- ------------------------- ------------- --------- 
0      dense_17_bias      1/4         weights/weights           const float             
1      dense_17_output    1/4         activations/**default**   float         /output   
2      dense_17_weights   24/96       weights/weights           const float             
3      input_0_output     1152/4608   activations/**default**   float         /input    
4      input_11_output    24/96       activations/**default**   float                   
------ ------------------ ----------- ------------------------- ------------- --------- 

C-Layers (1)
------ ---------------- ---- ------------ ------ ----- --------------------- ------------------ 
c_id   name (*_layer)   id   layer_type   macc   rom   tensors               shape (array id)   
------ ---------------- ---- ------------ ------ ----- --------------------- ------------------ 
0      dense_17         11   Dense        25     100   I: input_11_output    f32(1x24) (4)      
                                                       W: dense_17_weights   f32(1x24) (2)      
                                                       W: dense_17_bias      f32(1) (0)         
                                                       O: dense_17_output    f32(1x1) (1)       
------ ---------------- ---- ------------ ------ ----- --------------------- ------------------ 



Number of operations per c-layer
------- ------ ------------------ ----- -------------- 
c_id    m_id   name (type)          #op           type 
------- ------ ------------------ ----- -------------- 
0       11     dense_17 (Dense)      25   smul_f32_f32 
------- ------ ------------------ ----- -------------- 
total                                25 

Number of operation types
---------------- ---- ----------- 
operation type      #           % 
---------------- ---- ----------- 
smul_f32_f32       25      100.0% 

Complexity report (model)
------ ---------- ------------------------- ------------------------- ------ 
m_id   name       c_macc                    c_rom                     c_id   
------ ---------- ------------------------- ------------------------- ------ 
11     input_11   |||||||||||||||| 100.0%   |||||||||||||||| 100.0%   [0]    
------ ---------- ------------------------- ------------------------- ------ 
macc=25 weights=100 act=4,708 ram_io=0
 
 Requested memory size by section - "stm32h5" target
 ------------------------------- ------- -------- ------ ------- 
 module                             text   rodata   data     bss 
 ------------------------------- ------- -------- ------ ------- 
 NetworkRuntime1010_CM33_GCC.a     5,900        0      0       0 
 network.o                           408        8    692     116 
 network_data.o                       48       16     88       0 
 lib (toolchain)*                      0        0      0       0 
 ------------------------------- ------- -------- ------ ------- 
 RT total**                        6,356       24    780     116 
 ------------------------------- ------- -------- ------ ------- 
 weights                               0      104      0       0 
 activations                           0        0      0   4,708 
 io                                    0        0      0       0 
 ------------------------------- ------- -------- ------ ------- 
 TOTAL                             6,356      128    780   4,824 
 ------------------------------- ------- -------- ------ ------- 
 *  toolchain objects (libm/libgcc*)
 ** RT AI runtime objects (kernels+infrastructure)
  
  Summary - "stm32h5" target
  ---------------------------------------------------
               FLASH (ro)      %*   RAM (rw)       % 
  ---------------------------------------------------
  RT total          7,160   98.6%        896   16.0% 
  ---------------------------------------------------
  TOTAL             7,264              5,604         
  ---------------------------------------------------
  *  rt/total


Generated files (7)
--------------------------------------------------------------------- 
C:\Users\me\.stm32cubemx\network_output\network_data_params.h   
C:\Users\me\.stm32cubemx\network_output\network_data_params.c   
C:\Users\me\.stm32cubemx\network_output\network_data.h          
C:\Users\me\.stm32cubemx\network_output\network_data.c          
C:\Users\me\.stm32cubemx\network_output\network_config.h        
C:\Users\me\.stm32cubemx\network_output\network.h               
C:\Users\me\.stm32cubemx\network_output\network.c               

 

0 REPLIES 0