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pjb
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June 6, 2025
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Network analyze result is only on final layer

  • June 6, 2025
  • 1 reply
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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

 

Best answer by Julian E.

Hello @pjb,

 

I think there is a bug when using the Sequential API. I escalated the bug to the dev team to investigate.

In your case, please use something like this:

 

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, Dense, BatchNormalization, Flatten,ReLU

input_layer = Input(shape = (24,24,2), batch_size = 1)
x = Conv2D(32, kernel_size=(3, 3))(input_layer)
x = BatchNormalization(momentum=0.8, epsilon=1e-3)(x)
x = ReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)

x =Conv2D(16, kernel_size=(1, 1))(x)
x = BatchNormalization(momentum=0.9)(x)
x = ReLU()(x)

# Second Conv Block
x = Conv2D(32, kernel_size=(3, 3))(x)
x = BatchNormalization(momentum=0.9)(x)
x = ReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)

# Fully Connected Layers
x = Flatten()(x)
x = Dense(48, activation='relu')(x)
x = Dropout(0.1)(x)
x = Dense(24, activation='relu')(x)
x = Dropout(0.1)(x)
output_layer = Dense(1, activation='linear')(x)

# Create the model
model = Model(inputs=input_layer, outputs=output_layer)

model.summary()

model.save('non_seq_model.h5')

 

report:

 stedgeai.exe analyze --model non_seq_model.h5 --target stm32
ST Edge AI Core v2.1.0-20194 329b0e98d

Creating c (debug) info json file C:\Users\ettariaj\Downloads\st_ai_output\network_c_info.json

 Exec/report summary (analyze)
 -----------------------------------------------------------------------------------------
 model file : C:\Users\ettariaj\Downloads\non_seq_model.h5
 type : keras
 c_name : network
 compression : lossless
 options : allocate-inputs, allocate-outputs
 optimization : balanced
 target/series : stm32
 workspace dir : C:\Users\ettariaj\Downloads\st_ai_ws
 output dir : C:\Users\ettariaj\Downloads\st_ai_output
 model_fmt : float
 model_name : non_seq_model
 model_hash : 0x4067780f2ddae9bff15bae826f6379a1
 params # : 31,761 items (124.07 KiB)
 -----------------------------------------------------------------------------------------
 input 1/1 : 'input_layer_12', f32(1x24x24x2), 4.50 KBytes, activations
 output 1/1 : 'dense_36', f32(1x1), 4 Bytes, activations
 macc : 777,513
 weights (ro) : 126,276 B (123.32 KiB) (1 segment) / -768(-0.6%) vs float model
 activations (rw) : 24,200 B (23.63 KiB) (1 segment) *
 ram (total) : 24,200 B (23.63 KiB) = 24,200 + 0 + 0
 -----------------------------------------------------------------------------------------
 (*) 'input'/'output' buffers can be used from the activations buffer

Computing AI RT data/code size (target=stm32)..
 -> compiler "gcc:arm-none-eabi-gcc" is not in the PATH

 Model name - non_seq_model
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 m_id layer (original) oshape param/size macc connected to
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 0 input_layer_12 (InputLayer) [b:1,h:24,w:24,c:2]
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 1 conv2d_36 (Conv2D) [b:1,h:22,w:22,c:32] 608/2,432 278,816 input_layer_12
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 2 batch_normalization_36 (BatchNormalization) [b:1,h:22,w:22,c:32] 64/256 30,976 conv2d_36
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 3 re_lu_36 (ReLU) [b:1,h:22,w:22,c:32] 15,488 batch_normalization_36
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 4 max_pooling2d_24 (MaxPooling2D) [b:1,h:11,w:11,c:32] 15,488 re_lu_36
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 5 conv2d_37 (Conv2D) [b:1,h:11,w:11,c:16] 528/2,112 61,968 max_pooling2d_24
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 6 batch_normalization_37 (BatchNormalization) [b:1,h:11,w:11,c:16] 32/128 3,872 conv2d_37
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 7 re_lu_37 (ReLU) [b:1,h:11,w:11,c:16] 1,936 batch_normalization_37
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 8 conv2d_38 (Conv2D) [b:1,h:9,w:9,c:32] 4,640/18,560 373,280 re_lu_37
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 9 batch_normalization_38 (BatchNormalization) [b:1,h:9,w:9,c:32] 64/256 5,184 conv2d_38
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 10 re_lu_38 (ReLU) [b:1,h:9,w:9,c:32] 2,592 batch_normalization_38
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 11 max_pooling2d_25 (MaxPooling2D) [b:1,h:4,w:4,c:32] 2,048 re_lu_38
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 12 flatten_12 (Flatten) [b:1,c:512] max_pooling2d_25
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 13 dense_34_dense (Dense) [b:1,c:48] 24,624/98,496 24,624 flatten_12
 dense_34 (Dense) [b:1,c:48] 48 dense_34_dense
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 15 dense_35_dense (Dense) [b:1,c:24] 1,176/4,704 1,176 dense_34
 dense_35 (Dense) [b:1,c:24] 24 dense_35_dense
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 17 dense_36 (Dense) [b:1,c:1] 25/100 25 dense_35
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 model: macc=817,545 weights=127,044 activations=-- io=--

 Number of operations per c-layer
 ------- ------ ------------------------- --------- --------------
 c_id m_id name (type) #op type
 ------- ------ ------------------------- --------- --------------
 0 4 conv2d_36 (Conv2D) 309,792 smul_f32_f32
 1 6 conv2d_37 (Conv2D) 61,968 smul_f32_f32
 2 7 re_lu_37 (Nonlinearity) 1,936 op_f32_f32
 3 11 conv2d_38 (Conv2D) 377,920 smul_f32_f32
 4 13 dense_34_dense (Dense) 24,624 smul_f32_f32
 5 13 dense_34 (Nonlinearity) 48 op_f32_f32
 6 15 dense_35_dense (Dense) 1,176 smul_f32_f32
 7 15 dense_35 (Nonlinearity) 24 op_f32_f32
 8 17 dense_36 (Dense) 25 smul_f32_f32
 ------- ------ ------------------------- --------- --------------
 total 777,513

 Number of operation types
 ---------------- --------- -----------
 operation type # %
 ---------------- --------- -----------
 smul_f32_f32 775,505 99.7%
 op_f32_f32 2,008 0.3%

 Complexity report (model)
 ------ ------------------------ ------------------------- ------------------------- --------
 m_id name c_macc c_rom c_id
 ------ ------------------------ ------------------------- ------------------------- --------
 4 max_pooling2d_24 ||||||||||||| 39.8% | 1.9% [0]
 6 batch_normalization_37 ||| 8.0% | 1.7% [1]
 7 re_lu_37 | 0.2% | 0.0% [2]
 11 max_pooling2d_25 |||||||||||||||| 48.6% ||| 14.7% [3]
 13 dense_34_dense | 3.2% |||||||||||||||| 78.0% [4, 5]
 15 dense_35_dense | 0.2% | 3.7% [6, 7]
 17 dense_36 | 0.0% | 0.1% [8]
 ------ ------------------------ ------------------------- ------------------------- --------
 macc=777,513 weights=126,276 act=24,200 ram_io=0

Creating txt report file C:\Users\ettariaj\Downloads\st_ai_output\network_analyze_report.txt

Have a good day,

Julian

1 reply

Julian E.
Julian E.
Technical Moderator
June 9, 2025

Hello @pjb,

 

Can you share an example of model that you used and the code to generate it. I think you are doing something wrong.

If you take a look at the table describing your model, all the layers are called input_# and always taking in input "(Input,None)" , like:

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

 As if your model is not connected (the output of one layer is not the input of the following one), which may explain the fact that you only see the last layer appearing in the analyze.

 

Have a good day,

Julian

​In order to give better visibility on the answered topics, please click on 'Accept as Solution' on the reply which solved your issue or answered your question.
P
pjbAuthor
Associate
June 9, 2025

Hi Julian - I see what you mean. The model is nothing special and does train and inference well in a notebook. I don't have the original model in the above code as it has evolved since then. But here is latest, which is similar and same NONE input results. I've not tried using the add layer method of building a model but perhaps I should.  

def create_model():
 model = Sequential([
 Conv2D(32, (3, 3), input_shape=(24,24,2)),
 BatchNormalization(momentum=0.8, epsilon=1e-3),
 ReLU(),
 MaxPooling2D((2, 2)),

 Conv2D(16, (1, 1)),
 BatchNormalization(momentum=0.9),
 ReLU(),

 Conv2D(32, (3, 3)),
 BatchNormalization(momentum=0.9),
 ReLU(),
 MaxPooling2D((2, 2)),

 Flatten(),
 Dense(48, activation='relu'),
 Dropout(0.1),
 Dense(24, activation='relu'),
 Dropout(0.1),
 Dense(1, activation='linear')
 ])

 model.compile(optimizer='adam', loss='mse', metrics=['mae'])
 return model

 

Julian E.
Julian E.Best answer
Technical Moderator
June 10, 2025

Hello @pjb,

 

I think there is a bug when using the Sequential API. I escalated the bug to the dev team to investigate.

In your case, please use something like this:

 

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, Dense, BatchNormalization, Flatten,ReLU

input_layer = Input(shape = (24,24,2), batch_size = 1)
x = Conv2D(32, kernel_size=(3, 3))(input_layer)
x = BatchNormalization(momentum=0.8, epsilon=1e-3)(x)
x = ReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)

x =Conv2D(16, kernel_size=(1, 1))(x)
x = BatchNormalization(momentum=0.9)(x)
x = ReLU()(x)

# Second Conv Block
x = Conv2D(32, kernel_size=(3, 3))(x)
x = BatchNormalization(momentum=0.9)(x)
x = ReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)

# Fully Connected Layers
x = Flatten()(x)
x = Dense(48, activation='relu')(x)
x = Dropout(0.1)(x)
x = Dense(24, activation='relu')(x)
x = Dropout(0.1)(x)
output_layer = Dense(1, activation='linear')(x)

# Create the model
model = Model(inputs=input_layer, outputs=output_layer)

model.summary()

model.save('non_seq_model.h5')

 

report:

 stedgeai.exe analyze --model non_seq_model.h5 --target stm32
ST Edge AI Core v2.1.0-20194 329b0e98d

Creating c (debug) info json file C:\Users\ettariaj\Downloads\st_ai_output\network_c_info.json

 Exec/report summary (analyze)
 -----------------------------------------------------------------------------------------
 model file : C:\Users\ettariaj\Downloads\non_seq_model.h5
 type : keras
 c_name : network
 compression : lossless
 options : allocate-inputs, allocate-outputs
 optimization : balanced
 target/series : stm32
 workspace dir : C:\Users\ettariaj\Downloads\st_ai_ws
 output dir : C:\Users\ettariaj\Downloads\st_ai_output
 model_fmt : float
 model_name : non_seq_model
 model_hash : 0x4067780f2ddae9bff15bae826f6379a1
 params # : 31,761 items (124.07 KiB)
 -----------------------------------------------------------------------------------------
 input 1/1 : 'input_layer_12', f32(1x24x24x2), 4.50 KBytes, activations
 output 1/1 : 'dense_36', f32(1x1), 4 Bytes, activations
 macc : 777,513
 weights (ro) : 126,276 B (123.32 KiB) (1 segment) / -768(-0.6%) vs float model
 activations (rw) : 24,200 B (23.63 KiB) (1 segment) *
 ram (total) : 24,200 B (23.63 KiB) = 24,200 + 0 + 0
 -----------------------------------------------------------------------------------------
 (*) 'input'/'output' buffers can be used from the activations buffer

Computing AI RT data/code size (target=stm32)..
 -> compiler "gcc:arm-none-eabi-gcc" is not in the PATH

 Model name - non_seq_model
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 m_id layer (original) oshape param/size macc connected to
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 0 input_layer_12 (InputLayer) [b:1,h:24,w:24,c:2]
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 1 conv2d_36 (Conv2D) [b:1,h:22,w:22,c:32] 608/2,432 278,816 input_layer_12
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 2 batch_normalization_36 (BatchNormalization) [b:1,h:22,w:22,c:32] 64/256 30,976 conv2d_36
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 3 re_lu_36 (ReLU) [b:1,h:22,w:22,c:32] 15,488 batch_normalization_36
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 4 max_pooling2d_24 (MaxPooling2D) [b:1,h:11,w:11,c:32] 15,488 re_lu_36
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 5 conv2d_37 (Conv2D) [b:1,h:11,w:11,c:16] 528/2,112 61,968 max_pooling2d_24
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 6 batch_normalization_37 (BatchNormalization) [b:1,h:11,w:11,c:16] 32/128 3,872 conv2d_37
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 7 re_lu_37 (ReLU) [b:1,h:11,w:11,c:16] 1,936 batch_normalization_37
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 8 conv2d_38 (Conv2D) [b:1,h:9,w:9,c:32] 4,640/18,560 373,280 re_lu_37
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 9 batch_normalization_38 (BatchNormalization) [b:1,h:9,w:9,c:32] 64/256 5,184 conv2d_38
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 10 re_lu_38 (ReLU) [b:1,h:9,w:9,c:32] 2,592 batch_normalization_38
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 11 max_pooling2d_25 (MaxPooling2D) [b:1,h:4,w:4,c:32] 2,048 re_lu_38
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 12 flatten_12 (Flatten) [b:1,c:512] max_pooling2d_25
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 13 dense_34_dense (Dense) [b:1,c:48] 24,624/98,496 24,624 flatten_12
 dense_34 (Dense) [b:1,c:48] 48 dense_34_dense
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 15 dense_35_dense (Dense) [b:1,c:24] 1,176/4,704 1,176 dense_34
 dense_35 (Dense) [b:1,c:24] 24 dense_35_dense
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 17 dense_36 (Dense) [b:1,c:1] 25/100 25 dense_35
 ------ --------------------------------------------- ---------------------- --------------- --------- ------------------------
 model: macc=817,545 weights=127,044 activations=-- io=--

 Number of operations per c-layer
 ------- ------ ------------------------- --------- --------------
 c_id m_id name (type) #op type
 ------- ------ ------------------------- --------- --------------
 0 4 conv2d_36 (Conv2D) 309,792 smul_f32_f32
 1 6 conv2d_37 (Conv2D) 61,968 smul_f32_f32
 2 7 re_lu_37 (Nonlinearity) 1,936 op_f32_f32
 3 11 conv2d_38 (Conv2D) 377,920 smul_f32_f32
 4 13 dense_34_dense (Dense) 24,624 smul_f32_f32
 5 13 dense_34 (Nonlinearity) 48 op_f32_f32
 6 15 dense_35_dense (Dense) 1,176 smul_f32_f32
 7 15 dense_35 (Nonlinearity) 24 op_f32_f32
 8 17 dense_36 (Dense) 25 smul_f32_f32
 ------- ------ ------------------------- --------- --------------
 total 777,513

 Number of operation types
 ---------------- --------- -----------
 operation type # %
 ---------------- --------- -----------
 smul_f32_f32 775,505 99.7%
 op_f32_f32 2,008 0.3%

 Complexity report (model)
 ------ ------------------------ ------------------------- ------------------------- --------
 m_id name c_macc c_rom c_id
 ------ ------------------------ ------------------------- ------------------------- --------
 4 max_pooling2d_24 ||||||||||||| 39.8% | 1.9% [0]
 6 batch_normalization_37 ||| 8.0% | 1.7% [1]
 7 re_lu_37 | 0.2% | 0.0% [2]
 11 max_pooling2d_25 |||||||||||||||| 48.6% ||| 14.7% [3]
 13 dense_34_dense | 3.2% |||||||||||||||| 78.0% [4, 5]
 15 dense_35_dense | 0.2% | 3.7% [6, 7]
 17 dense_36 | 0.0% | 0.1% [8]
 ------ ------------------------ ------------------------- ------------------------- --------
 macc=777,513 weights=126,276 act=24,200 ram_io=0

Creating txt report file C:\Users\ettariaj\Downloads\st_ai_output\network_analyze_report.txt

Have a good day,

Julian

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