So Training for specific car commands did not work well.
Reading the above post, I guess my question is: Can the output from FOMO be the input for a second model on a micro-controller? The Portenta has 2 cores and I have good ways to communicate between the cores link here, It would be really intesting to have both cores running an Edgeimpulse model that sends data from one to the other. Too bad the inner M4 core is so slow.
OMG.
The crappy model above, trained on what the car should do (1right, 2left, 3fast) runs better than any of my other models trying to extract information from where the lines are showing up. So much for my predictive ability.
For any beginners wanting a good laugh.
I took the positive from the above model and tried a model fully based on what my Line following track might look like. Here are my model results
Oh well!
kind of connected to this post Passing FOMO results data (to a different core on the Arduino Portenta)
sorry, i somehow missed this last week…
so the main output of FOMO before the bounding box conversion is something equivalent to a segmentation map; by default a (96,96) inputs with 3 classes (e.g. {implied_background, white_line, witches_hat}) will reduce to a (12,12,3) tensor representing the distribution of classes across the entire 12x12 output . note: it’s the logits, not a softmax, at this point.
In expert mode it’s
model = Conv2D(filters=32, kernel_size=1, strides=1,
activation='relu', name='head')(head_of_mobile_net.output)
logits = Conv2D(filters=num_classes, kernel_size=1, strides=1,
activation=None, name='logits')(model). # say (12, 12, 3)
what you want to do is further reduce with two important points
{stop, right, fast, straight} class set.you can do 1) by a stack of strided 2d convolutions until you get to a “small enough” tensor that you can flatten to do 2) as standard logistic regression.
just writing from my head but it’ll be something like …
steering_head = Conv2D(filters=8, kernel_size=3, strides=2,
activation='relu')(logits) # (6,6,8)
steering_head = Conv2D(filters=8, kernel_size=3, strides=2,
activation='relu')(steering_head) # (3,3,8)
steering_head = Flatten()(steering_head) # (72)
steering_head = Dense(units=4, activation='softmax')(steering_head)
this won’t add much more compute, but will keep the important aspects you need. the main tunables for model size / latency are the #filters on the conv layers, try to make values small ( maybe even 4 would work? ). note: it’s important to Flatten not PoolXYZ before the classifier, the whole point is you don’t want to loss the spatial info…
Happy to elaborate more (unsure your level of familiarity with this)
Mat
Oh, forgot to add. This extra part could be trained independently after the original FOMO model. E.g. self drive the car around for a bit and record your actions. That gives you training data. You can add sensible augmentations include stuff like how a left/right flip of the FOMO output would correspond to a turn_left / turn_right label swap.
Thanks @matkelcey this is really interesting and makes some sense to me, actually was the answer I was hoping for. I am going to continue working with my students on post FOMO algorithms for the next 2 weeks until the course is finished, then may take a stab at this.
@matkelcey The part about training independently is interesting, my pre-edgeimpulse background was with tensorflowJS , using javascript I could “freeze” layers. Could I train a FOMO model using only my white lines, then add more layers and freeze the FOMO layers and then train it again with labels of: goLeft, goRight, goStraight-Faster. (stop is the default if nothing is classified)
Yeah spot on, I actually forgot to say this, but as you mention it’s usually more stable to freeze the torso of the model (FOMO) and only train the new classifier. You can think of it along the same lines as transfer learning.
the classifier will always return a result so you have to be a bit more explicit about stop; simplest way is to just a stop class and train for it (e.g. in original training data instances for where all output cells were background)
@matkelcey I have shared a FOMO lines model with you on the expert network ID=109651 called rocksetta-lines-fomo-0stop-1right-2left-3straight That link might work if you are logged in.
Presently I have trained it only on the FOMO lines and here is the code that seems relevant:
cut_point = mobile_net_v2.get_layer('block_6_expand_relu')
#! Now attach a small additional head on the MobileNet
model = Conv2D(filters=32, kernel_size=1, strides=1,
activation='relu', name='head')(cut_point.output)
logits = Conv2D(filters=num_classes, kernel_size=1, strides=1,
activation=None, name='logits')(model)
return Model(inputs=mobile_net_v2.input, outputs=logits)
I am in no rush to do this but would like to understand what you are thinking I should try.
And here is the entire model keras expert mode.
sys.path.append('./resources/libraries')
import os
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import BatchNormalization, Conv2D
from tensorflow.keras.models import Model
from ei_tensorflow.constrained_object_detection import models, dataset, metrics, util
import ei_tensorflow.training
def build_model(input_shape: tuple, weights: str, alpha: float,
num_classes: int) -> tf.keras.Model:
""" Construct a constrained object detection model.
Args:
input_shape: Passed to MobileNet construction.
weights: Weights for initialization of MobileNet where None implies
random initialization.
alpha: MobileNet alpha value.
num_classes: Number of classes, i.e. final dimension size, in output.
Returns:
Uncompiled keras model.
Model takes (B, H, W, C) input and
returns (B, H//8, W//8, num_classes) logits.
"""
#! First create full mobile_net_V2 from (HW, HW, C) input
#! to (HW/8, HW/8, C) output
mobile_net_v2 = MobileNetV2(input_shape=input_shape,
weights=weights,
alpha=alpha,
include_top=True)
#! Default batch norm is configured for huge networks, let's speed it up
for layer in mobile_net_v2.layers:
if type(layer) == BatchNormalization:
layer.momentum = 0.9
#! Cut MobileNet where it hits 1/8th input resolution; i.e. (HW/8, HW/8, C)
cut_point = mobile_net_v2.get_layer('block_6_expand_relu')
#! Now attach a small additional head on the MobileNet
model = Conv2D(filters=32, kernel_size=1, strides=1,
activation='relu', name='head')(cut_point.output)
logits = Conv2D(filters=num_classes, kernel_size=1, strides=1,
activation=None, name='logits')(model)
return Model(inputs=mobile_net_v2.input, outputs=logits)
def train(num_classes: int, learning_rate: float, num_epochs: int,
alpha: float, object_weight: int,
train_dataset: tf.data.Dataset,
validation_dataset: tf.data.Dataset,
best_model_path: str,
input_shape: tuple) -> tf.keras.Model:
""" Construct and train a constrained object detection model.
Args:
num_classes: Number of classes in datasets. This does not include
implied background class introduced by segmentation map dataset
conversion.
learning_rate: Learning rate for Adam.
num_epochs: Number of epochs passed to model.fit
alpha: Alpha used to construct MobileNet. Pretrained weights will be
used if there is a matching set.
object_weight: The weighting to give the object in the loss function
where background has an implied weight of 1.0.
train_dataset: Training dataset of (x, (bbox, one_hot_y))
validation_dataset: Validation dataset of (x, (bbox, one_hot_y))
best_model_path: location to save best model path. note: weights
will be restored from this path based on best val_f1 score.
input_shape: The shape of the model's input
max_training_time_s: Max training time (will exit if est. training time is over the limit)
is_enterprise_project: Determines what message we print if training time exceeds
Returns:
Trained keras model.
Constructs a new constrained object detection model with num_classes+1
outputs (denoting the classes with an implied background class of 0).
Both training and validation datasets are adapted from
(x, (bbox, one_hot_y)) to (x, segmentation_map). Model is trained with a
custom weighted cross entropy function.
"""
nonlocal callbacks
num_classes_with_background = num_classes + 1
input_width_height = None
width, height, input_num_channels = input_shape
if width != height:
raise Exception(f"Only square inputs are supported; not {input_shape}")
input_width_height = width
#! Use pretrained weights, if we have them for configured
weights = None
if input_num_channels == 1:
if alpha == 0.1:
weights = "./transfer-learning-weights/edgeimpulse/MobileNetV2.0_1.96x96.grayscale.bsize_64.lr_0_05.epoch_441.val_loss_4.13.val_accuracy_0.2.hdf5"
elif alpha == 0.35:
weights = "./transfer-learning-weights/edgeimpulse/MobileNetV2.0_35.96x96.grayscale.bsize_64.lr_0_005.epoch_260.val_loss_3.10.val_accuracy_0.35.hdf5"
elif input_num_channels == 3:
if alpha == 0.1:
weights = "./transfer-learning-weights/edgeimpulse/MobileNetV2.0_1.96x96.color.bsize_64.lr_0_05.epoch_498.val_loss_3.85.hdf5"
elif alpha == 0.35:
weights = "./transfer-learning-weights/keras/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_96.h5"
if (weights is not None) and (not os.path.exists(weights)):
print(f"WARNING: Pretrained weights {weights} unavailable; defaulting to random init")
weights = None
model = build_model(
input_shape=input_shape,
weights=weights,
alpha=alpha,
num_classes=num_classes_with_background
)
#! Derive output size from model
model_output_shape = model.layers[-1].output.shape
_batch, width, height, num_classes = model_output_shape
if width != height:
raise Exception(f"Only square outputs are supported; not {model_output_shape}")
output_width_height = width
#! Build weighted cross entropy loss specific to this model size
weighted_xent = models.construct_weighted_xent_fn(model.output.shape, object_weight)
model.compile(loss=weighted_xent,
optimizer=Adam(learning_rate=learning_rate))
#! Wrap bbox datasets with adapters for segmentation maps
train_segmentation_dataset = dataset.bbox_to_segmentation(
train_dataset, input_width_height, input_num_channels,
output_width_height, num_classes_with_background)
validation_segmentation_dataset = dataset.bbox_to_segmentation(
validation_dataset, input_width_height, input_num_channels,
output_width_height, num_classes_with_background)
#! Initialise bias of final classifier based on training data prior.
util.set_classifier_biases_from_dataset(
model, train_segmentation_dataset, num_classes_with_background)
#! Create callback that will do centroid scoring on end of epoch against
#! validation data. Include a callback to show % progress in slow cases.
callbacks = callbacks if callbacks else []
callbacks.append(metrics.CentroidScoring(validation_dataset, output_width_height, num_classes_with_background))
callbacks.append(metrics.PrintPercentageTrained(num_epochs))
#! Include a callback for model checkpointing based on the best validation f1.
callbacks.append(
tf.keras.callbacks.ModelCheckpoint(best_model_path,
monitor='val_f1', save_best_only=True, mode='max',
save_weights_only=True, verbose=0))
model.fit(train_segmentation_dataset,
validation_data=validation_segmentation_dataset,
epochs=num_epochs, callbacks=callbacks, verbose=0)
#! Restore best weights.
model.load_weights(best_model_path)
return model
model = train(num_classes=classes,
learning_rate=0.001,
num_epochs=700,
alpha=0.35,
object_weight=100,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
best_model_path=BEST_MODEL_PATH,
input_shape=MODEL_INPUT_SHAPE)
override_mode = 'segmentation'
disable_per_channel_quantization = False
Looks like I asked this question on a different forum and got a different suggestion. Here is the link
I like having multiple possible solutions.
HI @matkelcey I had to do some raw sensor work before coming back to this issue. I will DM you and see if there is a chance for a phone or zoom call. I get the gist of what you mentioned at Advanced Vision FOMO adding keras layers - #6 by matkelcey but not sure how to put it into practice.
It’s not the expert mode changes, they make sense it’s more the big picture of what to do. How to merge FOMO as a base model with a 4 output classification of the FOMO data. It does sound really interesting, I hope we can connect.
@matkelcey so what I am not yet understanding is do I train 2 models or just one in your suggestions above here?
For 2 models I use what I have already trained, a FOMO model detecting the white line the car follows, and then uses it as the base model for your suggestions above with 4 classes, and retrain that model. (I don’t yet understand how to use one model as a base model for the second model, basically how to do transfer training on edgeimpulse with your own model).
Or do I just make the changes you suggested and train the model ONCE using my 4 classes (stop, left, right, fast).
What I don’t understand with the one model idea is how the FOMO part understands that the line is the important thing to learn. The bounding boxes will be huge compared to the precise bounding boxes I have been using. Do I just trust, that the model, with enough training data will figure it out?
My thinking from the original post was that it’d be two models for training.
A FOMO one that takes the camera input (96, 96, 1) and returns (12,12,3) describing the location of {implied_background, white_line, witches_hat}
Then another model that takes the output of FOMO (12,12,3) and collapses all the way down to (4) (being {stop, right, fast, straight})
These could be trained as completely seperate projects; the input training data for the second coming from some output data of the first.
For deployment you can run them sequentially or, with some surgery, turn them into a single model.
Yes @matkelcey that makes so much sense. I could probably do it all on my own if I can load the vision-FOMO model as the base for a Vision-Classification model. Looking at expert mode for vision-classification the model is loaded as
WEIGHTS_PATH = './transfer-learning-weights/edgeimpulse/MobileNetV2.0_35.96x96.grayscale.bsize_64.lr_0_005.epoch_260.val_loss_3.10.val_accuracy_0.35.hdf5'
INPUT_SHAPE = (120, 120, 1)
base_model = tf.keras.applications.MobileNetV2(
input_shape = INPUT_SHAPE, alpha=0.35,
weights = WEIGHTS_PATH
)
base_model.trainable = False
model = Sequential()
I have loaded weights “.hdf5” or similar files from github before for tensorflowJS models. I wonder if anyone has ever loaded their own file as a base for Vision-Classification and if so, what changes did you make to the following code? I will do some testing.
base_model = tf.keras.applications.MobileNetV2(
input_shape = INPUT_SHAPE, alpha=0.35,
weights = WEIGHTS_PATH
)
Is this the right idea @matkelcey or were you thinking of something different?
@dansitu This is a very old post but is this still the situation that we can’t easily load our own models (my situation is for Vision Classification, not audio as the thread below)? Also any Opinions Daniel, I am trying to make a FOMO model the input base for a classification model.
Summary: Just trying to save the first FOMO weights h5 file and reload it as a classification model.
I have saved the fomo weights model.h5 file here if anyone can figure out how to load it using expert mode
Being creative here, in my FOMO model ProjectID 109651 rocksetta-1lines-fomo-part1 I saved the weights as
#! Add explicit softmax layer before export.
softmax_layer = Softmax()(model.layers[-1].output)
model = Model(model.input, softmax_layer)
model.save_weights('./rocksetta/fomolines')
return model
which should auto-name the weights file, but I don’t know where it is saving it. (This is probably a bug, since I probably should not be able to save a file here, but it seemed to work)
I then tried to load the FOMO weights in the classification model ProjectID 129338 which failed since it didn’t have the filename.
raise ValueError('The `weights` argument should be either '
ValueError: The `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded. Received `weights=./rocksetta/fomolines`
Application exited with code 1
Job failed (see above)
The code I used was:
WEIGHTS_PATH = './rocksetta/fomolines'
#WEIGHTS_PATH = './transfer-learning-weights/edgeimpulse/MobileNetV2.0_35.96x96.grayscale.bsize_64.lr_0_005.epoch_260.val_loss_3.10.val_accuracy_0.35.hdf5'
INPUT_SHAPE = (96, 96, 1)
Other than the logic issues loading a FOMO weights file as the base for a classification model, anyone have any idea how I can find the weights filename? It probably is similar to the project name which is rocksetta-fomo-to-classify-part2-v01 probably with some extension like weights.hdf5
The rest of my messing around is just getting errors. I tried to save the full model and then load the full model but that is also getting errors.
model.summary() is useful, tells me the model name but not really what it has been saved as.
At the moment saving / restoring the weights between models isn’t really possible. We have some things in progress that will make this much easier, but they are still under development.
I think the only way forward at the moment without some serious hacking is the training two models approach sorry 
Thanks for the reply @matkelcey. I agree it seems difficult. I might try Daniels hack to load the weights in base64 format. I am also trying a TensorflowJS method.
For the training of 2 models approach I simply have to redirect the ouput of the FOMO model as a completely new input for a classification model. That isn’t as awful as it sounds, since the data would be very similar to the raw data that we get from the Pixy2Cam (#, x, y, width, height), which I have always wanted to get working with EdgeImpulse and my latest maker101 work on uploading raw data is making that kind of thing easier to do.
Might take a while, thanks for your help.
By the way, having some success with TFJS. Does this look at all sensible for the FOMO model. Converting it to a tfjs layers model was a pain.
___________________________________________________________________________________________________________________
tfjs@3.19.0:17 Layer (type) Input Shape Output shape Param # Receives inputs
tfjs@3.19.0:17 ===================================================================================================================
tfjs@3.19.0:17 input_1 (InputLayer) [[null,96,96,1]] [null,96,96,1] 0
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 Conv1 (Conv2D) [[null,96,96,1]] [null,48,48,16] 144 input_1[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 bn_Conv1 (BatchNormalizati [[null,48,48,16]] [null,48,48,16] 64 Conv1[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 Conv1_relu (ReLU) [[null,48,48,16]] [null,48,48,16] 0 bn_Conv1[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 expanded_conv_depthwise (D [[null,48,48,16]] [null,48,48,16] 144 Conv1_relu[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 expanded_conv_depthwise_BN [[null,48,48,16]] [null,48,48,16] 64 expanded_conv_depthwise
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 expanded_conv_depthwise_re [[null,48,48,16]] [null,48,48,16] 0 expanded_conv_depthwise
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 expanded_conv_project (Con [[null,48,48,16]] [null,48,48,8] 128 expanded_conv_depthwise
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 expanded_conv_project_BN ( [[null,48,48,8]] [null,48,48,8] 32 expanded_conv_project[0
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_expand (Conv2D) [[null,48,48,8]] [null,48,48,48] 384 expanded_conv_project_B
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_expand_BN (BatchNo [[null,48,48,48]] [null,48,48,48] 192 block_1_expand[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_expand_relu (ReLU) [[null,48,48,48]] [null,48,48,48] 0 block_1_expand_BN[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_pad (ZeroPadding2D [[null,48,48,48]] [null,49,49,48] 0 block_1_expand_relu[0][
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_depthwise (Depthwi [[null,49,49,48]] [null,24,24,48] 432 block_1_pad[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_depthwise_BN (Batc [[null,24,24,48]] [null,24,24,48] 192 block_1_depthwise[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_depthwise_relu (Re [[null,24,24,48]] [null,24,24,48] 0 block_1_depthwise_BN[0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_project (Conv2D) [[null,24,24,48]] [null,24,24,8] 384 block_1_depthwise_relu[
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_1_project_BN (BatchN [[null,24,24,8]] [null,24,24,8] 32 block_1_project[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_expand (Conv2D) [[null,24,24,8]] [null,24,24,48] 384 block_1_project_BN[0][0
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_expand_BN (BatchNo [[null,24,24,48]] [null,24,24,48] 192 block_2_expand[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_expand_relu (ReLU) [[null,24,24,48]] [null,24,24,48] 0 block_2_expand_BN[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_depthwise (Depthwi [[null,24,24,48]] [null,24,24,48] 432 block_2_expand_relu[0][
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_depthwise_BN (Batc [[null,24,24,48]] [null,24,24,48] 192 block_2_depthwise[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_depthwise_relu (Re [[null,24,24,48]] [null,24,24,48] 0 block_2_depthwise_BN[0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_project (Conv2D) [[null,24,24,48]] [null,24,24,8] 384 block_2_depthwise_relu[
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_project_BN (BatchN [[null,24,24,8]] [null,24,24,8] 32 block_2_project[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_2_add (Add) [[null,24,24,8],[null,24,24 [null,24,24,8] 0 block_1_project_BN[0][0
tfjs@3.19.0:17 block_2_project_BN[0][0
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_expand (Conv2D) [[null,24,24,8]] [null,24,24,48] 384 block_2_add[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_expand_BN (BatchNo [[null,24,24,48]] [null,24,24,48] 192 block_3_expand[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_expand_relu (ReLU) [[null,24,24,48]] [null,24,24,48] 0 block_3_expand_BN[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_pad (ZeroPadding2D [[null,24,24,48]] [null,25,25,48] 0 block_3_expand_relu[0][
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_depthwise (Depthwi [[null,25,25,48]] [null,12,12,48] 432 block_3_pad[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_depthwise_BN (Batc [[null,12,12,48]] [null,12,12,48] 192 block_3_depthwise[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_depthwise_relu (Re [[null,12,12,48]] [null,12,12,48] 0 block_3_depthwise_BN[0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_project (Conv2D) [[null,12,12,48]] [null,12,12,16] 768 block_3_depthwise_relu[
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_3_project_BN (BatchN [[null,12,12,16]] [null,12,12,16] 64 block_3_project[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_expand (Conv2D) [[null,12,12,16]] [null,12,12,96] 1536 block_3_project_BN[0][0
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_expand_BN (BatchNo [[null,12,12,96]] [null,12,12,96] 384 block_4_expand[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_expand_relu (ReLU) [[null,12,12,96]] [null,12,12,96] 0 block_4_expand_BN[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_depthwise (Depthwi [[null,12,12,96]] [null,12,12,96] 864 block_4_expand_relu[0][
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_depthwise_BN (Batc [[null,12,12,96]] [null,12,12,96] 384 block_4_depthwise[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_depthwise_relu (Re [[null,12,12,96]] [null,12,12,96] 0 block_4_depthwise_BN[0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_project (Conv2D) [[null,12,12,96]] [null,12,12,16] 1536 block_4_depthwise_relu[
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_project_BN (BatchN [[null,12,12,16]] [null,12,12,16] 64 block_4_project[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_4_add (Add) [[null,12,12,16],[null,12,1 [null,12,12,16] 0 block_3_project_BN[0][0
tfjs@3.19.0:17 block_4_project_BN[0][0
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_expand (Conv2D) [[null,12,12,16]] [null,12,12,96] 1536 block_4_add[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_expand_BN (BatchNo [[null,12,12,96]] [null,12,12,96] 384 block_5_expand[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_expand_relu (ReLU) [[null,12,12,96]] [null,12,12,96] 0 block_5_expand_BN[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_depthwise (Depthwi [[null,12,12,96]] [null,12,12,96] 864 block_5_expand_relu[0][
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_depthwise_BN (Batc [[null,12,12,96]] [null,12,12,96] 384 block_5_depthwise[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_depthwise_relu (Re [[null,12,12,96]] [null,12,12,96] 0 block_5_depthwise_BN[0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_project (Conv2D) [[null,12,12,96]] [null,12,12,16] 1536 block_5_depthwise_relu[
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_project_BN (BatchN [[null,12,12,16]] [null,12,12,16] 64 block_5_project[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_5_add (Add) [[null,12,12,16],[null,12,1 [null,12,12,16] 0 block_4_add[0][0]
tfjs@3.19.0:17 block_5_project_BN[0][0
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_6_expand (Conv2D) [[null,12,12,16]] [null,12,12,96] 1536 block_5_add[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_6_expand_BN (BatchNo [[null,12,12,96]] [null,12,12,96] 384 block_6_expand[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 block_6_expand_relu (ReLU) [[null,12,12,96]] [null,12,12,96] 0 block_6_expand_BN[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 head (Conv2D) [[null,12,12,96]] [null,12,12,32] 3104 block_6_expand_relu[0][
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 logits (Conv2D) [[null,12,12,32]] [null,12,12,2] 66 head[0][0]
tfjs@3.19.0:17 ___________________________________________________________________________________________________________________
tfjs@3.19.0:17 softmax (Softmax) [[null,12,12,2]] [null,12,12,2] 0 logits[0][0]
tfjs@3.19.0:17 ===================================================================================================================
Or in netron.org format
yeah, training the second model in edge impulse will work for you, but only accidently since the output of your first model has 3 classes that you can “pack” into an image as RGB. it wouldn’t be as easy with a different number of classes.
that TFJS model all looks sensible
