Do you know how to get an output or repreaentation of the extracted features?

Means a lot, thanks!

Thanks a lot!

Exactly, I just came here to comment on this, but you already did 😆

Thanks a lot for your advice! You saved me!

You had me in the first half :)

@@NachiketaHebbar brother, that's a style of talk I adopted if I want someone not just to listen to me but also reply. brother again, thanks for your knowledge sharing .

Same case brother I used efficientnetb3 for hand gesture classification but its initial accuracy is around 20 and rising so slowly any suggestions 😢?

It's a name error. You must have done something wrong in naming, maybe capitalised a letter or something while writing val_ds at the two places.. Check on that once

the seed value makes sure that a certain pattern is used to select validation and training data

I was unable to make his code work until I saw your comment, thank you so much man!

It is very important to understand the data you are using before choosing the components of your neural network. The code in the video is correct since it utilizes label_mode="categorical" in conjunction with the categorical cross-entropy loss function. If you intend to use the sparse_categorical_cross_entropy loss function, you must specify label_mode="int". This is especially important because it appears that the data used in the video may not be identical to the data available to us (I did not verify this). The categorical cross-entropy loss function requires one-hot encoding for each class. For example, if there are three classes, A, B, and C, this loss function will work only if the class labels are encoded as [1, 0, 0], [0, 1, 0], and [0, 0, 1], respectively. This encoding is achieved by using the label_mode="categorical" argument. On the other hand, sparse_categorical_cross_entropy does not require such encoding and assigns integers (e.g., 1, 2, 3) to classes A, B, and C. This loss function is preferable when dealing with a large number of classes, such as in predicting words in a vocabulary, as it conserves memory resources A comprehensive introduction to loss functions can be found here: machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/

Try to add dropout and batch normalization and you can even try l2 and l1 regularizers too😊

J0

exactly I have also same doubt

Make sure you are using a number of neurons in softmax layer = number of categories

soution = replace loss='sparse_categorical_crossentropy'

Will try to make one on it soon

***Do not change your last layer to resnet_model.add(Dense(1, activation='softmax')) your NN needs to recognize between 5 classes(like in this video example). ***Try to use loss='sparse_categorical_crossentropy' in yourmodel.compile() function. That problem appears because your labels are in numbers(0,1,2,3...) not in one hot encoding.

@@tony-iy5xf Thanks, that fixed it, and training epochs reached 1.0000. Why did the code work for you, when you had loss='categorical_crossentropy'? Because I just copied your code from github, and there the loss was set to that method.

@@tony-iy5xf This. Anyone who have trouble should listen to this guy

Hii naman , did you find the solution to this ? I've got the same problem

@@dhruvil8 use sparse_categorical_crossentropy

@@hubertmsuya8071 it doesnt work

What error are you getting in this

I fixed it with using "label_mode='categorical'," in he val_ds and trains_ds. Then got error "only integer scalar arrays can be converted to a scalar index" but could be solved by using: plt.figure(figsize=(10, 10)) for images, labels in train_ds.take(1): for i in range(6): ax = plt.subplot(3, 3, i + 1) plt.imshow(images[i].numpy().astype("uint8")) plt.title(class_names[np.argmax(labels[i])]) # Convert tensor to integer plt.axis("off")

I got this exact error, thank you so much!@@anthonieschaap1625

Thanks and yes, i miss them too

set label_mode='categorical' in val_ds and train_ds

@@AkshayRakate thanks

@@AkshayRakate Thanks for your help brother. The Epochs are executing but the images are not Printing it seems now. ERROR: 'only integer scalar arrays can be converted to a scalar index'

@@AkshayRakate bt it does not help for me.. still i have the same error...could u plz help me

@@ayeshakhatun3114 what is the error ?

Just replace loss='categorical_crossentropy' to loss='sparse_categorical_crossentropy' It works fine 👍

model.add(Dense(number of classes, activation='softmax')) do this changes

I have updated the github link in the video description.

I have updated the video description with the source code link

in his blog the following block before the plt.figure is missing. class_names = train_ds.class_names print(class_names) It is shown in the video, however.

Try adding label_mode = "categorical" as a parameter when initiating the train_ds and val_ds