Hey nice tutorial, thanks. I have a question: suppose I want to output a 2D vector in the geometrical sense (x, y). I've divided the output into three parts: axis, sign, and absolute value. These are the conditions: if one axis value is > 0, then the other axis is assumed to be zero. For example, (0, 1) is a valid vector, (3, 0) is a valid vector, but (3, 4) is not a valid vector. Absolute values are bounded between 0 and 7, for example, (0, 7) is a valid vector, but (-1, 0) does not exist in this case. So, my question is this: should I train the network SEPARATELY for each case, meaning, the first nn will do binary classification of the axis, the second nn will do binary classification of the sign and the third nn will do multiclass classification of the values, with one hot encoded data? Thanks again
Thanks a lot for your video sir, it's very insightful. I just would like to make sure how many images are in your training datat&test data? Because I m little confused about number 800, during the training, does that mean that your whole dataset has 800 images?
Great content. I have one doubt - your validation_generator only apply rescale of 1/255, meaning it will not increase the data size (unlike train_generator where random transformation is applied). Base on this, 1) does the generator repeats the data in an infinite loop until stopped by epoch? 2) if the answer to (1) is no, can I feed a validation_dataset (instead of a generator) into the model.fit() method (b.t.w the TensorFlow docs claims that "model.fit_generator is deprecated and Please use Model.fit, which supports generators")
You can use validation data instead of generator, but remember to rescale validation data just like you did for training data. I used generator for validation data to easily mimic operations I performed for training data. I am not aware of TensorFlow depreciating model.fit_generator. I normally use Keras and I've never seen that message. If it is real then you can find alternate library.
Sir on line 39 Syntaxerror :cannot assign to operator Aa rha h mainy apny dataset k two folders bnai hn image_directory = 'C:/Users/OWNER/Desktop/single-grn-dataset/' SIZE = 224 Dataset = [ ] Label = [ ] Infected-Grains-Images = os.listdir(image_directory +'C:/Users/OWNER/Desktop/single-grn-dataset/Infected-Grains/ ') SyntaxError: cannot assign to operator Sir I am new plz guide me
It clearly says that the system cannot find the path. So please make sure the path you gave is correct and also remove the first / before images and try again.
Try data augmentation but you may run into overfitting. Also try traditional methods such as using Random Forest or SVM, they work better with small datasets. Watch my videos on image classification.
![[AI] Binary classification using tensorflow keras](http://i.ytimg.com/vi/PM6uvCLyeXM/mqdefault.jpg)
This channel is massively underrated! Great work 🔥
Appreciate it!
I love your videos. You show me the path when i was in confusion about start off learning deep learning. Keep going , sir. Great job!!!!!
Glad to hear that
8:30 no issue. Its part of learning and teaching... ❤
Your series are very helpful. Great videos to learn
Glad you like them!
Hey nice tutorial, thanks. I have a question: suppose I want to output a 2D vector in the geometrical sense (x, y). I've divided the output into three parts: axis, sign, and absolute value. These are the conditions: if one axis value is > 0, then the other axis is assumed to be zero. For example, (0, 1) is a valid vector, (3, 0) is a valid vector, but (3, 4) is not a valid vector. Absolute values are bounded between 0 and 7, for example, (0, 7) is a valid vector, but (-1, 0) does not exist in this case.
So, my question is this: should I train the network SEPARATELY for each case, meaning, the first nn will do binary classification of the axis, the second nn will do binary classification of the sign and the third nn will do multiclass classification of the values, with one hot encoded data? Thanks again
Thanks a lot for your video sir, it's very insightful. I just would like to make sure how many images are in your training datat&test data? Because I m little confused about number 800, during the training, does that mean that your whole dataset has 800 images?
Hi, thanks for the tutorial. If possible, could you talk about the implementation of vision transformer(ViT) using keras?
Spyser seems to work well with ML , any better IDE?
Great content. I have one doubt - your validation_generator only apply rescale of 1/255, meaning it will not increase the data size (unlike train_generator where random transformation is applied). Base on this, 1) does the generator repeats the data in an infinite loop until stopped by epoch? 2) if the answer to (1) is no, can I feed a validation_dataset (instead of a generator) into the model.fit() method (b.t.w the TensorFlow docs claims that "model.fit_generator is deprecated and Please use Model.fit, which supports generators")
You can use validation data instead of generator, but remember to rescale validation data just like you did for training data. I used generator for validation data to easily mimic operations I performed for training data.
I am not aware of TensorFlow depreciating model.fit_generator. I normally use Keras and I've never seen that message. If it is real then you can find alternate library.
Sir on line 39
Syntaxerror :cannot assign to operator
Aa rha h mainy apny dataset k two folders bnai hn
image_directory = 'C:/Users/OWNER/Desktop/single-grn-dataset/'
SIZE = 224
Dataset = [ ]
Label = [ ]
Infected-Grains-Images = os.listdir(image_directory +'C:/Users/OWNER/Desktop/single-grn-dataset/Infected-Grains/ ')
SyntaxError: cannot assign to operator
Sir I am new plz guide me
i have an errror, it is in line 39, how to resolve it [WinError 3] The system cannot find the path specified: '/images/cell_images/test/Parasitized/'
It clearly says that the system cannot find the path. So please make sure the path you gave is correct and also remove the first / before images and try again.
can you provide me with the dataset please
Really Appreciate and like your videos. Thank You.
My pleasure!
sir..great video ..tq so much...how to create this binary classification if my data quiet small ? just 200 dataset?
Try data augmentation but you may run into overfitting. Also try traditional methods such as using Random Forest or SVM, they work better with small datasets. Watch my videos on image classification.
Can I get the dataset?
Very good videos. Thank you!
Great job sir
great lectures
Glad you like them!
nice video man, very helpful!
Glad it helped!
permission to learn sir