What and excellent piece of work! Introduction is complete, allowed me to understand and conduct whole analysis. Author comments build background knowledge and explain how to solve potential problems. @Dr. Bharatendra Rai my complements on your didactic skills and quality of materials! Btw I had to do minor script adaptions (probably due to some changes in library) to work in my environment. ln56: WAS: pred % predict_classes(testx) / IS: pred % predict(testx) %>% k_argmax() ln59: WAS: prob % predict_proba(testx) / IS: prob % predict(testx) Have fun!
The problem is that the codes "predict_classes" and "predict_proba" according to the Error massage are deprecated and they are removed from tensorflow in version 2.6 (I have the latest version). The Error Massage suggests "predict() %>% k_argmax()" and "predict()" instead but they are not working for me.
Terrific intro to the MNIST data set! Thank you for sharing such a quality content. I would like to ask you for the hand written pictures used in the example for replicability purposes. I've carefully followed your steps but have only achieved a very poor 30% accuracy when evaluating the MNIST trained model with my own handwritten numbers.
If I have an entirely different set of images: A folder that contains a test and train folder with each containing two additional subfolders with labeled images. How can I replicate the same structure as the mnist dataset?
Thanks for the tutorial and the code,but I faced the error"imensions must be equal, but are 32 and 320 for 'loss/dense_8_loss/softmax_cross_entropy_with_logits' (op: 'SoftmaxCrossEntropyWithLogits') with input shapes: [32,10], [320,10]." while fitting the model. Could you please help me out.
Hi! I followed every instruction given, but R studio still throws an error: No gradients provided for any variable. Does somebody have a clue why this occurs? Thanks!
Excellent video sir..your steps are very clear and easy to follow...please do a classification problem in deep learning keras using cloudml package....thanks Sir
This is a great lecture. Thank you so much. Sir, i want to predict a unlabeled data set by using labeled data set, then how can i proceed? Thanks in advance.
@@bkrai I already know how to install keras and tensorflow. 1. #install packages install.packages("keras") 2.load it into the library library("keras") 3. You can look where it is loaded or not by typing search() #Search will give all loaded packages Run this command 4.install_keras() #Another way is to install from github using devtools Since I'm running R in windows 10 I'm facing many issues. Python version 3.8.3 Anaconda Latest Version Installed it with admin rights to configure it properly. Updated and upgraded pip and conda Installed Tensorflow CPU version with pip command from stackoverflow. Everthing is going well. But when I'm trying to load keras dataset I'm getting an error. Either python module so and so found or conda does not exist /Anaconda3/Scripts. This is where I'm struck.
@@bkrai Yes. I guess so. Sir you are indeed an amazing and inspiring teacher. Recently I had requested you to please do make a video on Plant disease detection. I hope you soon get it off your as well as my bucket list.
Sir, Error in install_tensorflow() : could not find function "install_tensorflow", getting this error though I have tensor flow installed for Python. PN I am using R 3.6
Again thanks for referring me to this video... do you have any recording where you used flow_images_from_directory() to read in your images, the concept is still confusing to me because i couldn't find a way to look at the str of the object generated. Many Thanks .
Hi, I hope that this late answer still helps you. relu is a activation function, which puts everything below 0 to 0 and is linear from 0 to + infinity. Softmax uses the sigmoid (1 / (1+e^-x)) function but divides everything by the highest neuron in the output layer to have values between 0 and 1 which add up to 1.
Great introduction! Isn´t it a problem that accuracy for validation data is better than for training data when using dropout layers? Why don´t you reduce the epochs at the point where the loss function of the training data is at its lowest point? Thanks in advance.
Thanks. So that is no Problem? It is unintuitive for me. And why don't you stop the Training ehen overfitting starts (after loss function of Validation Data reachs Minimum)?
can you help me?. every time when i want use mnist database. i have next error. please... i'm gonna go crazy. (Error in dir.exists(x) : file name conversion problem -- name too long?) this happens when I use keras library and I want to charge the data "mnist
Need your guidance as how we can solve sale prediction (Kaggle competition) . Below is problem snapshot - YYou are provided with daily historical sales data. The task is to forecast the total amount of products sold in every shop for the test set. Note that the list of shops and products slightly changes every month. Creating a robust model that can handle such situations is part of the challenge. File descriptions sales_train.csv - the training set. Daily historical data from January 2013 to October 2015. test.csv - the test set. You need to forecast the sales for these shops and products for November 2015. sample_submission.csv - a sample submission file in the correct format. items.csv - supplemental information about the items/products. item_categories.csv - supplemental information about the items categories. shops.csv- supplemental information about the shops.
What and excellent piece of work! Introduction is complete, allowed me to understand and conduct whole analysis. Author comments build background knowledge and explain how to solve potential problems. @Dr. Bharatendra Rai my complements on your didactic skills and quality of materials!
Btw I had to do minor script adaptions (probably due to some changes in library) to work in my environment.
ln56: WAS: pred % predict_classes(testx) / IS: pred % predict(testx) %>% k_argmax()
ln59: WAS: prob % predict_proba(testx) / IS: prob % predict(testx)
Have fun!
Thanks for the comments and code related update!
The problem is that the codes "predict_classes" and "predict_proba" according to the Error massage are deprecated and they are removed from tensorflow in version 2.6 (I have the latest version). The Error Massage suggests "predict() %>% k_argmax()" and "predict()" instead but they are not working for me.
same issue. please help
No one solved this
brilliant @
Bharatendra Rai !
Thanks!
Sir waiting for video related to GAN Thanks for easy explain guidance to all self learner's
Thanks, I've it on my list!
Great to see example with torch package
Thanks!
Sir, you are the one who inspired me choosing analytics career. Thank for sharing all the videos in UA-cam.
Thanks for comments!
Terrific intro to the MNIST data set! Thank you for sharing such a quality content. I would like to ask you for the hand written pictures used in the example for replicability purposes. I've carefully followed your steps but have only achieved a very poor 30% accuracy when evaluating the MNIST trained model with my own handwritten numbers.
Here is the link:
drive.google.com/open?id=1m_nKMCUZBP0O32Friuzck27QiiE9a7jz
If I have an entirely different set of images: A folder that contains a test and train folder with each containing two additional subfolders with labeled images. How can I replicate the same structure as the mnist dataset?
You can use the same approach that is used with my own handwritten images at the time of testing.
Great great tutorial on deep learning in R. Thank you very much Sir.
Thanks for comments!
Thanks for the tutorial and the code,but I faced the error"imensions must be equal, but are 32 and 320 for 'loss/dense_8_loss/softmax_cross_entropy_with_logits' (op: 'SoftmaxCrossEntropyWithLogits') with input shapes: [32,10], [320,10]." while fitting the model.
Could you please help me out.
Are your working with the same data/code used here? If yes, let me know which line you are referring to?
Hi! I followed every instruction given, but R studio still throws an error: No gradients provided for any variable. Does somebody have a clue why this occurs? Thanks!
Excellent video sir..your steps are very clear and easy to follow...please do a classification problem in deep learning keras using cloudml package....thanks Sir
Thanks for the suggestion, I've added this to my list.
This is a great lecture. Thank you so much.
Sir, i want to predict a unlabeled data set by using labeled data set, then how can i proceed? Thanks in advance.
Train the network with labeled data and then use the model to predict from unlabeled data.
thank you @@bkrai
Thank you for the video, but: 'Sequential' object has no attribute 'predict_classes'
Dear Dr. Rai, I'm having an error while I try to upload my own images. "cannot allocate vector of size 2.9 Mb"
Do you have any suggestion with this ?
If you have any unnecessary data files loaded, you can remove them to create some space.
@@bkrai Thank you for replying, I have no data before I load my 7000 image for classification . I have 16gb ram also.
Try with less number of images or if possible, use gpu computer.
@@bkrai Is it possible to use Gpu of our computer in R ? İnstead of cpu and ram ? (Thank you for replying)
I'm getting an error that specified conda library does not exist
I'll do a video to install keras and tensorflow in coming weeks.
@@bkrai I already know how to install keras and tensorflow.
1. #install packages
install.packages("keras")
2.load it into the library
library("keras")
3. You can look where it is loaded or not
by typing
search()
#Search will give all loaded packages
Run this command
4.install_keras()
#Another way is to install from github using devtools
Since I'm running R in windows 10 I'm facing many issues.
Python version 3.8.3
Anaconda Latest Version
Installed it with admin rights to configure it properly.
Updated and upgraded pip and conda
Installed Tensorflow CPU version with pip command from stackoverflow.
Everthing is going well.
But when I'm trying to load keras dataset I'm getting an error.
Either python module so and so found or conda does not exist /Anaconda3/Scripts.
This is where I'm struck.
Probably it's something computer specific.
@@bkrai Yes. I guess so. Sir you are indeed an amazing and inspiring teacher. Recently I had requested you to please do make a video on Plant disease detection. I hope you soon get it off your as well as my bucket list.
Very Helpful machine learning concept ,Thanks you ..
can you make video for image processing in R.
Here is the playlist of related videos:
ua-cam.com/play/PL34t5iLfZddsaZKDeCK2RD8lrhxBxp4hP.html
Sir, Error in install_tensorflow() : could not find function "install_tensorflow", getting this error though I have tensor flow installed for Python. PN I am using R 3.6
Try this:
ua-cam.com/video/-IYYqdxdYXk/v-deo.html
Again thanks for referring me to this video... do you have any recording where you used flow_images_from_directory() to read in your images, the concept is still confusing to me because i couldn't find a way to look at the str of the object generated. Many Thanks .
I've not used flow_images_from_directory() so far.
Bharatendra Rai I figured it out already... many thanks
Thanks for the update!
Hi, what is the activation ='relu' and 'softmax' means? Which one is better?
Hi, I hope that this late answer still helps you.
relu is a activation function, which puts everything below 0 to 0 and is linear from 0 to + infinity. Softmax uses the sigmoid (1 / (1+e^-x)) function but divides everything by the highest neuron in the output layer to have values between 0 and 1 which add up to 1.
Thanks!
Great introduction! Isn´t it a problem that accuracy for validation data is better than for training data when using dropout layers? Why don´t you reduce the epochs at the point where the loss function of the training data is at its lowest point? Thanks in advance.
When difference is very small, it can happen sometimes.
Thanks. So that is no Problem? It is unintuitive for me.
And why don't you stop the Training ehen overfitting starts (after loss function of Validation Data reachs Minimum)?
It's important to highlight the problem of over fitting and see how to identify it. And it is all the more important when it comes to deep learning.
Sir, One error is showing while running the code
"mnist
Try this link:
ua-cam.com/video/-IYYqdxdYXk/v-deo.html
@@bkrai thank you verymuch sir. 😃
Welcome!
can you help me?. every time when i want use mnist database. i have next error. please... i'm gonna go crazy.
(Error in dir.exists(x) : file name conversion problem -- name too long?)
this happens when I use keras library and I want to charge the data "mnist
I would suggest check your code again.
Yes!! Can you help me?
Sir, expecting a video on recommendation system...thanks
Thanks, I've it on my list!
thank you, very nice response..
Thanks for comments!
Need your guidance as how we can solve sale prediction (Kaggle competition) . Below is problem snapshot - YYou are provided with daily historical sales data. The task is to forecast the total amount of products sold in every shop for the test set. Note that the list of shops and products slightly changes every month. Creating a robust model that can handle such situations is part of the challenge.
File descriptions
sales_train.csv - the training set. Daily historical data from January 2013 to October 2015.
test.csv - the test set. You need to forecast the sales for these shops and products for November 2015.
sample_submission.csv - a sample submission file in the correct format.
items.csv - supplemental information about the items/products.
item_categories.csv - supplemental information about the items categories.
shops.csv- supplemental information about the shops.
Sorry I saw this today. I guess it's too late now.
Simplly Great :)
Thanks for comments!
not all heroes wear caps
thanks for comments!