Thank you very much! I learned a lot from this video. By the way, how do I choose the number of hidden layers before running the model each time? Thank you very much.
Dear Rai，You have made a series of outstanding deep learning videos, which helped me a lot. Thank you for your contribution and look forward to your more instructional videos on deep learning ( Long short-term memory network)
Dear Dr. Rai. thanks a lot for your informative video. I am trying to install keras in r on Mac Pro M1 . but it does not work and gives a fatal error each e=time I run the code. I used your video as an examples for my data. may I ask you if you have a step by step install and configure keras and tensforflow on Mac Pro M1? Thanks in advance!
I have a problem when I try to run the model at 7:40. The error says : Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ValueError: Only input tensors may be passed as positional arguments. The following argument value should be passed as a keyword argument: (of type ) Run `reticulate::py_last_error()` for details. Can you help me ?
This very interesting but complex process was simplified by this video. Easy to understand and replicate. What about forecasting? how does one forecast the prediction for future dates?
Thank you, I've learned a lot watching your videos ! Please can you do a tutorial to build a recurrent neural network (RNN) on R for data prediction ? An LSTM/GRU one would be awesome !
Again Dr. Bharatendra, Do you know why I am getting an error when I am creating the model. Please, guide me otherwise I am can't go forward using this code "model %>% + layer_dense(units = 5, activation = 'relu', input_shape = c(13)) %>% + layer_dense(units = 1) Error in eval(lhs, parent, parent) : object 'model' not found"
I am using rstudio cloud, but for some reason I get an error when I run the last bit of my code i.e Fit Model, would you be able to help where I may be missing it, here is the code and error >> model = keras_model_sequential() model %>% layer_dense(units = 5, activation = 'relu', input_shape = c(18)) %>% layer_dense(units = 1) #Compile model %>% compile(loss = 'mse', optimizer = 'rmsprop', metrics = 'mae') #Fit model mymodel = model %>% fit(training, trainingtarget, epochs = 50, batch_size = 10, validation_split = 0.2) Error Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: in user code: /home/rstudio-user/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:571 train_function * outputs = self.distribute_strategy.run( /home/rstudio-user/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run ** return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /home/rstudio-user/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /home/rstudio-user/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica return fn(*args, **kwargs) /home/rstudio-user/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-pac Please assist. Thanks
Prof., understood the principles. Excellent lecture. Could you please let us know how to use the selected model for forecasted vales for future period. Regards
One of the best and simple explanation of DNN. Can you please make a tutorial on time series forecasting using a combination of DL models such as CNN-LSTM?
@@bkrai I have install keras and tensorflow bit is not working install.packages("keras") install_keras() install.packages("tensorflow") install_tensorflow() miniconda package also installed .All library are also loaded But I am getting this error > model - keras_model_sequential() Error: Installation of TensorFlow not found. Python environments searched for 'tensorflow' package: C:\Users\User\AppData\Local -miniconcja\envs -reticulate\python.exe You can install TensorFlow using the install_tensorflow() function.
Thank you very much Dr. Rai for your informative videos. Your tutorials are like compressed 3 months of work in 20 minutes!!! Is there a chance you could post a multivariate forecasting and tuning example with LSTM.
Many Thanks Dr Rai, i wanted your guidance to predict 14th Variable for fresh dummy data 'newdata' that i created to see if i can use this for a similar problem. Code attached below, pls help where am i going wrong. Used exactly your code which ran fine. Post which created newdata in same format/class. Below attached is additional code. Getting error: ValueError: No data provided for "dense_2_input". Need data for each key in: ['dense_2_input'] when trying to fit model. Appreciate your support. val
@@bkrai Thank you for your answer and your great video. But I have the following question: How can I fix the choice of weights in Keras? Perhaps there is a function like set.Sees () in Keras that. enables this?
Thank you for a very clear and interesting video! About overfitting: should you stop when train and test error curves cross or can you keep training? What if both errors are still decreasing with continued iteration? Is the danger of over fitting mainly in thinking the training error is valid for the model applied to new data?
Hi there, How do we inteprete the results of the prediction ? I am not sure I understood it well. Really good video btw. Thanks for sharing your knowledge.
When I used this piece of code I am getting this error from your own code and dataset. I didn't change anything. Do you know why? "> model %>% + layer_dense(units = 5, activation = 'relu', input_shape = c(13)) %>% + layer_dense(units = 1) Error in eval(lhs, parent, parent) : object 'model' not found" Thank you so much
@@bkrai, Ok I am sharing the whole code previous and a preceding line of code after so that you see, Thank you so much for your answer. "# Libraries library(keras) library(mlbench) library(dplyr) library(magrittr) library(neuralnet) # Data data("BostonHousing") data
@@bkrai: I was just copying your code and run as you present it. Does it mean that I have to create the "model" somewhere else? I was going to your ode as it's so that I could apply it to my own dataset. I got the code from as it's. Thank you.
All your videos are very informative and everything is so well explained. I have been working on time series data. Your video on facebook's prophet library was amazing, although prophet only works well on an ideal dataset. I have a request sir, please do a tutorial for time series forecasting using lstm in R, would be really helpful
Thank you very much Dr. Your videos are amazing as usual. Unfortunately, I can not use this technique to predict the prices of used cars since not all variables are numeric, so Deep NN will not work.
Thank you sir for your nice video about deep learning method. Kindly suggest how to extract the k-fold 'cv' results. It will be very much helpful for better presentation of results. Kindly suggest Sir
Dr Rai, Thankyou very much for sharing your knowledge & educating the data science professionals, quick question, What would be your recommendation for saving trained Model & exporting to other machines , formats Something like JSON, or XML, Or CSV (more generic), or do we need to stick to just '.RDS' ?
First of all, thank you! This video is awesome. Everythings works fine except the viewer. I installed the packages tfruns but I dont have a diagram of the fit. I get this message: /session/tfruns-metrics381c56414d7e/index.html?viewer_pane=1&capabilities=1&host=http%3A%2F%2F127.0.0.1%3A43071 not found Do anyone know what is wrong and how I can solve the problem? Thank you!
Thank you. I have learned a lot from this video. by the way, how can I reproduce the result of the neural network every time I run the model? Thank you so much.
Thank you very much for such a nice tutorial. I have a question though, I want to make a neural network with custom connections between the neurons of the input and hidden layers, how to do that ? Basically, I am trying to apply prior knowledge to the neural net architecture and remove all those connections that do not satisfy the particular conditions. Please indicate a source or any other tutorial where I can find something like this. Many thanks in advance.
I am curious why you choose Adam versus RMSPROPS. Also for the learning rate, is there any guidance on how to pick the learning rate? Thank you for your help.
Great tutorial Sir. After a long time you have uploaded a new video. Are you busy Sir? I have been waiting for your video for a long a time. Thank you so much for this one.
I'll be waiting for your next video Sir. This video took almost a month which is a very long period for all of us who eagerly wait to learn from you. Thank you so much Sir.
Great Example but every time I try to fit the model I get the following error. I was wondering if you have insight. Again thanks for your amazing well articulated R training! Epoch 1/200 Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: in user code:
There could be some computer specific issue. You can try RStudio cloud that runs from the browser, ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-SFpzr21Pavg.html
Hi, thanks for the great tutorials. Can this model be used with and ordinal dependent variable. Like a horse race first, second, third, lose? Thanks again.
Awesome video. Thank you. Two quick questions 1. Curious why you remove all the column names? 2. For converting chas shouldn't you be creating two columns using to_categorical(...) similar to what you did in one of your previous videos? Thank you
1. This is not mandatory. I had done this for other purpose and kept the same code for this example. 2. Chas has only 2 levels, so one column with 0 and 1 values is fine here. We can use that earlier method when a factor variable has more levels.
Great Video! Are there any current ways to plot the Keras model similar to how the neural net package plotted the original node map? I've been doing some research on this, and I'm coming up empty handed.
Thank yoy very much Dr. Rai , I am getting this message "your cpu supports instructions that this tensorflow binary was not compiled to use avx2" can you please help me ?
Great Video. Question: In the other video you used the min max normalization, would it work here too? How do I know which normalization method to use? Thanks
It is important to do normalization. However there may not be much difference when it comes to model performance. So any one of the 2 methods will work fine.
There are so many elements to tune the model and hence so many ways, how is one supposed to understand which element need to be modified, is it the extra hidden layer or number of neurons or the learning rate?
@@bkrai Thank you sir, I just have one last question and quite basic but I am still confused. In case of quantitative variable error is MSE for neural networks but in case of qualitative variable the data is in 0,1 format ans y_predicted is generally a probability value so what is the error that is calculated and propagated back to neurons through gradient descent for updating weights??
Thank you so much for your useful tutorials. A quick question how we can obtain an exactly same result for the same model after running several times. I tried with (set.seed(1234)) but the results were different. Could you provide any suggestion for that?
Hi! Congrats for the great video! Do you have any recommendation of packages to use in a recurrent neural network for classification purposes? If you have a tutorial for that it is going to be amazing! Thanks!
Great video, thank you. I am having problem un-scaling the prediction value using unscale function (Error: Error in scale.default(data, center = FALSE, scale = 1/scale) : length of 'scale' must equal the number of columns of 'x'). Is there a way to unscale with keras function or some other way?
How someone can get prediction value (dependent) for independent variable values with model for future? I think many newbies are wondering how to get an estimation after model is ok. Any help?
Dear Sir, Thanks for good video. I am facing following error when, I am trying to excutse "keras_model_sequential()". Error: Installation of Python not found, Python bindings not loaded. Kindly guide me. Thanks
Also, curious when calling Tensorflow from Keras R, is it running inside R memory space or is it running in its own memory space / remote machine? Thank you...
@@jitendraupadhyay6218 You guys need to have Python already installed on your computer. Then you may need restart your R studio and reinstall all required libraries. Or possible even reinstall R studio to make sure all connections are updated.
Hi, I love your videos! Whilst trying to execute keras_model_sequential I get the following error: Error in initialize_python(required_module, use_environment): Installation of Python not found, Python bindings not loaded. I installed R but haven’t installed Python. Please can you advise if there are any easy fixes for this? Thanks 🙃
Hi, thanks for your reply. I managed to get this working eventually. One thing I feel is missing from this video, is to show how you would get the probability of an outcome using some new data? For example, a file that contains the same data every week. This would make the video complete as it fully walks through the process for creating a model and testing from a sample of data and results using the training/testing partition method, together with how to test on new data, and how to export the results from the new data, into a flat file, for example. I feel your viewers would benefit from this additional content.
thank you sir for the vedio plz i have i question when i create the model in R show me that Python module keras was not found.even that the library of keras works
@@bkrai yes i do solve the problem by install tensorflow in code it work know but the problem in the evaluation i get big number 1234567 like this i don't know why? And thank you sir
