TIPS & TRICKS - How to Reshape Input Data for Long Short-Term Memory (LSTM) Networks in Tensorflow

No video

TIPS & TRICKS - How to Reshape Input Data for Long Short-Term Memory (LSTM) Networks in Tensorflow

Рет қаралды 13,929

2 жыл бұрын

This video is to provide guidance on how to convert your 1D or 2D data to the required 3D format of the LSTM input layer.
To make it easy to follow, you can download this notebook at Github and follow along with this step by step tutorial.
---------------------------------------------------------------------------------------------------------------------------
⭐️⭐️⭐️Get the FREE notebook: github.com/ema...
⭐️⭐️⭐️Support FREE content: www.buymeacoff...

Пікірлер: 25

@jayktharwani9822 Жыл бұрын

To be honest, this video is among the best video I was looking for. I did not find any blog explaining this clearly. Really grateful to you.

@eMasterClassAcademy Жыл бұрын

Thanks for watching and your kind support!

@romankorotchenko9931 2 жыл бұрын

Wow, I finally got a clear explanation and understood out how to organize LSTM for data from a set of sensors! Grand merci!

@pepe6666 6 ай бұрын

thank you very much for making this. this specific topic is very much overlooked by other tutorials.

@eMasterClassAcademy 6 ай бұрын

Thanks

@iftikhar58 Жыл бұрын

Your videos help me a lot for understanding the input shape of data for sequenc model specially LSTM Model. Thanks again.

@eMasterClassAcademy 11 ай бұрын

Glad to hear that!

@bhopinderkambo9143 Жыл бұрын

Very clearly basic concepts explained

@Lory97hs Жыл бұрын

Thank you for this tutorial!

@eMasterClassAcademy Жыл бұрын

Thanks for watching.

@marcosagustinmel2294 2 жыл бұрын

YOU are a ROCKSTAR!

@mariaclaraassuncao9276 Жыл бұрын

Esse cara merece ser canonizado! melhor vídeo que encontrei

@kctay3253 6 ай бұрын

thank you for making this interesting video that's easy to understand. One question I have is what criteria you use to split samples by 48 ? I tried 36, 12 but both giving error.

@smithathurthi928 2 жыл бұрын

Thank you so much clear data prepartion for LSTM.

@batugkce10 2 жыл бұрын

Thx mister! Your work helped me a lot. I am appreciated to you.

@eighthseason9959 2 жыл бұрын

Thank you! Is it possible to show how to scale training set and then use its scaler to scale test/validation set?

@alvinjamur1 Жыл бұрын

this is wrong!! u cannot scale everything and then split data into train and test.

@vedantdalvi7523 7 ай бұрын

Thank you for the great explanation! Will the data processing be same for multiclass classification problem? I am doing classification of data with 5 classes

@eMasterClassAcademy 7 ай бұрын

Thanks for watching. Yes, it’s the same for features. For target, kindly change to the output layers to fit in to your problems. I also explained in other short video on how to setup the output layer for different problems. Please don’t hesitate to check that out. Cheers

@vedantdalvi7523 7 ай бұрын

Thank you so much for your prompt response! Instead of one data column as in your tutorial, I have 51 columns (features), so my doubt is that should I process the 51 columns in the same way as the one column in this tutorial?@@eMasterClassAcademy

@lordyoun8668 9 ай бұрын

hello good tutorial .just want to know if it is still same if i want to do binary classification

@eMasterClassAcademy 9 ай бұрын

Thanks for watching. Yes everything is the same, except the output layers. Should probably be Sigmoid, instead of Dense (linear). Hope it helps.

@folashadeolaitan6222 2 жыл бұрын

Thank you so much for the explanations. Is it necessary to use the np.stack()? If i have my X shape as (20050, 18), can't i just do np.reshape(X, 20050, 5, 18) assuming i want to use the last 5days data to predcit next day. I look forward to your response. Thank you.

@iftikhar58 Жыл бұрын

when make the features Y you are wrong there. Y should ahead from the input features