Comments (4)
ok, i will try and provide result later
from rmdl.
Thank you for your question,
The time for training and prediction depends on many factors such as the size of the dataset, how many models you want to generate (CNN, RNN, and DNN), your hardware, and number of epochs,
I think if you create 3 DNNs and 3 CNNs with mid-level hardware (one GPU), it will be learned models very fast (2-3 hours).
Best regards,
Kamran
from rmdl.
Thanks for answer.
Time for training is not important to me, most interesting time for a prediction. I think it will not depend on size of dataset and number of epochs.
Right now i use VGG19 to make a prediction on Video(60fps) and on my machine with Geforce 1060, it takes 20ms to make a prediction, for InceptionV1 as I remember 150ms.
It's very interesting to me to make approximate comparison of time for prediction, to know if you RMDL is suitable by performance, for my task.
Thanks. And great work
from rmdl.
Prediction time is very fast and even if you use several models it could be real-time,
best regards,
Kamran
from rmdl.
Related Issues (20)
- How to set the number of "n" in the experiment? HOT 3
- accuracy question
- accurcy problems HOT 11
- How to use saved model and weights for prediction? HOT 1
- Text classification task accuracy problem HOT 7
- Functional API HOT 1
- How to we make prediction on new data? HOT 1
- Error in model 0 try to re-generate an other model DNN 0 HOT 2
- Error in model 0 try to re-generate an other model DNN 0 HOT 4
- Upload Trained Model HOT 1
- Unseen data... HOT 1
- random_optimier not use
- Could not find %s Set GloVe Directory in Global.py HOT 1
- Multi label classifier in RMDL. HOT 1
- The dataset is not divided into train data, validation data and test data?
- Using my own image dataset HOT 1
- Error when trying to run for my dataset HOT 2
- How to use a trained model for predictions? HOT 3
- ValueError HOT 18
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