I am trying to train a LSTM model to predict speech veracity using video as input. I have a dataset per video and each dataset contains its own timestamp. I have been trying to prepare each dataset as a time series for model training. I have tried combining all the datasets into a single one, however i am not sure if that is the right procedure due to the videos not being ordered in the final dataset as a time series should be. I have also tried fitting the model with one dataset at a time.
This is how the video datasets look like
This is how the dataset looks after some preprocessing
What is the best way to apply the multiple datasets to a LSTM model?
Related
I have created an dataset with image and its label. Now I want a validation dataset. I'm worried that randomly segmenting dataset will cause some categories to appear only in the training dataset, or only in the validation dataset. Is there any method in tensorflow that can divide each category of the dataset evenly?
I'm using Colab Pro TPU that offers up to 35 GB of memory. My dataset contains 650,000 sequences. I'm trying to use BiDirectional LSTM to predict the next word.
When I attempt to generate the binary_class vector using to_categorical(), it crashes because of memory limits. I took the first 200k sequences, trained the model, accuracy almost stops at around 65%. Before tweaking the hyperparameters, I wanted to feed the whole dataset and train the model. Is it possible to split the dataset, generate sequences, join them for training?
Appreciate any suggestions.
Thanks.
I‘m new to tensorflow have a general question:
I have a certain amount of training data and want to do a time series prediction.
The intervals on my training data is one minute and I want to do a prediction for the following minutes based on the new input data which is provided via a REST API
What I don‘t understand is this:
Let‘s say I train the model with all the data till yesterday and this means I can predict the first values of today by a certain amount. But the new values of today have not been observed by the model that has been build yesterday.
How would you solve this problem?
Thanks
Djboblo,
I assume that you need to predict whole next day values on per minute basis.
In that case your options are:
recursive prediction, i.e. using predicted data as input for the next prediction
structuring the model to provide you with prediction for the whole next day
If it is just a matter of predicting for a single minute forward and your model is trained on reasonably large amount of data - don't worry, just feed it with the values up to the prediction minute. Periodically you may re-train the model using new data.
What I was looking for is this:
How to use a Keras RNN model to forecast for future dates or events?
to predict stateful events
and after a while use the .fit Method to update the network with new data
See: https://machinelearningmastery.com/update-lstm-networks-training-time-series-forecasting/
In my case, I would like to weekly tune/adjust the model parameters value.
I have pre-trained the model by using the 100K data rows, Keras, and saved the model.
Then, as the new data collection (10K data rows), I need to tune the model parameter but don't want to retrain the whole dataset (110K).
How can I just partially fit the data on the model? load model -> model.fit(10K_data)?
Yes, that is correct you will train only on the new dataset (10k) model.fit(10K_data). I will recommend to change the learning rate for the retraining (reducing the learning rate) as you will just want to do a minor update to the parameters while keeping the earlier learning intact (or trying to leavarage the earlier learning).
The problem:
I have a model that I would like to train with independent data sets. Afterwards, I would like to extract the weights of each model (the model is the same for each instance but trained using different datasets) and finally, compute and average of these weights. Basically, my intention is to mimic tensorflow running on multiple devices and then average their weights so that they are used by one model.
My solution:
I added this model multiple times to tensorflow and am currently training each of these models separately with its unique dataset.. but this is using GBs of memory, and am wondering if there is a better way to do this?
One of the possible solutions is that you can fine-tune your network weights with other similar networks(similar datasets, i.e, if your dataset is images, you can use AlexNet weights)don't afraid if your network has no same architecture, you can simply load weights of layers as much as you need by 'load_with_skip' function of
https://github.com/joelthchao/tensorflow-finetune-flickr-style/blob/master/network.py
Fine-tuning takes much less than train networks from scratch.