1

The normalisation you do does not re-scale to $[0,1]$ range! It normalises to have mean $0$ and std $1$ instead. To scale the tensor to be in $[0,1]$ range you should subtract min value and divide by absolute max-min value.


1

gaussian is different from sigma, because sigma is trainable while gaussian is not. It implies that the value of sigma can be optimized but not of gaussian. optimal is different from mse as in the case of mse, the final loss is computed as MSE. but in the case of optimal, we use gaussian as the loss function, with MSE acting as the variance of the gaussian ...


1

The task is a specific case of NER (technically NER is a sequence labeling task, a special case of classification). I think you would have two main options: Apply a pre-trained NER model: most deal with time entities but not always very accurately, and it wouldn't be specifically adapted to your data so you wouldn't obtain the distinction between the three ...


1

I understand that you are doing only inference, so you shouldn't need your model to be trainable, and therefore you can set the parameter trainable to False when invoking hub.Module.


1

There are several options when saving and loading a keras model, as explained at https://www.tensorflow.org/guide/keras/save_and_serialize: save the whole configuration, including the architecture, weights and even the last training state but also the model architecture and the weights can be saved as independent files, and that is what you might have ...


1

The Bayesian optimization algorithm selects points to test based on a balance between exploring uncertain regions and exploiting high-performing regions. But before you've tested very many points, there's not much information to go on. So, in this implementation you can specify a number of completely-at-random points to evaluate to start, and after that ...


1

Shuffling begins by making a buffer of size BUFFER_SIZE (which starts empty but has enough room to store that many elements). The buffer is then filled until it has no more capacity with elements from the dataset, then an element is chosen uniformly at random. This means that each example in the buffer is equally likely to be chosen, with probability 1/...


1

You are still able to calculate metrics such as loss and accuracy on training data (or any data for that matter), however the important thing to keep in mind is that it is by definition training data. Therefore the metrics from the training data are not how you would expect the model to perform on new unseen data as the model has been training on these ...


1

I think the best practice is to overfit intentionally to make sure your model is not underfitted (pick a huge number of epochs). Then, you will know for sure if the learning process is over and you are just overfitting... In your case, it is not certain whether your model can still improve on the validation set. I would be more suspicious about the better ...


1

NO. It's not Underfitting( Yay!!! ). But yes, there are some scopes of improvement such as:- I can see too many dropout layers. Try to oust them with BatchNormalization. That way you can have a more generalized model along with leveraging Try to reduce no. of Dense layers and increase no. of Conv1D layers. It will have two effects. First, your training ...


1

I don't think you need to worry, instead I would ask myself if the accuracy I'm getting is good enough for the task that the NN is supposed to do. Having higher training loss than validation loss can mean different things: Your validation data is easier to assess than training data. If the train/validation split is done randomly and there is enough data in ...


1

There are two parts to it. Does Keras means Tensorflow = Yes Does Tensorflow means Keras = No Keras is part of the TensorFlow API now. It was not so earlier From Keras official docs - Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. ...


1

Currently, Keras is part of Tensorflow, so if you are using the Keras that comes bundled with Tensorflow (tf.keras), technically one is a subset of the other. This was not always like that (see this), so if you are using the old version of Keras that is a separate package, then technically it hides the complexity of Tensorflow, so your experience would be ...


1

A common approach is what you suggest in 1. - apply time-shift as a Data Augmentation strategy. The augmentation is generally beneficial with deep learning models, and GPUs are fast so the compute time is rarely a big problem. Another strategy, less common, would be to make sure that the event is always located at the same position inside the analysis window ...


1

My thoughts are to save Keras model to .pb(protobuf) file and then load this model with Tensorflow C API. Do you think this will be possible? the answer is yes, absolutely. there are many ways to do this and i'll list some here so that you can pick up what suites your needs. you can use cppflow, which is a c++ wrapper for the tensorflow C API, and actually ...


1

The result of not freezing the pretrained layers will be to destroy the information they contain during future training rounds. See Transfer learning and fine-tuning guide from TensorFlow.


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