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LSTM to multivariate sequence classification

In addition, this article explains some generalized methods for clustering using DTW on multivariate data. Especially useful for IOT data https://link.springer.com/article/10.1007/s10618-020-00727-3
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Why does accuracy remain the same

be carefull: you shouldn't consider sigmoid & softmax activation functions to be interchangeable, because the first is Only for BinaryClf, the second is Only for MultiClassClf -- because having ...
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I am getting (loss: nan - accuracy: 0.0000e+00) for all epochs after training the model

you should one_hot target(y) before model.fit & give it in training in such a form y= tf.one_hot(y,10,) you've got ok, just display results of each batch ...
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Keras Binary Classification - Maximizing Recall

This is the kind of solution that I was looking for: https://github.com/huanglau/Keras-Weighted-Binary-Cross-Entropy/blob/master/DynCrossEntropy.py
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RNN/LSTM timeseries, with fixed attributes per run

You can create a sort of encoder-decoder network with two different inputs. ...
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Using the first 3 layers of a pretrained network in Keras

Defining a new network using a part of a pretained network in Keras is best done layer by layer: ...
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Input shape error

You simply need to add another dimension to your data, you can either use numpy.expand_dims again or use numpy indexing to add the two extra dimensions at once: <...
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What kind of neural network am I using? How can I build a specific kind of network?

Here you have designed a simple ANN architecture. Anyhow if you want to build CNN architecture(Refer https://keras.io/api/layers/convolution_layers/) RNN architecture(Refer https://keras.io/api/layers/...
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Imbalanced Binary Dataset in Keras. Finding the best threshold after fit s.t. sensitivity and specificity is maximized?

It's impossible in general to optimize both sensitivity and specificity, in the sense of finding a threshold for which sensitivity is maximum and specificity is maximum: sensitivity is high when TP ...
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Genetic Algorithms (Specifically with Keras)

You have many 1000s of neural network parameters that need to be set up correctly to generate a policy function for your game. In addition, many of the parameters are co-dependent - a "good" ...
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Genetic Algorithms (Specifically with Keras)

this algorythm is not the best for solving a Snake Game (best proven are RLs), however, I think this can actually work if your model is well built. As far I understand, your knowledge about DGA is ...
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When using padding in sequence models, is Keras validation accuracy valid/ reliable?

I know this answer is too late but I think it can be useful for other readers. The short answer is YES! The padding influences the accuracy. For handling the bad effect of padding, you can define new ...
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How Did Keras Determine The Number of Parameters In My Model

As you are using dense layers, Keras determine the params in models as following calculations : # of params = # of outputs * (# of inputs + 1) Hence, the ...
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How to compute the mean of weights of multiple models?

In your function, you get weights by each model layer but you always assign them to the same variable. Hence at the end, you will have weights assigned to the ...
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Keras: ambiguity regarding state maintenance in RNNs

First, let's clarify some things: Each sequence in a batch is totally independent of the rest of the sequences from the same batch. This behavior is fixed and cannot be changed. The initial state is ...
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Keras: ambiguity regarding state maintenance in RNNs

Internal state of RNN is reset every time it sees a new batch. The layer will only maintain the state while processing the samples in a batch. If you think logically if a model resets its internal ...
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Using Z-test score to evaluate model performance

From your description, you can not use Z-test because the z-test requires knowing the population variance.
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