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I got this batch-based GRU to work: import numpy as np import tensorflow as tf units = 2 seq_length = 32 X = (np.random.rand(int(1e4), seq_length, 1) > 0.5).astype(float) Y = 1 - X model = tf.keras.models.Sequential([ tf.keras.layers.Input(shape=X.shape[1:]), tf.keras.layers.GRU(units, return_sequences=True), tf.keras.layers.Conv1D(1, 1, ...

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The time complexity of an algorithm always depends on its implementation (e.g. searching in a red-black tree has a different time complexity than searching in an unbalanced binary search tree). This also applies to the case of computing the time complexity of the algorithm that tests a neural network with multiple LSTM layers, so one may need to assume how ...

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I don't think time series model necessarily makes sense if you have one conductivity value to predict for each time series. A regression like setup makes more sense here: you could model this by letting the vector of time points represent the input. So you'd end up with a $n \mbox{ x } t$ matrix as input to predict the conductivity value.

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