Category : lstm

My total length of data is l=132011. Initially when I try to run this code(without @jit) on CPU normally,its taking 35 mins to run for length = 100 iterations and around 6 hours for length = 1000 iterations which means it would take more than 60 hours to run through the full length(132011 iterations). @jit(target ..

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X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1])) X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1])) Trying to train my dataset on the Residual(skip connections) LSTM model via wrapper class: import tensorflow as tf class ResidualWrapper(tf.keras.Model): def __init__(self, model): super().__init__() self.model = model def call(self, inputs, *args, **kwargs): delta = self.model(inputs, *args, **kwargs) The prediction for each timestep is ..

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I am trying to implement Residual LSTM on my dataset containg ECG readings sequences as input : from keras.layers import LSTM, Lambda from keras.layers.merge import add def make_residual_lstm_layers(input, rnn_width, rnn_depth, rnn_dropout): x = input for i in range(rnn_depth): return_sequences = i < rnn_depth – 1 x_rnn = LSTM(rnn_width, recurrent_dropout=rnn_dropout, dropout=rnn_dropout, return_sequences=return_sequences)(x) if return_sequences: if i ..

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How do I resolve this error? ValueError: Layer model_101 expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor ‘IteratorGetNext:0’ shape=(None, 1, 1, 64) dtype=float32>] I am following the guide from Jason Brownlee on how to develop Encoder-Decoder Model for Sequence-to-Sequence Prediction. Instead of using LSTM units, I would like to also be ..

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