When working with an LSTM network in Keras. The first layer has the input_shape parameter show below. model.add(LSTM(50, input_shape=(window_size, num_features), return_sequences=True)) I don't quite follow the window size parameter and the effect it will have on the model. As far as I understand, to make a decision the network not only makes use of current … Read more
I need to know if the number of hidden layers affect the performance and accuracy of a neural network. In other words, does increasing the number of hidden layers of a neural network increase the accuracy but decrease the performance? Or there is no rule for that? Best Answer Ultimately, the best way to think … Read more
I have made a LSTM network (hidden size 16) where I give a sequence of 10 numbers as the input and feed its output to a fully connected layer. The numbers in the sequence are corresponding to the words in the dictionary that I created (Eg: number 12 in the sequence corresponds to the 12th … Read more
I am trying to modify Andrej Karpathy's char-RNN code. As far as I understand, the loss function used in his code for a LSTM is the Softmax function function (in the file model/LSTM.lua ). I understand Softmax is the multi-class equivalent of the Logistic loss function (used for 2-class classification). The site here says that … Read more
I am trying to modify Andrej Karpathy's char-RNN code. As far as I understand, the loss function used in his code for a LSTM is the Softmax function function (in the file model/LSTM.lua ). I understand Softmax is the multi-class equivalent of the Logistic loss function (used for 2-class classification). The site here says that … Read more
LSTM network is assumed to be about memory, keeping the important information for predictions. If it is the case, why do we need to consider delayed inputs as well? My assumption would be that the LSTM – if the model is sufficiently complex – shall somehow remember the very last inputs if relevant. (Similar trick … Read more
LSTM network is assumed to be about memory, keeping the important information for predictions. If it is the case, why do we need to consider delayed inputs as well? My assumption would be that the LSTM – if the model is sufficiently complex – shall somehow remember the very last inputs if relevant. (Similar trick … Read more
I am trying to train a keras model for recognising human motion, where the input are the extracted features, such as 2D position of face, torso, etc. Recently, I have managed to train a model with high accuracy for subject-dependent case, where the derivatives of the original human motion were recognised correctly with >90% accuracy … Read more
I am trying to train a keras model for recognising human motion, where the input are the extracted features, such as 2D position of face, torso, etc. Recently, I have managed to train a model with high accuracy for subject-dependent case, where the derivatives of the original human motion were recognised correctly with >90% accuracy … Read more
It's hard to find literature where LTSM are used with multidimensional input. I know that LTSM admits various time series as input (multidimensional input) with the shape : (samples,look back,dimension).Dimension could be, electricity demand, temperature, pressure, day of the week,… among others. The question is: If you want to forecast the next X samples of … Read more