I wrote a custom Tree-RNN-CELL that can handle several different inputs when they are provided as a tuple. This is working fine, but now I wanted to put it together in a submodel, so that i can sum the 4 lines up in 2 lines and to have a better overview ( the tree gets big so its worth it)
Tag: recurrent-neural-network
How to include future values in a time series prediction of a RNN in Keras
I currently have a RNN model for time series predictions. It uses 3 input features “value”, “temperature” and “hour of the day” of the last 96 time steps to predict the next 96 time steps of the feature “value”. Here you can see a schema of it: and here you have the current code: Here you have some test data
Lstm for multivariate sequence prediction
I am confused with my Stacked LSTM model. Lstm has different type of applications. For example, in the image, two types of LSTM are shown, machine translation and video classification. My model is as follow. Input x has shape (1269, 4, 7). A few samples of input x and output y are as follows. Does this implementation fall into machine
Modeling Encoder-Decoder according to instructions from a paper [closed]
Closed. This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 2 years ago. Improve this question I am new to this field and I was reading a paper “Predicting citation counts based on deep neural network learning
Neural Machine Translation model predictions are off-by-one
Problem Summary In the following example, my NMT model has high loss because it correctly predicts target_input instead of target_output. As is evident, the prediction matches up almost 100% with target_input instead of target_output, as it should (off-by-one). Loss and gradients are being calculated using target_output, so it is strange that predictions are matching up to target_input. Model Overview An