Questions tagged [stacked-lstm]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
1
vote
0answers
20 views

LSTM for multiple time series regression with extremely large ranges

I have the following question for those which encountered the same dilemma as me: My target is to develop a LSTM RNN for multi-step prediction for multiple time series representing daily sales of ...
0
votes
0answers
42 views

How multi layer LSTM are interconnected?

I am trying to understand the layers in LSTM for my own implementation using Python. I started with Keras to getting familiarized with the layer flow. I have tried the below code in Keras and I have ...
0
votes
0answers
22 views

Stacking model using 100 seeds

i want to build a stacking model either by PNN or SVM of different 3 classifiers SVM, KNN, PNN and useing their optimized best paramters retrieved using Randomized grid search method but i need help ...
0
votes
1answer
49 views

What are h(t-1) and c(t-1) for the first LSTM cell?

I know in a LSTM chain you should connect the h(t) of the previous cell to the h(t+1) of the next cell, and doing so for c(t). But what about the first cell? What does it get as h(t-1) and c(t-1)? I ...
1
vote
0answers
92 views

Splitting and training multiple datasets at the same time

I've got 15 different datasets at about 10GB each. Each dataset comes with a binary 2D ground truth (10486147ish, 1) that I pull from it. I'm trying to figure out how to load each dataset, split them ...
1
vote
0answers
23 views

Initialising states in a multilayer sequence to sequence model

With a sequence to sequence model where the enocoder and decoder are both comprised of one layer each, the initial state of the decoder is initialised to use the final states of the encoder layer. In ...
3
votes
1answer
468 views

Connect a dense layer to a LSTM architecture

I am trying to implement an LSTM structure in plain numpy for didactic reason. I clearly understand how to input the data, but not how to output. Suppose I give as inputs a tensor of dimension (n, b, ...
1
vote
0answers
70 views

LSTM Produces Random Predictions

I have trained an LSTM in PyTorch on financial data where a series of 14 values predicts the 15th. I split the data into Train, Test, and Validation sets. I trained the model until the loss ...
0
votes
1answer
188 views

How can I detect anomalies/outliers in my online streaming data on a real-time basis?

Say, I've a huge set of data(infinite in size) consisting of alternating sine wave and step pulses one after the other. What I want from my model is to parse the data sequence wise or point wise and ...
2
votes
1answer
3k views

Stacking LSTM layers

Can someone please tell me the difference between those stacked LSTM layers? First image is given in this question and second image is given in this article. So far what I learned about stacking LSTM ...
0
votes
1answer
269 views

Why does my LSTM perform better when randomizing training subset vs. standard batch training?

I am training a simple LSTM network using Keras to predict time series values. It is a simple 2-layer LSTM. I get the best performance when I train on subsets of the training set that start at random ...
10
votes
2answers
5k views

Dropout on which layers of LSTM?

Using a multi-layer LSTM with dropout, is it advisable to put dropout on all hidden layers as well as the output Dense layers? In Hinton's paper (which proposed ...
3
votes
1answer
95 views

How is error back-propagated in a multi-layer RNN

Let's say I have a 2 layer LSTM cell, and I'm using this network to perform regression for input sequences of length 10 along the time axis. From what I understand, when this network is 'unfolded', ...