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Keras Tuner for a (stacked) LSTM model?

I have a question about how to correctly setup a Keras Tuner model for a stacked LSTM model. What I have tried is the normal tutorial with a loop and the hp.Int() function to define the size of each ...
Barry's user avatar
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Confusion about LSTM network with multiple LSTM units

An LSTM layer can have multiple LSTM units ( LSTM memory cells each with input/update, output, and forget gates). In this question, we have 2 LSTM layers each with two cells, and according to the ...
John adams's user avatar
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After training an LSTM model using log returns as the input and obtaining a binary output of either 0 or 1, how can I predict stock movement?

I have been working on a personal project called "Predicting Stock Movement using LSTM." For my project, I have selected log returns as the input (X) and the target (y) is whether today's ...
Murtuza's user avatar
1 vote
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What is the reason behind Keras choice of default (recurrent) activation functions in LSTM networks

Activation function between LSTM layers In the above link, the answer to the question whether activation function are required for LSTM layers was answered as follows: as an LSTM unit already consists ...
Lauramvp's user avatar
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Wiggle in the initial part of an LSTM prediction

I working on using LSTMs and GRUs to make time series predictions. For the most part the predictions are pretty good. However, there seems to be a wiggle (or initial up-then-down) before the ...
AGirlHasNoUsername's user avatar
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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 ...
user86335's user avatar
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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 ...
felix Antony's user avatar
1 vote
1 answer

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 ...
user3486308's user avatar
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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 ...
Hexadecimalism's user avatar
1 vote
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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 ...
Aesir's user avatar
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4 votes
1 answer

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, ...
Alexbrini's user avatar
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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 ...
Skiddles's user avatar
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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 ...
Goutam Bose's user avatar
2 votes
1 answer

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 ...
ARAT's user avatar
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1 vote
1 answer

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 ...
Mati K's user avatar
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15 votes
2 answers

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 ...
BigBadMe's user avatar
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4 votes
1 answer

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', ...
Ali250's user avatar
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