Improving the accuracy of the acclerometer based LSTM model in python.

I am performing time series classification using accelerometer data. I want to use a deep learning method for my project. I am classifying accelerometer data into various road conditions. I have a labeled dataset for various road conditions. I am presently using LSTM to perform the time series classification

Working Code:

I have a working LSTM classification code which runs perfectly on a different dataset.

# The code directory name is

--Description of the working project

--Six Classes

--Dataset used UCI HAR dataset

--Result 70 percent Accuracy

--- This was just to justify that the code works to check please

The Problem:

I am trying a different dataset with the same structure and format. The dataset and project is inside the rar file name [login to view URL]

I am getting strange results like 31% accuracy and a confusion matrix in which only a single class is showing any [login to view URL] attachment name of the confusion matrix is [login to view URL]

The training and testing dataset is balanced among various classes.

Comparison Tests

When I use the same dataset and ensemble machine learning method, I obtain 50% percent accuracy using an ensemble machine learning method.

When I use the same dataset and calculate features and then use ensemble machine learning method, I obtain 78% percent accuracy with a better confusion matrix. Confusion matrix of Ensemble machine learning classifier is present in the attachment name [login to view URL]

I want to understand what I am doing wrong to get strange results with LSTM.

Things to try:

1. Trying hyperparameter optimization changing the parameters such as learning rate, number of hidden layers etc.

2. Come up with a feature to train deep learning neural network.

The rar file having the Code having the problem is [login to view URL]

Beceriler: Makine Öğrenimi, Python

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İşveren Hakkında:
( 0 değerlendirme ) delhi, India

Proje NO: #18652111

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