1. CNN Implementation

Description:

In this task, you are asked to develop a CNN model by giving the dataset below.

(https://mavsuta-

my.sharepoint.com/:f:/q/personal/chaowei wang mavs uta edu/Eo6iRY2sJKdOp5V7xCmDAt8

BcU7LoCulE0k2ETqrxpM1-A?e=4RUuDa)

There are two files in the dataset:

• data.csv is the training data. Column ACCX is collected from one channel of accelerator.

Column Light indicate environment light condition.

•

label.csv is the label for the training data (data.csv).

o There are 55 labels, and 137500 rows in training data. Therefore, each label

corresponds to 2500 rows of training data. In other words, the input size of one

instance is 2500*2.

You are free to choose any deep learning library you are familiar with. After finish training,

please save the output as a file named "output.txt". The model's performance is not

important. However, it is important the model runs with no error.

Deliverables:

- Source code. The code should be organized in a clear structure and easy to follow.

The output files.

A README.txt with necessary instructions included.