Difference between revisions of "AI: Anomaly Detection in logfiles"
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model = Sequential() | model = Sequential() | ||
Our model will use 1 input layer 1 hidden layer with 128 nodes and 1 output layer with a single node. | Our model will use 1 input layer, 1 hidden layer with 128 nodes and 1 output layer with a single node. | ||
WIP | WIP | ||
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* [[Jetson AGX Xavier Development Kit]] | * [[Jetson AGX Xavier Development Kit]] | ||
[[Category:Documentation]] | [[Category:Documentation]] | ||
Revision as of 19:18, 12 July 2022
➤ IMPORTANT: This page is still under construction.
Summary
This guide will create a basic AI model to perform binary classification in order to detect anomalies in logfiles. This AI model is also suitable for the Jetson AGX Xavier Development Kit
Requirements
- Packages: TensorFlow, Keras, Pandas, sklearn, numpy, seaborn, matplotlib
- Software: Pycharm or any other python editor
Description
Step 1 - Create a model
First you need to create a sequential model, which can be trained later.
model = Sequential()
Our model will use 1 input layer, 1 hidden layer with 128 nodes and 1 output layer with a single node.
WIP
Step 2
WIP