- Transform Features: Assess whether the dataset can be transformed into a two-dimensional format, like a matrix or heatmap, and then apply convolutional layers to this transformed data.
- Modify the Model: Adapt the pretrained GoogleNet model to accept non-image data. These models typically expect 2D matrices with three color channels. Since the features are likely 1D vectors, modify the first layer to accept a 1D vector instead of a 3D image. You might also need to replace certain convolutional layers with dense (fully connected) layers that are more suitable for 1D data. Then adjust the final layers to output the desired number of classes.
Classification of .xlsx formatted features with deep learning.
조회 수: 3 (최근 30일)
이전 댓글 표시
I have handcraftd Features of Images dataset how i can classify these features with pretrain deep learning Models GoogleNet etc?
댓글 수: 0
답변 (1개)
Jayanti
2024년 10월 25일
Hi Asaf,
Deep learning models like GoogleNet are primarily designed for tasks involving image data, such as classification and segmentation. However, if you want to apply them to your handcrafted features dataset, you can follow some of the below strategies:
Hope this will resolve your query!
댓글 수: 0
참고 항목
카테고리
Help Center 및 File Exchange에서 Deep Learning Toolbox에 대해 자세히 알아보기
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!