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CS 5542 BigData Lab Report #09
Amy Lin edited this page Mar 29, 2017
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Implement a CNN model for image classification for the datasets relevant to the project. Report accuracy. Visualizations(Tensor Board): Training and Testing.
Re-scale raw images into same pixel sizes, label and build vectors based on datasets. ( 100 X 100 pixel in this project ) Split data into Test and Training. 70% Training and 30% for Testing. Since we have a small datasets for three of Kansas City Fountains, I set the 10 Testing Images for each group. The rest of the image goes to training. Run 400 iterations to build the CNN model.
- Tensorflow CNN Net
- Accuracy
- Convolution Net 1
- Convolution Net 2
- Convolution Net Weight
- Cross Entropy
- Dropout ReLU
- OnConvolution
I haven't been able to work the Heroku and the conversation API for our own KC Fountain Dataset in time! Only put the code up but doesn't have any screenshots yet.