Training accuracy 86% validation 94%, required training accuracy 99% validation accuracy 95%

[Deepti_Prasad]

ok I finally achieved 99% accuracy with the help of mentor @balaji.ambresh guidance and this post. I just wanted to point what I understood through week 4 assignment, hoping it will help someone in future just the way this attached post link from @shiro help me.

1. When training model between two classes, image augmentation can vary from rotation range to horizontal flip based on the image one is training. So that explains keep the training_datagen as simple as possible to make it less complicated when using more than two classes.

2. I also noticed the importance of batch_size, as the batch size increases, the neural network layer should be lesser.

3. Also emphasising on the data augmentation for this exercise is must to understand. I removed these three hyper parameters: rotation_range, horizontal_flip, shear_range to achieve accuracy 99% explaining that training dotage to be as simple as possible when model training for multi-class.

Refer link is below

C2W4 Assignment accuracy

Hi, Taras. “A reasonable benchmark is to achieve over 99% accuracy for training and over 95% accuracy for validation within 15 epochs.” This seems too high for me because the task is 26 class classification. I checked more sample images than 100. I think shear and rotation are useless or limited gain since all images I checked are upright. And horizontal flips will be harmful because they are all right hand. I reflect those findings on the settings of ImageDataGenerator but my result was fa…

Thank you
DP