VALIDATION CRITERION MET DURING TRAINING OF VGG19

Is this network is overfitting? There is condition called "validation criterion met " arises and stop the training process why? what is the solution for that? Is this is possible to train a neural network using transfer learning(vgg19) by having single 8gb ram cpu ? Is there any chance that this 8 gb ram will lead to these kind of errors ?

Thank You in advance..

Answer 1

[Srivardhan Gadila]

I think you have mentioned some function for the 'OutputFcn' Name-Value pair argument in trainingOptions and this function stopped the training after seeing that the validation has loss increased from previous step.

You can used pretrained VGG19 network for transfer learning and you can refer to the following workflows:

1. Deep learning approach to train new models faster by using pretrained models
2. Get Started with Transfer Learning
3. Transfer Learning Using AlexNet
4. Deep Learning Tips and Tricks

Make use of the imageDatastore(also refer to Datastores for Deep Learning) for training the network on the mentioned hardware. Specify the appropriate Name-Value pair arguments for 'ExecutionEnvironment' & 'MniBatchSize' in the trainingOptions.

[Deepika B]

Thank you so much, for your kind response sir.

what are all the information i got from this confusion matrix ? Based on this confusion matrix, Is my model is a good one or bad one ?

how do i test my model using test data ?

thank you in advance sir.

[Srivardhan Gadila]

The rows correspond to the predicted class (Output Class) and the columns correspond to the true class (Target Class). The diagonal cells correspond to observations that are correctly classified. The off-diagonal cells correspond to incorrectly classified observations. Both the number of observations and the percentage of the total number of observations are shown in each cell.

Refer to Examples of plotconfusion function.