False positive in Multi class Image classification

I am training a neural network with some convolution layers for multi class image classification. I am using keras to build and train the model. I am using 1600 images for all categories for training. I have used softmax as final layer activation function.

The model predicts well on all True categories with high softmax probability. But when I test model on new or unknown data, it predicts with high softmax probability. How can I reduce that? Should I make changes in model architecture or data?

Topic cnn keras image-classification computer-vision deep-learning

Category Data Science

1
answered at

Seems like the network is overfit, if new/unknown data gives similar results. Implementing dropout in the layers should help with that.

Unclear if there are 1600 images total, or 1600 examples per class - if it's the former, taking a look at the ratio of positive to negative examples in the training data could be insightful.

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