Above is my config of the network.
l am training a CNN network on picture size of 192*192.
my target is a classification network of 11 kinds.
However, the loss and the accuracy on testing dataset appears to be very unstable. l have to run 15+ epochs to get a stable accuracy and loss. The maximum accuracy is only 50%.
What can l do to improve the performance?
>Solution :
I would recommend you to first refer to models which are widely known like VGG-16, LeNET or VGG-19 and check out the way how the conv2D and max-pooling layers are placed.
Start with a very basic model without any batch normalization and Leaky ReLU layers. You just keep the conv2D and max pooling layers and train your model for a few epochs.
Next, try other activations like ReLU to TanH. Try Changing the max pooling to average pooling.
If you are solving a classification problem then use the softmax layer at the end. Also, introduce Dense layer(s) after flattening.
Your dataset should be large and also the target should be one-hot encoded if you wish to use the softmax layer.