I am training a Squeeze-net model for binary classification of images. I have 79968 images for training (50:50 for and against) and 8892 images in the validation set. After 35000 iterations my training accuracy fluctuates between 1 and 0.96875. The validation accuracy is more or less constant between [0.985, 0.986]. The base learning rate is 0.01 and decreased upto 0.00001. As far as I can tell visually training loss doesn't exactly fluctuate between two numbers as such but is in the range [0.02, 0.09] for most part except a few occasional spike.
My question is what can i infer from this? (1) Overfit? (2) Model has converged? If not should I reduce the learning rate? (3) Is the model stuck at a local minima? I am using softmax-with-loss as my loss layer.