Tensorflow: loss decreasing, but accuracy stable

Question

My team is training a CNN in Tensorflow for binary classification of damaged/acceptable parts. We created our code by modifying the cifar10 example code. In my prior experience

Answer 1

A decrease in binary cross-entropy loss does not imply an increase in accuracy. Consider label 1, predictions 0.2, 0.4 and 0.6 at timesteps 1, 2, 3 and classification threshold 0.5. timesteps 1 and 2 will produce a decrease in loss but no increase in accuracy.

Ensure that your model has enough capacity by overfitting the training data. If the model is overfitting the training data, avoid overfitting by using regularization techniques such as dropout, L1 and L2 regularization and data augmentation.

Last, confirm your validation data and training data come from the same distribution.