Classification predictions completely different base on data size, though data doesn’t change

Hello, I’ve just started learning and messing around with neural networks. I’m not sure if this is a problem, or this is how neural networks work, but I’ve noticed, that whenever I try to predict a binary classification outcome with my model, the predictions vary completely based on the size of the data i pass it.

For example, if I try to predict a single outcome with one row of data, I get something like 0.4. Then if I add another row of data and predict again, the first prediction of row 1 becomes 0.9, even though the data in row 1 did not change, I only added an additional row of data for an additional prediction.

My training data consists of 1266 entries with 54 features. I’ve tried reducing the batch_size to 1, different optimizers, number of layers, number of neurons and the result is mostly the same. Is this normal behavior?