TensorFlow Sigmoid activation function as output layer - value interpretation
In short, value of model.predict() function is interpreted as mentioned in option 2.

In order to clarify, let's assume we are talking about spam detection application. Label 0 represents that text/email is not spam and label 1 represents that text/email is spam.

Suppose, after running the function model.predict(), we get value 0.9899. Then we can interpret it as:


chance of given text/email being spam is 98.99%
chance of given text/email not being spam is 1.01%


Usually, when we talk about a binary classification problem, we will often use labels 0 &amp; 1 to represent the classes. So the predicted value being closer to 0 means it is more likely to fall under the class 0 (in our example class 0 represents text/email not being spam.). Likewise, if the predicted value being closer to 1 means it is more likely to fall under the class 1 (in our example class 1 represents text/email being spam.)