machine learning - What is the difference between Keras model.evaluate() and model.predict()?

Question

I used Keras biomedical image segmentation to segment brain neurons. I used model.evaluate() it gave me Dice coefficient: 0.916. However, when I used model.predict(), then loop through the predicted images by calculating the Dice coefficient, the Dice coefficient is 0.82. Why are these two values different?

Answer 1

The model.evaluate function predicts the output for the given input and then computes the metrics function specified in the model.compile and based on y_true and y_pred and returns the computed metric value as the output.

The model.predict just returns back the y_pred

So if you use model.predict and then compute the metrics yourself, the computed metric value should turn out to be the same as model.evaluate

For example, one would use model.predict instead of model.evaluate in evaluating an RNN/ LSTM based models where the output needs to be fed as input in next time step