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A professor of mine gave me a small script that he uses to visualize the evolution of his neural net after every epoch of learning. This is a plot of 3 values: train loss, train error, and test error.

What is the difference between the first two?

1 Answer

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Train Loss is the value of the objective function that you are minimizing. This value could be a positive or negative number, depending on the specific objective function of your training data. The training loss is calculated over the entire training dataset.

Train Error involves the human interpretable metric of your model's performance. Normally it means what percentage of training examples the model got incorrect. This is always a value between 0 and 1. Training error is calculated by using the same data for training the model and calculating its error rate.

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