![]() ![]() ![]() The dataset should cover the full range of inputs that the model is expected to handle. To prevent overfitting, the best solution is to use more complete training data. Understanding how to train for an appropriate number of epochs as you'll explore below is a useful skill. If you train for too long though, the model will start to overfit and learn patterns from the training data that don't generalize to the test data. This means the network has not learned the relevant patterns in the training data. This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. Underfitting occurs when there is still room for improvement on the train data. The opposite of overfitting is underfitting. Although it's often possible to achieve high accuracy on the training set, what you really want is to develop models that generalize well to a testing set (or data they haven't seen before). Learning how to deal with overfitting is important. In other words, your model would overfit to the training data. In both of the previous examples- classifying text and predicting fuel efficiency-the accuracy of models on the validation data would peak after training for a number of epochs and then stagnate or start decreasing. ![]() As always, the code in this example will use the tf.keras API, which you can learn more about in the TensorFlow Keras guide. ![]()
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