evaluate ( x_test, y_test, batch_size = 128 ) ) model. fit ( x_train, y_train, batch_size = 64, epochs = 3, validation_data = ( x_val, y_val ) ) results = model. categorical_ crossentropy ( ), metrics = ) history = model. The typical training loop in Keras looks like this: model = keras. This evaluation is carried out at the end. Because the training and validation datasets have been used in the training loop, it is necessary to withhold yet another split of the training dataset, called the testing dataset, to report the actual error metrics that could be expected on new and unseen data. Therefore, evaluation needs to be done within the training loop, and error metrics on a withheld split of the training data, called the validation dataset, have to be monitored as well. Unfortunately, you cannot know whether the model complexity is too high for a particular dataset until you actually train that model on that dataset. Overfitting can happen if the model complexity is higher than can be afforded by the size and coverage of the dataset. Because of this, the error (called the loss) on the training dataset has to be monitored. SGD finds a minimum, but is not a closed-form solution, and so we have to detect whether the model convergence has happened. This is called stochastic gradient descent (SGD), and extensions of SGD (such as Adam and Adagrad) are the de facto optimizers used in modern-day machine learning frameworks.īecause SGD requires training to take place iteratively on small batches of the training dataset, training a machine learning model happens in a loop. On large datasets, gradient descent is applied to mini-batches of the input data to train everything from linear models and boosted trees to deep neural networks (DNNs) and support vector machines (SVMs).
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