From the course: Deep Learning: Getting Started

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Training and evaluation

Training and evaluation

From the course: Deep Learning: Getting Started

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Training and evaluation

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- We have now prepared the input data and created a model in the previous videos. Let's now go ahead and train a model. The core block for this video is in section 4.4 of the notebook. We set the variables flat to one, so Iris will print out details of progress during training. We then set the hyper parameters for training. These are of course set initially on intuition and then fine tuned on experimentation as we improve the model. We set the batch size to 16, which is in the two power end range. We set the number of epochs to 10. We will use a validation split of 20%. This means that 20% of the training data will be used by carers for validation after each epoch. Though the recommended percentage of validation is 10, we are using more samples since the total sample size is only 150. Training a model is straightforward in carers with a single function call. The model.fit method is used to train and also capture details about the training. The first parameter is the input feature…

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