Tutorial Overview There is not a lot of code required, but we are going to step over it slowly so that you will know how to create your own models in the future. The steps you are going to cover in this tutorial are as follows: • Load Data.

• Define Model. • Compile Model. • Evaluate Model. • Tie It All Together. This tutorial has a few requirements: • You have installed and configured. • You have (including NumPy) installed and configured (e.g.

• If you need help, see the post. • You have and a backend ( or ) installed and configured. • If you need help, see the post. Create a new file called keras_first_network.py and type or copy-and-paste the code into the file as you go.

Seed ( seed ) Now we can load our data. In this tutorial, we are going to use the. This is a standard machine learning dataset from the UCI Machine Learning repository. It describes patient medical record data for Pima Indians and whether they had an onset of diabetes within five years. As such, it is a binary classification problem (onset of diabetes as 1 or not as 0).

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All of the input variables that describe each patient are numerical. This makes it easy to use directly with neural networks that expect numerical input and output values, and ideal for our first neural network in Keras. And place it in your local working directory, the same as your python file. Save it with the file name.

Y = dataset [:, 8 ] We have initialized our random number generator to ensure our results are reproducible and loaded our data. We are now ready to define our neural network model. Define Model Models in Keras are defined as a sequence of layers.

We create a Sequential model and add layers one at a time until we are happy with our network topology. The first thing to get right is to ensure the input layer has the right number of inputs. This can be specified when creating the first layer with the input_dim argument and setting it to 8 for the 8 input variables. How do we know the number of layers and their types? This is a very hard question.

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