AI News, R Interface to the Keras Deep Learning Library

R Interface to the Keras Deep Learning Library

To show the power of neural networks we need a larger dataset to make use of.

A popular first dataset for applying neural networks is the MNIST Handwriting dataset, consisting of small black and white scans of handwritten numeric digits (0-9).

The values are pixel intensities between 0 and 255, so we will also normalize the values to be between 0 and 1: Finally, we want to process the response vector y into a different format as well.

These are fairly well-known choices for a simple dense neural network and allow us to show off many of the possibilities within the kerasR interface: We then compile the model with the “categorical_crossentropy” loss and fit it on the training data: Now that the model is trained, we could use the function keras_predict once again, however this would give us an output matrix with 10 columns.

It’s possible to get slightly higher with strictly dense layers by employing additional tricks and using larger models with more regularization.

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