Deep Learning with R Cookbook

How it works...

In step 1, we initialized a sequential model by calling the keras_model_sequential() function. In the next step, we stacked hidden and output layers by using a series of layer functions. The layer_dense() function adds a densely-connected layer to the defined model. The first layer of the sequential model needs to know what input shape it should expect, so we passed a value to the input_shape argument of the first layer. In our case, the input shape was equal to the number of features in the dataset. When we add layers to the keras sequential model, the model object is modified in-place, and we do not need to assign the updated object back to the original. The keras object's behavior is unlike most R objects (R objects are ...