Input layer design requires an understanding of how the data flows into the system. We have CSV data as input, and we need to inspect the features to decide on the input attributes. Layers are core components in neural network architecture. In this recipe, we will configure input layers for the neural network.
Designing input layers for the neural network model
Getting ready
We need to decide the number of input neurons before designing the input layer. It can be derived from the feature shape. For instance, we have 13 input features (excluding the label). But after applying the transformation, we have a total of 11 feature columns present in the dataset. Noise features are removed and categorical variables are transformed...
