Keras uses the concept of layers when working with models. There are two ways to do so. The simplest way is by using a sequential model for a linear stack of layers. The other is the functional API for building complex models such as multi-output models, directed acyclic graphs, or models with shared layers. This means that the tensor output from a layer can be used to define a model, or a model itself can become a layer:
- Let's use the Keras library and create a Sequential model:
In [ ]: from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers.normalization import BatchNormalization num_features = train_scaled_x.shape[1] ...
