Modularity

Keras is highly modular, meaning that models, layers, loss functions, optimizers, metrics, and more can all be independently defined and reused. This enables users to easily experiment with different architectures and techniques.

• Layers: Layers are the building blocks of neural networks in Keras. Common layer types include Dense, Conv2D, LSTM, and BatchNormalization.
• Models: Keras allows you to define multiple model types (Sequential or Functional) based on your application.
• Optimizers: Keras has built-in optimizers like Adam, SGD, RMSprop, which are used to update model parameters.pythonCopy codefrom keras.optimizers import Adam optimizer = Adam(learning_rate=0.001)