This is done by subclassing the Model class and implementing a call method.įor example: from keras.layers import Dense,Dropout,BatchNormalizationĭef _init_(self, use_bn=False, use_dp=False, num_classes=10): But you may create your own fully-customizable models in Keras. Functional API also only has a little of customization available for you. Sequential model does not allow you much flexibility to create your models. Model=Model(inputs=,outputs=) Model Subclassing in Keras To create model with multiple inputs and outputs: Also its easy to model the graph here and access its nodes as well.īelow is the Example for Functional API: from keras.models import Model
we can make graphs of layers using Keras functional API.Īs functional API is a data structure, it is easy to save it as a single file that helps in recreating the exact model without having the original code. Functional API allows us to create models that have multiple input or output. It provides more flexibility to define a model and add layers in keras. Here is an example for Sequential model: from keras.models import Sequential Even if we want non-linear topology, it is not suited. This model is not suited when any of the layer in the stack has multiple inputs or outputs. It is best for simple stack of layers which have 1 input tensor and 1 output tensor. But it does not allow us to create models that have multiple inputs or outputs. It allows us to create models layer by layer in sequential order. Stay updated with latest technology trends