Sequential vs Functional Keras API Part 2 explanation _ Day 15
Keras API Example Let’s continue from day 14 which we explained the 3 Keras API types and compare them Understanding Sequential vs. Functional API in Keras with a Simple Example When building neural networks in Keras, there are two main ways to define models: the Sequential API and the Functional API. In this post, we’ll explore the differences between these two approaches using a simple mathematical example. Sequential API The Sequential API in Keras is a linear stack of layers. It’s easy to use but limited to single-input, single-output stacks of layers. Here’s a simple example to illustrate how it works. Objective: Multiply the input $x$ by 2. Add 3 to the result. Let’s implement this using the Sequential API: from keras.models import Sequential from keras.layers import Lambda # Define a simple sequential model model = Sequential() model.add(Lambda(lambda x: 2 * x, input_shape=(1,))) model.add(Lambda(lambda x: x + 3)) model.summary() Functional API The Functional API in Keras is more flexible and allows for the creation of complex models with multiple inputs and outputs. We’ll use the same mathematical operations to illustrate how it works. Objective: Multiply the input $x$ by 2. Add 3 to the result. Mathematical Operations: $y_1 = 2...
