Keras Model API

There are mainly two API of keras model.

1. Sequential

• Sequential API is the simplest and commonly used way to create a Keras model. A sequential keras model is created by adding layers with one input and output tensor sequentially. It does not work for multi input and output tensors.
• A sequential model with linear stack of Convolutional and Dense layers.

from keras import models
from keras import layers
model=models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation="relu"))
model.add(layers.Dense(32))

2. Functional

• Functional API in Keras is introduced to create models with complex architectures flexibly. To deal with multi input and output tensors functional keras model is used.
• In case of functional API the outputs of one layer are connected to inputs of next layer externally for more flexibility and control over architecture of model. Complex network architectures like graph models can be created with this API.
• A keras functional model where layers are connected externally.

from keras import models
from keras import layers
inputs = layers.Input(shape=(784,))
x = layers.Dense(64)(inputs)
x = layers.Activation('relu')(x)
outputs = layers.Dense(10, activation='softmax')(x)
model = models.Model(inputs=inputs, outputs=outputs)

In conclusion, the Keras Layers API offers a rich and versatile framework for building a wide range of neural network architectures, from simple to complex. It provides a variety of layers that are essential for constructing different types of neural networks, suitable for a broad spectrum of applications from image and video processing to text analysis

What Next?? If you want dive more into keras layer API and know to create a model, so please read this article – Click Here

Keras is a powerful API built on top of deep learning libraries like TensorFlow and PyTorch. The Layers API is a key component of Keras, allowing you to stack predefined layers or create custom layers for your model. In this article, we will discuss the Keras layers API.