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Keras applications module is used to provide pre-trained model for deep neural networks. Keras models are used for prediction, feature extraction and fine tuning. This chapter explains about Keras applications in detail.

Pre-trained models

Trained model consists of two parts model Architecture and model Weights. Model weights are large file so we have to download and extract the feature from ImageNet database. Some of the popular pre-trained models are listed below,

• ResNet
• VGG16
• MobileNet
• InceptionResNetV2
• InceptionV3

Loading a model

Keras pre-trained models can be easily loaded as specified below −

import keras 
import numpy as np 

from keras.applications import vgg16, inception_v3, resnet50, mobilenet 

#Load the VGG model 
vgg_model = vgg16.VGG16(weights = 'imagenet') 

#Load the Inception_V3 model 
inception_model = inception_v3.InceptionV3(weights = 'imagenet') 

#Load the ResNet50 model 
resnet_model = resnet50.ResNet50(weights = 'imagenet') 

#Load the MobileNet model mobilenet_model = mobilenet.MobileNet(weights = 'imagenet')

Once the model is loaded, we can immediately use it for prediction purpose. Let us check each pre-trained model in the upcoming chapters.

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