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In this chapter, we will learn about the pre-trained models in Keras. Let us begin with VGG16.

**VGG16** is another pre-trained model. It is also trained using ImageNet. The syntax to load the model is as follows −

keras.applications.vgg16.VGG16( include_top = True, weights = 'imagenet', input_tensor = None, input_shape = None, pooling = None, classes = 1000 )

The default input size for this model is 224x224.

**MobileNetV2** is another pre-trained model. It is also trained uing **ImageNet**.

The syntax to load the model is as follows −

keras.applications.mobilenet_v2.MobileNetV2 ( input_shape = None, alpha = 1.0, include_top = True, weights = 'imagenet', input_tensor = None, pooling = None, classes = 1000 )

Here,

**alpha** controls the width of the network. If the value is below 1, decreases the number of filters in each layer. If the value is above 1, increases the number of filters in each layer. If alpha = 1, default number of filters from the paper are used at each layer.

The default input size for this model is **224x224**.

**InceptionResNetV2** is another pre-trained model. It is also trained using **ImageNet**. The syntax to load the model is as follows −

keras.applications.inception_resnet_v2.InceptionResNetV2 ( include_top = True, weights = 'imagenet', input_tensor = None, input_shape = None, pooling = None, classes = 1000)

This model and can be built both with ‘channels_first’ data format (channels, height, width) or ‘channels_last’ data format (height, width, channels).

The default input size for this model is **299x299**.

**InceptionV3** is another pre-trained model. It is also trained uing **ImageNet**. The syntax to load the model is as follows −

keras.applications.inception_v3.InceptionV3 ( include_top = True, weights = 'imagenet', input_tensor = None, input_shape = None, pooling = None, classes = 1000 )

Here,

The default input size for this model is **299x299**.

Keras is very simple, extensible and easy to implement neural network API, which can be used to build deep learning applications with high level abstraction. Keras is an optimal choice for deep leaning models.

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