How to use the UpSampling2D and Conv2DTranspose Layers in Keras

Last Updated on July 12, 2019

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.

The GAN architecture is comprised of both a generator and a discriminator model. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The generator model is typically implemented using a deep convolutional neural network and results-specialized layers that learn to fill in features in an image rather than extract features from an input image.

Two common types of layers that can be used in the generator model are a upsample layer (UpSampling2D) that simply doubles the dimensions of the input and the transpose convolutional layer (Conv2DTranspose) that performs an inverse convolution operation.

In this tutorial, you will discover how to use UpSampling2D and Conv2DTranspose Layers in Generative Adversarial Networks when generating images.

After completing this tutorial, you will know:

• Generative models in the GAN architecture are required to upsample input data in order to generate an output image.
• The Upsampling layer is a simple layer with no weights that will double the dimensions of input and can be used in
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