Deep Learning with R Cookbook

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In a simple autoencoder, the decoder and encoder networks have fully connected dense layers. A convolutional autoencoder extends this underlying autoencoder architecture by replacing its dense layers with convolutional layers. Like simple autoencoders, the size of the input layer is the same as the output layers in a convolutional autoencoder. The encoder network of this autoencoder has convolutional layers, while the decoder network has transposed convolutional layers or an upsampling layer coupled with a convolutional layer.

In the following code block, we have implemented a convolutional autoencoder, where the decoder network consists of an upsampling layer combined with a convolutional layer. This approach scales up the ...