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PyTorch MNIST autoencoder Raw This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters. Training a deep autoencoder or a classifier. on MNIST digits. Code provided by Ruslan Salakhutdinov and Geoff Hinton. Permission is granted for anyone to copy, use, modify, or distribute this program and accompanying programs and documents for any purpose, provided this copyright notice is retained and prominently displayed, along with a note.

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Load the MNIST dataset with the mnist.load_data() function provided by keras. Load the images in two separate lists, x_train and x_test. Create the first part of the autoencoder - the encoder model. The encoder model compresses the input image down to a lower dimensional latent space.

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