Gist 2d7353459b99c1bc219a
This visualization demonstrates unsupervised learning of a two-dimensional dataset using a denoising autoencoder implemented in Pylearn2. The model, defined in the YAML configuration, learns to reconstruct input points through a hidden layer with tanh activations, minimizing mean squared reconstruction error via stochastic gradient descent. The example shows how the autoencoder’s latent representation evolves over training epochs, with the model saving checkpoints after each epoch. It relies on Pylearn2’s `Train` class, `SGD` algorithm, and `MeanSquaredReconstructionError` cost function, while the data comes from a pickled `training-test.pkl` file.