Denoising autoencoder with CIFAR10. Overfitting like there is no tomorrow.

dae_cifar.yaml

!obj:pylearn2.train.Train {
    "dataset": !obj:pylearn2.datasets.cifar10.CIFAR10 &dataset {
        "which_set" : 'train'
    },
    "model": !obj:pylearn2.models.autoencoder.DenoisingAutoencoder {
        "nvis" : 3072,
        "nhid" : 400,
        "irange" : 0.05,
        "corruptor": !obj:pylearn2.corruption.BinomialCorruptor {
            "corruption_level": 0.5,
        },
        "act_enc": "tanh",
        "act_dec": null,    # Linear activation on the decoder side.
    },
    "algorithm": !obj:pylearn2.training_algorithms.sgd.SGD {
        "learning_rate" : 1e-3,
        "batch_size" : 10,
        "monitoring_batches" : 5,
        "monitoring_dataset" : *dataset,
        "cost" : !obj:pylearn2.costs.autoencoder.MeanSquaredReconstructionError {},
        "termination_criterion" : !obj:pylearn2.training_algorithms.sgd.EpochCounter {
            "max_epochs": 25,
        },
    },
    "save_path": "./dae_cifar.pkl",
    save_freq: 1
}