Reproduce behavior described in pylearn2 issue #1083: https://github.com/lisa-lab/pylearn2/issues/1083

1083.yaml

!obj:pylearn2.train.Train {

  dataset: &train !obj:pylearn2.datasets.mnist.MNIST {
    which_set: 'train',
    start: 0,
    stop: 50000
  },

  model: !obj:pylearn2.models.mlp.MLP {
    layers: [
      !obj:pylearn2.models.mlp.Softmax {
        layer_name: 'y',
        n_classes: 10,
        irange: 0.0,
        init_bias_target_marginals: *train
      }],
    nvis: 784
  },

  algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
    batch_size: 100,
    learning_rate: 0.01,
    monitoring_dataset: {
      'train': *train,
      'valid': !obj:pylearn2.datasets.mnist.MNIST {
        which_set: 'train',
        start: 50000,
        stop: 51000
      }
    },

    termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {
      max_epochs: 100
    }
  }
}