sgd_mnist_reboot.yaml

!obj:pylearn2.train.Train {
    dataset: &train !obj:pylearn2.datasets.mnist.MNIST {
        which_set: 'train',
        one_hot: 1,
        start: 0,
        stop: 50000
    },
    model: !obj:galatea.sample_prop.basic.SimpleModel2 {
        # convert to irange .005 for all 3 layers
        nvis: 784,
        num_hid: 1200,
        num_hid_2: 1200,
        num_class: 10,
        h0_max_col_norm: 1.9,
        h1_max_col_norm: 1.9,
        y_max_col_norm: 1.9
    },
    algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
        batch_size: 100,
        learning_rate: .1,
        learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
            init_momentum: .5,
        },
        monitoring_dataset:
            {
                'train' : *train,
                'valid' : !obj:pylearn2.datasets.mnist.MNIST {
                              which_set: 'train',
                              one_hot: 1,
                              start: 50000,
                              stop:  60000
                          },
                'test'  : !obj:pylearn2.datasets.mnist.MNIST {
                              which_set: 'test',
                              one_hot: 1,
                          }
            },
        cost : !obj:galatea.sample_prop.basic.SamplingCost3 {
               },
        termination_criterion: !obj:pylearn2.termination_criteria.MonitorBased {
            channel_name: "valid_y_misclass",
            prop_decrease: 0.,
            N: 100
        },
        update_callbacks: !obj:pylearn2.training_algorithms.sgd.ExponentialDecay {
            decay_factor: 1.000004,
            min_lr: .000001
        }
    },
    extensions: [
        !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
             channel_name: 'valid_y_misclass',
             save_path: "${PYLEARN2_TRAIN_FILE_FULL_STEM}_best.pkl"
        },
        !obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
            start: 1,
            saturate: 250,
            final_momentum: .7
        }
    ],
    save_path: "${PYLEARN2_TRAIN_FILE_FULL_STEM}.pkl",
    save_freq: 1
}