reboot.yaml

gistfile1.txt

!obj:pylearn2.train.Train {
    dataset: &train !obj:pylearn2.datasets.mnist.MNIST {
        which_set: 'train',
        start: 0,
        stop: 50000
    },
    model: !obj:galatea.adversarial.AdversaryPair {
        generator: !obj:galatea.adversarial.Generator {
            mlp: !obj:pylearn2.models.mlp.MLP {
            layers: [
                     !obj:pylearn2.models.mlp.RectifiedLinear {
                         layer_name: 'h0',
                         dim: 1200,
                         irange: .05,
                         max_col_norm: 1.9365,
                     },
                     !obj:pylearn2.models.mlp.RectifiedLinear {
                         layer_name: 'h1',
                         dim: 1200,
                         irange: .05,
                         max_col_norm: 1.9365,
                     },
                     !obj:pylearn2.models.mlp.Sigmoid {
                         max_col_norm: 1.9365,
                         init_bias: !obj:pylearn2.models.dbm.init_sigmoid_bias_from_marginals { dataset: *train},
                         layer_name: 'y',
                         irange: .05,

                        output_space: !obj:pylearn2.space.Conv2DSpace {
                            shape: [28, 28],
                            num_channels: 1
                        }
                     }
                    ],
        }},
        discriminator: 
            !obj:pylearn2.models.mlp.MLP {
            

            input_space: !obj:pylearn2.space.Conv2DSpace {
                shape: [28, 28],
                num_channels: 1
            },
            layers: [
                     !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                         #W_lr_scale: .1,
                         #b_lr_scale: .1,
                         
                          layer_name: 'h0',
                          kernel_shape: [100, 100],
                          output_channels: 1,
                          pool_shape: [50, 50],
                          pool_stride: [20, 20],
                          max_kernel_norm: 1.9365,
                          irange: .005,
                          #max_col_norm: 1.9365,
                     },
                     !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                         #W_lr_scale: .1,
                         #b_lr_scale: .1,
                         layer_name: 'h1',
                         output_channels: 1,
                         kernel_shape: [100, 100],
                         pool_shape: [50, 50],
                         pool_stride: [20, 20],
                         max_kernel_norm: 1.9365,                       
                         irange: .005,
                     },
                     !obj:pylearn2.models.mlp.Sigmoid {
                         #W_lr_scale: .1,
                         #b_lr_scale: .1,
                         max_col_norm: 1.9365,
                         layer_name: 'y',
                         dim: 1,
                         irange: .005
                     }
                    ],
        },
    },
    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',
                              start: 50000,
                              stop:  60000
                          },
                'test'  : !obj:pylearn2.datasets.mnist.MNIST {
                              which_set: 'test',
                          }
            },
        cost: !obj:galatea.adversarial.AdversaryCost2 {
            scale_grads: 0,
            #target_scale: 1.,
            discriminator_default_input_include_prob: .5,
            discriminator_input_include_probs: {
                'h0': .8
            },
            discriminator_default_input_scale: 2.,
            discriminator_input_scales: {
                'h0': 1.25   
            }
            },
        #!obj:pylearn2.costs.mlp.dropout.Dropout {
        #    input_include_probs: { 'h0' : .8 },
        #    input_scales: { 'h0': 1. }
        #},
        #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
}