<commit_before>!obj:pylearn2.train.Train {
    dataset: &train !obj:neukrill_net.dense_dataset.DensePNGDataset {
            settings_path: %(settings_path)s,
            run_settings: %(run_settings_path)s,
            training_set_mode: "train"
    },
    model: !obj:pylearn2.models.mlp.MLP {
        batch_size: &batch_size 128,
        layers: [
                 !obj:pylearn2.models.maxout.MaxoutConvC01B {
                     layer_name: 'h0',
                     pad: 4,
                     tied_b: 1,
                     W_lr_scale: .05,
                     b_lr_scale: .05,
                     num_channels: 96,
                     num_pieces: 2,
                     kernel_shape: [8, 8],
                     pool_shape: [4, 4],
                     pool_stride: [2, 2],
                     irange: .005,
                     max_kernel_norm: .9,
                     partial_sum: 49
                 },
                 !obj:pylearn2.models.maxout.MaxoutConvC01B {
                     layer_name: 'h1',
                     pad: 3,
                     tied_b: 1,
                     W_lr_scale: .05,
                     b_lr_scale: .05,
                     num_channels: 192,
                     num_pieces: 2,
                     kernel_shape: [8, 8],
                     pool_shape: [4, 4],
                     pool_stride: [2, 2],
                     irange: .005,
                     max_kernel_norm: 1.9365,
                     partial_sum: 23
                 },
                 !obj:pylearn2.models.maxout.MaxoutConvC01B {
                     pad: 3,
                     layer_name: 'h2',
                     tied_b: 1,
                     W_lr_scale: .05,
                     b_lr_scale: .05,
                     num_channels: 192,
                     num_pieces: 2,
                     kernel_shape: [5, 5],
                     pool_shape: [2, 2],
                     pool_stride: [2, 2],
                     irange: .005,
                     max_kernel_norm: 1.9365
                 },
                 !obj:pylearn2.models.maxout.Maxout {
                    layer_name: 'h3',
                    irange: .005,
                    num_units: 500,
                    num_pieces: 5,
                    max_col_norm: 1.9
                 },
                 !obj:pylearn2.models.mlp.Softmax {
                     max_col_norm: 1.9365,
                     layer_name: 'y',
                     n_classes: %(n_classes)i,
                     irange: .005
                 }
                ],
        input_space: !obj:pylearn2.space.Conv2DSpace {
            shape: %(final_shape)s,
            num_channels: 1,
            axes: ['c', 0, 1, 'b']
        }
    },
    algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
        learning_rate: .17,
        learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
            init_momentum: .5
            },
        train_iteration_mode: 'even_shuffled_sequential',
        monitor_iteration_mode: 'even_sequential',
        monitoring_dataset:
            {
                'test' : !obj:neukrill_net.dense_dataset.DensePNGDataset  {
                                settings_path: %(settings_path)s,
                                run_settings: %(run_settings_path)s,
                                training_set_mode: "test"
                }
            },
        cost: !obj:pylearn2.costs.mlp.dropout.Dropout {
            input_include_probs: { 'h0' : .8 },
            input_scales: { 'h0' : 1. }
        },
        termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {
            max_epochs: 474 
        }
    },
    extensions: [
        !obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
            start: 1,
            saturate: 250,
            final_momentum: .65
        },
        !obj:pylearn2.training_algorithms.sgd.LinearDecayOverEpoch {
            start: 1,
            saturate: 500,
            decay_factor: .01
        },
        !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
            channel_name: test_y_misclass,
            save_path: '%(save_path)s'
        }
    ]
}
<commit_msg>Changed monitoring data set and objective to valid<commit_after>!obj:pylearn2.train.Train {
    dataset: &train !obj:neukrill_net.dense_dataset.DensePNGDataset {
            settings_path: %(settings_path)s,
            run_settings: %(run_settings_path)s,
            training_set_mode: "train"
    },
    model: !obj:pylearn2.models.mlp.MLP {
        batch_size: &batch_size 128,
        layers: [
                 !obj:pylearn2.models.maxout.MaxoutConvC01B {
                     layer_name: 'h0',
                     pad: 4,
                     tied_b: 1,
                     W_lr_scale: .05,
                     b_lr_scale: .05,
                     num_channels: 96,
                     num_pieces: 2,
                     kernel_shape: [8, 8],
                     pool_shape: [4, 4],
                     pool_stride: [2, 2],
                     irange: .005,
                     max_kernel_norm: .9,
                     partial_sum: 49
                 },
                 !obj:pylearn2.models.maxout.MaxoutConvC01B {
                     layer_name: 'h1',
                     pad: 3,
                     tied_b: 1,
                     W_lr_scale: .05,
                     b_lr_scale: .05,
                     num_channels: 192,
                     num_pieces: 2,
                     kernel_shape: [8, 8],
                     pool_shape: [4, 4],
                     pool_stride: [2, 2],
                     irange: .005,
                     max_kernel_norm: 1.9365,
                     partial_sum: 23
                 },
                 !obj:pylearn2.models.maxout.MaxoutConvC01B {
                     pad: 3,
                     layer_name: 'h2',
                     tied_b: 1,
                     W_lr_scale: .05,
                     b_lr_scale: .05,
                     num_channels: 192,
                     num_pieces: 2,
                     kernel_shape: [5, 5],
                     pool_shape: [2, 2],
                     pool_stride: [2, 2],
                     irange: .005,
                     max_kernel_norm: 1.9365
                 },
                 !obj:pylearn2.models.maxout.Maxout {
                    layer_name: 'h3',
                    irange: .005,
                    num_units: 500,
                    num_pieces: 5,
                    max_col_norm: 1.9
                 },
                 !obj:pylearn2.models.mlp.Softmax {
                     max_col_norm: 1.9365,
                     layer_name: 'y',
                     n_classes: %(n_classes)i,
                     irange: .005
                 }
                ],
        input_space: !obj:pylearn2.space.Conv2DSpace {
            shape: %(final_shape)s,
            num_channels: 1,
            axes: ['c', 0, 1, 'b']
        }
    },
    algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
        learning_rate: .17,
        learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
            init_momentum: .5
            },
        train_iteration_mode: 'even_shuffled_sequential',
        monitor_iteration_mode: 'even_sequential',
        monitoring_dataset:
            {
                'train' : *train,
                'valid' : !obj:neukrill_net.dense_dataset.DensePNGDataset  {
                                settings_path: %(settings_path)s,
                                run_settings: %(run_settings_path)s,
                                training_set_mode: "validation"
                }
            },
        cost: !obj:pylearn2.costs.mlp.dropout.Dropout {
            input_include_probs: { 'h0' : .8 },
            input_scales: { 'h0' : 1. }
        },
        termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {
            max_epochs: 474 
        }
    },
    extensions: [
        !obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
            start: 1,
            saturate: 250,
            final_momentum: .65
        },
        !obj:pylearn2.training_algorithms.sgd.LinearDecayOverEpoch {
            start: 1,
            saturate: 500,
            decay_factor: .01
        },
        !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
            channel_name: valid_objective,
            save_path: '%(save_path)s'
        }
    ]
}

{'fraction_non_alphanumeric': 0.09282849728709601, 'fraction_numerical': 0.03349846661948573, 'mean_word_length': 1.0665366804776992, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7529359', 'n_tokens_mistral': 2731, 'n_tokens_neox': 2438, 'n_words': 385}