Test tied biases for ConvRectifiedLinear

test_conv_relu.yaml

!obj:pylearn2.train.Train {
    dataset: &train !obj:pylearn2.datasets.mnist.MNIST {
        which_set: 'train',
        one_hot: 1,
        start: 0,
        stop: 50000
    },
    model: !obj:pylearn2.models.mlp.MLP {
        batch_size: 128,
        layers: [
                 !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                     layer_name: 'h0',
                     output_channels: 64,
                     kernel_shape: [8, 8],
                     pool_shape: [4, 4],
                     tied_b: True,
                     pool_stride: [2, 2],
                     irange: .005,
                     max_kernel_norm: .9,
                 },
                 !obj:pylearn2.models.mlp.Softmax {
                     max_col_norm: 1.9365,
                     layer_name: 'y',
                     n_classes: 10,
                     irange: .005
                 }
                ],
        input_space: !obj:pylearn2.space.Conv2DSpace {
            shape: [28, 28],
            num_channels: 1,
            axes: ['c', 0, 1, 'b'],
        },
    },
    algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
        learning_rate: .05,
        learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
            init_momentum: .5,
        },
        monitoring_dataset:
            {
                '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
                          },

            },
        termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {
            max_epochs: 100
        },
        update_callbacks: !obj:pylearn2.training_algorithms.sgd.ExponentialDecay {
            decay_factor: 1.00004,
            min_lr: .000001
        },
    },
    extensions: [
        !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
             channel_name: 'valid_y_misclass',
             save_path: "${PYLEARN2_TRAIN_DIR}relu.pkl"
        },
        !obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
            start: 1,
            saturate: 50,
            final_momentum: .99
        }
    ]
}