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!obj:pylearn2.train.Train {
dataset: &train !obj:research.code.pylearn2.datasets.timit_sparse.TIMITSparse {
which_set: 'valid',
frame_length: &flen 1600,
overlap: &olap 1400,
frames_per_example: &fpe 1,
n_next_phones: 1,
n_prev_phones: 1,
start: 0,
stop: 100,
},
model: !obj:mlp_with_source.MLPWithSource {
batch_size: 256,
layers: [
!obj:mlp_with_source.CompositeLayerWithSource {
layer_name: 'c',
layers: [
!obj:pylearn2.models.mlp.Sigmoid {
layer_name: 'h1',
dim: 2000,
irange: 0.1,
},
!obj:pylearn2.models.mlp.Sigmoid {
layer_name: 'h2',
dim: 250,
irange: 0.1,
},
],
},
!obj:pylearn2.models.mlp.Sigmoid {
layer_name: 'h3',
dim: 1536,
irange: 0.1,
},
!obj:conditional_gater.SmoothTimesStochastic {
dim: 1536,
hidden_dim: 900,
layer_name: 'y',
hidden_activation: 'sigmoid',
sparsity_target: 0.10,
sparsity_cost_coeff: 1.0,
irange: [0.1,0.1,0.1],
},
],
input_space: !obj:pylearn2.space.CompositeSpace {
components: [
!obj:pylearn2.space.VectorSpace {
dim: 1536,
},
!obj:pylearn2.space.VectorSpace {
dim: 186,
},
],
},
input_source: ['features', 'phones'],
},
algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
theano_function_mode: !obj:pylearn2.devtools.nan_guard.NanGuardMode {
nan_is_error: True,
inf_is_error: True
},
learning_rate: .01,
monitoring_dataset: {
'train': *train,
'valid': !obj:research.code.pylearn2.datasets.timit_sparse.TIMITSparse {
which_set: 'valid',
frame_length: *flen,
overlap: *olap,
frames_per_example: *fpe,
n_next_phones: 1,
n_prev_phones: 1,
start: 101,
stop: 110,
},
'test': !obj:research.code.pylearn2.datasets.timit_sparse.TIMITSparse {
which_set: 'valid',
frame_length: *flen,
overlap: *olap,
frames_per_example: *fpe,
n_next_phones: 1,
n_prev_phones: 1,
start: 111,
stop: 130,
},
},
cost: !obj:conditional_gater.Conditional1Cost {},
termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {
max_epochs: 2000
}
},
extensions: [
!obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
channel_name: 'valid_objective',
save_path: "sp1600_conditional_overlap.pkl"
}
]
}