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!obj:pylearn2.train.Train {
dataset: &train !obj:research.code.pylearn2.datasets.timit.TIMIT {
which_set: 'train',
frame_length: &flen 160,
frames_per_example: &fpe 1,
samples_to_predict: &ylen 1,
n_next_phones: 1,
n_prev_phones: 1,
filter_fn: 'male_speakers'
#start: 0,
#stop: 100,
},
model: !obj:mlp_with_source.MLPWithSource {
batch_size: 512,
layers: [
!obj:mlp_with_source.CompositeLayerWithSource {
layer_name: 'c',
layers: [
!obj:pylearn2.models.mlp.RectifiedLinear {
layer_name: 'h1',
dim: 250,
irange: 0.05,
},
!obj:pylearn2.models.mlp.RectifiedLinear {
layer_name: 'h2',
dim: 250,
irange: 0.05,
},
],
},
!obj:pylearn2.models.mlp.RectifiedLinear {
layer_name: 'h3',
dim: 100,
irange: 0.05,
},
!obj:pylearn2.models.mlp.Linear {
layer_name: 'y',
dim: *ylen,
irange: 0.05,
},
],
input_space: !obj:pylearn2.space.CompositeSpace {
components: [
!obj:pylearn2.space.VectorSpace {
dim: *flen,
},
!obj:pylearn2.space.VectorSpace {
dim: 186,
},
],
},
input_source: ['features', 'phones'],
},
algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
learning_rate: .01,
monitoring_dataset: {
'train': *train,
'valid': !obj:research.code.pylearn2.datasets.timit.TIMIT {
which_set: 'valid',
frame_length: *flen,
frames_per_example: *fpe,
samples_to_predict: *ylen,
n_next_phones: 1,
n_prev_phones: 1,
filter_fn: 'male_speakers'
},
'test': !obj:research.code.pylearn2.datasets.timit.TIMIT {
which_set: 'test',
frame_length: *flen,
frames_per_example: *fpe,
samples_to_predict: *ylen,
n_next_phones: 1,
n_prev_phones: 1,
filter_fn: 'male_speakers'
},
},
cost: !obj:pylearn2.costs.mlp.Default {},
termination_criterion: !obj:pylearn2.termination_criteria.And {
criteria: [
!obj:pylearn2.termination_criteria.MonitorBased {
channel_name: "valid_objective",
prop_decrease: 0.0000000001,
N: 10
},
!obj:pylearn2.termination_criteria.EpochCounter {
max_epochs: 200
}
]
}
},
extensions: [
!obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
channel_name: 'valid_objective',
save_path: "ac160_ph3_rl2_malespkr.pkl"
}
]
}