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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" | |
} | |
] | |
} |