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info.py
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info.py
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import torch
import fdob
import fdob.model
import fdob.processing
hparam = {
"sgd": {
"optimizer": torch.optim.SGD,
"n_params": 1,
"param_names": ["lr"],
"lb": [-4],
"ub": [0],
"reversed": [False],
},
"momentum": {
"optimizer": torch.optim.SGD,
"n_params": 2,
"param_names": ["lr", "momentum"],
"lb": [-4, -3],
"ub": [0, 0],
"reversed": [False, True],
},
"rmsprop": {
"optimizer": torch.optim.RMSprop,
"n_params": 4,
"param_names": ["lr", "momentum", "alpha", "eps"],
"lb": [-4, -3, -3, -10],
"ub": [-1, 0, 0, 0],
"reversed": [False, True, True, False],
},
"adam": {
"optimizer": torch.optim.Adam,
"n_params": 4,
"param_names": ["lr", "beta1", "beta2", "eps"],
"lb": [-4, -3, -4, -10],
"ub": [-1, 0, -1, 0],
"reversed": [False, True, True, False],
},
}
model = {
"stimcnn": {
"model": fdob.model.STIMCNN,
"sample_length": 784,
"tf": [fdob.processing.NpToTensor(), fdob.processing.ToImage(28, 28, 1)],
},
"stftcnn": {
"model": fdob.model.STFTCNN,
"sample_length": 512,
"tf": [
fdob.processing.STFT(window_length=128, noverlap=120, nfft=128),
fdob.processing.Resize(64, 64),
fdob.processing.NpToTensor(),
fdob.processing.ToImage(64, 64, 1),
],
},
"wdcnn": {
"model": fdob.model.WDCNN,
"sample_length": 2048,
"tf": [fdob.processing.NpToTensor(), fdob.processing.ToSignal()],
},
"wdcnnrnn": {
"model": fdob.model.WDCNNRNN,
"sample_length": 4096,
"tf": [fdob.processing.NpToTensor(), fdob.processing.ToSignal()],
},
"ticnn": {
"model": fdob.model.TICNN,
"sample_length": 2048,
"tf": [fdob.processing.NpToTensor(), fdob.processing.ToSignal()],
},
"dcn": {
"model": fdob.model.DCN,
"sample_length": 784,
"tf": [fdob.processing.NpToTensor(), fdob.processing.ToSignal()],
},
"srdcnn": {
"model": fdob.model.SRDCNN,
"sample_length": 1024,
"tf": [fdob.processing.NpToTensor(), fdob.processing.ToSignal()],
},
}