val_length = len(dset) - train_length
train_set, val_set = random_split(dset, [train_length, val_length])
# Prepare dataloaders
train_loader = DataLoader(train_set,
batch_size=5000,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_set,
batch_size=10000,
shuffle=False,
num_workers=4)
# Prepare trainer
#trainer = Trainer(cpic(), CrossEntropyLoss(), lr=0.1)
trainer = Trainer(polarity(), CrossEntropyLoss(), lr=0.01)
# Train model over training dataset
trainer.train(train_loader, val_loader, epochs=50, print_freq=100)
#resume='checkpoint_best.pth.tar')
# Save training results to disk
trainer.results(path='scsn_polarity_results.pth.tar')
# Validate saved model
results = torch.load('scsn_polarity_results.pth.tar')
#model = cpic()
model = polarity()
model.load_state_dict(results['model'])
trainer = Trainer(model, CrossEntropyLoss(), lr=0.1)
trainer.validate(val_loader, print_freq=100)
train_set, val_set = random_split(dset, [train_length, val_length])
# Prepare dataloaders
train_loader = DataLoader(train_set,
batch_size=20,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_set,
batch_size=20,
shuffle=False,
num_workers=4)
# Prepare trainer
#trainer = Trainer(cpic(), CrossEntropyLoss(), lr=0.1)
#trainer = Trainer(polarity(), CrossEntropyLoss(), lr=0.1)
trainer = Trainer(focal_mechanism(), CrossEntropyLoss(), lr=0.0001)
# Train model over training dataset
trainer.train(train_loader, val_loader, epochs=100, print_freq=10)
#resume='checkpoint_best.pth.tar')
# Save training results to disk
trainer.results(path='taiwan_focal_mechanism_results.pth.tar')
# Validate saved model
results = torch.load('taiwan_focal_mechanism_results.pth.tar')
#model = cpic()
#model = polarity()
model = focal_mechanism()
model.load_state_dict(results['model'])
trainer = Trainer(model, CrossEntropyLoss(), lr=0.1)
sample_transform=waveform_transform,
target_transform=target_transform)
train_length = int(len(dset) * 0.8)
val_length = len(dset) - train_length
train_set, val_set = random_split(dset, [train_length, val_length])
# Uncomment for chronological split
# train_set = Subset(dset, range(train_length))
# val_set = Subset(dset, range(train_length, len(dset)))
# Dataloaders
train_loader = DataLoader(train_set,
batch_size=512,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_set,
batch_size=10000,
shuffle=False,
num_workers=8)
# CNN model
model = Cpic40()
# Trainer
trainer = Trainer(model, CrossEntropyLoss(), lr=0.1)
# Training process
trainer.train(train_loader, val_loader, epochs=200, print_freq=1000)
# Save training results to disk
trainer.results(path='ok_results.pth.tar')
train_length = int(len(dset) * 0.8)
val_length = len(dset) - train_length
train_set, val_set = random_split(dset, [train_length, val_length])
# Prepare dataloaders
train_loader = DataLoader(train_set,
batch_size=100,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_set,
batch_size=1000,
shuffle=False,
num_workers=8)
# Prepare trainer
trainer = Trainer(cpic(), CrossEntropyLoss(), lr=0.1)
# Train model over training dataset
trainer.train(train_loader, val_loader, epochs=100, print_freq=100)
#resume='checkpoint_best.pth.tar')
# Save training results to disk
trainer.results(path='wenchuan_results.pth.tar')
# Validate saved model
results = torch.load('wenchuan_results.pth.tar')
model = cpic()
model.load_state_dict(results['model'])
trainer = Trainer(model, CrossEntropyLoss(), lr=0.1)
trainer.validate(val_loader, print_freq=100)