def run(root_experiment_name):
activations = load_nilmtk_activations(appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS)
for get_net in [ae]:
for target_appliance in ['kettle']:
pipeline = get_pipeline(target_appliance, activations)
# Build net
batch = pipeline.get_batch()
net = get_net(batch)
# Trainer
trainer = Trainer(
net=net,
data_pipeline=pipeline,
experiment_id=[
root_experiment_name, get_net.__name__, target_appliance
],
metrics=Metrics(
state_boundaries=[2]), # was 3 up until 230000ish
learning_rates={0: 1E-2},
repeat_callbacks=[(5000, Trainer.validate),
(5000, Trainer.save_params),
(5000, Trainer.plot_estimates)])
report = trainer.submit_report()
print(report)
# Run!
trainer.fit(None)
def run(root_experiment_name):
activations = load_nilmtk_activations(appliances=APPLIANCES,
filename=NILMTK_FILENAME,
sample_period=SAMPLE_PERIOD,
windows=WINDOWS)
for get_net in [ae]:
for target_appliance in APPLIANCES[2:]:
print("Starting training for net {}, appliance {}.".format(
get_net.__name__, target_appliance))
pipeline = get_pipeline(target_appliance, activations)
# Build net
batch = pipeline.get_batch()
net = get_net(batch)
# Trainer
trainer = Trainer(net=net,
data_pipeline=pipeline,
experiment_id=[
root_experiment_name, get_net.__name__,
target_appliance
],
metrics=Metrics(state_boundaries=[2.5]),
learning_rates={
0: 1e-2,
200000: 1e-3
},
repeat_callbacks=[(25000, Trainer.validate),
(25000, Trainer.save_params),
(25000, Trainer.plot_estimates)
])
report = trainer.submit_report()
print(report)
# Run!
trainer.fit(300000)