def exp_b(name):
# 5 appliances and normal cost func
# avg valid cost = 1.6496223211
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(
appliances=[
['fridge freezer', 'fridge', 'freezer'],
'hair straighteners',
'television',
'dish washer',
['washer dryer', 'washing machine']
],
skip_probability=0.7
))
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source,
loss_function=lambda x, t: mdn_nll(x, t).mean(),
))
net_dict_copy['layers_config'].extend([
{
'type': MixtureDensityLayer,
'num_units': source.n_outputs,
'num_components': 2
}
])
net = Net(**net_dict_copy)
return net
def exp_b(name):
# 5 appliances and normal cost func
# avg valid cost = 1.6496223211
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(
dict(appliances=[['fridge freezer', 'fridge', 'freezer'],
'hair straighteners', 'television', 'dish washer',
['washer dryer', 'washing machine']],
skip_probability=0.7))
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(
dict(
experiment_name=name,
source=source,
loss_function=lambda x, t: mdn_nll(x, t).mean(),
))
net_dict_copy['layers_config'].extend([{
'type': MixtureDensityLayer,
'num_units': source.n_outputs,
'num_components': 2
}])
net = Net(**net_dict_copy)
return net
def exp_b(name):
# 5 appliances and normal cost func
global source
# source_dict_copy = deepcopy(source_dict)
# source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(
dict(
experiment_name=name,
source=source,
loss_function=lambda x, t: mdn_nll(x, t).mean(),
))
net_dict_copy['layers_config'].extend([{
'type': MixtureDensityLayer,
'num_units': source.n_outputs,
'num_components': 2
}])
net = Net(**net_dict_copy)
return net
def exp_b(name):
# 5 appliances and normal cost func
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source,
loss_function=lambda x, t: mdn_nll(x, t).mean(),
))
net_dict_copy['layers_config'].extend([
{
'type': MixtureDensityLayer,
'num_units': source.n_outputs,
'num_components': 2
}
])
net = Net(**net_dict_copy)
return net
reshape_target_to_2D=True
# input_stats={'mean': np.array([ 0.05526326], dtype=np.float32),
# 'std': np.array([ 0.12636775], dtype=np.float32)},
# target_stats={
# 'mean': np.array([ 0.04066789, 0.01881946,
# 0.24639061, 0.17608672, 0.10273963],
# dtype=np.float32),
# 'std': np.array([ 0.11449792, 0.07338708,
# 0.26608968, 0.33463112, 0.21250485],
# dtype=np.float32)}
)
net_dict = dict(
save_plot_interval=SAVE_PLOT_INTERVAL,
loss_function=lambda x, t: mdn_nll(x, t).mean(),
# loss_function=lambda x, t: mse(x, t).mean(),
updates_func=momentum,
learning_rate=1e-03,
learning_rate_changes_by_iteration={
100: 5e-04,
500: 1e-04
# 1000: 5e-05,
# 2000: 1e-05,
# 3000: 5e-06,
# 4000: 1e-06,
# 10000: 5e-07,
# 50000: 1e-07
},
plotter=MDNPlotter
)
def callback(net, epoch):
net.source.reshape_target_to_2D = True
net.plotter = MDNPlotter(net)
net.generate_validation_data_and_set_shapes()
net.loss_function = lambda x, t: mdn_nll(x, t).mean()
net.learning_rate = 1e-05
def callback(net, epoch):
net.source.reshape_target_to_2D = True
net.plotter = MDNPlotter(net)
net.generate_validation_data_and_set_shapes()
net.loss_function = lambda x, t: mdn_nll(x, t).mean()
net.learning_rate = 1e-05
