def exp_g(name):
global source
try:
a = source
except NameError:
source = RealApplianceSource(**source_dict)
source.lag = 5
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
net_dict_copy['layers_config'] = [
{
'type': LSTMLayer,
'num_units': 200,
'gradient_steps': GRADIENT_STEPS,
'peepholes': False,
'W_in_to_cell': Normal(std=1.)
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': None,
'W': Normal(std=(1/sqrt(200)))
}
]
net = Net(**net_dict_copy)
return net
def exp_b(name):
global source
try:
a = source
except NameError:
source = RealApplianceSource(**source_dict)
source.lag = 5
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
net_dict_copy['layers_config'].append({
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': None,
'W': Normal(std=(1 / sqrt(100)))
})
net = Net(**net_dict_copy)
return net
def exp_b(name):
global source
try:
a = source
except NameError:
source = RealApplianceSource(**source_dict)
source.lag = 5
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
net_dict_copy['layers_config'].append(
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': None,
'W': Normal(std=(1/sqrt(100)))
}
)
net = Net(**net_dict_copy)
return net
def exp_g(name):
global source
try:
a = source
except NameError:
source = RealApplianceSource(**source_dict)
source.lag = 5
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
net_dict_copy['layers_config'] = [{
'type': LSTMLayer,
'num_units': 200,
'gradient_steps': GRADIENT_STEPS,
'peepholes': False,
'W_in_to_cell': Normal(std=1.)
}, {
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': None,
'W': Normal(std=(1 / sqrt(200)))
}]
net = Net(**net_dict_copy)
return net
