def exp_a(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
source.subsample_target = 4
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
net_dict_copy['layers_config'] = [
{
'type': BidirectionalRecurrentLayer,
'num_units': 25,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1.),
'nonlinearity': tanh
},
{
'type': FeaturePoolLayer,
'ds': 2, # number of feature maps to be pooled together
'axis': 1, # pool over the time axis
'pool_function': T.mean
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 10,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1/sqrt(25)),
'nonlinearity': tanh
},
{
'type': FeaturePoolLayer,
'ds': 2, # number of feature maps to be pooled together
'axis': 1, # pool over the time axis
'pool_function': T.mean
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 5,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1/sqrt(10)),
'nonlinearity': tanh
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 10,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1/sqrt(5)),
'nonlinearity': tanh
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 25,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1/sqrt(10)),
'nonlinearity': tanh
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': None,
'W': Normal(std=(1/sqrt(25)))
}
]
net = Net(**net_dict_copy)
return net
def exp_a(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
source.subsample_target = 4
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
net_dict_copy['layers_config'] = [
{
'type': BidirectionalRecurrentLayer,
'num_units': 25,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1.),
'nonlinearity': tanh
},
{
'type': FeaturePoolLayer,
'ds': 2, # number of feature maps to be pooled together
'axis': 1, # pool over the time axis
'pool_function': T.mean
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 10,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1 / sqrt(25)),
'nonlinearity': tanh
},
{
'type': FeaturePoolLayer,
'ds': 2, # number of feature maps to be pooled together
'axis': 1, # pool over the time axis
'pool_function': T.mean
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 5,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1 / sqrt(10)),
'nonlinearity': tanh
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 10,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1 / sqrt(5)),
'nonlinearity': tanh
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 25,
'gradient_steps': GRADIENT_STEPS,
'W_in_to_hid': Normal(std=1 / sqrt(10)),
'nonlinearity': tanh
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': None,
'W': Normal(std=(1 / sqrt(25)))
}
]
net = Net(**net_dict_copy)
return net
