def exp_a(name, target_appliance, seq_length):
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(
dict(target_appliance=target_appliance,
logger=logging.getLogger(name),
seq_length=seq_length))
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 256,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_e(name):
# conv then pool
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(logger=logging.getLogger(name)))
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy["layers_config"] = [
{"type": DimshuffleLayer, "pattern": (0, 2, 1)}, # (batch, features, time)
{
"type": Conv1DLayer, # convolve over the time axis
"num_filters": NUM_FILTERS,
"filter_size": 4,
"stride": 1,
"nonlinearity": None,
"border_mode": "same",
},
{
"type": FeaturePoolLayer,
"pool_size": 2, # number of feature maps to be pooled together
"axis": 2, # pool over the time axis
"pool_function": T.max,
},
{"type": DimshuffleLayer, "pattern": (0, 2, 1)}, # back to (batch, time, features)
{"type": DenseLayer, "num_units": 512, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 256, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 128, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": target_seq_length, "nonlinearity": None},
]
net = Net(**net_dict_copy)
return net
def exp_a(name):
# no conv
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(logger=logging.getLogger(name)))
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy['layers_config'] = [{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
}, {
'type': DenseLayer,
'num_units': 256,
'nonlinearity': rectify
}, {
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
}, {
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}]
net = Net(**net_dict_copy)
return net
def exp_a(name, target_appliance, seq_length):
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(
dict(target_appliance=target_appliance, logger=logging.getLogger(name), seq_length=seq_length)
)
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy["layers_config"] = [
{"type": DimshuffleLayer, "pattern": (0, 2, 1)}, # (batch, features, time)
{
"type": Conv1DLayer, # convolve over the time axis
"num_filters": NUM_FILTERS,
"filter_size": 4,
"stride": 1,
"nonlinearity": None,
"border_mode": "valid",
},
{"type": DimshuffleLayer, "pattern": (0, 2, 1)}, # back to (batch, time, features)
{"type": DenseLayer, "num_units": 512, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 256, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 128, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": target_seq_length, "nonlinearity": sigmoid},
]
net = Net(**net_dict_copy)
return net
def exp_e(name):
# conv then pool
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(logger=logging.getLogger(name)))
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'same'
},
{
'type': FeaturePoolLayer,
'pool_size': 2, # number of feature maps to be pooled together
'axis': 2, # pool over the time axis
'pool_function': T.max
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 256,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_a(name, target_appliance, seq_length):
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(
target_appliance=target_appliance,
logger=logging.getLogger(name),
seq_length=seq_length
))
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 256,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_a(name):
# no conv
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(logger=logging.getLogger(name)))
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy["layers_config"] = [
{"type": DenseLayer, "num_units": 512, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 256, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 128, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": target_seq_length, "nonlinearity": None},
]
net = Net(**net_dict_copy)
return net
def exp_a(name, target_appliance, seq_length):
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(
target_appliance=target_appliance,
logger=logging.getLogger(name),
seq_length=seq_length
))
source = SameLocation(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy['layers_config'] = [
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 256,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': sigmoid
}
]
net = Net(**net_dict_copy)
return net
