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 = RandomSegmentsInMemory(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DenseLayer,
'num_units': seq_length,
'nonlinearity': rectify
},
{
'type': PolygonOutputLayer,
'num_units': 2,
'seq_length': seq_length
},
{
'type': ReshapeLayer,
'shape': source.output_shape()
}
]
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 = RandomSegmentsInMemory(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DenseLayer,
'num_units': seq_length,
'nonlinearity': rectify
},
{
'type': PolygonOutputLayer,
'num_units': 2,
'seq_length': seq_length
},
{
'type': ReshapeLayer,
'shape': source.output_shape()
}
]
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 = RandomSegmentsInMemory(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
NUM_FILTERS = 4
target_seq_length = seq_length // source.subsample_target
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'label': 'conv0',
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_length': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'label': 'dense0',
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'label': 'dense2',
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': (target_seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_f(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RandomSegmentsInMemory(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 4
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'label': 'conv0',
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_length': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'label': 'dense0',
'type': DenseLayer,
'num_units': (SEQ_LENGTH - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': ReshapeLayer,
'shape': (N_SEQ_PER_BATCH, SEQ_LENGTH - 3, NUM_FILTERS)
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': DeConv1DLayer,
'num_output_channels': 1,
'filter_length': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'full'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
}
]
net = Net(**net_dict_copy)
return net
def exp_g(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RandomSegmentsInMemory(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
NUM_FILTERS = 4
net_dict_copy['layers_config'] = [{
'label': 'dense0',
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
}, {
'label': 'dense1',
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
}, {
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}]
net = Net(**net_dict_copy)
return net