'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': Conv1DLayer,
'num_filters': 80,
'filter_length': 5,
'stride': 5,
'nonlinearity': sigmoid
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': LSTMLayer,
'num_units': 80,
'W_in_to_cell': Uniform(5)
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': sigmoid
}
]
)
net.print_net()
net.compile()
net.fit()
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': Conv1DLayer,
'num_filters': 80,
'filter_length': 5,
'stride': 5,
'nonlinearity': sigmoid
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': LSTMLayer,
'num_units': 80,
'W_in_to_cell': Uniform(5)
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': sigmoid
}
])
net.print_net()
net.compile()
net.fit()
from __future__ import print_function, division
from neuralnilm import Net, ToySource
from lasagne.nonlinearities import sigmoid
source = ToySource(seq_length=300, n_seq_per_batch=30)
net = Net(source=source,
n_cells_per_hidden_layer=[10],
output_nonlinearity=sigmoid,
learning_rate=1e-1)
net.fit(n_iterations=1000)
net.plot_costs()
net.plot_estimates()
from __future__ import print_function, division
from neuralnilm import Net, RealApplianceSource
from lasagne.nonlinearities import sigmoid
source = RealApplianceSource(
'/data/dk3810/ukdale.h5',
['fridge freezer', 'hair straighteners', 'television'],
max_input_power=1000, max_output_power=300,
window=("2013-06-01", "2014-06-01")
)
net = Net(
source=source,
n_cells_per_hidden_layer=[50,50,50],
output_nonlinearity=sigmoid,
learning_rate=1e-1,
n_dense_cells_per_layer=50
)
net.fit(n_iterations=1600)
net.plot_costs()
net.plot_estimates()
from __future__ import print_function, division
from neuralnilm import Net, RealApplianceSource
from lasagne.nonlinearities import sigmoid
source = RealApplianceSource(
'/data/dk3810/ukdale.h5',
['fridge freezer', 'hair straighteners', 'television'],
max_input_power=1000,
max_output_power=300,
window=("2013-06-01", "2014-06-01"))
net = Net(source=source,
n_cells_per_hidden_layer=[50, 50, 50],
output_nonlinearity=sigmoid,
learning_rate=1e-1,
n_dense_cells_per_layer=50)
net.fit(n_iterations=1600)
net.plot_costs()
net.plot_estimates()
from __future__ import print_function, division
from neuralnilm import Net, ToySource
from lasagne.nonlinearities import sigmoid
source = ToySource(
seq_length=300,
n_seq_per_batch=30
)
net = Net(
source=source,
n_cells_per_hidden_layer=[10],
output_nonlinearity=sigmoid,
learning_rate=1e-1
)
net.fit(n_iterations=1000)
net.plot_costs()
net.plot_estimates()
