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()
