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()