'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()