Python neuralnilm.Net类代码示例

def exp_a(name):
    # ReLU hidden layers
    # linear output
    # output one appliance
    # 0% skip prob for first appliance
    # 100% skip prob for other appliances
    # input is diff
    global source
    source_dict_copy = deepcopy(source_dict)
    source = RealApplianceSource(**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': BidirectionalRecurrentLayer,
            'num_units': 50,
            'W_in_to_hid': Normal(std=1),
            'W_hid_to_hid': Identity(scale=0.9),
            'nonlinearity': rectify,
            'learn_init': False, 
            'precompute_input': True
        },
        {
            'type': DenseLayer,
            'num_units': source.n_outputs,
            'nonlinearity': None,
            'W': Normal(std=1/sqrt(50))
        }
    ]
    net = Net(**net_dict_copy)
    net.load_params(5000)
    return net

def exp_c(name):
    global source
    MAX_TARGET_POWER = 200
    source_dict_copy = deepcopy(source_dict)
    source_dict_copy.update(dict(
        logger=logging.getLogger(name),
        appliances=[
            'HTPC',
            'dish washer',
            ['fridge freezer', 'fridge', 'freezer'],
            ['washer dryer', 'washing machine'],
            'kettle'
        ],
        max_appliance_powers=[MAX_TARGET_POWER, 2500, 300, 2400, 2600],
        on_power_thresholds=[5] * 5,
        min_on_durations=[60, 1800, 60, 1800, 30],
        min_off_durations=[12, 1800, 12, 600, 1],
        seq_length=2048
    ))
    source = RealApplianceSource(**source_dict_copy)
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=source,
        plotter=StartEndMeanPlotter(
            n_seq_to_plot=32, max_target_power=MAX_TARGET_POWER),
        learning_rate_changes_by_iteration={
            150000: 1e-4,
            275000: 1e-5
        }
    ))
    net = Net(**net_dict_copy)
    net.load_params(146758)
    return net

def exp_a(name):
    # 5 appliances
    global source
    source_dict_copy = deepcopy(source_dict)
    source_dict_copy.update(dict(
        appliances=[
            ['fridge freezer', 'fridge', 'freezer'], 
            'hair straighteners', 
            'television',
            'dish washer',
            ['washer dryer', 'washing machine']
        ],
        skip_probability=0.7
    ))
    source = RealApplianceSource(**source_dict_copy)
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=source
    ))
    net_dict_copy['layers_config'].extend([
        {
            'type': MixtureDensityLayer,
            'num_units': source.n_outputs,
            'num_components': 2
        }
    ])
    net = Net(**net_dict_copy)
    net.load_params(iteration=4000)
    return net

def exp_a(name):
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=multi_source,
        plotter=StartEndMeanPlotter(
            n_seq_to_plot=32, max_target_power=MAX_TARGET_POWER)
    ))
    net = Net(**net_dict_copy)
    net.load_params(350000)
    return net

def exp_c(name):
    global source
    source_dict_copy = deepcopy(source_dict)
    source_dict_copy['random_window'] = 256
    source = RealApplianceSource(**source_dict_copy)
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=source,
        learning_rate=1e-5
    ))
    N = 512 * 8
    output_shape = source.output_shape_after_processing()
    net_dict_copy['layers_config'] = [
        {
            'type': DenseLayer,
            'num_units': N * 2,
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': N,
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': N // 2,
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': N // 4,
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': output_shape[1] * output_shape[2],
            'nonlinearity': sigmoid
        }
    ]
    net = Net(**net_dict_copy)
    net.load_params(30000)
    return net

def exp_a(name):
    global source
    source_dict_copy = deepcopy(source_dict)
    source = RealApplianceSource(**source_dict_copy)
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=source
    ))
    net_dict_copy['layers_config'].extend([
        {
            'type': DenseLayer,
            'num_units': source.n_outputs,
            'nonlinearity': T.nnet.softplus
        }
    ])
    net = Net(**net_dict_copy)
    net.load_params(1000)
    return net

def exp_a(name):
    # 5 appliances
    global source
    source_dict_copy = deepcopy(source_dict)
    source = RealApplianceSource(**source_dict_copy)
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=source
    ))
    net_dict_copy['layers_config'].extend([
        {
            'type': MixtureDensityLayer,
            'num_units': source.n_outputs,
            'num_components': 2
        }
    ])
    net = Net(**net_dict_copy)
    net.load_params(397)
    return net

def exp_a(name):
    global source
    source_dict_copy = deepcopy(source_dict)
    source = RealApplianceSource(**source_dict_copy)
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=source
    ))
    N = 50
    net_dict_copy['layers_config'] = [
        {
            'type': BidirectionalRecurrentLayer,
            'num_units': N,
            'gradient_steps': GRADIENT_STEPS,
            'W_in_to_hid': Normal(std=1.),
            'nonlinearity': tanh
        },
        {
            'type': FeaturePoolLayer,
            'ds': 4, # number of feature maps to be pooled together
            'axis': 1, # pool over the time axis
            'pool_function': T.max
        },
        {
            'type': BidirectionalRecurrentLayer,
            'num_units': N,
            'gradient_steps': GRADIENT_STEPS,
            'W_in_to_hid': Normal(std=1/sqrt(N)),
            'nonlinearity': tanh
        },
        {
            'type': MixtureDensityLayer,
            'num_units': source.n_outputs,
            'num_components': 2
        }
    ]
    net = Net(**net_dict_copy)
    net.load_params(iteration=5000)
    return net

def exp_a(name):
    global source
    # source_dict_copy = deepcopy(source_dict)
    # source = RealApplianceSource(**source_dict_copy)
    net_dict_copy = deepcopy(net_dict)
    net_dict_copy.update(dict(
        experiment_name=name,
        source=source
    ))
    N = 512
    output_shape = source.output_shape_after_processing()
    net_dict_copy['layers_config'] = [
        {
            'type': DenseLayer,
            'num_units': N,
            'W': Normal(std=1/sqrt(N)),
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': N // 2,
            'W': Normal(std=1/sqrt(N)),
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': N // 4,
            'W': Normal(std=1/sqrt(N // 2)),
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': output_shape[1] * output_shape[2],
            'W': Normal(std=1/sqrt(N // 4)),
            'nonlinearity': T.nnet.softplus
        }
    ]
    net = Net(**net_dict_copy)
    net.load_params(25000)
    return net

def exp_a(name):
    global source
    source_dict_copy = deepcopy(source_dict)
    source_dict_copy.update(dict(logger=logging.getLogger(name)))
    source = RealApplianceSource(**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": DimshuffleLayer, "pattern": (0, 2, 1)},  # (batch, features, time)
        {"type": PadLayer, "width": 4},
        {
            "type": Conv1DLayer,  # convolve over the time axis
            "num_filters": 16,
            "filter_size": 4,
            "stride": 1,
            "nonlinearity": None,
            "border_mode": "valid",
        },
        {
            "type": Conv1DLayer,  # convolve over the time axis
            "num_filters": 16,
            "filter_size": 4,
            "stride": 1,
            "nonlinearity": None,
            "border_mode": "valid",
        },
        {"type": DimshuffleLayer, "pattern": (0, 2, 1), "label": "dimshuffle3"},  # back to (batch, time, features)
        {"type": DenseLayer, "num_units": 512 * 16, "nonlinearity": rectify, "label": "dense0"},
        {"type": DenseLayer, "num_units": 512 * 8, "nonlinearity": rectify, "label": "dense1"},
        {"type": DenseLayer, "num_units": 512 * 4, "nonlinearity": rectify, "label": "dense2"},
        {"type": DenseLayer, "num_units": 512, "nonlinearity": rectify},
        {"type": DenseLayer, "num_units": 3, "nonlinearity": None},
    ]
    net = Net(**net_dict_copy)
    net.load_params(300000)
    return net

def get_net(appliance, architecture):
    """
    Parameters
    ----------
    appliance : string
    architecture : {'rnn', 'ae', 'rectangles'}
    """
    NET_DICTS = {
        'rnn': net_dict_rnn,
        'ae': net_dict_ae,
        'rectangles': net_dict_rectangles
    }
    net_dict_func = NET_DICTS[architecture]
    source = get_source(
        appliance,
        logger,
        target_is_start_and_end_and_mean=(architecture == 'rectangles'),
        is_rnn=(architecture == 'rnn'),
        window_per_building={  # just load a tiny bit of data. Won't be used.
            1: ("2013-04-12", "2013-05-12"),
            2: ("2013-05-22", "2013-06-22"),
            3: ("2013-02-27", "2013-03-27"),
            4: ("2013-03-09", "2013-04-09"),
            5: ("2014-06-29", "2014-07-29")
        },
        source_type='real_appliance_source',
        filename=UKDALE_FILENAME
    )
    seq_length = source.seq_length
    net_dict = net_dict_func(seq_length)
    if appliance == 'dish washer' and architecture == 'rectangles':
        epochs = 200000
        net_dict.pop('epochs')
    else:
        epochs = net_dict.pop('epochs')
    net_dict_copy = deepcopy(net_dict)
    experiment_name = EXPERIMENT + "_" + appliance + "_" + architecture
    net_dict_copy.update(dict(
        source=source,
        logger=logger,
        experiment_name=experiment_name
    ))
    net = Net(**net_dict_copy)
    net.plotter.max_target_power = source.max_appliance_powers.values()[0]
    net.load_params(iteration=epochs,
                    path=join(NET_BASE_PATH, experiment_name))
    net.print_net()
    net.compile()
    return net

def exp_a(name):
    global source
    source_dict_copy = deepcopy(source_dict)
    source_dict_copy.update(dict(
        logger=logging.getLogger(name)
    ))
    source = RealApplianceSource(**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': DimshuffleLayer,
            'pattern': (0, 2, 1)  # (batch, features, time)
        },
        {
            'type': PadLayer,
            'width': 4
        },
        {
            'type': Conv1DLayer,  # convolve over the time axis
            'num_filters': 16,
            'filter_size': 4,
            'stride': 1,
            'nonlinearity': None,
            'border_mode': 'valid'
        },
        {
            'type': Conv1DLayer,  # convolve over the time axis
            'num_filters': 16,
            'filter_size': 4,
            'stride': 1,
            'nonlinearity': None,
            'border_mode': 'valid'
        },
        {
            'type': DimshuffleLayer,
            'pattern': (0, 2, 1),  # back to (batch, time, features)
            'label': 'dimshuffle3'
        },
        {
            'type': DenseLayer,
            'num_units': 512 * 16,
            'nonlinearity': rectify,
            'label': 'dense0'
        },
        {
            'type': DenseLayer,
            'num_units': 512 * 8,
            'nonlinearity': rectify,
            'label': 'dense1'
        },
        {
            'type': DenseLayer,
            'num_units': 512 * 4,
            'nonlinearity': rectify,
            'label': 'dense2'
        },
        {
            'type': DenseLayer,
            'num_units': 512,
            'nonlinearity': rectify
        },
        {
            'type': DenseLayer,
            'num_units': 3,
            'nonlinearity': None
        }
    ]
    net = Net(**net_dict_copy)
    net.load_params(300000)
    return net

def exp_a(name):
    global source
    source_dict_copy = deepcopy(source_dict)
    source = SameLocation(**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
        },
        {
            'label': 'dense1',
            'type': DenseLayer,
            'num_units': SEQ_LENGTH - 3,
            'nonlinearity': rectify
        },
        {
            '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)
    net.load_params(15000)
    return net

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()

net = Net(
    experiment_name="e49b",
    source=source,
    learning_rate=1e-1,
    save_plot_interval=50,
    loss_function=crossentropy,
    layers_config=[
        {
            'type': DenseLayer,
            'num_units': 50,
            'nonlinearity': sigmoid,
            'W': Uniform(25),
            'b': Uniform(25)
        },
        {
            'type': DenseLayer,
            'num_units': 50,
            'nonlinearity': sigmoid,
            'W': Uniform(10),
            'b': Uniform(10)
        },
        {
            'type': LSTMLayer,
            'num_units': 20,
            'W_in_to_cell': Uniform(5)
        },
        {
            'type': ReshapeLayer,
            'shape': (5, 20, 1000)
        },
        {
            'type': Conv1DLayer,
            'num_filters': 20,
            'filter_length': 5,
            'stride': 5
        },
        {
            'type': ReshapeLayer,
            'shape': (5, 200, 20)
        },
        {
            'type': LSTMLayer,
            'num_units': 40,
            'W_in_to_cell': Uniform(5)
        },
        {
            'type': ReshapeLayer,
            'shape': (5, 40, 200)
        },
        {
            'type': Conv1DLayer,
            'num_filters': 40,
            'filter_length': 5,
            'stride': 5
        },
        {
            'type': ReshapeLayer,
            'shape': (5, 40, 40)
        },
        {
            'type': LSTMLayer,
            'num_units': 80,
            'W_in_to_cell': Uniform(5)
        },
        {
            'type': DenseLayer,
            'num_units': source.n_outputs,
            'nonlinearity': sigmoid
        }
    ]
)