def test_mse():
from lasagne.objectives import mse
output = np.array([[1.0, 0.0, 1.0, 0.0], [-1.0, 0.0, -1.0, 0.0]])
target = np.zeros((2, 4))
result = mse(output, target)
assert result.eval() == 0.5
validation_buildings=[1],
n_seq_per_batch=N_SEQ_PER_BATCH,
standardise_input=True,
standardise_targets=True,
ignore_incomplete=True,
offset_probability=0.5,
ignore_offset_activations=True
)
net_dict = dict(
save_plot_interval=SAVE_PLOT_INTERVAL,
# loss_function=partial(ignore_inactive, loss_func=mdn_nll, seq_length=SEQ_LENGTH),
# loss_function=lambda x, t: mdn_nll(x, t).mean(),
# loss_function=lambda x, t: (mse(x, t) * MASK).mean(),
loss_function=lambda x, t: mse(x, t).mean(),
# loss_function=lambda x, t: binary_crossentropy(x, t).mean(),
# loss_function=partial(scaled_cost, loss_func=mse),
# loss_function=ignore_inactive,
# loss_function=partial(scaled_cost3, ignore_inactive=False),
# updates_func=momentum,
updates_func=clipped_nesterov_momentum,
updates_kwargs={'clip_range': (0, 10)},
learning_rate=1e-2,
learning_rate_changes_by_iteration={
1000: 1e-3,
5000: 1e-4
},
do_save_activations=True,
auto_reshape=False,
# plotter=CentralOutputPlotter
def exp_j(name):
# like a but with max power = 1000W and 5 appliances
# tanh and softplus output
# sane inits for other layers
# output one appliance
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(
standardise_targets=True,
unit_variance_targets=True,
max_input_power=1000,
skip_probability=0.9,
output_one_appliance=True
))
source_dict_copy['appliances'] = [
['fridge freezer', 'fridge', 'freezer'],
'hair straighteners',
'television',
'dish washer',
['washer dryer', 'washing machine']
]
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source,
loss_function=lambda x, t: mse(x, t).mean(),
learning_rate=1e-3,
learning_rate_changes_by_iteration={
1000: 1e-4,
2000: 1e-5
}
))
net_dict_copy['layers_config']= [
{
'type': DenseLayer,
'num_units': 200,
'nonlinearity': tanh,
'W': Uniform(25),
'b': Uniform(25)
},
{
'type': DenseLayer,
'num_units': 50,
'nonlinearity': tanh,
'W': Normal(std=1/sqrt(200)),
'b': Normal(std=1/sqrt(200))
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 40,
'W_in_to_hid': Normal(std=1/sqrt(50)),
'gradient_steps': GRADIENT_STEPS,
'nonlinearity': tanh,
'learn_init': False,
'precompute_input': False
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': Conv1DLayer,
'num_filters': 20,
'filter_length': 4,
'stride': 4,
'nonlinearity': tanh,
'W': Normal(std=1/sqrt(50))
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 80,
'W_in_to_hid': Normal(std=1/sqrt(50)),
'gradient_steps': GRADIENT_STEPS,
'nonlinearity': tanh,
'learn_init': False,
'precompute_input': False
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': T.nnet.softplus
}
]
net = Net(**net_dict_copy)
return net
def exp_d(name):
# tanh and softplus output
# sane inits for other layers
# batch norm
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(
standardise_targets=True,
unit_variance_targets=True
))
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source,
loss_function=lambda x, t: mse(x, t).mean(),
learning_rate=1e-3,
learning_rate_changes_by_iteration={
1000: 1e-4,
2000: 1e-5
}
))
net_dict_copy['layers_config']= [
{
'type': DenseLayer,
'num_units': 50,
'nonlinearity': identity,
'W': Uniform(25),
'b': Uniform(25)
},
{
'type': BatchNormLayer,
'axes': (0, 1),
'nonlinearity': tanh
},
{
'type': DenseLayer,
'num_units': 50,
'nonlinearity': identity,
'W': Normal(std=1/sqrt(50)),
'b': Normal(std=1/sqrt(50))
},
{
'type': BatchNormLayer,
'axes': (0, 1),
'nonlinearity': tanh
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 40,
'W_in_to_hid': Normal(std=1/sqrt(50)),
'gradient_steps': GRADIENT_STEPS,
'nonlinearity': tanh, # need nonlinearity for hid_to_hid
'learn_init': False,
'precompute_input': False
},
{
'type': BatchNormLayer,
'axes': (0, 1),
'nonlinearity': identity
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': Conv1DLayer,
'num_filters': 20,
'filter_length': 4,
'stride': 4,
'nonlinearity': identity,
'W': Normal(std=1/sqrt(50))
},
{
'type': BatchNormLayer,
'nonlinearity': tanh
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 80,
'W_in_to_hid': Normal(std=1/sqrt(50)),
'gradient_steps': GRADIENT_STEPS,
'nonlinearity': tanh,
'learn_init': False,
'precompute_input': False
},
{
'type': BatchNormLayer,
'nonlinearity': tanh,
'axes': (0, 1)
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': T.nnet.softplus
}
]
net = Net(**net_dict_copy)
return net
# 'std': np.array([ 0.12636775], dtype=np.float32)},
# target_stats={
# 'mean': np.array([ 0.04066789, 0.01881946,
# 0.24639061, 0.17608672, 0.10273963],
# dtype=np.float32),
# 'std': np.array([ 0.11449792, 0.07338708,
# 0.26608968, 0.33463112, 0.21250485],
# dtype=np.float32)}
)
net_dict = dict(
save_plot_interval=SAVE_PLOT_INTERVAL,
# loss_function=partial(ignore_inactive, loss_func=mdn_nll, seq_length=SEQ_LENGTH),
# loss_function=lambda x, t: mdn_nll(x, t).mean(),
loss_function=lambda x, t: (mse(x, t) * MASK).mean(),
# loss_function=lambda x, t: binary_crossentropy(x, t).mean(),
# loss_function=partial(scaled_cost, loss_func=mse),
# loss_function=ignore_inactive,
# loss_function=partial(scaled_cost3, ignore_inactive=False),
# updates_func=momentum,
updates_func=clipped_nesterov_momentum,
updates_kwargs={'clip_range': (0, 10)},
learning_rate=1e-7,
learning_rate_changes_by_iteration={
# 5000: 1e-6,
# 7000: 1e-7
# 800: 1e-4
# 500: 1e-3
# 4000: 1e-03,
# 6000: 5e-06,
# 'std': np.array([ 0.12636775], dtype=np.float32)},
# target_stats={
# 'mean': np.array([ 0.04066789, 0.01881946,
# 0.24639061, 0.17608672, 0.10273963],
# dtype=np.float32),
# 'std': np.array([ 0.11449792, 0.07338708,
# 0.26608968, 0.33463112, 0.21250485],
# dtype=np.float32)}
)
N = 50
net_dict = dict(
save_plot_interval=SAVE_PLOT_INTERVAL,
# loss_function=partial(ignore_inactive, loss_func=mdn_nll, seq_length=SEQ_LENGTH),
# loss_function=lambda x, t: mdn_nll(x, t).mean(),
loss_function=lambda x, t: mse(x, t).mean(),
# loss_function=lambda x, t: binary_crossentropy(x, t).mean(),
# loss_function=partial(scaled_cost, loss_func=mse),
# loss_function=ignore_inactive,
# loss_function=partial(scaled_cost3, ignore_inactive=False),
# updates_func=momentum,
updates_func=clipped_nesterov_momentum,
updates_kwargs={'clip_range': (0, 10)},
learning_rate=1e-5,
learning_rate_changes_by_iteration={
5000: 1e-6,
7000: 1e-7
# 800: 1e-4
# 500: 1e-3
# 4000: 1e-03,
# 6000: 5e-06,
def exp_c(name):
# tanh and softplus output
source_dict_copy = deepcopy(source_dict)
source_dict_copy.update(dict(
standardise_targets=True,
unit_variance_targets=True
))
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source,
loss_function=lambda x, t: mse(x, t).mean(),
learning_rate=1e-3,
learning_rate_changes_by_iteration={
1000: 1e-4,
2000: 1e-5
}
))
net_dict_copy['layers_config']= [
{
'type': DenseLayer,
'num_units': 50,
'nonlinearity': tanh,
'W': Uniform(25),
'b': Uniform(25)
},
{
'type': DenseLayer,
'num_units': 50,
'nonlinearity': tanh,
'W': Uniform(10),
'b': Uniform(10)
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 40,
'W_in_to_hid': Uniform(5),
'gradient_steps': GRADIENT_STEPS,
'nonlinearity': tanh,
'learn_init': False,
'precompute_input': False
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': Conv1DLayer,
'num_filters': 20,
'filter_length': 4,
'stride': 4,
'nonlinearity': tanh
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': BidirectionalRecurrentLayer,
'num_units': 80,
'W_in_to_hid': Uniform(5),
'gradient_steps': GRADIENT_STEPS,
'nonlinearity': tanh,
'learn_init': False,
'precompute_input': False
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': T.nnet.softplus
}
]
net = Net(**net_dict_copy)
return net