def exp_a(name):
# conv, conv
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))
NUM_FILTERS = 16
target_seq_length = (source.output_shape_after_processing()[1] *
source.output_shape_after_processing()[2])
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': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'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 * 2,
'nonlinearity': rectify,
'label': 'dense0'
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_e(name):
global source
source_dict_copy = deepcopy(source_dict)
source_dict_copy['random_window'] = 0
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(experiment_name=name, source=source))
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)
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': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 32,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'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': output_shape[1] * output_shape[2],
'nonlinearity': sigmoid
}
]
net = Net(**net_dict_copy)
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
))
output_shape = source.output_shape_after_processing()
net_dict_copy['layers_config'] = [
{
'type': BLSTMLayer,
'num_units': 40,
'gradient_steps': GRADIENT_STEPS,
'peepholes': False,
'nonlinearity_cell': rectify,
'nonlinearity_out': rectify
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': Conv1DLayer,
'num_filters': 20,
'filter_length': 4,
'stride': 4,
'nonlinearity': rectify
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': BLSTMLayer,
'num_units': 80,
'gradient_steps': GRADIENT_STEPS,
'peepholes': False,
'nonlinearity_cell': rectify,
'nonlinearity_out': rectify
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': T.nnet.softplus
}
]
net = Net(**net_dict_copy)
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': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 32,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'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': output_shape[1] * output_shape[2],
'nonlinearity': sigmoid
}
]
net = Net(**net_dict_copy)
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
))
output_shape = source.output_shape_after_processing()
net_dict_copy['layers_config'] = [
{
'type': BLSTMLayer,
'num_units': 40,
'gradient_steps': GRADIENT_STEPS,
'peepholes': False
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': Conv1DLayer,
'num_filters': 20,
'filter_length': 4,
'stride': 4,
'nonlinearity': sigmoid
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1)
},
{
'type': BLSTMLayer,
'num_units': 80,
'gradient_steps': GRADIENT_STEPS,
'peepholes': False
},
{
'type': DenseLayer,
'num_units': source.n_outputs,
'nonlinearity': T.nnet.softplus
}
]
net = Net(**net_dict_copy)
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):
# conv, conv
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))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy["layers_config"] = [
{"type": DimshuffleLayer, "pattern": (0, 2, 1)}, # (batch, features, time)
{
"type": Conv1DLayer, # convolve over the time axis
"num_filters": NUM_FILTERS,
"filter_size": 4,
"stride": 1,
"nonlinearity": None,
"border_mode": "valid",
},
{
"type": Conv1DLayer, # convolve over the time axis
"num_filters": NUM_FILTERS,
"filter_size": 4,
"stride": 1,
"nonlinearity": None,
"border_mode": "valid",
},
{"type": DimshuffleLayer, "pattern": (0, 2, 1)}, # back to (batch, time, features)
{"type": DenseLayer, "num_units": 512, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 512, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": 512, "nonlinearity": rectify},
{"type": DenseLayer, "num_units": target_seq_length, "nonlinearity": None},
]
net = Net(**net_dict_copy)
return net
def exp_a(name):
# conv, conv
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
))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_a(name):
# conv, conv
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
))
NUM_FILTERS = 16
target_seq_length = source.output_shape_after_processing()[1]
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'border_mode': 'valid'
},
# Need to do ugly dimshuffle, reshape, reshape, dimshuffle
# to get output of first Conv1DLayer ready for
# ConcatLayer
# {
# 'type': DimshuffleLayer,
# 'pattern': (0, 2, 1), # back to (batch, time, features)
# 'label': 'dimshuffle1'
# },
# {
# 'type': ReshapeLayer,
# 'shape': (N_SEQ_PER_BATCH, -1),
# 'label': 'reshape0'
# },
# {
# 'type': ReshapeLayer,
# 'shape': (N_SEQ_PER_BATCH, NUM_FILTERS, -1)
# },
# {
# 'type': DimshuffleLayer,
# 'pattern': (0, 2, 1), # back to (batch, time, features)
# 'label': 'dimshuffle2'
# },
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': NUM_FILTERS,
'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,
'nonlinearity': rectify,
'label': 'dense0'
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify,
'label': 'dense1'
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify,
'label': 'dense2'
},
{
'type': ConcatLayer,
'axis': 1,
'incomings': ['dense0', 'dense2']
},
{
'type': DenseLayer,
'num_units': 512,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': target_seq_length,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
