def ae(batch):
NUM_FILTERS = 8
input_shape = batch.input.shape
target_shape = input_shape
seq_length = input_shape[1]
output_layer = build_net(
input_shape=input_shape,
layers=[
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
# Output
{
'type': DenseLayer,
'num_units': target_shape[1] * target_shape[2],
'nonlinearity': None
},
{
'type': ReshapeLayer,
'shape': target_shape
}
]
)
net = Net(
output_layer,
tags=['AE'],
description="Identical AE to e576 but with rectify.",
predecessor_experiment="e576"
)
return net
def ae(batch):
NUM_FILTERS = 8
input_shape = batch.input.shape
target_shape = input_shape
seq_length = input_shape[1]
output_layer = build_net(
input_shape=input_shape,
layers=[
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
# Output
{
'type': DenseLayer,
'num_units': target_shape[1] * target_shape[2],
'nonlinearity': None
},
{
'type': ReshapeLayer,
'shape': target_shape
}
])
net = Net(output_layer,
tags=['AE'],
description="Identical AE to e576 but with rectify.",
predecessor_experiment="e576")
return net
def ae(batch):
NUM_FILTERS = 8
input_shape = batch.input.shape
seq_length = input_shape[1]
output_layer = build_net(
input_shape=input_shape,
layers=[
{
'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,
'pad': 'valid'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': ReshapeLayer,
'shape': (-1, (seq_length - 3), NUM_FILTERS)
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{ # DeConv
'type': Conv1DLayer,
'num_filters': 1,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'pad': 'full'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
}
])
net = Net(output_layer,
tags=['AE', 'Conv1D'],
description="Identical AE to e567. Trained on 5 appliances.",
predecessor_experiment="e567")
return net
def ae(batch):
NUM_FILTERS = 8
input_shape = batch.input.shape
seq_length = input_shape[1]
output_layer = build_net(
input_shape=input_shape,
layers=[
{
'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,
'pad': 'valid'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': 128,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': (seq_length - 3) * NUM_FILTERS,
'nonlinearity': rectify
},
{
'type': ReshapeLayer,
'shape': (-1, (seq_length - 3), NUM_FILTERS)
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{ # DeConv
'type': Conv1DLayer,
'num_filters': 1,
'filter_size': 4,
'stride': 1,
'nonlinearity': None,
'pad': 'full'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
}
]
)
net = Net(
output_layer,
tags=['AE', 'Conv1D'],
description="Identical AE to e567. Trained on 5 appliances.",
predecessor_experiment="e567"
)
return net