def create_regressor(rng=np.random,
batchsize=1,
window=240,
input=1,
dropout=0.25):
print('inside create_regressor')
return Network(
#DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(64, input, 45),
input_shape=(batchsize, input, window),
rng=rng),
BiasLayer(shape=(64, 1)),
ActivationLayer(),
#DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(128, 64, 25),
input_shape=(batchsize, 64, window),
rng=rng),
BiasLayer(shape=(128, 1)),
ActivationLayer(),
#DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(256, 128, 15),
input_shape=(batchsize, 128, window),
rng=rng),
BiasLayer(shape=(256, 1)),
ActivationLayer(),
Pool1DLayer(input_shape=(batchsize, 256, window)))
def create_footstepper(rng=np.random, batchsize=1, window=250, dropout=0.25):
return Network(
DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(64, 3, 65),
input_shape=(batchsize, 3, window),
rng=rng),
BiasLayer(shape=(64, 1)),
ActivationLayer(),
DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(5, 64, 45),
input_shape=(batchsize, 64, window),
rng=rng),
BiasLayer(shape=(5, 1)),
)
def createcore_rightleg(rng=np.random,
batchsize=1,
window=240,
dropout=0.25,
depooler='random'):
return Network(
Network(
DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(256, 12, 25),
input_shape=(batchsize, 12, window),
rng=rng),
BiasLayer(shape=(256, 1)),
ActivationLayer(),
Pool1DLayer(input_shape=(batchsize, 256, window)),
),
Network(
Depool1DLayer(output_shape=(batchsize, 256, window),
depooler='random',
rng=rng), DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(12, 256, 25),
input_shape=(batchsize, 256, window),
rng=rng), BiasLayer(shape=(12, 1))))
def create_core(rng=np.random,
batchsize=1,
window=240,
dropout=0.25,
depooler='random'):
print('inside create_core')
return Network(
Network(
DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(256, 73, 25),
input_shape=(batchsize, 73, window),
rng=rng),
BiasLayer(shape=(256, 1)),
ActivationLayer(),
Pool1DLayer(input_shape=(batchsize, 256, window)),
),
Network(
Depool1DLayer(output_shape=(batchsize, 256, window),
depooler='random',
rng=rng), DropoutLayer(amount=dropout, rng=rng),
Conv1DLayer(filter_shape=(73, 256, 25),
input_shape=(batchsize, 256, window),
rng=rng), BiasLayer(shape=(73, 1))))