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 create_regressor(rng=np.random,
batchsize=1,
window=240,
input=4,
dropout=0.25):
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 __init__(self, rng=rng, input_shape=1, output_shape=1, dropout=0.7):
self.nslices = 4
self.dropout0 = DropoutLayer(dropout, rng=rng)
self.dropout1 = DropoutLayer(dropout, rng=rng)
self.dropout2 = DropoutLayer(dropout, rng=rng)
self.activation = ActivationLayer('ELU')
self.W0 = HiddenLayer((self.nslices, 512, input_shape-1), rng=rng, gamma=0.01)
self.W1 = HiddenLayer((self.nslices, 512, 512), rng=rng, gamma=0.01)
self.W2 = HiddenLayer((self.nslices, output_shape, 512), rng=rng, gamma=0.01)
self.b0 = BiasLayer((self.nslices, 512))
self.b1 = BiasLayer((self.nslices, 512))
self.b2 = BiasLayer((self.nslices, output_shape))
self.layers = [
self.W0, self.W1, self.W2,
self.b0, self.b1, self.b2]
self.params = sum([layer.params for layer in self.layers], [])
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))))
def __init__(self, rng=rng, input_shape=1, output_shape=1, dropout=0.7):
self.nslices = 4
self.dropout0 = DropoutLayer(dropout, rng=rng)
self.dropout1 = DropoutLayer(dropout, rng=rng)
self.dropout2 = DropoutLayer(dropout, rng=rng)
self.activation = ActivationLayer('ELU')
self.W0 = HiddenLayer((self.nslices, 512, input_shape - 1),
rng=rng,
gamma=0.01)
self.W1 = HiddenLayer((self.nslices, 512, 512), rng=rng, gamma=0.01)
self.W2 = HiddenLayer((self.nslices, output_shape, 512),
rng=rng,
gamma=0.01)
self.b0 = BiasLayer((self.nslices, 512))
self.b1 = BiasLayer((self.nslices, 512))
self.b2 = BiasLayer((self.nslices, output_shape))
self.ang_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.ang_b = BiasLayer((self.nslices, 512))
self.chi_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.chi_b = BiasLayer((self.nslices, 512))
self.dep_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.dep_b = BiasLayer((self.nslices, 512))
self.neu_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.neu_b = BiasLayer((self.nslices, 512))
self.old_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.old_b = BiasLayer((self.nslices, 512))
self.pro_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.pro_b = BiasLayer((self.nslices, 512))
self.sex_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.sex_b = BiasLayer((self.nslices, 512))
self.str_W = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.str_b = BiasLayer((self.nslices, 512))
self.layers = [
self.W0, self.W1, self.W2, self.b0, self.b1, self.b2, self.ang_W,
self.ang_b, self.chi_W, self.chi_b, self.dep_W, self.dep_b,
self.neu_W, self.neu_b, self.old_W, self.old_b, self.pro_W,
self.pro_b, self.sex_W, self.sex_b, self.str_W, self.str_b
]
self.params = sum([layer.params for layer in self.layers], [])
ang_label = np.zeros(L.shape[1])
ang_label[w * 0:w * 1] = 1
chi_label = np.zeros(L.shape[1])
chi_label[w * 1:w * 2] = 1
dep_label = np.zeros(L.shape[1])
dep_label[w * 2:w * 3] = 1
neu_label = np.zeros(L.shape[1])
neu_label[w * 3:w * 4] = 1
old_label = np.zeros(L.shape[1])
old_label[w * 4:w * 5] = 1
pro_label = np.zeros(L.shape[1])
pro_label[w * 5:w * 6] = 1
sex_label = np.zeros(L.shape[1])
sex_label[w * 6:w * 7] = 1
str_label = np.zeros(L.shape[1])
str_label[w * 7:w * 8] = 1
self.ang_label = theano.shared(ang_label, borrow=True)
self.chi_label = theano.shared(chi_label, borrow=True)
self.dep_label = theano.shared(dep_label, borrow=True)
self.neu_label = theano.shared(neu_label, borrow=True)
self.old_label = theano.shared(old_label, borrow=True)
self.pro_label = theano.shared(pro_label, borrow=True)
self.sex_label = theano.shared(sex_label, borrow=True)
self.str_label = theano.shared(str_label, borrow=True)
zeros = np.zeros((1, output_shape))
self.zeros = T.addbroadcast(theano.shared(zeros, borrow=True), 0)
def __init__(self,
rng=rng,
input_shape=1,
output_shape=1,
dropout=0.7,
dropout_res=0.5,
style='Balance',
batchsize=20):
self.style = style
self.batchsize = batchsize
self.nslices = 4
self.dropout0 = DropoutLayer(dropout, rng=rng)
self.dropout1 = DropoutLayer(dropout, rng=rng)
self.dropout_res = DropoutLayer(dropout_res, rng=rng)
self.dropout2 = DropoutLayer(dropout, rng=rng)
self.activation = ActivationLayer('ELU')
W0_load = np.empty((self.nslices, 512, input_shape - 1),
dtype=np.float32)
W1_load = np.empty((self.nslices, 512, 512), dtype=np.float32)
W2_load = np.empty((self.nslices, output_shape, 512), dtype=np.float32)
b0_load = np.empty((self.nslices, 512), dtype=np.float32)
b1_load = np.empty((self.nslices, 512), dtype=np.float32)
b2_load = np.empty((self.nslices, output_shape), dtype=np.float32)
for i in range(4):
W0_load[i] = np.fromfile(
'./Parameters/' + mname + '/W0_%03i.bin' % (int)(i * 12.5),
dtype=np.float32).reshape(512, input_shape - 1)
W1_load[i] = np.fromfile('./Parameters/' + mname + '/W1_%03i.bin' %
(int)(i * 12.5),
dtype=np.float32).reshape(512, 512)
W2_load[i] = np.fromfile(
'./Parameters/' + mname + '/W2_%03i.bin' % (int)(i * 12.5),
dtype=np.float32).reshape(output_shape, 512)
b0_load[i] = np.fromfile('./Parameters/' + mname + '/b0_%03i.bin' %
(int)(i * 12.5),
dtype=np.float32)
b1_load[i] = np.fromfile('./Parameters/' + mname + '/b1_%03i.bin' %
(int)(i * 12.5),
dtype=np.float32)
b2_load[i] = np.fromfile('./Parameters/' + mname + '/b2_%03i.bin' %
(int)(i * 12.5),
dtype=np.float32)
self.W0 = HiddenLayer((self.nslices, 512, input_shape - 1),
rng=rng,
gamma=0.01)
self.W1 = HiddenLayer((self.nslices, 512, 512), rng=rng, gamma=0.01)
self.W2 = HiddenLayer((self.nslices, output_shape, 512),
rng=rng,
gamma=0.01)
self.b0 = BiasLayer((self.nslices, 512))
self.b1 = BiasLayer((self.nslices, 512))
self.b2 = BiasLayer((self.nslices, output_shape))
self.W0.W.set_value(W0_load)
self.W1.W.set_value(W1_load)
self.W2.W.set_value(W2_load)
self.b0.b.set_value(b0_load)
self.b1.b.set_value(b1_load)
self.b2.b.set_value(b2_load)
self.style_W0 = DiagLayer((self.nslices, 1, 512), rng=rng, gamma=0.01)
self.style_b = BiasLayer((self.nslices, 512))
self.layers = [self.style_W0, self.style_b]
self.params = sum(
[layer.params for layer in self.layers], []
) # The only parameters we want to update are the residual adapter ones
style_label = np.zeros(L.shape[1])
style_label[w * styletransfer_styles.index(self.style):w *
(styletransfer_styles.index(self.style) + 1)] = 1
self.style_label = theano.shared(style_label, borrow=True)
zeros = np.zeros((1, output_shape))
self.zeros = T.addbroadcast(theano.shared(zeros, borrow=True), 0)
