class Kawamura_Y:
def __init__(self):
self.model0 = FunctionSet(l=F.Convolution2D(1, 1, 5, stride=1, pad=2, nobias=True))
self.model1 = FunctionSet(l=F.Convolution2D(1, 1, 5, stride=1, pad=2, nobias=True))
#print self.model.l.W.shape
self.model0.l.W[0,0,:,:] = np.array([[0,0,2,0,0],[0,0,-12,0,0],[0,0,6,0,0],[0,0,4,0,0],[0,0,0,0,0]]).astype(np.float32)/12.0
self.model1.l.W[0,0,:,:] = np.array([[0,0,0,0,0],[0,0,-4,0,0],[0,0,-6,0,0],[0,0,12,0,0],[0,0,-2,0,0]]).astype(np.float32)/12.0
#print self.model.l.W.shape
self.model0.to_gpu()
self.model1.to_gpu()
def forward(self, x_data):
y0 = self.model0.l(x_data)
y1 = self.model1.l(x_data)
return y0,y1
class FTCS_Y:
def __init__(self):
self.model0 = FunctionSet(l=F.Convolution2D(1, 1, 3, pad=1, nobias=True))
self.model0.l.W[0,0,:,:] = np.array([[0,-1,0],[0,0,0],[0,1,0]]).astype(np.float32)/2
self.model0.to_gpu()
def forward(self, x_data):
y0 = self.model0.l(x_data)
return y0
class FTCS_Y:
def __init__(self):
self.model0 = FunctionSet(
l=F.Convolution2D(1, 1, 3, pad=1, nobias=True))
self.model0.l.W[0, 0, :, :] = np.array(
[[0, -1, 0], [0, 0, 0], [0, 1, 0]]).astype(np.float32) / 2
self.model0.to_gpu()
def forward(self, x_data):
y0 = self.model0.l(x_data)
return y0
class Kawamura_Y:
def __init__(self):
self.model0 = FunctionSet(
l=F.Convolution2D(1, 1, 5, stride=1, pad=2, nobias=True))
self.model1 = FunctionSet(
l=F.Convolution2D(1, 1, 5, stride=1, pad=2, nobias=True))
#print self.model.l.W.shape
self.model0.l.W[0, 0, :, :] = np.array(
[[0, 0, 2, 0, 0], [0, 0, -12, 0, 0], [0, 0, 6, 0, 0],
[0, 0, 4, 0, 0], [0, 0, 0, 0, 0]]).astype(np.float32) / 12.0
self.model1.l.W[0, 0, :, :] = np.array(
[[0, 0, 0, 0, 0], [0, 0, -4, 0, 0], [0, 0, -6, 0, 0],
[0, 0, 12, 0, 0], [0, 0, -2, 0, 0]]).astype(np.float32) / 12.0
#print self.model.l.W.shape
self.model0.to_gpu()
self.model1.to_gpu()
def forward(self, x_data):
y0 = self.model0.l(x_data)
y1 = self.model1.l(x_data)
return y0, y1
