def grad(self, inputs, output_gradients):
W, b, d, H, RShape = inputs
dCdR, = output_gradients
dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
WShape = W.shape
dCdW = convGrad3D(dCdR, d, WShape, H)
dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
dCdd = None #not differentiable, since d is not continuous
dCdRShape = None #not differentiable, since RShape is not continuous
if 'name' in dir(dCdR) and dCdR.name is not None:
dCdR_name = dCdR.name
else:
dCdR_name = 'anon'
if 'name' in dir(H) and H.name is not None:
H_name = H.name
else:
H_name = 'anon'
if 'name' in dir(W) and W.name is not None:
W_name = W.name
else:
W_name = 'anon'
if 'name' in dir(b) and b.name is not None:
b_name = b.name
else:
b_name = 'anon'
dCdW.name = 'ConvTransp3D_dCdW.H=' + H_name + ',dCdR=' + dCdR_name + ',W=' + W_name
dCdb.name = 'ConvTransp3D_dCdb.H=' + H_name + ',dCdR=' + dCdR_name + ',W=' + W_name + ',b=' + b_name
dCdH.name = 'ConvTransp3D_dCdH.H=' + H_name + ',dCdR=' + dCdR_name
return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
def grad(self,inputs, output_gradients):
C,d, WShape, B = inputs
dLdA ,= output_gradients
z = T.zeros_like(C[0,0,0,0,:])
dLdC = convTransp3D( dLdA, z, d, B, C.shape[1:4])
dLdd = None #not differentiable, since d is not continuous
dLdWShape = None #not differentiable, since d is not continuous
dLdB = conv3D( C, dLdA, T.zeros_like(B[0,0,0,0,:]), d)
return [ dLdC, dLdd, dLdWShape, dLdB ]
def grad(self, inputs, output_gradients):
C, d, WShape, B = inputs
dLdA, = output_gradients
z = T.zeros_like(C[0, 0, 0, 0, :])
dLdC = convTransp3D(dLdA, z, d, B, C.shape[1:4])
# d actually does affect the outputs, so it's not disconnected
dLdd = grad_undefined(self, 1, d)
# The shape of the weights doesn't affect the output elements
dLdWShape = DisconnectedType()()
dLdB = conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
return [dLdC, dLdd, dLdWShape, dLdB]
def grad(self, inputs, output_gradients):
C, d, WShape, B = inputs
dLdA, = output_gradients
z = T.zeros_like(C[0, 0, 0, 0, :])
dLdC = convTransp3D(dLdA, z, d, B, C.shape[1:4])
# d actually does affect the outputs, so it's not disconnected
dLdd = grad_undefined(self, 1, d)
# The shape of the weights doesn't affect the output elements
dLdWShape = DisconnectedType()()
dLdB = conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
return [dLdC, dLdd, dLdWShape, dLdB]
def grad(self, inputs, output_gradients):
W, b, d, H, RShape = inputs
dCdR, = output_gradients
dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
WShape = W.shape
dCdW = convGrad3D(dCdR, d, WShape, H)
dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
# not differentiable, since d affects the output elements
dCdd = grad_undefined(self, 2, d)
# disconnected, since RShape just determines the output shape
dCdRShape = DisconnectedType()()
if 'name' in dir(dCdR) and dCdR.name is not None:
dCdR_name = dCdR.name
else:
dCdR_name = 'anon_dCdR'
if 'name' in dir(H) and H.name is not None:
H_name = H.name
else:
H_name = 'anon_H'
if 'name' in dir(W) and W.name is not None:
W_name = W.name
else:
W_name = 'anon_W'
if 'name' in dir(b) and b.name is not None:
b_name = b.name
else:
b_name = 'anon_b'
dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
dCdH.name = 'ConvTransp3D_dCdH.H=' + H_name + ',dCdR=' + dCdR_name
return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
def grad(self, inputs, output_gradients):
W, b, d, H, RShape = inputs
dCdR, = output_gradients
dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
WShape = W.shape
dCdW = convGrad3D(dCdR, d, WShape, H)
dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
# not differentiable, since d affects the output elements
dCdd = grad_undefined(self, 2, d)
# disconnected, since RShape just determines the output shape
dCdRShape = DisconnectedType()()
if 'name' in dir(dCdR) and dCdR.name is not None:
dCdR_name = dCdR.name
else:
dCdR_name = 'anon_dCdR'
if 'name' in dir(H) and H.name is not None:
H_name = H.name
else:
H_name = 'anon_H'
if 'name' in dir(W) and W.name is not None:
W_name = W.name
else:
W_name = 'anon_W'
if 'name' in dir(b) and b.name is not None:
b_name = b.name
else:
b_name = 'anon_b'
dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
dCdH.name = 'ConvTransp3D_dCdH.H=' + H_name + ',dCdR=' + dCdR_name
return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
def grad(self,inputs, output_gradients):
W,b,d,H, RShape = inputs
dCdR ,= output_gradients
dCdH = conv3D( dCdR, W, T.zeros_like(H[0,0,0,0,:]), d)
WShape = W.shape
dCdW = convGrad3D(dCdR,d,WShape,H)
dCdb = T.sum(dCdR,axis=(0,1,2,3))
dCdd = None #not differentiable, since d is not continuous
dCdRShape = None #not differentiable, since RShape is not continuous
if 'name' in dir(dCdR) and dCdR.name is not None:
dCdR_name = dCdR.name
else:
dCdR_name = 'anon'
if 'name' in dir(H) and H.name is not None:
H_name = H.name
else:
H_name = 'anon'
if 'name' in dir(W) and W.name is not None:
W_name = W.name
else:
W_name = 'anon'
if 'name' in dir(b) and b.name is not None:
b_name = b.name
else:
b_name = 'anon'
dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
dCdH.name = 'ConvTransp3D_dCdH.H='+H_name+',dCdR='+dCdR_name
return [ dCdW, dCdb, dCdd, dCdH, dCdRShape ]