def grad(self, inp, cost_grad):
"""
Notes
-----
The gradient is currently implemented for matrices only.
"""
a, val, offset = inp
grad = cost_grad[0]
height, width = grad.shape
if a.dtype.startswith("complex"):
return [None, None]
# only valid for matrices
wr_a = fill_diagonal_offset(grad, 0, offset)
offset_abs = basic.abs_(offset)
pos_offset_flag = basic.ge(offset, 0)
neg_offset_flag = basic.lt(offset, 0)
min_wh = basic.minimum(width, height)
start = offset * pos_offset_flag + offset_abs * width * neg_offset_flag
num_of_step = basic.minimum(
min_wh,
width * pos_offset_flag + height * neg_offset_flag - offset_abs)
step = a.shape[1] + 1
end = start + step * num_of_step
# input of slice should be integer
start = basic.cast(start, "int32")
step = basic.cast(step, "int32")
end = basic.cast(end, "int32")
wr_val = grad.flatten()[start:end:step].sum()
wr_offset = grad_undefined(
self,
2,
offset,
"offset is not defined for non-integer offset so"
" fill_diagonal_offset(a,val,offset+eps) is undefined",
)
return [wr_a, wr_val, wr_offset]
def grad(self, inp, cost_grad):
"""
Notes
-----
The gradient is currently implemented for matrices only.
"""
a, val, offset = inp
grad = cost_grad[0]
height, width = grad.shape
if a.dtype.startswith("complex"):
return [None, None]
# only valid for matrices
wr_a = fill_diagonal_offset(grad, 0, offset)
offset_abs = basic.abs_(offset)
pos_offset_flag = basic.ge(offset, 0)
neg_offset_flag = basic.lt(offset, 0)
min_wh = basic.minimum(width, height)
start = offset * pos_offset_flag + offset_abs * width * neg_offset_flag
num_of_step = basic.minimum(min_wh, width * pos_offset_flag + height * neg_offset_flag - offset_abs)
step = a.shape[1] + 1
end = start + step * num_of_step
# input of slice should be integer
start = basic.cast(start, "int32")
step = basic.cast(step, "int32")
end = basic.cast(end, "int32")
wr_val = grad.flatten()[start:end:step].sum()
wr_offset = theano.gradient.grad_undefined(
self,
2,
offset,
"offset is not defined for non-integer offset so" " fill_diagonal_offset(a,val,offset+eps) is undefined",
)
return [wr_a, wr_val, wr_offset]
def infer_shape(self, fgraph, node, in_shapes):
temp = node.inputs[0]
M = basic.switch(basic.lt(temp, 0), basic.cast(0, temp.dtype), temp)
return [[M]]
