def d(x):
return sp.sp_sum(sp.square_diagonal(x), sparse_grad=True)
def d(x, s):
return sp.sp_sum(sp.col_scale(x, s), sparse_grad=True)
def d(x, s):
return sp.sp_sum(sp.col_scale(x, s), sparse_grad=True)
def d(x):
return sp.sp_sum(sp.square_diagonal(x), sparse_grad=True)
if x.type.dtype != y.type.dtype:
raise NotImplementedError()
return gof.Apply(self,
[x, y],
[SparseType(dtype=x.type.dtype,
format=x.type.format).make_variable()])
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and not _is_sparse(y)
assert x.shape[1] == y.shape[0]
out[0] = x.__class__(x.toarray() * y)
def grad(self, (x, y), (gz,)):
assert _is_sparse_variable(x) and _is_dense_variable(y)
assert _is_sparse_variable(gz)
return mul_s_v(gz, y), sp_sum(x * gz, axis=0, sparse_grad=True)
mul_s_v = MulSV()
class MulSVCSR(gof.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, a_data, a_indices, a_indptr, b):
assert b.type.ndim == 1
return gof.Apply(self, [a_data, a_indices, a_indptr, b],
[tensor.tensor(b.dtype, (False,))])
if x.type.dtype != y.type.dtype:
raise NotImplementedError()
return gof.Apply(self, [x, y], [
SparseType(dtype=x.type.dtype,
format=x.type.format).make_variable()
])
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and not _is_sparse(y)
assert x.shape[1] == y.shape[0]
out[0] = x.__class__(x.toarray() * y)
def grad(self, (x, y), (gz, )):
assert _is_sparse_variable(x) and _is_dense_variable(y)
assert _is_sparse_variable(gz)
return mul_s_v(gz, y), sp_sum(x * gz, axis=0, sparse_grad=True)
mul_s_v = MulSV()
class MulSVCSR(gof.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, a_data, a_indices, a_indptr, b):
assert b.type.ndim == 1
return gof.Apply(self, [a_data, a_indices, a_indptr, b],
if x.type.dtype != y.type.dtype:
raise NotImplementedError()
return gof.Apply(self,
[x, y],
[SparseType(dtype=x.type.dtype,
format=x.type.format).make_variable()])
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and not _is_sparse(y)
assert x.shape[1] == y.shape[0]
out[0] = x.__class__(x + (x.toarray() != 0) * y)
def grad(self, (x, y), (gz,)):
assert _is_sparse_variable(x) and not _is_sparse_variable(y)
assert _is_sparse_variable(gz)
return gz, sp_sum(gz, axis=0, sparse_grad=True)
structured_add_s_v = StructuredAddSV()
class StrucutedAddSVCSR(gof.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, a_data, a_indices, a_indptr, b):
b = tensor.as_tensor_variable(b)
a_data = tensor.as_tensor_variable(a_data)
a_indices = tensor.as_tensor_variable(a_indices)
a_indptr = tensor.as_tensor_variable(a_indptr)