def test_grad_ob_aux_return(self):
class Test(bp.Base):
def __init__(self):
super(Test, self).__init__()
self.a = bm.TrainVar(bm.ones(10))
self.b = bm.TrainVar(bm.random.randn(10))
self.c = bm.TrainVar(bm.random.uniform(size=10))
def __call__(self):
return bm.sum(self.a + self.b + self.c), (bm.sin(100), bm.exp(0.1))
bm.random.seed(0)
t = Test()
f_grad = bm.grad(t, grad_vars=[t.a, t.b], dyn_vars=t.vars(),
has_aux=True, return_value=True)
grads, returns, aux = f_grad()
for g in grads: assert (g == 1.).all()
assert returns == bm.sum(t.a + t.b + t.c)
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
t = Test()
f_grad = bm.grad(t, grad_vars=t.a, dyn_vars=t.vars(),
has_aux=True, return_value=True)
grads, returns, aux = f_grad()
assert (grads == 1.).all()
assert returns == bm.sum(t.a + t.b + t.c)
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
def derivative(self, u, t, Iext):
r1 = bm.square(u)
r2 = 1.0 + self.k * bm.sum(r1)
r = r1 / r2
Irec = bm.dot(self.conn_mat, r)
du = (-u + Irec + Iext) / self.tau
return du
def update(self, _t, _dt): r1 = bm.square(self.u) r2 = 1.0 + self.k * bm.sum(r1) self.r.value = r1 / r2 Irec = bm.dot(self.conn_mat, self.r) self.u.value = self.u + (-self.u + Irec + self.input) / self.tau * _dt self.input[:] = 0.
def update(self, _t, _dt):
r1 = bm.square(self.u)
r2 = 1.0 + self.k * bm.sum(r1)
self.r.value = r1 / r2
r = bm.fft.fft2(self.r)
jjft = bm.fft.fft2(self.conn_mat)
interaction = bm.real(bm.fft.ifft2(r * jjft))
self.u.value = self.u + (-self.u + self.input +
interaction) / self.tau * _dt
self.input[:] = 0.
def test_grad_pure_func_return1(self):
def call(a, b, c): return bm.sum(a + b + c)
bm.random.seed(1)
a = bm.ones(10)
b = bm.random.randn(10)
c = bm.random.uniform(size=10)
f_grad = bm.grad(call, return_value=True)
grads, returns = f_grad(a, b, c)
assert (grads == 1.).all()
assert returns == bm.sum(a + b + c)
def test_grad_func_return_aux1(self):
def call(a, b, c):
return bm.sum(a + b + c), (bm.sin(100), bm.exp(0.1))
bm.random.seed(1)
a = bm.ones(10)
b = bm.random.randn(10)
c = bm.random.uniform(size=10)
f_grad = bm.grad(call, return_value=True, has_aux=True)
grads, returns, aux = f_grad(a, b, c)
assert (grads == 1.).all()
assert returns == bm.sum(a + b + c)
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
def __call__(self): return bm.sum(self.a + self.b + self.c)
def call(a, b, c): return bm.sum(a + b + c), (bm.sin(100), bm.exp(0.1))
def call(a, b, c): return bm.sum(a + b + c)
bm.random.seed(1)
def __call__(self, d): return bm.sum(self.a + self.b + self.c + 2 * d), (bm.sin(100), bm.exp(0.1))
def __call__(self, d): return bm.sum(self.a + self.b + self.c + 2 * d)