def main(): model = MLP() optimizer = optimizers.Adam(0.01) optimizer.setup(model) optimizer.use_cleargrads() bs = 8 x_data = np.random.randn(bs, 100).astype(np.float32) x = Variable(x_data) y = model(x) z = F.sum(y) with function_hooks.PrintHook(): z.backward()
def setUp(self): self.h = function_hooks.PrintHook() self.f = functions.Exp() self.f.add_hook(self.h) self.x = numpy.random.uniform(-0.1, 0.1, (3, 5)).astype(numpy.float32) self.gy = numpy.random.uniform(-0.1, 0.1, (3, 5)).astype(numpy.float32)
def setUp(self): self.io = six.StringIO() self.h = function_hooks.PrintHook(file=self.io) self.f = DummyFunction() self.x = numpy.random.uniform(-0.1, 0.1, (3, 5)).astype(numpy.float32) self.gy = numpy.random.uniform(-0.1, 0.1, (3, 5)).astype(numpy.float32)
def setUp(self): self.h = function_hooks.PrintHook() self.l = links.Linear(5, 5) self.x = numpy.random.uniform(-0.1, 0.1, (3, 5)).astype(numpy.float32) self.gy = numpy.random.uniform(-0.1, 0.1, (3, 5)).astype(numpy.float32)