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)