class TestLinear(TestCase):
def setUp(self):
self.func = Linear(3, 2)
self.func.W = numpy.random.uniform(-1, 1, self.func.W.shape).astype(
numpy.float32)
self.func.b = numpy.random.uniform(-1, 1, self.func.b.shape).astype(
numpy.float32)
self.func.gW.fill(0)
self.func.gb.fill(0)
self.W = self.func.W.copy() # fixed on CPU
self.b = self.func.b.copy() # fixed on CPU
self.x = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (4, 2)).astype(numpy.float32)
self.y = self.x.dot(self.func.W.T) + self.func.b
def check_forward(self, x_data):
x = Variable(x_data)
y = self.func(x)
y_expect = self.x.dot(self.W.T) + self.b
assert_allclose(y_expect, y.data)
def test_forward_cpu(self):
self.check_forward(self.x)
@attr.gpu
def test_forward_gpu(self):
self.func.to_gpu()
self.check_forward(to_gpu(self.x))
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = self.func(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, ))
gx, gW, gb = numerical_grad(f, (x.data, func.W, func.b), (y.grad, ),
eps=1e-2)
assert_allclose(gx, x.grad)
assert_allclose(gW, func.gW)
assert_allclose(gb, func.gb)
def test_backward_cpu(self):
self.check_backward(self.x, self.gy)
@attr.gpu
def test_backward_gpu(self):
self.func.to_gpu()
self.check_backward(to_gpu(self.x), to_gpu(self.gy))
class TestLinear(TestCase):
def setUp(self):
self.func = Linear(3, 2)
self.func.W = numpy.random.uniform(-1, 1, self.func.W.shape).astype(numpy.float32)
self.func.b = numpy.random.uniform(-1, 1, self.func.b.shape).astype(numpy.float32)
self.func.gW.fill(0)
self.func.gb.fill(0)
self.W = self.func.W.copy() # fixed on CPU
self.b = self.func.b.copy() # fixed on CPU
self.x = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (4, 2)).astype(numpy.float32)
self.y = self.x.dot(self.func.W.T) + self.func.b
def check_forward(self, x_data):
x = Variable(x_data)
y = self.func(x)
y_expect = self.x.dot(self.W.T) + self.b
assert_allclose(y_expect, y.data)
def test_forward_cpu(self):
self.check_forward(self.x)
@attr.gpu
def test_forward_gpu(self):
self.func.to_gpu()
self.check_forward(to_gpu(self.x))
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = self.func(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, gW, gb = numerical_grad(f, (x.data, func.W, func.b), (y.grad,), eps=1e-2)
assert_allclose(gx, x.grad)
assert_allclose(gW, func.gW)
assert_allclose(gb, func.gb)
def test_backward_cpu(self):
self.check_backward(self.x, self.gy)
@attr.gpu
def test_backward_gpu(self):
self.func.to_gpu()
self.check_backward(to_gpu(self.x), to_gpu(self.gy))
def setUp(self):
self.func = Linear(3, 2)
self.func.W = numpy.random.uniform(-1, 1, self.func.W.shape).astype(
numpy.float32)
self.func.b = numpy.random.uniform(-1, 1, self.func.b.shape).astype(
numpy.float32)
self.func.gW.fill(0)
self.func.gb.fill(0)
self.W = self.func.W.copy() # fixed on CPU
self.b = self.func.b.copy() # fixed on CPU
self.x = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (4, 2)).astype(numpy.float32)
self.y = self.x.dot(self.func.W.T) + self.func.b
def __init__(self, optimizer):
self.model = FunctionSet(l=Linear(self.UNIT_NUM, 2))
self.optimizer = optimizer
# true parameters
self.w = np.random.uniform(-1, 1,
(self.UNIT_NUM, 1)).astype(np.float32)
self.b = np.random.uniform(-1, 1, (1, )).astype(np.float32)
def setUp(self):
self.func = Linear(3, 2)
self.func.W = numpy.random.uniform(-1, 1, self.func.W.shape).astype(numpy.float32)
self.func.b = numpy.random.uniform(-1, 1, self.func.b.shape).astype(numpy.float32)
self.func.gW.fill(0)
self.func.gb.fill(0)
self.W = self.func.W.copy() # fixed on CPU
self.b = self.func.b.copy() # fixed on CPU
self.x = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (4, 2)).astype(numpy.float32)
self.y = self.x.dot(self.func.W.T) + self.func.b
def setUp(self):
self.fs = FunctionSet(a=Linear(3, 2), b=Linear(3, 2))
