def test_device_instance(self):
tensor = tF.raw_input([], [0])
dev = tensor.device()
self.assertIs(dev, self.device)
node = F.raw_input([], [0])
dev = node.device()
self.assertIs(dev, self.device)
my_device = Naive()
self.assertIsNot(my_device, self.device)
node = F.raw_input([], [0], dev=my_device)
dev = node.device()
self.assertIs(dev, my_device)
param = Parameter([], I.Constant(1))
dev = param.device()
self.assertIs(dev, self.device)
def train_func(optimizer):
dev = D.Naive(12345)
Device.set_default(dev)
g = Graph()
Graph.set_default(g)
pw1 = Parameter([8, 2], I.XavierUniform())
pb1 = Parameter([8], I.Constant(0))
pw2 = Parameter([1, 8], I.XavierUniform())
pb2 = Parameter([1], I.Constant(0))
optimizer.add(pw1, pb1, pw2, pb2)
input_data = [1, 1, 1, -1, -1, 1, -1, -1]
output_data = [1, -1, -1, 1]
for i in range(10):
g.clear()
x = F.raw_input(Shape([2], 4), input_data)
w1 = F.parameter(pw1)
b1 = F.parameter(pb1)
w2 = F.parameter(pw2)
b2 = F.parameter(pb2)
h = F.tanh(w1 @ x + b1)
y = w2 @ h + b2
t = F.raw_input(Shape([], 4), output_data)
diff = t - y
loss = F.batch.mean(diff * diff)
optimizer.reset_gradients()
loss.backward()
optimizer.update()
return [
pw1.value.to_list(),
pb1.value.to_list(),
pw2.value.to_list(),
pb2.value.to_list()
]
def test_functions_input_argument(self):
# list[ndarray] w/o shape
x = F.input(self.ndarray_data)
self.assertEqual(x.to_list(), self.list_data)
self.assertEqual(x.shape(), Shape([4, 3], 2))
# ndarray w/o shape
x = F.input(self.ndarray_data[0])
self.assertEqual(x.to_list(), self.list_data[:12])
self.assertEqual(x.shape(), Shape([4, 3], 1))
# list[float] w/o shape
self.assertRaises(TypeError, lambda: F.input(self.list_data))
# list[float] w/ shape
x = F.raw_input(Shape([4, 3], 2), self.list_data)
self.assertEqual(x.to_list(), self.list_data)
self.assertEqual(x.shape(), Shape([4, 3], 2))
def test_graph_instance(self):
node = F.raw_input([], [0])
g = node.graph()
self.assertIs(g, self.graph)