def test_mean_with_grad(self):
""" Test mean of all elements of tensor, and input data to backward function
"""
tensor1 = Tensor([1, 2, 3], requires_grad=True)
tensor2 = tensor1.mean()
tensor2.backward(Tensor(3.))
assert tensor1.grad.data.tolist() == [1, 1, 1]
def test_simple_mean(self):
""" Test mean of all elements of tensor
"""
tensor1 = Tensor([3, 6, 9], requires_grad=True)
tensor2 = tensor1.mean()
tensor2.backward()
assert tensor2.data.tolist() == 6
assert np.round(tensor1.grad.data, 2).tolist() == [0.33, 0.33, 0.33]
def test_mean_with_grad_torch(self):
""" Test mean of all elements of tensor, and input data to backward function
and compare with torch
"""
tensor1 = Tensor([1, 2, 3], requires_grad=True)
tensor2 = tensor1.mean()
tensor_torch1 = tensor([1, 2, 3], dtype=float, requires_grad=True)
tensor_torch2 = tensor_torch1.mean()
tensor2.backward(Tensor(3.))
tensor_torch2.backward(tensor(3.))
assert tensor1.grad.data.tolist() == tensor1.grad.data.tolist()
def test_simple_mean_torch(self):
""" Test mean of all elements of tensor and compare with torch
"""
tensor1 = Tensor([3, 6, 9], requires_grad=True)
tensor2 = tensor1.mean()
tensor_torch1 = tensor([3, 6, 9], dtype=float, requires_grad=True)
tensor_torch2 = tensor_torch1.mean()
tensor2.backward()
tensor_torch2.backward()
assert tensor2.data.tolist() == tensor_torch2.data.tolist()
assert tensor1.grad.data.tolist() == tensor_torch1.grad.data.tolist()
