def test_cross_entropy_derivative_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = ((outputs.asnumpyarray() - targets.asnumpyarray()) /
(outputs.asnumpyarray() * (1 - outputs.asnumpyarray())))
assert_tensor_near_equal(
expected_result, cross_entropy_derivative(be, outputs, targets, temp))
def test_cross_entropy_derivative_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = ((outputs.asnumpyarray() - targets.asnumpyarray()) /
(outputs.asnumpyarray() * (1 - outputs.asnumpyarray())))
assert_tensor_near_equal(expected_result,
cross_entropy_derivative(be, outputs,
targets, temp))
def test_cross_entropy_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum((- targets.asnumpyarray()) *
np.log(outputs.asnumpyarray()) -
(1 - targets.asnumpyarray()) *
np.log(1 - outputs.asnumpyarray()), keepdims=True)
assert_tensor_near_equal(expected_result, cross_entropy(be, outputs,
targets, temp))
def test_cross_entropy_cputensor():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 0.9, 0.1, 0.0001])
targets = CPUTensor([0.5, 0.99, 0.01, 0.2])
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum(
(-targets.asnumpyarray()) * np.log(outputs.asnumpyarray()) -
(1 - targets.asnumpyarray()) * np.log(1 - outputs.asnumpyarray()),
keepdims=True)
assert_tensor_near_equal(expected_result,
cross_entropy(be, outputs, targets, temp))
def test_cross_entropy_limits():
be = CPU(rng_seed=0)
outputs = CPUTensor([0.5, 1.0, 0.0, 0.0001])
targets = CPUTensor([0.5, 0.0, 1.0, 0.2])
eps = 2**-23
temp = [be.zeros(outputs.shape), be.zeros(outputs.shape)]
expected_result = np.sum(
(-targets.asnumpyarray()) * np.log(outputs.asnumpyarray() + eps) -
(1 - targets.asnumpyarray()) *
np.log(1 - outputs.asnumpyarray() + eps),
keepdims=True)
assert_tensor_near_equal(expected_result,
cross_entropy(be, outputs, targets, temp, eps))
def test_asnumpyarray(self):
tns = CPUTensor([[1, 2], [3, 4]])
res = tns.asnumpyarray()
assert isinstance(res, np.ndarray)
assert_tensor_equal(res, np.array([[1, 2], [3, 4]]))
