def test_square(self):
np_x = np.array([1, 2, 3])
dtype = plaidml2.DType.FLOAT32
x = edsl.Tensor(edsl.LogicalShape(dtype, np_x.shape))
y = op.square(x)
test_result = get_jacobian([x], [np_x], y, x)
true_result = np.array([[2, 0, 0], [0, 4, 0], [0, 0, 6]])
npt.assert_allclose(test_result, true_result)
def test_assign(self):
np_x = np.array([1, 2, 3])
np_b = np.array([1, 1, 1])
dtype = plaidml2.DType.FLOAT32
x = edsl.Tensor(edsl.LogicalShape(dtype, np_x.shape))
b = edsl.Tensor(edsl.LogicalShape(dtype, np_b.shape))
y = op.square(dist(x, b))
test_result = get_jacobian([x, b], [np_x, np_b], y, x)
true_result = np.zeros((3, 3, 3))
true_result[0, :, 0] = 0
true_result[1, :, 1] = 2
true_result[2, :, 2] = 4
npt.assert_allclose(test_result, true_result)
def square(x):
logger.debug('square(x: {})'.format(x))
return _KerasNode('square', tensor=plaidml_op.square(x.tensor))
def square(x):
return _KerasNode('square', tensor=plaidml_op.square(x.tensor))