def testXlog1pyWithNegOne(self):
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
x = constant_op.constant(np.zeros((2, 3)), dtype=dtype)
y = constant_op.constant([[0.1, 0.2, 3.5], [-1., 1., 2.]], dtype=dtype)
with test_util.use_gpu():
xlog1py_tf_np = self.evaluate(math_ops.xlog1py(x, y))
zeros_np = self.evaluate(array_ops.zeros_like(y))
self.assertAllClose(xlog1py_tf_np, zeros_np)
def testXlog1pyNoNeg1(self):
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
x = constant_op.constant([[0.1, 0.2, 3.5], [-2., -5., 30.]], dtype=dtype)
y = constant_op.constant([[-0.1, -0.2, 3.5], [3.1, -0.9, 2.]],
dtype=dtype)
with test_util.use_gpu():
xlog1py = self.evaluate(math_ops.xlog1py(x, y))
xtimeslog1py = self.evaluate(x * math_ops.log1p(y))
self.assertAllClose(xlog1py, xtimeslog1py)
def testXlog1pyWithZeroBroadcast(self):
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
x = constant_op.constant([[0.], [1.]], dtype=dtype)
y = constant_op.constant([[-0.1, -0.2, -1.], [0., 1., 2.]], dtype=dtype)
with test_util.use_gpu():
xlog1py_tf_np = self.evaluate(math_ops.xlog1py(x, y))
zeros_np = self.evaluate(array_ops.zeros_like(y[0]))
xtimes_log1py = self.evaluate(math_ops.log1p(y[1]))
self.assertAllClose(zeros_np, xlog1py_tf_np[0])
self.assertAllClose(xtimes_log1py, xlog1py_tf_np[1])
def _xlog1py_gradients(self, x, y):
xlog1py_xgrad = self.evaluate(
gradients.gradients(math_ops.xlog1py(x, y), x)[0])
xlog1py_ygrad = self.evaluate(
gradients.gradients(math_ops.xlog1py(x, y), y)[0])
return xlog1py_xgrad, xlog1py_ygrad