def f(a, b, reverse=False):
res = tf_np.sum(tf_np.sqrt(tf_np.exp(a)) + b)
res = (res, 10)
if allow_static_outputs:
res = res + (Thing(20),)
if reverse:
res = tuple(reversed(res))
return res
def testRematLambdaFunction(self):
f = lambda a, b: tf_np.sum(tf_np.sqrt(tf_np.exp(a)) + b)
f_remat = extensions.remat(f)
shape = [10]
a = tf_np.random.randn(*shape)
b = tf_np.random.randn(*shape)
actual = extensions.grad(f_remat)(a, b)
expected = extensions.grad(f)(a, b)
self.assertAllClose(actual, expected)
def f(a, b): return tf_np.sum(tf_np.sqrt(tf_np.exp(a)) + b)
def f(a, b):
y = tf_np.sum(tf_np.sqrt(tf_np.exp(a)) + b)
if has_aux:
return y, tf_np.asarray(1)
else:
return y
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))