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Make a couple of remaining tests in math_ops_test.py run in eager mode.

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PiperOrigin-RevId: 317756434
Change-Id: I14911d5f460131f4b9bdf6031cc7011ddd6a1d53
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A. Unique TensorFlower 2020-06-22 16:16:21 -07:00 committed by TensorFlower Gardener
parent 23fc134a17
commit a26044ac2f
1 changed files with 30 additions and 10 deletions
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40
tensorflow/python/ops/math_ops_test.py
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@ -397,7 +397,6 @@ class AddNTest(test_util.TensorFlowTestCase):
self.assertAllEqual(x[0] * num_inputs, self.assertAllEqual(x[0] * num_inputs,
math_ops.add_n([tf_x[0]] * num_inputs)) math_ops.add_n([tf_x[0]] * num_inputs))
@test_util.deprecated_graph_mode_only
def testGrad(self): def testGrad(self):
np.random.seed(42) np.random.seed(42)
for num_inputs in range(1, 10): for num_inputs in range(1, 10):
@ -406,9 +405,16 @@ class AddNTest(test_util.TensorFlowTestCase):
variables.Variable(10.0 * np.random.random()) variables.Variable(10.0 * np.random.random())
for _ in range(0, num_inputs) for _ in range(0, num_inputs)
] ]
addn = math_ops.add_n(input_vars)
self.evaluate(variables.global_variables_initializer()) self.evaluate(variables.global_variables_initializer())
add_n_grad = gradients.gradients(addn, input_vars) if context.executing_eagerly():
with backprop.GradientTape() as tape:
tape.watch(input_vars)
addn = math_ops.add_n(input_vars)
add_n_grad = tape.gradient(addn, input_vars)
else:
addn = math_ops.add_n(input_vars)
add_n_grad = gradients.gradients(addn, input_vars)
self.assertAllEqual( self.assertAllEqual(
np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1
[self.evaluate(g) for g in add_n_grad]) [self.evaluate(g) for g in add_n_grad])
@ -515,18 +521,32 @@ class DivAndModTest(test_util.TensorFlowTestCase):
_ = math_ops.divide(foo, 1.) _ = math_ops.divide(foo, 1.)
_ = math_ops.div(foo, 2.) _ = math_ops.div(foo, 2.)
@test_util.deprecated_graph_mode_only
def testFloorDivGrad(self): def testFloorDivGrad(self):
a = variables.Variable(2.) a = variables.Variable(2.)
b = variables.Variable(4.) b = variables.Variable(4.)
input_vars = [a, b]
self.evaluate(variables.global_variables_initializer()) self.evaluate(variables.global_variables_initializer())
c_grad = gradients.gradients(math_ops.divide(a, b), [a, b]) if context.executing_eagerly():
self.assertAllEqual([self.evaluate(x) for x in c_grad], [.25, -.125]) # TDOO(rmlarsen): Is there a more compact way of
c_grad = gradients.gradients(math_ops.div(a, b), [a, b]) # writing this for multiple expressions?
self.assertAllEqual([self.evaluate(x) for x in c_grad], [.25, -.125]) with backprop.GradientTape() as tape:
c_grad = gradients.gradients(math_ops.floordiv(a, b), [a, b]) tape.watch(input_vars)
c_grad0 = tape.gradient(math_ops.divide(a, b), input_vars)
with backprop.GradientTape() as tape:
tape.watch(input_vars)
c_grad1 = tape.gradient(math_ops.div(a, b), input_vars)
with backprop.GradientTape() as tape:
tape.watch(input_vars)
c_grad2 = tape.gradient(math_ops.floordiv(a, b), input_vars)
else:
c_grad0 = gradients.gradients(math_ops.divide(a, b), input_vars)
c_grad1 = gradients.gradients(math_ops.div(a, b), input_vars)
c_grad2 = gradients.gradients(math_ops.floordiv(a, b), input_vars)
self.assertAllEqual([self.evaluate(x) for x in c_grad0], [.25, -.125])
self.assertAllEqual([self.evaluate(x) for x in c_grad1], [.25, -.125])
self.assertAllEqual( self.assertAllEqual(
[None if x is None else self.evaluate(x) for x in c_grad], [None, None]) [None if x is None else self.evaluate(x) for x in c_grad2],
[None, None])
def testConsistent(self): def testConsistent(self):
nums, divs = self.intTestData() nums, divs = self.intTestData()