def testInt(self):
np.random.seed(54321)
x = [np.random.randint(-128, 128, (5, 4, 3, 2, 1)) for _ in range(6)]
tf_x = ops.convert_n_to_tensor(x)
with self.test_session(use_gpu=True):
self.assertAllEqual(sum(x), av2.accumulate_n_v2(tf_x).eval())
self.assertAllEqual(x[0] * 6, av2.accumulate_n_v2([tf_x[0]] * 6).eval())
def testInt(self):
np.random.seed(54321)
x = [np.random.randint(-128, 128, (5, 4, 3, 2, 1)) for _ in range(6)]
tf_x = ops.convert_n_to_tensor(x)
with self.test_session(use_gpu=True):
self.assertAllEqual(sum(x), av2.accumulate_n_v2(tf_x).eval())
self.assertAllEqual(x[0] * 6, av2.accumulate_n_v2([tf_x[0]] * 6).eval())
def testFloat(self):
np.random.seed(12345)
x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
tf_x = ops.convert_n_to_tensor(x)
with self.test_session(use_gpu=True):
self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).eval())
self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).eval())
def testFloat(self):
np.random.seed(12345)
x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
tf_x = ops.convert_n_to_tensor(x)
with self.test_session(use_gpu=True):
self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).eval())
self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).eval())
def testWrongShape(self):
with self.test_session():
with self.assertRaises(ValueError):
a = variables.Variable(0.2)
b = variables.Variable(0.1)
tf_val = av2.accumulate_n_v2([a, b],
shape=[2,
2]) # Should be shape=[]
def testGrad(self):
np.random.seed(42)
for num_inputs in range(1, 10):
with self.test_session(use_gpu=True) as sess:
input_vars = [
variables.Variable(10.0 * np.random.random())
for i in range(0, num_inputs)
]
accum_n = av2.accumulate_n_v2(input_vars)
sess.run(variables.global_variables_initializer())
accum_n_grad = gradients.gradients(accum_n, input_vars)
self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1
[g.eval() for g in accum_n_grad])
def testSimple(self):
with self.test_session():
random_arrays = [
np.random.rand(16, 16, 16, 16).astype(np.float32) for _ in range(20)
]
random_tensors = [
ops.convert_to_tensor(x, dtype=dtypes_lib.float32)
for x in random_arrays
]
tf_val = av2.accumulate_n_v2(random_tensors)
np_val = random_arrays[0]
for random_array in random_arrays[1:]:
np_val += random_array
self.assertAllClose(np_val, tf_val.eval())
def testSimple(self):
with self.test_session():
random_arrays = [
np.random.rand(16, 16, 16, 16).astype(np.float32) for _ in range(20)
]
random_tensors = [
ops.convert_to_tensor(
x, dtype=dtypes_lib.float32) for x in random_arrays
]
tf_val = av2.accumulate_n_v2(random_tensors)
np_val = random_arrays[0]
for random_array in random_arrays[1:]:
np_val += random_array
self.assertAllClose(np_val, tf_val.eval())
def testMinimalEagerMode(self):
forty = constant_op.constant(40)
two = constant_op.constant(2)
answer = av2.accumulate_n_v2([forty, two])
self.assertEqual(42, answer.numpy())
def testZeroArgs(self):
with self.test_session():
with self.assertRaises(ValueError):
tf_val = av2.accumulate_n_v2([])
tf_val.eval()
def testWrongTypeOneInput(self):
# Scenario that used to trigger a bug, even when testWrongType() worked
with self.test_session():
with self.assertRaises(TypeError):
a = variables.Variable(0.2, dtype=np.float32)
tf_val = av2.accumulate_n_v2([a], tensor_dtype=np.int32)
def testWrongType(self):
with self.test_session():
with self.assertRaises(TypeError):
a = variables.Variable(0.2, dtype=np.float32)
b = variables.Variable(0.1, dtype=np.float32)
tf_val = av2.accumulate_n_v2([a, b], tensor_dtype=np.int32)
def testIncompatibleShapes(self):
with self.test_session():
with self.assertRaises(ValueError):
a = variables.Variable(np.array([0.1, 0.2]))
b = variables.Variable(np.array([[0.3], [0.4]]))
tf_val = av2.accumulate_n_v2([a, b])
def testIncompatibleShapes(self):
with self.test_session():
with self.assertRaises(ValueError):
a = variables.Variable(np.array([0.1, 0.2]))
b = variables.Variable(np.array([[0.3], [0.4]]))
tf_val = av2.accumulate_n_v2([a, b])
def testWrongShape(self):
with self.test_session():
with self.assertRaises(ValueError):
a = variables.Variable(0.2)
b = variables.Variable(0.1)
tf_val = av2.accumulate_n_v2([a, b], shape=[2, 2]) # Should be shape=[]
def testZeroArgs(self):
with self.test_session():
with self.assertRaises(ValueError):
tf_val = av2.accumulate_n_v2([])
tf_val.eval()
def fn(first, second, third):
return av2.accumulate_n_v2([first, second, third])
def testWrongTypeOneInput(self):
# Scenario that used to trigger a bug, even when testWrongType() worked
with self.test_session():
with self.assertRaises(TypeError):
a = variables.Variable(0.2, dtype=np.float32)
tf_val = av2.accumulate_n_v2([a], tensor_dtype=np.int32)
def testWrongType(self):
with self.test_session():
with self.assertRaises(TypeError):
a = variables.Variable(0.2, dtype=np.float32)
b = variables.Variable(0.1, dtype=np.float32)
tf_val = av2.accumulate_n_v2([a, b], tensor_dtype=np.int32)
def fn(first, second, third): return av2.accumulate_n_v2([first, second, third])
def testMinimalEagerMode(self): forty = constant_op.constant(40) two = constant_op.constant(2) answer = av2.accumulate_n_v2([forty, two]) self.assertEqual(42, answer.numpy())
