experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Added additional gradient tests for where_v2

Browse Source 
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
This commit is contained in:
Yong Tang 2019-05-14 18:30:56 +00:00
parent 518a2bad14
commit 0566e47d73
1 changed files with 125 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

128
tensorflow/python/kernel_tests/cwise_ops_test.py
View File

@ -326,14 +326,19 @@ class SelectOpTest(test.TestCase):
self.assertAllEqual(np_ans, tf_ans) self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, out) self.assertShapeEqual(np_ans, out)
def _compareGradientX(self, fn, c, x, y, numeric_gradient_type=None): def _compareGradientX(self, fn, c, x, y, numeric_gradient_type=None,
x_init_value=None):
with self.cached_session(): with self.cached_session():
inx = ops.convert_to_tensor(x) inx = ops.convert_to_tensor(x)
iny = ops.convert_to_tensor(y) iny = ops.convert_to_tensor(y)
out = fn(c, inx, iny) out = fn(c, inx, iny)
s = list(np.shape(c)) s = list(np.shape(c))
if x_init_value is None:
x_init_value = x
if x.shape != y.shape:
x_init_value = np.broadcast_to(y, x.shape)
jacob_t, jacob_n = gradient_checker.compute_gradient( jacob_t, jacob_n = gradient_checker.compute_gradient(
inx, s, out, s, x_init_value=x) inx, s, out, s, x_init_value=x_init_value)
if numeric_gradient_type is not None: if numeric_gradient_type is not None:
xf = x.astype(numeric_gradient_type) xf = x.astype(numeric_gradient_type)
yf = y.astype(numeric_gradient_type) yf = y.astype(numeric_gradient_type)
@ -357,7 +362,7 @@ class SelectOpTest(test.TestCase):
out = fn(c, inx, iny) out = fn(c, inx, iny)
s = list(np.shape(c)) s = list(np.shape(c))
jacob_t, jacob_n = gradient_checker.compute_gradient( jacob_t, jacob_n = gradient_checker.compute_gradient(
iny, s, out, s, x_init_value=y, delta=1.0) iny, s, out, s, x_init_value=x, delta=1.0)
if numeric_gradient_type is not None: if numeric_gradient_type is not None:
xf = x.astype(numeric_gradient_type) xf = x.astype(numeric_gradient_type)
yf = y.astype(numeric_gradient_type) yf = y.astype(numeric_gradient_type)
@ -392,6 +397,49 @@ class SelectOpTest(test.TestCase):
self._testScalar(array_ops.where) self._testScalar(array_ops.where)
self._testScalar(array_ops.where_v2) self._testScalar(array_ops.where_v2)
def _testScalarBroadcast(self, fn, c, x, y):
for t in [
np.float16, np.float32, np.float64, np.int32, np.int64, np.complex64,
np.complex128
]:
xt = x.astype(t)
yt = y.astype(t)
self._compare(fn, c, xt, yt, use_gpu=False)
if t in [np.float16, np.float32, np.float64]:
self._compare(fn, c, xt, yt, use_gpu=True)
def testScalarBroadcast(self):
c = True
# where_v2 only
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1, 1) * 100
self._testScalarBroadcast(array_ops.where_v2, c, x, y)
self._testScalarBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 3, 1) * 100
self._testScalarBroadcast(array_ops.where_v2, c, x, y)
self._testScalarBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1, 2) * 100
self._testScalarBroadcast(array_ops.where_v2, c, x, y)
self._testScalarBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1) * 100
self._testScalarBroadcast(array_ops.where_v2, c, x, y)
self._testScalarBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1) * 100
self._testScalarBroadcast(array_ops.where_v2, c, x, y)
self._testScalarBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 2) * 100
self._testScalarBroadcast(array_ops.where_v2, c, x, y)
self._testScalarBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(3, 2) * 100
self._testScalarBroadcast(array_ops.where_v2, c, x, y)
self._testScalarBroadcast(array_ops.where_v2, c, y, x)
def _testBasic(self, fn): def _testBasic(self, fn):
c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2) c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2)
x = np.random.rand(1, 3, 2) * 100 x = np.random.rand(1, 3, 2) * 100
@ -410,6 +458,53 @@ class SelectOpTest(test.TestCase):
self._testBasic(array_ops.where) self._testBasic(array_ops.where)
self._testBasic(array_ops.where_v2) self._testBasic(array_ops.where_v2)
def _testBasicBroadcast(self, fn, c, x, y):
for t in [
np.float16, np.float32, np.float64, np.int32, np.int64, np.complex64,
np.complex128
]:
xt = x.astype(t)
yt = y.astype(t)
self._compare(fn, c, xt, yt, use_gpu=False)
if t in [np.float16, np.float32, np.float64]:
self._compare(fn, c, xt, yt, use_gpu=True)
def testBasicBroadcast(self):
c0 = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2)
c1 = np.random.randint(0, 2, 2).astype(np.bool).reshape(1, 1, 2)
c2 = np.random.randint(0, 2, 3).astype(np.bool).reshape(1, 3, 1)
c3 = np.random.randint(0, 2, 1).astype(np.bool).reshape(1, 1, 1)
for c in [c0, c1, c2, c3]:
# where_v2 only
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1, 1) * 100
self._testBasicBroadcast(array_ops.where_v2, c, x, y)
self._testBasicBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 3, 1) * 100
self._testBasicBroadcast(array_ops.where_v2, c, x, y)
self._testBasicBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1, 2) * 100
self._testBasicBroadcast(array_ops.where_v2, c, x, y)
self._testBasicBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1) * 100
self._testBasicBroadcast(array_ops.where_v2, c, x, y)
self._testBasicBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1) * 100
self._testBasicBroadcast(array_ops.where_v2, c, x, y)
self._testBasicBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 2) * 100
self._testBasicBroadcast(array_ops.where_v2, c, x, y)
self._testBasicBroadcast(array_ops.where_v2, c, y, x)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(3, 2) * 100
self._testBasicBroadcast(array_ops.where_v2, c, x, y)
self._testBasicBroadcast(array_ops.where_v2, c, y, x)
def _testGradients(self, fn): def _testGradients(self, fn):
c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2) c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2)
x = np.random.rand(1, 3, 2) * 100 x = np.random.rand(1, 3, 2) * 100
@ -434,6 +529,33 @@ class SelectOpTest(test.TestCase):
self._testGradients(array_ops.where) self._testGradients(array_ops.where)
self._testGradients(array_ops.where_v2) self._testGradients(array_ops.where_v2)
@test_util.run_deprecated_v1
def testGradientsBroadcast(self):
c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2)
for t in [np.float32, np.float64]:
# where_v2 only
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1, 1) * 100
self._compareGradientX(array_ops.where_v2, c, x.astype(t), y.astype(t))
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 3, 1) * 100
self._compareGradientX(array_ops.where_v2, c, x.astype(t), y.astype(t))
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1, 2) * 100
self._compareGradientX(array_ops.where_v2, c, x.astype(t), y.astype(t))
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 1) * 100
self._compareGradientX(array_ops.where_v2, c, x.astype(t), y.astype(t))
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1) * 100
self._compareGradientX(array_ops.where_v2, c, x.astype(t), y.astype(t))
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(1, 2) * 100
self._compareGradientX(array_ops.where_v2, c, x.astype(t), y.astype(t))
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(3, 2) * 100
self._compareGradientX(array_ops.where_v2, c, x.astype(t), y.astype(t))
def _testShapeMismatch(self, fn): def _testShapeMismatch(self, fn):
c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2) c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2)
x = np.random.rand(1, 3, 2) * 100 x = np.random.rand(1, 3, 2) * 100