def _testTypes(self, vals):
for dtype in [np.float32, np.float64, np.int32, np.int64]:
x = np.zeros(vals.shape).astype(dtype)
y = vals.astype(dtype)
var_value, op_value = self._initAssignFetch(x, y, use_gpu=False)
self.assertAllEqual(y, var_value)
self.assertAllEqual(y, op_value)
var_value, op_value = self._initAssignAddFetch(x, y, use_gpu=False)
self.assertAllEqual(x + y, var_value)
self.assertAllEqual(x + y, op_value)
var_value, op_value = self._initAssignSubFetch(x, y, use_gpu=False)
self.assertAllEqual(x - y, var_value)
self.assertAllEqual(x - y, op_value)
if test.is_built_with_gpu_support() and dtype in [
np.float32, np.float64
]:
var_value, op_value = self._initAssignFetch(x, y, use_gpu=True)
self.assertAllEqual(y, var_value)
self.assertAllEqual(y, op_value)
var_value, op_value = self._initAssignAddFetch(x,
y,
use_gpu=True)
self.assertAllEqual(x + y, var_value)
self.assertAllEqual(x + y, op_value)
var_value, op_value = self._initAssignSubFetch(x,
y,
use_gpu=False)
self.assertAllEqual(x - y, var_value)
self.assertAllEqual(x - y, op_value)
def testInvalidLabel(self):
features = [[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 2., 3., 4.],
[1., 2., 3., 4.]]
labels = [4, 3, 0, -1]
if test.is_built_with_gpu_support() and test.is_gpu_available():
with self.session(use_gpu=True) as sess:
loss, backprop = (
gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
features, labels))
tf_loss, tf_backprop = self.evaluate([loss, backprop])
self.assertAllClose(
[[np.nan] * 4, [0.25, 0.25, 0.25, -0.75],
[-0.968, 0.087, 0.237, 0.6439], [np.nan] * 4],
tf_backprop,
rtol=1e-3,
atol=1e-3)
self.assertAllClose([np.nan, 1.3862, 3.4420, np.nan],
tf_loss,
rtol=1e-3,
atol=1e-3)
with self.session(use_gpu=False) as sess:
loss, backprop = (
gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
features, labels))
with self.assertRaisesOpError("Received a label value of"):
self.evaluate([loss, backprop])
def testImportedFunctionsRegistered(self):
if test.is_built_with_gpu_support():
self.skipTest(
"Disabling this new test due to errors with cuda and rocm")
with ops.Graph().as_default() as graph:
x = array_ops.placeholder(dtypes.variant, shape=[], name='foo')
ds = dataset_ops.from_variant(x,
structure=(structure.TensorStructure(
dtypes.int32, [])))
y = ds.reduce(array_ops.zeros([], dtype=dtypes.int32),
lambda p, q: p + q)
graph_def = graph.as_graph_def()
def fn_to_wrap(a):
returned_elements = graph_def_importer.import_graph_def(
graph_def, input_map={x.name: a}, return_elements=[y.name])
return returned_elements[0]
wrapped_fn = wrap_function.wrap_function(
fn_to_wrap, [tensor_spec.TensorSpec((), dtypes.variant)])
ds = dataset_ops.Dataset.from_tensor_slices([10, 20])
v = dataset_ops.to_variant(ds)
self.evaluate(wrapped_fn(v))
class SoftmaxTest(test.TestCase):
def _npSoftmax(self, features, dim=-1, log=False):
if dim == -1:
dim = len(features.shape) - 1
one_only_on_dim = list(features.shape)
one_only_on_dim[dim] = 1
is_fp16 = features.dtype == np.float16
if is_fp16:
# Do the compute in fp32 and cast the input back to fp32.
features = features.astype(np.float32)
e = np.exp(features -
np.reshape(np.amax(features, axis=dim), one_only_on_dim))
softmax = e / np.reshape(np.sum(e, axis=dim), one_only_on_dim)
if log:
res = np.log(softmax)
else:
res = softmax
if is_fp16:
res = res.astype(np.float16)
return res
def _testSoftmax(self, np_features, dim=-1, log=False, use_gpu=False):
# A previous version of the code checked the op name rather than the op type
# to distinguish between log and non-log. Use an arbitrary name to catch
# this bug in future.
name = "arbitrary"
np_softmax = self._npSoftmax(np_features, dim=dim, log=log)
with self.cached_session(use_gpu=use_gpu):
if log:
tf_softmax = nn_ops.log_softmax(np_features,
axis=dim,
name=name)
else:
tf_softmax = nn_ops.softmax(np_features, axis=dim, name=name)
out = self.evaluate(tf_softmax)
self.assertAllCloseAccordingToType(np_softmax, out)
self.assertShapeEqual(np_softmax, tf_softmax)
if not log:
# Bonus check: the softmaxes should add to one in dimension dim.
sum_along_dim = np.sum(out, axis=dim)
self.assertAllCloseAccordingToType(np.ones(sum_along_dim.shape),
sum_along_dim)
def _testAll(self, features):
self._testSoftmax(features, use_gpu=True)
self._testSoftmax(features, log=True, use_gpu=True)
self._testOverflow(use_gpu=True)
def testNpSoftmax(self):
features = [[1., 1., 1., 1.], [1., 2., 3., 4.]]
# Batch 0: All exps are 1. The expected result is
# Softmaxes = [0.25, 0.25, 0.25, 0.25]
# LogSoftmaxes = [-1.386294, -1.386294, -1.386294, -1.386294]
#
# Batch 1:
# exps = [1., 2.718, 7.389, 20.085]
# sum = 31.192
# Softmaxes = exps / sum = [0.0320586, 0.08714432, 0.23688282, 0.64391426]
# LogSoftmaxes = [-3.44019 , -2.44019 , -1.44019 , -0.44019]
np_sm = self._npSoftmax(np.array(features))
self.assertAllClose(np.array(
[[0.25, 0.25, 0.25, 0.25],
[0.0320586, 0.08714432, 0.23688282, 0.64391426]]),
np_sm,
rtol=1.e-5,
atol=1.e-5)
np_lsm = self._npSoftmax(np.array(features), log=True)
self.assertAllClose(np.array(
[[-1.386294, -1.386294, -1.386294, -1.386294],
[-3.4401897, -2.4401897, -1.4401897, -0.4401897]]),
np_lsm,
rtol=1.e-5,
atol=1.e-5)
def _testOverflow(self, use_gpu=False):
if use_gpu:
type = np.float32 # pylint: disable=redefined-builtin
else:
type = np.float64 # pylint: disable=redefined-builtin
max = np.finfo(type).max # pylint: disable=redefined-builtin
features = np.array([[1., 1., 1., 1.], [max, 1., 2., 3.]]).astype(type)
with self.cached_session(use_gpu=use_gpu):
tf_log_softmax = nn_ops.log_softmax(features)
out = self.evaluate(tf_log_softmax)
self.assertAllClose(
np.array([[-1.386294, -1.386294, -1.386294, -1.386294],
[0, -max, -max, -max]]),
out,
rtol=1.e-5,
atol=1.e-5)
def testFloat(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float32))
@unittest.skipUnless(test.is_built_with_gpu_support(),
"Test only applicable when running on GPUs")
def testFloatGPU(self):
if test.is_gpu_available(cuda_only=True):
rows = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
cols = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
for row, col in zip(rows, cols):
logging.info("Testing softmax float dtype in shape [%d, %d]",
row, col)
data = np.random.rand(row, col)
self._testAll(data.astype(np.float32))
def testHalf(self):
self._testAll(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16))
@unittest.skipUnless(test.is_built_with_gpu_support(),
"Test only applicable when running on GPUs")
def testHalfGPU(self):
if test.is_gpu_available(cuda_only=True):
rows = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
cols = [2**x + np.random.randint(0, 16) for x in range(1, 4)]
for row, col in zip(rows, cols):
logging.info("Testing softmax half dtype in shape [%d, %d]",
row, col)
data = np.random.rand(row, col)
self._testAll(data.astype(np.float16))
def testDouble(self):
self._testSoftmax(
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64))
self._testOverflow()
def test1DTensorAsInput(self):
self._testSoftmax(np.array([3., 2., 3., 9.]).astype(np.float64),
use_gpu=False)
self._testOverflow(use_gpu=False)
def test1DTensorAsInputNoReshape(self):
self._testSoftmax(np.array([3., 2., 3., 9.]).astype(np.float64),
use_gpu=False)
self._testOverflow(use_gpu=False)
def test3DTensorAsInput(self):
self._testSoftmax(np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.],
[1., 2., 3., 4.]]]).astype(np.float32),
use_gpu=False)
self._testOverflow(use_gpu=False)
def test3DTensorAsInputNoReshape(self):
self._testSoftmax(np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.],
[1., 2., 3., 4.]]]).astype(np.float32),
use_gpu=False)
self._testOverflow(use_gpu=False)
def testAlongFirstDimension(self):
self._testSoftmax(np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.],
[1., 2., 3., 4.]]]).astype(np.float32),
dim=0,
use_gpu=False)
self._testOverflow(use_gpu=False)
def testAlongSecondDimension(self):
self._testSoftmax(np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.],
[1., 2., 3., 4.]]]).astype(np.float32),
dim=1,
use_gpu=False)
self._testOverflow(use_gpu=False)
def testAlongNegativeDimension(self):
self._testSoftmax(np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.],
[1., 2., 3., 4.]]]).astype(np.float32),
dim=-2,
use_gpu=False)
self._testOverflow(use_gpu=False)
def testShapeInference(self):
op = nn_ops.softmax([[[1., 1., 1., 1.], [1., 2., 3., 4.]],
[[2., 3., 4., 5.], [6., 7., 8., 9.]],
[[5., 4., 3., 2.], [1., 2., 3., 4.]]])
self.assertEqual([3, 2, 4], op.get_shape())
@test_util.run_deprecated_v1
def testEmptyInput(self):
with self.cached_session():
x = array_ops.placeholder(dtypes.float32, shape=[0, 3])
self.assertEqual(0, array_ops.size(x).eval())
# reshape would raise if logits is empty
with self.assertRaises(errors_impl.InvalidArgumentError):
nn_ops.softmax(x, axis=0).eval()
def testDimTooLarge(self):
with self.cached_session():
# Use placeholder to make sure we get runtime error instead of shape
# inference error.
dim = array_ops.placeholder_with_default(100, shape=[])
with self.assertRaises(errors_impl.InvalidArgumentError):
nn_ops.softmax([1., 2., 3., 4.], axis=dim).eval()
def testInvalidAxis(self):
# Test case for GitHub issue 22793.
with self.cached_session():
ones = array_ops.ones(shape=[2, 3])
with self.assertRaises(errors_impl.InvalidArgumentError):
nn_ops.softmax(ones, axis=2).eval()
@test_util.run_deprecated_v1
def testLargeDims(self):
# Make sure that we properly handle large inputs. See
# https://github.com/tensorflow/tensorflow/issues/4425 for details
for dims in [129, 256]:
ones = np.random.rand(dims, dims).astype(np.float32)
np_softmax = self._npSoftmax(ones)
for use_gpu in [True, False]:
with self.cached_session(use_gpu=use_gpu) as sess:
x = array_ops.placeholder(dtypes.float32)
y = nn_ops.softmax(x)
tf_softmax = sess.run(y, feed_dict={x: ones})
self.assertAllClose(tf_softmax, np_softmax)
