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

Fix sparse_softmax_cross_entropy_with_logits for empty tensor

Browse Source 
If the batch size is zero, we need to avoid calling into Eigen because Eigen
will explode.  Zero classes is an error.
Change: 126359444
This commit is contained in:
Geoffrey Irving 2016-06-30 15:49:37 -08:00 committed by TensorFlower Gardener
parent 69abcd7ec2
commit aabfe1d03e
2 changed files with 37 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

50
tensorflow/core/kernels/sparse_xent_op.cc
View File

@ -35,38 +35,42 @@ class SparseSoftmaxXentWithLogitsOp : public OpKernel {
: OpKernel(context) {}
void Compute(OpKernelContext* context) override {
const Tensor& logits_in = context->input(0);
const Tensor& labels_in = context->input(1);
OP_REQUIRES(context, logits_in.shape().dim_size(0) == labels_in.NumElements(),
const Tensor& logits = context->input(0);
const Tensor& labels = context->input(1);
OP_REQUIRES(context, TensorShapeUtils::IsMatrix(logits.shape()),
errors::InvalidArgument("logits must be 2-D, but got shape ",
logits.shape().DebugString()));
OP_REQUIRES(context, TensorShapeUtils::IsVector(labels.shape()),
errors::InvalidArgument("labels must be 1-D, but got shape ",
labels.shape().DebugString()));
OP_REQUIRES(context, logits.dim_size(0) == labels.dim_size(0),
errors::InvalidArgument(
"logits first dimension must match labels size. logits shape=",
logits_in.shape().DebugString(), " labels shape=",
labels_in.shape().DebugString()));
OP_REQUIRES(context, TensorShapeUtils::IsMatrix(logits_in.shape()),
errors::InvalidArgument("logits must be 2-dimensional"));
// As we already tested that both inputs have the same shape no need to
// check that "labels" is a matrix too.
// loss is 1-D (one per example), and size is batch_size.
"logits and labels must have the same first dimension, "
"got logits shape ",
logits.shape().DebugString(), " and labels shape ",
labels.shape().DebugString()));
OP_REQUIRES(context, logits.dim_size(1) > 0,
errors::InvalidArgument(
"Must have at least one class, but got logits shape ",
logits.shape().DebugString()));
Tensor scratch;
OP_REQUIRES_OK(
context, context->allocate_temp(DataTypeToEnum<T>::value,
TensorShape({logits_in.dim_size(0)}),
&scratch));
OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum<T>::value,
labels.shape(), &scratch));
Tensor* loss_out = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(
0, TensorShape({logits_in.dim_size(0)}), &loss_out));
context->allocate_output(0, labels.shape(), &loss_out));
Tensor* back_out = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(1, logits_in.shape(), &back_out));
context->allocate_output(1, logits.shape(), &back_out));
functor::SparseXentFunctor<Device, T, Index> functor;
functor(context->eigen_device<Device>(), logits_in.matrix<T>(),
labels_in.vec<Index>(), scratch.vec<T>(), loss_out->vec<T>(),
back_out->matrix<T>());
if (logits.dim_size(0) > 0) {
functor::SparseXentFunctor<Device, T, Index> functor;
functor(context->eigen_device<Device>(), logits.matrix<T>(),
labels.vec<Index>(), scratch.vec<T>(), loss_out->vec<T>(),
back_out->matrix<T>());
}
}
};

10
tensorflow/python/kernel_tests/sparse_xent_op_test.py
View File

@ -120,6 +120,13 @@ class SparseXentTest(tf.test.TestCase):
tf.nn.sparse_softmax_cross_entropy_with_logits(
tf.constant(1.0), tf.constant(0))
def testLabelsPlaceholderScalar(self):
with self.test_session():
labels = tf.placeholder(np.int32)
y = tf.nn.sparse_softmax_cross_entropy_with_logits([[7.]], labels)
with self.assertRaisesOpError("labels must be 1-D"):
y.eval(feed_dict={labels: 0})
def testVector(self):
with self.test_session():
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
@ -145,6 +152,9 @@ class SparseXentTest(tf.test.TestCase):
np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16),
np.array([3, 0]).astype(label_dtype))
def testEmpty(self):
self._testXent(np.zeros((0, 3)), np.zeros((0,), dtype=np.int32))
def testGradient(self):
with self.test_session():
l = tf.constant([3, 0, 1], name="l")