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Losses should support scalar logits/labels.

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Change: 144366312
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A. Unique TensorFlower 2017-01-12 13:46:06 -08:00 committed by TensorFlower Gardener
parent 3689c21345
commit 5ccc994e7e
2 changed files with 12 additions and 2 deletions
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7
tensorflow/python/kernel_tests/losses_test.py
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@ -766,6 +766,13 @@ class MeanSquaredErrorTest(test.TestCase):
losses.mean_squared_error( losses.mean_squared_error(
self._predictions, self._predictions, weights=None) self._predictions, self._predictions, weights=None)
def testScalar(self):
with self.test_session():
self.assertEqual(
0.0,
losses.mean_squared_error(predictions=constant_op.constant(0),
labels=constant_op.constant(0)).eval())
def testAllCorrectNoLossWeight(self): def testAllCorrectNoLossWeight(self):
loss = losses.mean_squared_error(self._predictions, self._predictions) loss = losses.mean_squared_error(self._predictions, self._predictions)
with self.test_session(): with self.test_session():

3
tensorflow/python/ops/losses/losses_impl.py
View File

@ -119,6 +119,9 @@ def _num_present(losses, weights, per_batch=False):
""" """
# If weights is a scalar, its easy to compute: # If weights is a scalar, its easy to compute:
if weights.get_shape().ndims == 0: if weights.get_shape().ndims == 0:
if losses.get_shape().ndims == 0:
batch_size = 1
else:
batch_size = array_ops.reshape(array_ops.slice(array_ops.shape(losses), batch_size = array_ops.reshape(array_ops.slice(array_ops.shape(losses),
[0], [1]), []) [0], [1]), [])
num_per_batch = math_ops.div(math_ops.to_float(array_ops.size(losses)), num_per_batch = math_ops.div(math_ops.to_float(array_ops.size(losses)),