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Fix expand_dims of dim argument has been deprecated with axis #18567

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merged 2 commits into from
Sep 24, 2018
Merged

Fix expand_dims of dim argument has been deprecated with axis #18567

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c22d996
Fix expand_dims of dims argument has been deprecated with axis
imsheridan Apr 16, 2018
413ac36
Merge branch 'master' into fix_expand_dims
martinwicke Sep 22, 2018
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4 changes: 2 additions & 2 deletions tensorflow/contrib/layers/python/layers/target_column.py
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Original file line number Diff line number Diff line change
Expand Up @@ -396,7 +396,7 @@ def logits_to_predictions(self, logits, proba=False):
def _mean_squared_loss(logits, target):
# To prevent broadcasting inside "-".
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
target = array_ops.expand_dims(target, axis=1)

logits.get_shape().assert_is_compatible_with(target.get_shape())
return math_ops.square(logits - math_ops.to_float(target))
Expand All @@ -405,7 +405,7 @@ def _mean_squared_loss(logits, target):
def _log_loss_with_two_classes(logits, target):
# sigmoid_cross_entropy_with_logits requires [batch_size, 1] target.
if len(target.get_shape()) == 1:
target = array_ops.expand_dims(target, dim=[1])
target = array_ops.expand_dims(target, axis=1)
loss_vec = nn.sigmoid_cross_entropy_with_logits(
labels=math_ops.to_float(target), logits=logits)
return loss_vec
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10 changes: 5 additions & 5 deletions tensorflow/contrib/learn/python/learn/estimators/head.py
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Expand Up @@ -563,10 +563,10 @@ def _mean_squared_loss(labels, logits, weights=None):
labels = ops.convert_to_tensor(labels)
# To prevent broadcasting inside "-".
if len(labels.get_shape()) == 1:
labels = array_ops.expand_dims(labels, axis=(1,))
labels = array_ops.expand_dims(labels, axis=1)
# TODO(zakaria): make sure it does not recreate the broadcast bug.
if len(logits.get_shape()) == 1:
logits = array_ops.expand_dims(logits, axis=(1,))
logits = array_ops.expand_dims(logits, axis=1)
logits.get_shape().assert_is_compatible_with(labels.get_shape())
loss = math_ops.square(logits - math_ops.to_float(labels), name=name)
return _compute_weighted_loss(loss, weights)
Expand All @@ -579,10 +579,10 @@ def _poisson_loss(labels, logits, weights=None):
labels = ops.convert_to_tensor(labels)
# To prevent broadcasting inside "-".
if len(labels.get_shape()) == 1:
labels = array_ops.expand_dims(labels, axis=(1,))
labels = array_ops.expand_dims(labels, axis=1)
# TODO(zakaria): make sure it does not recreate the broadcast bug.
if len(logits.get_shape()) == 1:
logits = array_ops.expand_dims(logits, axis=(1,))
logits = array_ops.expand_dims(logits, axis=1)
logits.get_shape().assert_is_compatible_with(labels.get_shape())
loss = nn.log_poisson_loss(labels, logits, compute_full_loss=True,
name=name)
Expand Down Expand Up @@ -797,7 +797,7 @@ def _log_loss_with_two_classes(labels, logits, weights=None):
# TODO(ptucker): This will break for dynamic shapes.
# sigmoid_cross_entropy_with_logits requires [batch_size, 1] labels.
if len(labels.get_shape()) == 1:
labels = array_ops.expand_dims(labels, axis=(1,))
labels = array_ops.expand_dims(labels, axis=1)
loss = nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits,
name=name)
return _compute_weighted_loss(loss, weights)
Expand Down
2 changes: 1 addition & 1 deletion tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -909,7 +909,7 @@ def get_broadcasted_observation_model(self, times):
elif unbroadcasted_shape.ndims == 2:
# Unbroadcasted shape [num features x state dimension]
broadcasted_model = array_ops.tile(
array_ops.expand_dims(unbroadcasted_model, dim=0),
array_ops.expand_dims(unbroadcasted_model, axis=0),
[array_ops.shape(times)[0], 1, 1])
elif unbroadcasted_shape.ndims == 3:
broadcasted_model = unbroadcasted_model
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2 changes: 1 addition & 1 deletion tensorflow/tools/compatibility/testdata/test_file_v0_11.py
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Expand Up @@ -94,7 +94,7 @@ def testArgRenames(self):
self.assertAllClose(
tf.reduce_logsumexp(a, [0, 1]).eval(), 6.45619344711)
self.assertAllEqual(
tf.expand_dims([[1, 2], [3, 4]], dim=1).eval(),
tf.expand_dims([[1, 2], [3, 4]], axis=1).eval(),
[[[1, 2]], [[3, 4]]])

def testArgMinMax(self):
Expand Down