Automated g4 rollback of changelist 179258973

PiperOrigin-RevId: 179260538

2 files changed, 0 insertions, 101 deletions

diff --git a/tensorflow/contrib/nn/__init__.py b/tensorflow/contrib/nn/__init__.py
index 96d60e1498..0bc133a00e 100644
--- a/tensorflow/contrib/nn/__init__.py
+++ b/tensorflow/contrib/nn/__init__.py
@@ -21,7 +21,6 @@
 @@deprecated_flipped_sigmoid_cross_entropy_with_logits
 @@nth_element
 @@rank_sampled_softmax_loss
-@@sampled_sparse_softmax_loss
 @@scaled_softplus
 """
 
diff --git a/tensorflow/contrib/nn/python/ops/sampling_ops.py b/tensorflow/contrib/nn/python/ops/sampling_ops.py
index 63fc487dca..98749cff7e 100644
--- a/tensorflow/contrib/nn/python/ops/sampling_ops.py
+++ b/tensorflow/contrib/nn/python/ops/sampling_ops.py
@@ -24,8 +24,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
-from tensorflow.python.ops import nn_impl
-from tensorflow.python.ops import nn_ops
 
 
 def _rank_resample(weights, biases, inputs, sampled_values, num_resampled,
@@ -242,101 +240,3 @@ def rank_sampled_softmax_loss(weights,
         remove_accidental_hits=remove_accidental_hits,
         partition_strategy=partition_strategy,
         name=name)
-
-
-def sampled_sparse_softmax_loss(weights,
-                                biases,
-                                labels,
-                                inputs,
-                                num_sampled,
-                                num_classes,
-                                sampled_values=None,
-                                remove_accidental_hits=True,
-                                partition_strategy="mod",
-                                name="sampled_sparse_softmax_loss"):
-  """Computes and returns the sampled sparse softmax training loss.
-
-  This is a faster way to train a softmax classifier over a huge number of
-  classes.
-
-  This operation is for training only.  It is generally an underestimate of
-  the full softmax loss.
-
-  A common use case is to use this method for training, and calculate the full
-  softmax loss for evaluation or inference. In this case, you must set
-  `partition_strategy="div"` for the two losses to be consistent, as in the
-  following example:
-
-  ```python
-  if mode == "train":
-    loss = tf.nn.sampled_sparse_softmax_loss(
-        weights=weights,
-        biases=biases,
-        labels=labels,
-        inputs=inputs,
-        ...,
-        partition_strategy="div")
-  elif mode == "eval":
-    logits = tf.matmul(inputs, tf.transpose(weights))
-    logits = tf.nn.bias_add(logits, biases)
-    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
-        labels=tf.squeeze(labels),
-        logits=logits)
-  ```
-
-  See our [Candidate Sampling Algorithms Reference]
-  (https://www.tensorflow.org/extras/candidate_sampling.pdf)
-
-  Also see Section 3 of [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
-  ([pdf](http://arxiv.org/pdf/1412.2007.pdf)) for the math.
-
-  Args:
-    weights: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
-        objects whose concatenation along dimension 0 has shape
-        [num_classes, dim].  The (possibly-sharded) class embeddings.
-    biases: A `Tensor` of shape `[num_classes]`.  The class biases.
-    labels: A `Tensor` of type `int64` and shape `[batch_size, 1]`.
-        The index of the single target class for each row of logits.  Note that
-        this format differs from the `labels` argument of
-        `nn.sparse_softmax_cross_entropy_with_logits`.
-    inputs: A `Tensor` of shape `[batch_size, dim]`.  The forward
-        activations of the input network.
-    num_sampled: An `int`.  The number of classes to randomly sample per batch.
-    num_classes: An `int`. The number of possible classes.
-    sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
-        `sampled_expected_count`) returned by a `*_candidate_sampler` function.
-        (if None, we default to `log_uniform_candidate_sampler`)
-    remove_accidental_hits:  A `bool`.  whether to remove "accidental hits"
-        where a sampled class equals one of the target classes.  Default is
-        True.
-    partition_strategy: A string specifying the partitioning strategy, relevant
-        if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
-        Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
-    name: A name for the operation (optional).
-
-  Returns:
-    A `batch_size` 1-D tensor of per-example sampled softmax losses.
-
-  """
-  logits, _ = nn_impl._compute_sampled_logits(
-      weights=weights,
-      biases=biases,
-      labels=labels,
-      inputs=inputs,
-      num_sampled=num_sampled,
-      num_classes=num_classes,
-      num_true=1,
-      sampled_values=sampled_values,
-      subtract_log_q=True,
-      remove_accidental_hits=remove_accidental_hits,
-      partition_strategy=partition_strategy,
-      name=name)
-
-  # There is only one true label. _compute_sampled_logits puts the true logit
-  # at index 0.
-  labels = array_ops.zeros([array_ops.shape(logits)[0], 1], dtype=dtypes.int64)
-
-  sampled_losses = nn_ops.sparse_softmax_cross_entropy_with_logits(
-      labels=array_ops.squeeze(labels), logits=logits)
-  # sampled_losses is a [batch_size] tensor.
-  return sampled_losses