expose contrib.nn module.

Change: 143121948

5 files changed, 271 insertions, 0 deletions

diff --git a/tensorflow/contrib/nn/BUILD b/tensorflow/contrib/nn/BUILD
new file mode 100644
index 0000000000..13a05bf378
--- /dev/null
+++ b/tensorflow/contrib/nn/BUILD
@@ -0,0 +1,32 @@
+# Description:
+#   Contains deprecated ops to calculate cross entropy.
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+package(default_visibility = ["//visibility:public"])
+
+py_library(
+    name = "nn_py",
+    srcs = [
+        "__init__.py",
+        "python/__init__.py",
+        "python/ops/__init__.py",
+        "python/ops/cross_entropy.py",
+    ],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = ["//tensorflow/python:nn"],
+)
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+)
diff --git a/tensorflow/contrib/nn/__init__.py b/tensorflow/contrib/nn/__init__.py
new file mode 100644
index 0000000000..c2fe913b59
--- /dev/null
+++ b/tensorflow/contrib/nn/__init__.py
@@ -0,0 +1,23 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Module for deprecated ops in tf.nn."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.nn.python.ops.cross_entropy import *
+# pylint: enable=unused-import,wildcard-import
diff --git a/tensorflow/contrib/nn/python/__init__.py b/tensorflow/contrib/nn/python/__init__.py
new file mode 100644
index 0000000000..285c094ca7
--- /dev/null
+++ b/tensorflow/contrib/nn/python/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Module for deprecated ops in tf.nn."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
diff --git a/tensorflow/contrib/nn/python/ops/__init__.py b/tensorflow/contrib/nn/python/ops/__init__.py
new file mode 100644
index 0000000000..a945bcdccf
--- /dev/null
+++ b/tensorflow/contrib/nn/python/ops/__init__.py
@@ -0,0 +1,20 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Module for deprecated ops in tf.nn."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
diff --git a/tensorflow/contrib/nn/python/ops/cross_entropy.py b/tensorflow/contrib/nn/python/ops/cross_entropy.py
new file mode 100644
index 0000000000..7e698f86de
--- /dev/null
+++ b/tensorflow/contrib/nn/python/ops/cross_entropy.py
@@ -0,0 +1,177 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Deprecated Wrappers for Neural Net (NN) Cross Entropy Operations."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.ops import nn
+
+
+# TODO(b/33392402): Formally deprecate this API.
+# After LSC (see b/33392402#comment1), this API will be deprecated and callers
+# will be suggested to use the (updated version of)
+# tf.nn.softmax_cross_entropy_with_logits.
+def deprecated_flipped_softmax_cross_entropy_with_logits(logits,
+                                                         labels,
+                                                         dim=-1,
+                                                         name=None):
+  """Computes softmax cross entropy between `logits` and `labels`.
+
+  This function diffs from tf.nn.softmax_cross_entropy_with_logits only in the
+  argument order.
+
+  Measures the probability error in discrete classification tasks in which the
+  classes are mutually exclusive (each entry is in exactly one class).  For
+  example, each CIFAR-10 image is labeled with one and only one label: an image
+  can be a dog or a truck, but not both.
+
+  **NOTE:**  While the classes are mutually exclusive, their probabilities
+  need not be.  All that is required is that each row of `labels` is
+  a valid probability distribution.  If they are not, the computation of the
+  gradient will be incorrect.
+
+  If using exclusive `labels` (wherein one and only
+  one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`.
+
+  **WARNING:** This op expects unscaled logits, since it performs a `softmax`
+  on `logits` internally for efficiency.  Do not call this op with the
+  output of `softmax`, as it will produce incorrect results.
+
+  `logits` and `labels` must have the same shape `[batch_size, num_classes]`
+  and the same dtype (either `float16`, `float32`, or `float64`).
+
+  Args:
+    logits: Unscaled log probabilities.
+    labels: Each row `labels[i]` must be a valid probability distribution.
+    dim: The class dimension. Defaulted to -1 which is the last dimension.
+    name: A name for the operation (optional).
+
+  Returns:
+    A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
+    softmax cross entropy loss.
+  """
+  return nn.softmax_cross_entropy_with_logits(
+      logits=logits, labels=labels, dim=dim, name=name)
+
+
+# TODO(b/33392402): Formally deprecate this API.
+# After LSC (see b/33392402#comment1), this API will be deprecated and callers
+# will be suggested to use the (updated version of)
+# tf.nn.sparse_softmax_cross_entropy_with_logits.
+def deprecated_flipped_sparse_softmax_cross_entropy_with_logits(logits,
+                                                                labels,
+                                                                name=None):
+  """Computes sparse softmax cross entropy between `logits` and `labels`.
+
+  This function diffs from tf.nn.sparse_softmax_cross_entropy_with_logits only
+  in the argument order.
+
+  Measures the probability error in discrete classification tasks in which the
+  classes are mutually exclusive (each entry is in exactly one class).  For
+  example, each CIFAR-10 image is labeled with one and only one label: an image
+  can be a dog or a truck, but not both.
+
+  **NOTE:**  For this operation, the probability of a given label is considered
+  exclusive.  That is, soft classes are not allowed, and the `labels` vector
+  must provide a single specific index for the true class for each row of
+  `logits` (each minibatch entry).  For soft softmax classification with
+  a probability distribution for each entry, see
+  `softmax_cross_entropy_with_logits`.
+
+  **WARNING:** This op expects unscaled logits, since it performs a softmax
+  on `logits` internally for efficiency.  Do not call this op with the
+  output of `softmax`, as it will produce incorrect results.
+
+  A common use case is to have logits of shape `[batch_size, num_classes]` and
+  labels of shape `[batch_size]`. But higher dimensions are supported.
+
+  Args:
+
+    logits: Unscaled log probabilities of rank `r` and shape
+      `[d_0, d_1, ..., d_{r-2}, num_classes]` and dtype `float32` or `float64`.
+    labels: `Tensor` of shape `[d_0, d_1, ..., d_{r-2}]` and dtype `int32` or
+      `int64`. Each entry in `labels` must be an index in `[0, num_classes)`.
+      Other values will raise an exception when this op is run on CPU, and
+      return `NaN` for corresponding corresponding loss and gradient rows
+      on GPU.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of the same shape as `labels` and of the same type as `logits`
+    with the softmax cross entropy loss.
+
+  Raises:
+    ValueError: If logits are scalars (need to have rank >= 1) or if the rank
+      of the labels is not equal to the rank of the labels minus one.
+  """
+  return nn.sparse_softmax_cross_entropy_with_logits(
+      logits=logits, labels=labels, name=name)
+
+
+# TODO(b/33392402): Formally deprecate this API.
+# After LSC (see b/33392402#comment1), this API will be deprecated and callers
+# will be suggested to use the (updated version of)
+# tf.nn.sigmoid_cross_entropy_with_logits.
+def deprecated_flipped_sigmoid_cross_entropy_with_logits(logits,
+                                                         targets,
+                                                         name=None):
+  """Computes sigmoid cross entropy given `logits`.
+
+  This function diffs from tf.nn.sigmoid_cross_entropy_with_logits only in the
+  argument order.
+
+  Measures the probability error in discrete classification tasks in which each
+  class is independent and not mutually exclusive.  For instance, one could
+  perform multilabel classification where a picture can contain both an elephant
+  and a dog at the same time.
+
+  For brevity, let `x = logits`, `z = targets`.  The logistic loss is
+
+        z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
+      = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
+      = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
+      = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
+      = (1 - z) * x + log(1 + exp(-x))
+      = x - x * z + log(1 + exp(-x))
+
+  For x < 0, to avoid overflow in exp(-x), we reformulate the above
+
+        x - x * z + log(1 + exp(-x))
+      = log(exp(x)) - x * z + log(1 + exp(-x))
+      = - x * z + log(1 + exp(x))
+
+  Hence, to ensure stability and avoid overflow, the implementation uses this
+  equivalent formulation
+
+      max(x, 0) - x * z + log(1 + exp(-abs(x)))
+
+  `logits` and `targets` must have the same type and shape.
+
+  Args:
+    logits: A `Tensor` of type `float32` or `float64`.
+    targets: A `Tensor` of the same type and shape as `logits`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of the same shape as `logits` with the componentwise
+    logistic losses.
+
+  Raises:
+    ValueError: If `logits` and `targets` do not have the same shape.
+  """
+  return nn.sigmoid_cross_entropy_with_logits(
+      logits=logits, targets=targets, name=name)