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TensorFlow: upstream changes to git (doc fixes).

Browse Source 
Changes:

- Fix typos across several files contributed by Erik Erwitt,
  and Michael R. Berstein

- Fix bug in translate example (fr->en typo) by schuster

- Updates to some documentation (mcoram,shlens,vrv,joshl)

- Fix to Android camera demo app window size detection (andrewharp)

- Fix to support lookup table of high rank tensors (yleon)

- Fix invalid op names for parse_example (dga)

Base CL: 107531031
This commit is contained in:
Vijay Vasudevan 2015-11-10 15:23:01 -08:00
parent 9274f5aa47
commit c61c39614a
25 changed files with 209 additions and 272 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
tensorflow/core/kernels/lookup_table_op.cc
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@ -109,9 +109,6 @@ class LookupTableFindOp : public OpKernel {
OP_REQUIRES_OK(ctx, ctx->MatchSignature(expected_inputs, expected_outputs)); OP_REQUIRES_OK(ctx, ctx->MatchSignature(expected_inputs, expected_outputs));
const Tensor& input = ctx->input(1); const Tensor& input = ctx->input(1);
OP_REQUIRES(ctx, TensorShapeUtils::IsVector(input.shape()),
errors::InvalidArgument("Input must be a vector, not ",
input.shape().DebugString()));
const Tensor& default_value = ctx->input(2); const Tensor& default_value = ctx->input(2);
OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(default_value.shape()), OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(default_value.shape()),
@ -119,8 +116,7 @@ class LookupTableFindOp : public OpKernel {
default_value.shape().DebugString())); default_value.shape().DebugString()));
Tensor* out; Tensor* out;
OP_REQUIRES_OK(ctx, OP_REQUIRES_OK(ctx, ctx->allocate_output("values", input.shape(), &out));
ctx->allocate_output("output_values", input.shape(), &out));
OP_REQUIRES_OK(ctx, table->Find(input, out, default_value)); OP_REQUIRES_OK(ctx, table->Find(input, out, default_value));
} }

52
tensorflow/core/ops/data_flow_ops.cc
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@ -284,29 +284,28 @@ handle: The handle to a queue.
size: The number of elements in the given queue. size: The number of elements in the given queue.
)doc"); )doc");
// -------------------------------------------------------------------------- // --------------------------------------------------------------------------
REGISTER_OP("LookupTableFind") REGISTER_OP("LookupTableFind")
.Input("table_handle: Ref(string)") .Input("table_handle: Ref(string)")
.Input("input_values: Tin") .Input("keys: Tin")
.Input("default_value: Tout") .Input("default_value: Tout")
.Output("output_values: Tout") .Output("values: Tout")
.Attr("Tin: type") .Attr("Tin: type")
.Attr("Tout: type") .Attr("Tout: type")
.Doc(R"doc( .Doc(R"doc(
Maps elements of a tensor into associated values given a lookup table. Looks up keys in a table, outputs the corresponding values.
If an element of the input_values is not present in the table, the The tensor `keys` must of the same type as the keys of the table.
specified default_value is used. The output `values` is of the type of the table values.
The table needs to be initialized and the input and output types correspond The scalar `default_value` is the value output for keys not present in the
to the table key and value types. table. It must also be of the same type as the table values.
table_handle: A handle for a lookup table. table_handle: Handle to the table.
input_values: A vector of key values. keys: Any shape. Keys to look up.
default_value: A scalar to return if the input is not found in the table. values: Same shape as `keys`. Values found in the table, or `default_values`
output_values: A vector of values associated to the inputs. for missing keys.
)doc"); )doc");
REGISTER_OP("LookupTableSize") REGISTER_OP("LookupTableSize")
@ -315,8 +314,8 @@ REGISTER_OP("LookupTableSize")
.Doc(R"doc( .Doc(R"doc(
Computes the number of elements in the given table. Computes the number of elements in the given table.
table_handle: The handle to a lookup table. table_handle: Handle to the table.
size: The number of elements in the given table. size: Scalar that contains number of elements in the table.
)doc"); )doc");
REGISTER_OP("HashTable") REGISTER_OP("HashTable")
@ -326,18 +325,19 @@ REGISTER_OP("HashTable")
.Attr("key_dtype: type") .Attr("key_dtype: type")
.Attr("value_dtype: type") .Attr("value_dtype: type")
.Doc(R"doc( .Doc(R"doc(
Creates and holds an immutable hash table. Creates a non-initialized hash table.
The key and value types can be specified. After initialization, the table This op creates a hash table, specifying the type of its keys and values.
becomes immutable. Before using the table you will have to initialize it. After initialization the
table will be immutable.
table_handle: a handle of a the lookup table. table_handle: Handle to a table.
container: If non-empty, this hash table is placed in the given container. container: If non-empty, this table is placed in the given container.
Otherwise, a default container is used. Otherwise, a default container is used.
shared_name: If non-empty, this hash table is shared under the given name across shared_name: If non-empty, this table is shared under the given name across
multiple sessions. multiple sessions.
key_dtype: the type of the table key. key_dtype: Type of the table keys.
value_dtype: the type of the table value. value_dtype: Type of the table values.
)doc"); )doc");
REGISTER_OP("InitializeTable") REGISTER_OP("InitializeTable")
@ -349,9 +349,9 @@ REGISTER_OP("InitializeTable")
.Doc(R"doc( .Doc(R"doc(
Table initializer that takes two tensors for keys and values respectively. Table initializer that takes two tensors for keys and values respectively.
table_handle: a handle of the lookup table to be initialized. table_handle: Handle to a table which will be initialized.
keys: a vector of keys of type Tkey. keys: Keys of type Tkey.
values: a vector of values of type Tval. values: Values of type Tval. Same shape as `keys`.
)doc"); )doc");
} // namespace tensorflow } // namespace tensorflow

2
tensorflow/core/public/tensor.h
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@ -93,7 +93,7 @@ class Tensor {
/// \brief Slice this tensor along the 1st dimension. /// \brief Slice this tensor along the 1st dimension.
/// I.e., the returned tensor satisifies /// I.e., the returned tensor satisfies
/// returned[i, ...] == this[dim0_start + i, ...]. /// returned[i, ...] == this[dim0_start + i, ...].
/// The returned tensor shares the underlying tensor buffer with this /// The returned tensor shares the underlying tensor buffer with this
/// tensor. /// tensor.

2
tensorflow/core/util/events_writer.cc
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@ -25,7 +25,7 @@ bool EventsWriter::Init() {
if (FileHasDisappeared()) { if (FileHasDisappeared()) {
// Warn user of data loss and let .reset() below do basic cleanup. // Warn user of data loss and let .reset() below do basic cleanup.
if (num_outstanding_events_ > 0) { if (num_outstanding_events_ > 0) {
LOG(WARNING) << "Re-intialization, attempting to open a new file, " LOG(WARNING) << "Re-initialization, attempting to open a new file, "
<< num_outstanding_events_ << " events will be lost."; << num_outstanding_events_ << " events will be lost.";
} }
} else { } else {

31
tensorflow/examples/android/README.md
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@ -6,41 +6,46 @@ This folder contains a simple camera-based demo application utilizing Tensorflow
This demo uses a Google Inception model to classify camera frames in real-time, This demo uses a Google Inception model to classify camera frames in real-time,
displaying the top results in an overlay on the camera image. See displaying the top results in an overlay on the camera image. See
assets/imagenet_comp_graph_label_strings.txt for the possible classificiations. [`assets/imagenet_comp_graph_label_strings.txt`](assets/imagenet_comp_graph_label_strings.txt)
for the possible classifications.
## To build/install/run ## To build/install/run
As a pre-requisite, Bazel, the Android NDK, and the Android SDK must all be As a prerequisite, Bazel, the Android NDK, and the Android SDK must all be
installed on your system. The Android build tools may be obtained from: installed on your system. The Android build tools may be obtained from:
https://developer.android.com/tools/revisions/build-tools.html https://developer.android.com/tools/revisions/build-tools.html
The Android entries in [<workspace_root>/WORKSPACE](../../WORKSPACE) must be The Android entries in [`<workspace_root>/WORKSPACE`](../../WORKSPACE) must be
uncommented with the paths filled in appropriately depending on where you uncommented with the paths filled in appropriately depending on where you
installed the NDK and SDK. Otherwise an error such as: installed the NDK and SDK. Otherwise an error such as:
"The external label '//external:android/sdk' is not bound to anything" will "The external label '//external:android/sdk' is not bound to anything" will
be reported. be reported.
To build the APK, run this from your workspace root: To build the APK, run this from your workspace root:
```bash
$ bazel build //tensorflow/examples/android:tensorflow_demo -c opt --copt=-mfpu=neon
``` ```
bazel build //tensorflow/examples/android:tensorflow_demo -c opt --copt=-mfpu=neon
``` Note that `-c opt` is currently required; if not set, an assert (for an
Note that "-c opt" is currently required; if not set, an assert (for an
otherwise non-problematic issue) in Eigen will halt the application during otherwise non-problematic issue) in Eigen will halt the application during
execution. This issue will be corrected in an upcoming release. execution. This issue will be corrected in an upcoming release.
If adb debugging is enabled on your Android 5.0 or later device, you may then If adb debugging is enabled on your Android 5.0 or later device, you may then
use the following command from your workspace root to install the APK once use the following command from your workspace root to install the APK once
built: built:
'''
adb install -r -g bazel-bin/tensorflow/examples/android/tensorflow_demo_incremental.apk ```bash
''' $ adb install -r -g bazel-bin/tensorflow/examples/android/tensorflow_demo_incremental.apk
```
Alternatively, a streamlined means of building, installing and running in one Alternatively, a streamlined means of building, installing and running in one
command is: command is:
```
bazel mobile-install //tensorflow/examples/android:tensorflow_demo -c opt --start_app --copt=-mfpu=neon ```bash
$ bazel mobile-install //tensorflow/examples/android:tensorflow_demo -c opt --start_app --copt=-mfpu=neon
``` ```
If camera permission errors are encountered (possible on Android Marshmallow or If camera permission errors are encountered (possible on Android Marshmallow or
above), then the adb install command above should be used instead, as it above), then the `adb install` command above should be used instead, as it
automatically grants the required camera permissions with '-g'. automatically grants the required camera permissions with `-g`.

9
tensorflow/examples/android/src/org/tensorflow/demo/CameraConnectionFragment.java
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@ -63,6 +63,12 @@ import java.util.concurrent.TimeUnit;
public class CameraConnectionFragment extends Fragment { public class CameraConnectionFragment extends Fragment {
private static final Logger LOGGER = new Logger(); private static final Logger LOGGER = new Logger();
/**
* The camera preview size will be chosen to be the smallest frame by pixel size capable of
* containing a DESIRED_SIZE x DESIRED_SIZE square.
*/
private static final int MINIMUM_PREVIEW_SIZE = 320;
private RecognitionScoreView scoreView; private RecognitionScoreView scoreView;
/** /**
@ -227,8 +233,7 @@ public class CameraConnectionFragment extends Fragment {
// Collect the supported resolutions that are at least as big as the preview Surface // Collect the supported resolutions that are at least as big as the preview Surface
final List<Size> bigEnough = new ArrayList<>(); final List<Size> bigEnough = new ArrayList<>();
for (final Size option : choices) { for (final Size option : choices) {
// TODO(andrewharp): Choose size intelligently. if (option.getHeight() >= MINIMUM_PREVIEW_SIZE && option.getWidth() >= MINIMUM_PREVIEW_SIZE) {
if (option.getHeight() == 320 && option.getWidth() == 480) {
LOGGER.i("Adding size: " + option.getWidth() + "x" + option.getHeight()); LOGGER.i("Adding size: " + option.getWidth() + "x" + option.getHeight());
bigEnough.add(option); bigEnough.add(option);
} else { } else {

2
tensorflow/g3doc/api_docs/cc/ClassTensor.md
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@ -178,7 +178,7 @@ This tensor shares other&apos;s underlying storage. Returns `true` iff `other.sh
Slice this tensor along the 1st dimension. Slice this tensor along the 1st dimension.
I.e., the returned tensor satisifies returned[i, ...] == this[dim0_start + i, ...]. The returned tensor shares the underlying tensor buffer with this tensor. I.e., the returned tensor satisfies returned[i, ...] == this[dim0_start + i, ...]. The returned tensor shares the underlying tensor buffer with this tensor.
NOTE: The returned tensor may not satisfies the same alignment requirement as this tensor depending on the shape. The caller must check the returned tensor&apos;s alignment before calling certain methods that have alignment requirement (e.g., ` flat() `, `tensor()`). NOTE: The returned tensor may not satisfies the same alignment requirement as this tensor depending on the shape. The caller must check the returned tensor&apos;s alignment before calling certain methods that have alignment requirement (e.g., ` flat() `, `tensor()`).

52
tensorflow/g3doc/api_docs/cc/ClassTensorBuffer.md
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@ -1,52 +0,0 @@
# Class `tensorflow::TensorBuffer` <a class="md-anchor" id="AUTOGENERATED-class--tensorflow--tensorbuffer-"></a>
##Member Summary <a class="md-anchor" id="AUTOGENERATED-member-summary"></a>
* [`tensorflow::TensorBuffer::~TensorBuffer() override`](#tensorflow_TensorBuffer_TensorBuffer)
* [`virtual void* tensorflow::TensorBuffer::data() const =0`](#virtual_void_tensorflow_TensorBuffer_data)
* [`virtual size_t tensorflow::TensorBuffer::size() const =0`](#virtual_size_t_tensorflow_TensorBuffer_size)
* [`virtual TensorBuffer* tensorflow::TensorBuffer::root_buffer()=0`](#virtual_TensorBuffer_tensorflow_TensorBuffer_root_buffer)
* [`virtual void tensorflow::TensorBuffer::FillAllocationDescription(AllocationDescription *proto) const =0`](#virtual_void_tensorflow_TensorBuffer_FillAllocationDescription)
* [`T* tensorflow::TensorBuffer::base() const`](#T_tensorflow_TensorBuffer_base)
##Member Details <a class="md-anchor" id="AUTOGENERATED-member-details"></a>
#### `tensorflow::TensorBuffer::~TensorBuffer() override` <a class="md-anchor" id="tensorflow_TensorBuffer_TensorBuffer"></a>
#### `virtual void* tensorflow::TensorBuffer::data() const =0` <a class="md-anchor" id="virtual_void_tensorflow_TensorBuffer_data"></a>
#### `virtual size_t tensorflow::TensorBuffer::size() const =0` <a class="md-anchor" id="virtual_size_t_tensorflow_TensorBuffer_size"></a>
#### `virtual TensorBuffer* tensorflow::TensorBuffer::root_buffer()=0` <a class="md-anchor" id="virtual_TensorBuffer_tensorflow_TensorBuffer_root_buffer"></a>
#### `virtual void tensorflow::TensorBuffer::FillAllocationDescription(AllocationDescription *proto) const =0` <a class="md-anchor" id="virtual_void_tensorflow_TensorBuffer_FillAllocationDescription"></a>
#### `T* tensorflow::TensorBuffer::base() const` <a class="md-anchor" id="T_tensorflow_TensorBuffer_base"></a>

45
tensorflow/g3doc/api_docs/cc/ClassTensorShapeIter.md
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@ -1,45 +0,0 @@
# Class `tensorflow::TensorShapeIter` <a class="md-anchor" id="AUTOGENERATED-class--tensorflow--tensorshapeiter-"></a>
##Member Summary <a class="md-anchor" id="AUTOGENERATED-member-summary"></a>
* [`tensorflow::TensorShapeIter::TensorShapeIter(const TensorShape *shape, int d)`](#tensorflow_TensorShapeIter_TensorShapeIter)
* [`bool tensorflow::TensorShapeIter::operator==(const TensorShapeIter &rhs)`](#bool_tensorflow_TensorShapeIter_operator_)
* [`bool tensorflow::TensorShapeIter::operator!=(const TensorShapeIter &rhs)`](#bool_tensorflow_TensorShapeIter_operator_)
* [`void tensorflow::TensorShapeIter::operator++()`](#void_tensorflow_TensorShapeIter_operator_)
* [`TensorShapeDim tensorflow::TensorShapeIter::operator*()`](#TensorShapeDim_tensorflow_TensorShapeIter_operator_)
##Member Details <a class="md-anchor" id="AUTOGENERATED-member-details"></a>
#### `tensorflow::TensorShapeIter::TensorShapeIter(const TensorShape *shape, int d)` <a class="md-anchor" id="tensorflow_TensorShapeIter_TensorShapeIter"></a>
#### `bool tensorflow::TensorShapeIter::operator==(const TensorShapeIter &rhs)` <a class="md-anchor" id="bool_tensorflow_TensorShapeIter_operator_"></a>
#### `bool tensorflow::TensorShapeIter::operator!=(const TensorShapeIter &rhs)` <a class="md-anchor" id="bool_tensorflow_TensorShapeIter_operator_"></a>
#### `void tensorflow::TensorShapeIter::operator++()` <a class="md-anchor" id="void_tensorflow_TensorShapeIter_operator_"></a>
#### `TensorShapeDim tensorflow::TensorShapeIter::operator*()` <a class="md-anchor" id="TensorShapeDim_tensorflow_TensorShapeIter_operator_"></a>

12
tensorflow/g3doc/api_docs/cc/index.md
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@ -27,17 +27,15 @@ write the graph to a file.
##Classes <a class="md-anchor" id="AUTOGENERATED-classes"></a> ##Classes <a class="md-anchor" id="AUTOGENERATED-classes"></a>
* [tensorflow::Env](../../api_docs/cc/ClassEnv.md) * [tensorflow::Env](../../api_docs/cc/ClassEnv.md)
* [tensorflow::EnvWrapper](../../api_docs/cc/ClassEnvWrapper.md)
* [tensorflow::RandomAccessFile](../../api_docs/cc/ClassRandomAccessFile.md) * [tensorflow::RandomAccessFile](../../api_docs/cc/ClassRandomAccessFile.md)
* [tensorflow::WritableFile](../../api_docs/cc/ClassWritableFile.md)
* [tensorflow::EnvWrapper](../../api_docs/cc/ClassEnvWrapper.md)
* [tensorflow::Session](../../api_docs/cc/ClassSession.md) * [tensorflow::Session](../../api_docs/cc/ClassSession.md)
* [tensorflow::Status](../../api_docs/cc/ClassStatus.md) * [tensorflow::Status](../../api_docs/cc/ClassStatus.md)
* [tensorflow::Tensor](../../api_docs/cc/ClassTensor.md) * [tensorflow::Tensor](../../api_docs/cc/ClassTensor.md)
* [tensorflow::TensorBuffer](../../api_docs/cc/ClassTensorBuffer.md)
* [tensorflow::TensorShape](../../api_docs/cc/ClassTensorShape.md) * [tensorflow::TensorShape](../../api_docs/cc/ClassTensorShape.md)
* [tensorflow::TensorShapeIter](../../api_docs/cc/ClassTensorShapeIter.md)
* [tensorflow::TensorShapeUtils](../../api_docs/cc/ClassTensorShapeUtils.md) * [tensorflow::TensorShapeUtils](../../api_docs/cc/ClassTensorShapeUtils.md)
* [tensorflow::Thread](../../api_docs/cc/ClassThread.md) * [tensorflow::Thread](../../api_docs/cc/ClassThread.md)
* [tensorflow::WritableFile](../../api_docs/cc/ClassWritableFile.md)
##Structs <a class="md-anchor" id="AUTOGENERATED-structs"></a> ##Structs <a class="md-anchor" id="AUTOGENERATED-structs"></a>
@ -50,17 +48,15 @@ write the graph to a file.
<div class='sections-order' style="display: none;"> <div class='sections-order' style="display: none;">
<!-- <!--
<!-- ClassEnv.md --> <!-- ClassEnv.md -->
<!-- ClassEnvWrapper.md -->
<!-- ClassRandomAccessFile.md --> <!-- ClassRandomAccessFile.md -->
<!-- ClassWritableFile.md -->
<!-- ClassEnvWrapper.md -->
<!-- ClassSession.md --> <!-- ClassSession.md -->
<!-- ClassStatus.md --> <!-- ClassStatus.md -->
<!-- ClassTensor.md --> <!-- ClassTensor.md -->
<!-- ClassTensorBuffer.md -->
<!-- ClassTensorShape.md --> <!-- ClassTensorShape.md -->
<!-- ClassTensorShapeIter.md -->
<!-- ClassTensorShapeUtils.md --> <!-- ClassTensorShapeUtils.md -->
<!-- ClassThread.md --> <!-- ClassThread.md -->
<!-- ClassWritableFile.md -->
<!-- StructSessionOptions.md --> <!-- StructSessionOptions.md -->
<!-- StructState.md --> <!-- StructState.md -->
<!-- StructTensorShapeDim.md --> <!-- StructTensorShapeDim.md -->

2
tensorflow/g3doc/api_docs/python/constant_op.md
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@ -345,7 +345,7 @@ print sess.run(norm)
print sess.run(norm) print sess.run(norm)
``` ```
Another common use of random values is the intialization of variables. Also see Another common use of random values is the initialization of variables. Also see
the [Variables How To](../../how_tos/variables/index.md). the [Variables How To](../../how_tos/variables/index.md).
```python ```python

14
tensorflow/g3doc/api_docs/python/state_ops.md
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@ -854,10 +854,10 @@ for an extensive description of how reusing works. Here is a basic example:
```python ```python
with tf.variable_scope("foo"): with tf.variable_scope("foo"):
v = get_variable("v", [1]) # v.name == "foo/v:0" v = tf.get_variable("v", [1]) # v.name == "foo/v:0"
w = get_variable("w", [1]) # w.name == "foo/w:0" w = tf.get_variable("w", [1]) # w.name == "foo/w:0"
with tf.variable_scope("foo", reuse=True) with tf.variable_scope("foo", reuse=True)
v1 = get_variable("v") # The same as v above. v1 = tf.get_variable("v") # The same as v above.
``` ```
If initializer is `None` (the default), the default initializer passed in If initializer is `None` (the default), the default initializer passed in
@ -919,7 +919,7 @@ Basic example of sharing a variable:
```python ```python
with tf.variable_scope("foo"): with tf.variable_scope("foo"):
v = get_variable("v", [1]) v = tf.get_variable("v", [1])
with tf.variable_scope("foo", reuse=True): with tf.variable_scope("foo", reuse=True):
v1 = tf.get_variable("v", [1]) v1 = tf.get_variable("v", [1])
assert v1 == v assert v1 == v
@ -929,7 +929,7 @@ Sharing a variable by capturing a scope and setting reuse:
```python ```python
with tf.variable_scope("foo") as scope. with tf.variable_scope("foo") as scope.
v = get_variable("v", [1]) v = tf.get_variable("v", [1])
scope.reuse_variables() scope.reuse_variables()
v1 = tf.get_variable("v", [1]) v1 = tf.get_variable("v", [1])
assert v1 == v assert v1 == v
@ -940,7 +940,7 @@ getting an existing variable in a non-reusing scope.
```python ```python
with tf.variable_scope("foo") as scope. with tf.variable_scope("foo") as scope.
v = get_variable("v", [1]) v = tf.get_variable("v", [1])
v1 = tf.get_variable("v", [1]) v1 = tf.get_variable("v", [1])
# Raises ValueError("... v already exists ..."). # Raises ValueError("... v already exists ...").
``` ```
@ -950,7 +950,7 @@ does not exist in reuse mode.
```python ```python
with tf.variable_scope("foo", reuse=True): with tf.variable_scope("foo", reuse=True):
v = get_variable("v", [1]) v = tf.get_variable("v", [1])
# Raises ValueError("... v does not exists ..."). # Raises ValueError("... v does not exists ...").
``` ```

10
tensorflow/g3doc/api_docs/python/train.md
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@ -1664,10 +1664,12 @@ Adds a `Summary` protocol buffer to the event file.
This method wraps the provided summary in an `Event` procotol buffer This method wraps the provided summary in an `Event` procotol buffer
and adds it to the event file. and adds it to the event file.
You can pass the output of any summary op, as-is, to this function. You You can pass the result of evaluating any summary op, using
can also pass a `Summary` procotol buffer that you manufacture with your [`Session.run()`](client.md#Session.run] or
own data. This is commonly done to report evaluation results in event [`Tensor.eval()`](framework.md#Tensor.eval), to this
files. function. Alternatively, you can pass a `tf.Summary` protocol
buffer that you populate with your own data. The latter is
commonly done to report evaluation results in event files.
##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> ##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a>

2
tensorflow/g3doc/get_started/os_setup.md
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@ -222,7 +222,7 @@ From the root of your source tree, run:
``` bash ``` bash
$ ./configure $ ./configure
Do you wish to bulid TensorFlow with GPU support? [y/n] y Do you wish to build TensorFlow with GPU support? [y/n] y
GPU support will be enabled for TensorFlow GPU support will be enabled for TensorFlow
Please specify the location where CUDA 7.0 toolkit is installed. Refer to Please specify the location where CUDA 7.0 toolkit is installed. Refer to

11
tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
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@ -83,7 +83,9 @@ You're now all set to visualize this data using TensorBoard.
To run TensorBoard, use the command To run TensorBoard, use the command
python tensorflow/tensorboard/tensorboard.py --logdir=path/to/log-directory ```bash
python tensorflow/tensorboard/tensorboard.py --logdir=path/to/log-directory
```
where `logdir` points to the directory where the `SummaryWriter` serialized its where `logdir` points to the directory where the `SummaryWriter` serialized its
data. If this `logdir` directory contains subdirectories which contain data. If this `logdir` directory contains subdirectories which contain
@ -91,9 +93,12 @@ serialized data from separate runs, then TensorBoard will visualize the data
from all of those runs. Once TensorBoard is running, navigate your web browser from all of those runs. Once TensorBoard is running, navigate your web browser
to `localhost:6006` to view the TensorBoard. to `localhost:6006` to view the TensorBoard.
If you have pip installed TensorBoard, you can use the simpler command If you have pip installed TensorFlow, `tensorboard` is installed into
the system path, so you can use the simpler command
tensorboard --logdir=/path/to/log-directory ```bash
tensorboard --logdir=/path/to/log-directory
```
When looking at TensorBoard, you will see the navigation tabs in the top right When looking at TensorBoard, you will see the navigation tabs in the top right
corner. Each tab represents a set of serialized data that can be visualized. corner. Each tab represents a set of serialized data that can be visualized.

2
tensorflow/g3doc/how_tos/variables/index.md
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@ -93,7 +93,7 @@ weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
# Create another variable with the same value as 'weights'. # Create another variable with the same value as 'weights'.
w2 = tf.Variable(weights.initialized_value(), name="w2") w2 = tf.Variable(weights.initialized_value(), name="w2")
# Create another variable with twice the value of 'weights' # Create another variable with twice the value of 'weights'
w_twice = tf.Variable(weights.initialized_value() * 0.2, name="w_twice") w_twice = tf.Variable(weights.initialized_value() * 2.0, name="w_twice")
``` ```
### Custom Initialization <a class="md-anchor" id="AUTOGENERATED-custom-initialization"></a> ### Custom Initialization <a class="md-anchor" id="AUTOGENERATED-custom-initialization"></a>

21
tensorflow/g3doc/tutorials/deep_cnn/index.md
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@ -178,8 +178,9 @@ building operations for training a model.
> **EXERCISE:** The model architecture in `inference()` differs slightly from > **EXERCISE:** The model architecture in `inference()` differs slightly from
the CIFAR-10 model specified in the CIFAR-10 model specified in
[cuda-convnet](https://code.google.com/p/cuda-convnet/). In particular, the top [cuda-convnet](https://code.google.com/p/cuda-convnet/). In particular, the top
layers are locally connected and not fully connected. Try editing the layers of Alex's original model are locally connected and not fully connected.
architecture to exactly replicate that fully connected model. Try editing the architecture to exactly reproduce the locally connected
architecture in the top layer.
### Model Training <a class="md-anchor" id="model-training"></a> ### Model Training <a class="md-anchor" id="model-training"></a>
@ -224,7 +225,7 @@ the script `cifar10_train.py`.
python cifar10_train.py python cifar10_train.py
``` ```
**NOTE:** The first time you run any target in the CIFAR-10 tutorial, > **NOTE:** The first time you run any target in the CIFAR-10 tutorial,
the CIFAR-10 dataset is automatically downloaded. The data set is ~160MB the CIFAR-10 dataset is automatically downloaded. The data set is ~160MB
so you may want to grab a quick cup of coffee for your first run. so you may want to grab a quick cup of coffee for your first run.
@ -256,8 +257,8 @@ obtained on a Tesla K40c. If you are running on a CPU, expect slower performance
> **EXERCISE:** When experimenting, it is sometimes annoying that the first > **EXERCISE:** When experimenting, it is sometimes annoying that the first
training step can take so long. Try decreasing the number of images initially training step can take so long. Try decreasing the number of images that
that initially fill up the queue. Search for `NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN` initially fill up the queue. Search for `NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN`
in `cifar10.py`. in `cifar10.py`.
`cifar10_train.py` periodically [saves](../../api_docs/python/state_ops.md#Saver) `cifar10_train.py` periodically [saves](../../api_docs/python/state_ops.md#Saver)
@ -303,7 +304,7 @@ for this purpose.
## Evaluating a Model <a class="md-anchor" id="evaluating-a-model"></a> ## Evaluating a Model <a class="md-anchor" id="evaluating-a-model"></a>
Let us now evaluate how well the trained model performs on a hold-out data set. Let us now evaluate how well the trained model performs on a hold-out data set.
the model is evaluated by the script `cifar10_eval.py`. It constructs the model The model is evaluated by the script `cifar10_eval.py`. It constructs the model
with the `inference()` function and uses all 10,000 images in the evaluation set with the `inference()` function and uses all 10,000 images in the evaluation set
of CIFAR-10. It calculates the *precision at 1:* how often the top prediction of CIFAR-10. It calculates the *precision at 1:* how often the top prediction
matches the true label of the image. matches the true label of the image.
@ -415,9 +416,8 @@ See how-to on [Sharing Variables](../../how_tos/variable_scope/index.md).
### Launching and Training the Model on Multiple GPU cards <a class="md-anchor" id="AUTOGENERATED-launching-and-training-the-model-on-multiple-gpu-cards"></a> ### Launching and Training the Model on Multiple GPU cards <a class="md-anchor" id="AUTOGENERATED-launching-and-training-the-model-on-multiple-gpu-cards"></a>
If you have several GPU cards installed on your machine you can use them to If you have several GPU cards installed on your machine you can use them to
train the model faster with the `cifar10_multi_gpu_train.py` script. It is a train the model faster with the `cifar10_multi_gpu_train.py` script. This
variation of the training script that parallelizes the model across multiple GPU version of the training script parallelizes the model across multiple GPU cards.
cards.
```shell ```shell
python cifar10_multi_gpu_train.py --num_gpus=2 python cifar10_multi_gpu_train.py --num_gpus=2
@ -451,7 +451,8 @@ completed the CIFAR-10 tutorial.
If you are now interested in developing and training your own image If you are now interested in developing and training your own image
classification system, we recommend forking this tutorial and replacing classification system, we recommend forking this tutorial and replacing
components to build address your image classification problem. components to address your image classification problem.
> **EXERCISE:** Download the > **EXERCISE:** Download the
[Street View House Numbers (SVHN)](http://ufldl.stanford.edu/housenumbers/) data set. [Street View House Numbers (SVHN)](http://ufldl.stanford.edu/housenumbers/) data set.

2
tensorflow/g3doc/tutorials/mnist/beginners/index.md
View File

@ -310,7 +310,7 @@ cross_entropy = -tf.reduce_sum(y_*tf.log(y))
``` ```
First, `tf.log` computes the logarithm of each element of `y`. Next, we multiply First, `tf.log` computes the logarithm of each element of `y`. Next, we multiply
each element of `y_` with the corresponding element of `tf.log(y_)`. Finally, each element of `y_` with the corresponding element of `tf.log(y)`. Finally,
`tf.reduce_sum` adds all the elements of the tensor. (Note that this isn't `tf.reduce_sum` adds all the elements of the tensor. (Note that this isn't
just the cross-entropy of the truth with a single prediction, but the sum of the just the cross-entropy of the truth with a single prediction, but the sum of the
cross-entropies for all 100 images we looked at. How well we are doing on 100 cross-entropies for all 100 images we looked at. How well we are doing on 100

2
tensorflow/g3doc/tutorials/seq2seq/index.md
View File

@ -311,7 +311,7 @@ translator, end-to-end. Run it and see how the model performs for yourself.
While it has reasonable quality, the default parameters will not give you While it has reasonable quality, the default parameters will not give you
the best translation model. Here are a few things you can improve. the best translation model. Here are a few things you can improve.
First of all, we use a very promitive tokenizer, the `basic_tokenizer` function First of all, we use a very primitive tokenizer, the `basic_tokenizer` function
in `data_utils`. A better tokenizer can be found on the in `data_utils`. A better tokenizer can be found on the
[WMT'15 Website](http://www.statmt.org/wmt15/translation-task.html). [WMT'15 Website](http://www.statmt.org/wmt15/translation-task.html).
Using that tokenizer, and a larger vocabulary, should improve your translations. Using that tokenizer, and a larger vocabulary, should improve your translations.

2
tensorflow/models/rnn/translate/data_utils.py
View File

@ -254,7 +254,7 @@ def prepare_wmt_data(data_dir, en_vocabulary_size, fr_vocabulary_size):
fr_train_ids_path = train_path + (".ids%d.fr" % fr_vocabulary_size) fr_train_ids_path = train_path + (".ids%d.fr" % fr_vocabulary_size)
en_train_ids_path = train_path + (".ids%d.en" % en_vocabulary_size) en_train_ids_path = train_path + (".ids%d.en" % en_vocabulary_size)
data_to_token_ids(train_path + ".fr", fr_train_ids_path, fr_vocab_path) data_to_token_ids(train_path + ".fr", fr_train_ids_path, fr_vocab_path)
data_to_token_ids(train_path + ".en", fr_train_ids_path, fr_vocab_path) data_to_token_ids(train_path + ".en", en_train_ids_path, en_vocab_path)
# Create token ids for the development data. # Create token ids for the development data.
fr_dev_ids_path = dev_path + (".ids%d.fr" % fr_vocabulary_size) fr_dev_ids_path = dev_path + (".ids%d.fr" % fr_vocabulary_size)

19
tensorflow/python/kernel_tests/lookup_table_op_test.py
View File

@ -26,6 +26,25 @@ class HashTableOpTest(tf.test.TestCase):
result = output.eval() result = output.eval()
self.assertAllEqual([0, 1, -1], result) self.assertAllEqual([0, 1, -1], result)
def testHashTableFindHighRank(self):
with self.test_session():
shared_name = ''
default_val = -1
table = tf.HashTable(tf.string, tf.int64, default_val, shared_name)
# Initialize with keys and values tensors.
keys = tf.constant(['brain', 'salad', 'surgery'])
values = tf.constant([0, 1, 2], tf.int64)
init = table.initialize_from(keys, values)
init.run()
self.assertAllEqual(3, table.size().eval())
input_string = tf.constant([['brain', 'salad'], ['tank', 'tarkus']])
output = table.lookup(input_string)
result = output.eval()
self.assertAllEqual([[0, 1], [-1, -1]], result)
def testHashTableInitWithPythonArrays(self): def testHashTableInitWithPythonArrays(self):
with self.test_session(): with self.test_session():
shared_name = '' shared_name = ''

16
tensorflow/python/kernel_tests/parsing_ops_test.py
View File

@ -85,13 +85,14 @@ class ParseExampleTest(tf.test.TestCase):
sess.run(result) sess.run(result)
def testEmptySerializedWithAllDefaults(self): def testEmptySerializedWithAllDefaults(self):
dense_keys = ["a", "b", "c"] cname = "c:has_a_tricky_name"
dense_keys = ["a", "b", cname]
dense_shapes = [(1, 3), (3, 3), (2,)] dense_shapes = [(1, 3), (3, 3), (2,)]
dense_types = [tf.int64, tf.string, tf.float32] dense_types = [tf.int64, tf.string, tf.float32]
dense_defaults = { dense_defaults = {
"a": [0, 42, 0], "a": [0, 42, 0],
"b": np.random.rand(3, 3).astype(np.str), "b": np.random.rand(3, 3).astype(np.str),
"c": np.random.rand(2).astype(np.float32), cname: np.random.rand(2).astype(np.float32),
} }
expected_st_a = ( # indices, values, shape expected_st_a = ( # indices, values, shape
@ -103,7 +104,7 @@ class ParseExampleTest(tf.test.TestCase):
"st_a": expected_st_a, "st_a": expected_st_a,
"a": np.array(2 * [[dense_defaults["a"]]]), "a": np.array(2 * [[dense_defaults["a"]]]),
"b": np.array(2 * [dense_defaults["b"]]), "b": np.array(2 * [dense_defaults["b"]]),
"c": np.array(2 * [dense_defaults["c"]]), cname: np.array(2 * [dense_defaults[cname]]),
} }
self._test( self._test(
@ -210,14 +211,15 @@ class ParseExampleTest(tf.test.TestCase):
}, expected_output) }, expected_output)
def testSerializedContainingDense(self): def testSerializedContainingDense(self):
bname = "b*has+a:tricky_name"
original = [ original = [
example(features=features({ example(features=features({
"a": float_feature([1, 1]), "a": float_feature([1, 1]),
"b": bytes_feature(["b0_str"]), bname: bytes_feature(["b0_str"]),
})), })),
example(features=features({ example(features=features({
"a": float_feature([-1, -1]), "a": float_feature([-1, -1]),
"b": bytes_feature(["b1"]), bname: bytes_feature(["b1"]),
})) }))
] ]
@ -227,14 +229,14 @@ class ParseExampleTest(tf.test.TestCase):
expected_output = { expected_output = {
"a": np.array([[1, 1], [-1, -1]], dtype=np.float32).reshape(2, 1, 2, 1), "a": np.array([[1, 1], [-1, -1]], dtype=np.float32).reshape(2, 1, 2, 1),
"b": np.array(["b0_str", "b1"], dtype=np.str).reshape(2, 1, 1, 1, 1), bname: np.array(["b0_str", "b1"], dtype=np.str).reshape(2, 1, 1, 1, 1),
} }
# No defaults, values required # No defaults, values required
self._test( self._test(
{ {
"serialized": tf.convert_to_tensor(serialized), "serialized": tf.convert_to_tensor(serialized),
"dense_keys": ["a", "b"], "dense_keys": ["a", bname],
"dense_types": [tf.float32, tf.string], "dense_types": [tf.float32, tf.string],
"dense_shapes": dense_shapes, "dense_shapes": dense_shapes,
}, expected_output) }, expected_output)

2
tensorflow/python/ops/constant_op.py
View File

@ -58,7 +58,7 @@ print sess.run(norm)
print sess.run(norm) print sess.run(norm)
``` ```
Another common use of random values is the intialization of variables. Also see Another common use of random values is the initialization of variables. Also see
the [Variables How To](../../how_tos/variables/index.md). the [Variables How To](../../how_tos/variables/index.md).
```python ```python

43
tensorflow/python/ops/data_flow_ops.py
View File

@ -412,10 +412,9 @@ class LookupTableBase(object):
"""Construct a table object from a table reference. """Construct a table object from a table reference.
Args: Args:
key_dtype: The key data type of the table. key_dtype: The table key type.
value_dtype: The kvalue data type of the table. value_dtype: The table value type.
default_value: The scalar tensor to be used when a key is not present in default_value: The value to use if a key is missing in the table.
the table.
table_ref: The table reference, i.e. the output of the lookup table ops. table_ref: The table reference, i.e. the output of the lookup table ops.
""" """
self._key_dtype = types.as_dtype(key_dtype) self._key_dtype = types.as_dtype(key_dtype)
@ -434,12 +433,12 @@ class LookupTableBase(object):
@property @property
def key_dtype(self): def key_dtype(self):
"""The key dtype supported by the table.""" """The table key dtype."""
return self._key_dtype return self._key_dtype
@property @property
def value_dtype(self): def value_dtype(self):
"""The value dtype supported by the table.""" """The table value dtype."""
return self._value_dtype return self._value_dtype
@property @property
@ -466,20 +465,19 @@ class LookupTableBase(object):
return gen_data_flow_ops._lookup_table_size(self._table_ref, name=name) return gen_data_flow_ops._lookup_table_size(self._table_ref, name=name)
def lookup(self, keys, name=None): def lookup(self, keys, name=None):
"""Returns the values for the given 'keys' tensor. """Looks up `keys` in a table, outputs the corresponding values.
If an element on the key tensor is not found in the table, the default_value The `default_value` is use for keys not present in the table.
is used.
Args: Args:
keys: The tensor for the keys. keys: Keys to look up.
name: Optional name for the op. name: Optional name for the op.
Returns: Returns:
The operation that looks up the keys. The operation that looks up the keys.
Raises: Raises:
TypeError: when 'keys' or 'default_value' doesn't match the table data TypeError: when `keys` or `default_value` doesn't match the table data
types. types.
""" """
if name is None: if name is None:
@ -493,7 +491,7 @@ class LookupTableBase(object):
self._table_ref, keys, self._default_value, name=name) self._table_ref, keys, self._default_value, name=name)
def initialize_from(self, keys, values, name=None): def initialize_from(self, keys, values, name=None):
"""Initialize the lookup table with the provided keys and values tensors. """Initialize the table with the provided keys and values tensors.
Construct an initializer object from keys and value tensors. Construct an initializer object from keys and value tensors.
@ -503,10 +501,10 @@ class LookupTableBase(object):
name: Optional name for the op. name: Optional name for the op.
Returns: Returns:
The operation that initializes a lookup table. The operation that initializes the table.
Raises: Raises:
TypeError: when the 'keys' and 'values' data type do not match the table TypeError: when the keys and values data types do not match the table
key and value data types. key and value data types.
""" """
if name is None: if name is None:
@ -545,22 +543,23 @@ class HashTable(LookupTableBase):
def __init__(self, key_dtype, value_dtype, default_value, shared_name=None, def __init__(self, key_dtype, value_dtype, default_value, shared_name=None,
name="hash_table"): name="hash_table"):
"""Create a generic hash table. """Creates a non-initialized hash table.
A table holds a key-value pairs. The key and value types are This op creates a hash table, specifying the type of its keys and values.
described by key_dtype and value_dtype respectively. Before using the table you will have to initialize it. After initialization
the table will be immutable.
Args: Args:
key_dtype: The key data type of the table. key_dtype: Type of the table keys.
value_dtype: The kvalue data type of the table. value_dtype: Type of the table values.
default_value: The scalar tensor to be used when a key is not present in default_value: The scalar tensor to be used when a key is missing in the
the table. table.
shared_name: Optional. If non-empty, this table will be shared under shared_name: Optional. If non-empty, this table will be shared under
the given name across multiple sessions. the given name across multiple sessions.
name: Optional name for the hash table op. name: Optional name for the hash table op.
Returns: Returns:
A table object that can be used to lookup data. A `HashTable` object.
""" """
table_ref = gen_data_flow_ops._hash_table( table_ref = gen_data_flow_ops._hash_table(
shared_name=shared_name, key_dtype=key_dtype, shared_name=shared_name, key_dtype=key_dtype,

120
tensorflow/python/ops/parsing_ops.py
View File

@ -1,4 +1,5 @@
"""Parsing Ops.""" """Parsing Ops."""
import re
from tensorflow.python.framework import ops from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_shape
@ -166,9 +167,10 @@ def parse_example(serialized,
``` ```
Args: Args:
serialized: A list of strings, a batch of binary serialized `Example` serialized: A vector (1-D Tensor) of strings, a batch of binary
protos. serialized `Example` protos.
names: A list of strings, the names of the serialized protos. names: A vector (1-D Tensor) of strings (optional), the names of
the serialized protos.
sparse_keys: A list of string keys in the examples' features. sparse_keys: A list of string keys in the examples' features.
The results for these keys will be returned as `SparseTensor` objects. The results for these keys will be returned as `SparseTensor` objects.
sparse_types: A list of `DTypes` of the same length as `sparse_keys`. sparse_types: A list of `DTypes` of the same length as `sparse_keys`.
@ -192,67 +194,69 @@ def parse_example(serialized,
ValueError: If sparse and dense key sets intersect, or input lengths do not ValueError: If sparse and dense key sets intersect, or input lengths do not
match up. match up.
""" """
names = [] if names is None else names with ops.op_scope([serialized, names], name, "parse_example"):
dense_defaults = {} if dense_defaults is None else dense_defaults names = [] if names is None else names
sparse_keys = [] if sparse_keys is None else sparse_keys dense_defaults = {} if dense_defaults is None else dense_defaults
sparse_types = [] if sparse_types is None else sparse_types sparse_keys = [] if sparse_keys is None else sparse_keys
dense_keys = [] if dense_keys is None else dense_keys sparse_types = [] if sparse_types is None else sparse_types
dense_types = [] if dense_types is None else dense_types dense_keys = [] if dense_keys is None else dense_keys
dense_shapes = [ dense_types = [] if dense_types is None else dense_types
[]] * len(dense_keys) if dense_shapes is None else dense_shapes dense_shapes = [
[]] * len(dense_keys) if dense_shapes is None else dense_shapes
num_dense = len(dense_keys) num_dense = len(dense_keys)
num_sparse = len(sparse_keys) num_sparse = len(sparse_keys)
if len(dense_shapes) != num_dense: if len(dense_shapes) != num_dense:
raise ValueError("len(dense_shapes) != len(dense_keys): %d vs. %d" raise ValueError("len(dense_shapes) != len(dense_keys): %d vs. %d"
% (len(dense_shapes), num_dense)) % (len(dense_shapes), num_dense))
if len(dense_types) != num_dense: if len(dense_types) != num_dense:
raise ValueError("len(dense_types) != len(num_dense): %d vs. %d" raise ValueError("len(dense_types) != len(num_dense): %d vs. %d"
% (len(dense_types), num_dense)) % (len(dense_types), num_dense))
if len(sparse_types) != num_sparse: if len(sparse_types) != num_sparse:
raise ValueError("len(sparse_types) != len(sparse_keys): %d vs. %d" raise ValueError("len(sparse_types) != len(sparse_keys): %d vs. %d"
% (len(sparse_types), num_sparse)) % (len(sparse_types), num_sparse))
if num_dense + num_sparse == 0: if num_dense + num_sparse == 0:
raise ValueError("Must provide at least one sparse key or dense key") raise ValueError("Must provide at least one sparse key or dense key")
if not set(dense_keys).isdisjoint(set(sparse_keys)): if not set(dense_keys).isdisjoint(set(sparse_keys)):
raise ValueError( raise ValueError(
"Dense and sparse keys must not intersect; intersection: %s" % "Dense and sparse keys must not intersect; intersection: %s" %
set(dense_keys).intersection(set(sparse_keys))) set(dense_keys).intersection(set(sparse_keys)))
dense_defaults_vec = [] dense_defaults_vec = []
for i, key in enumerate(dense_keys): for i, key in enumerate(dense_keys):
default_value = dense_defaults.get(key) default_value = dense_defaults.get(key)
if default_value is None: if default_value is None:
default_value = constant_op.constant([], dtype=dense_types[i]) default_value = constant_op.constant([], dtype=dense_types[i])
elif not isinstance(default_value, ops.Tensor): elif not isinstance(default_value, ops.Tensor):
default_value = ops.convert_to_tensor( key_name = "key_" + re.sub("[^A-Za-z0-9_.\\-/]", "_", key)
default_value, dtype=dense_types[i], name=key) default_value = ops.convert_to_tensor(
default_value = array_ops.reshape(default_value, dense_shapes[i]) default_value, dtype=dense_types[i], name=key_name)
default_value = array_ops.reshape(default_value, dense_shapes[i])
dense_defaults_vec.append(default_value) dense_defaults_vec.append(default_value)
dense_shapes = [tensor_util.MakeTensorShapeProto(shape) dense_shapes = [tensor_util.MakeTensorShapeProto(shape)
if isinstance(shape, (list, tuple)) else shape if isinstance(shape, (list, tuple)) else shape
for shape in dense_shapes] for shape in dense_shapes]
outputs = gen_parsing_ops._parse_example( outputs = gen_parsing_ops._parse_example(
serialized=serialized, serialized=serialized,
names=names, names=names,
dense_defaults=dense_defaults_vec, dense_defaults=dense_defaults_vec,
sparse_keys=sparse_keys, sparse_keys=sparse_keys,
sparse_types=sparse_types, sparse_types=sparse_types,
dense_keys=dense_keys, dense_keys=dense_keys,
dense_shapes=dense_shapes, dense_shapes=dense_shapes,
name=name) name=name)
(sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs (sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs
sparse_tensors = [ops.SparseTensor(ix, val, shape) for (ix, val, shape) sparse_tensors = [ops.SparseTensor(ix, val, shape) for (ix, val, shape)
in zip(sparse_indices, sparse_values, sparse_shapes)] in zip(sparse_indices, sparse_values, sparse_shapes)]
return dict( return dict(
zip(sparse_keys + dense_keys, sparse_tensors + dense_values)) zip(sparse_keys + dense_keys, sparse_tensors + dense_values))
def parse_single_example(serialized, # pylint: disable=invalid-name def parse_single_example(serialized, # pylint: disable=invalid-name
@ -280,9 +284,9 @@ def parse_single_example(serialized, # pylint: disable=invalid-name
See also `parse_example`. See also `parse_example`.
Args: Args:
serialized: A scalar string, a single serialized Example. serialized: A scalar string Tensor, a single serialized Example.
See parse_example documentation for more details. See parse_example documentation for more details.
names: (Optional) A scalar string, the associated name. names: (Optional) A scalar string Tensor, the associated name.
See parse_example documentation for more details. See parse_example documentation for more details.
sparse_keys: See parse_example documentation for more details. sparse_keys: See parse_example documentation for more details.
sparse_types: See parse_example documentation for more details. sparse_types: See parse_example documentation for more details.
@ -298,7 +302,7 @@ def parse_single_example(serialized, # pylint: disable=invalid-name
Raises: Raises:
ValueError: if "scalar" or "names" have known shapes, and are not scalars. ValueError: if "scalar" or "names" have known shapes, and are not scalars.
""" """
with ops.op_scope([serialized], name, "parse_single_example"): with ops.op_scope([serialized, names], name, "parse_single_example"):
serialized = ops.convert_to_tensor(serialized) serialized = ops.convert_to_tensor(serialized)
serialized_shape = serialized.get_shape() serialized_shape = serialized.get_shape()
if serialized_shape.ndims is not None: if serialized_shape.ndims is not None: