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A remote repository cannot refer to the main repository that way and a fix in a future Bazel release will break TensorFlow with the current set-up.

Error: http://ci.bazel.io/job/TensorFlow/BAZEL_VERSION=HEAD,PLATFORM_NAME=linux-x86_64/74/console
Change: 112056797
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A. Unique TensorFlower 2016-01-13 14:05:16 -08:00 committed by Vijay Vasudevan
parent 5515148be8
commit 0c62942e2b
29 changed files with 121 additions and 121 deletions
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@ -1,6 +1,6 @@
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package(default_visibility = ["//tensorflow:internal"]) package(default_visibility = ["//visibility:public"])
filegroup( filegroup(
name = "iron-ajax", name = "iron-ajax",

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@ -3037,7 +3037,7 @@ op {
} }
} }
summary: "Outputs a `Summary` protocol buffer with a histogram." summary: "Outputs a `Summary` protocol buffer with a histogram."
description: "The generated\n[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)\nhas one summary value containing a histogram for `values`.\n\nThis op reports an `OutOfRange` error if any value is not finite." description: "The generated\n[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)\nhas one summary value containing a histogram for `values`.\n\nThis op reports an `OutOfRange` error if any value is not finite."
} }
op { op {
name: "IFFT2D" name: "IFFT2D"
@ -4385,7 +4385,7 @@ op {
minimum: 1 minimum: 1
} }
summary: "Merges summaries." summary: "Merges summaries."
description: "This op creates a\n[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)\nprotocol buffer that contains the union of all the values in the input\nsummaries.\n\nWhen the Op is run, it reports an `InvalidArgument` error if multiple values\nin the summaries to merge use the same tag." description: "This op creates a\n[`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)\nprotocol buffer that contains the union of all the values in the input\nsummaries.\n\nWhen the Op is run, it reports an `InvalidArgument` error if multiple values\nin the summaries to merge use the same tag."
} }
op { op {
name: "Min" name: "Min"

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@ -45,7 +45,7 @@ REGISTER_OP("HistogramSummary")
Outputs a `Summary` protocol buffer with a histogram. Outputs a `Summary` protocol buffer with a histogram.
The generated The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`. has one summary value containing a histogram for `values`.
This op reports an `OutOfRange` error if any value is not finite. This op reports an `OutOfRange` error if any value is not finite.
@ -118,7 +118,7 @@ REGISTER_OP("MergeSummary")
Merges summaries. Merges summaries.
This op creates a This op creates a
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input protocol buffer that contains the union of all the values in the input
summaries. summaries.

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@ -53,7 +53,7 @@ with tf.Session() as sess:
``` ```
The [`ConfigProto`] The [`ConfigProto`]
(https://www.tensorflow.org/code/tensorflow/core/framework/config.proto) (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/config.proto)
protocol buffer exposes various configuration options for a protocol buffer exposes various configuration options for a
session. For example, to create a session that uses soft constraints session. For example, to create a session that uses soft constraints
for device placement, and log the resulting placement decisions, for device placement, and log the resulting placement decisions,
@ -87,7 +87,7 @@ the session constructor.
Defaults to using an in-process engine. At present, no value Defaults to using an in-process engine. At present, no value
other than the empty string is supported. other than the empty string is supported.
* <b>`graph`</b>: (Optional.) The `Graph` to be launched (described above). * <b>`graph`</b>: (Optional.) The `Graph` to be launched (described above).
* <b>`config`</b>: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/framework/config.proto) * <b>`config`</b>: (Optional.) A [`ConfigProto`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/config.proto)
protocol buffer with configuration options for the session. protocol buffer with configuration options for the session.

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@ -113,7 +113,7 @@ This method is thread-safe.
##### Returns: ##### Returns:
A [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) A [`GraphDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto)
protocol buffer. protocol buffer.
##### Raises: ##### Raises:
@ -570,7 +570,7 @@ Note that this is unrelated to the
The GraphDef version of this graph. The GraphDef version of this graph.
For details on the meaning of each version, see [`GraphDef`] For details on the meaning of each version, see [`GraphDef`]
(https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto). (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto).
@ -858,7 +858,7 @@ Returns a serialized `NodeDef` representation of this operation.
##### Returns: ##### Returns:
A A
[`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) [`NodeDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto)
protocol buffer. protocol buffer.
@ -871,7 +871,7 @@ Returns the `OpDef` proto that represents the type of this op.
##### Returns: ##### Returns:
An An
[`OpDef`](https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto) [`OpDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op_def.proto)
protocol buffer. protocol buffer.
@ -1316,7 +1316,7 @@ Converts the given `type_value` to a `DType`.
* <b>`type_value`</b>: A value that can be converted to a `tf.DType` * <b>`type_value`</b>: A value that can be converted to a `tf.DType`
object. This may currently be a `tf.DType` object, a object. This may currently be a `tf.DType` object, a
[`DataType` enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto), [`DataType` enum](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/types.proto),
a string type name, or a `numpy.dtype`. a string type name, or a `numpy.dtype`.
##### Returns: ##### Returns:
@ -1518,7 +1518,7 @@ after calling this function will result in undefined behavior.
Imports the TensorFlow graph in `graph_def` into the Python `Graph`. Imports the TensorFlow graph in `graph_def` into the Python `Graph`.
This function provides a way to import a serialized TensorFlow This function provides a way to import a serialized TensorFlow
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) [`GraphDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto)
protocol buffer, and extract individual objects in the `GraphDef` as protocol buffer, and extract individual objects in the `GraphDef` as
[`Tensor`](#Tensor) and [`Operation`](#Operation) objects. See [`Tensor`](#Tensor) and [`Operation`](#Operation) objects. See
[`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a [`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a

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@ -1049,9 +1049,9 @@ Reinterpret the bytes of a string as a vector of numbers.
TensorFlow's [recommended format for training TensorFlow's [recommended format for training
examples](../../how_tos/reading_data/index.md#standard-tensorflow-format) examples](../../how_tos/reading_data/index.md#standard-tensorflow-format)
is serialized `Example` protocol buffers, [described is serialized `Example` protocol buffers, [described
here](https://www.tensorflow.org/code/tensorflow/core/example/example.proto). here](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/example.proto).
They contain `Features`, [described They contain `Features`, [described
here](https://www.tensorflow.org/code/tensorflow/core/example/feature.proto). here](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/feature.proto).
- - - - - -
@ -1148,7 +1148,7 @@ Alias for field number 0
Parses `Example` protos into a `dict` of tensors. Parses `Example` protos into a `dict` of tensors.
Parses a number of serialized [`Example`] Parses a number of serialized [`Example`]
(https://www.tensorflow.org/code/tensorflow/core/example/example.proto) (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/example.proto)
protos given in `serialized`. protos given in `serialized`.
`example_names` may contain descriptive names for the corresponding serialized `example_names` may contain descriptive names for the corresponding serialized

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@ -1459,13 +1459,13 @@ the list of all threads.
## Summary Operations ## Summary Operations
The following ops output The following ops output
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffers as serialized string tensors. protocol buffers as serialized string tensors.
You can fetch the output of a summary op in a session, and pass it to You can fetch the output of a summary op in a session, and pass it to
a [SummaryWriter](../../api_docs/python/train.md#SummaryWriter) to append it a [SummaryWriter](../../api_docs/python/train.md#SummaryWriter) to append it
to an event file. Event files contain to an event file. Event files contain
[`Event`](https://www.tensorflow.org/code/tensorflow/core/util/event.proto) [`Event`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/util/event.proto)
protos that can contain `Summary` protos along with the timestamp and protos that can contain `Summary` protos along with the timestamp and
step. You can then use TensorBoard to visualize the contents of the step. You can then use TensorBoard to visualize the contents of the
event files. See [TensorBoard and event files. See [TensorBoard and
@ -1554,7 +1554,7 @@ build the `tag` of the summary values:
Outputs a `Summary` protocol buffer with a histogram. Outputs a `Summary` protocol buffer with a histogram.
The generated The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`. has one summary value containing a histogram for `values`.
This op reports an `OutOfRange` error if any value is not finite. This op reports an `OutOfRange` error if any value is not finite.
@ -1607,7 +1607,7 @@ This is useful in summaries to measure and report sparsity. For example,
Merges summaries. Merges summaries.
This op creates a This op creates a
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input protocol buffer that contains the union of all the values in the input
summaries. summaries.
@ -1816,9 +1816,9 @@ for e in tf.summary_iterator(path to events file):
``` ```
See the protocol buffer definitions of See the protocol buffer definitions of
[Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto) [Event](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/util/event.proto)
and and
[Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [Summary](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
for more information about their attributes. for more information about their attributes.
##### Args: ##### Args:

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@ -290,6 +290,6 @@ with tf.Session() as sess:
A `placeholder()` operation generates an error if you do not supply a feed for A `placeholder()` operation generates an error if you do not supply a feed for
it. See the it. See the
[MNIST fully-connected feed tutorial](../tutorials/mnist/tf/index.md) [MNIST fully-connected feed tutorial](../tutorials/mnist/tf/index.md)
([source code](https://www.tensorflow.org/code/tensorflow/g3doc/tutorials/mnist/fully_connected_feed.py)) ([source code](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/g3doc/tutorials/mnist/fully_connected_feed.py))
for a larger-scale example of feeds. for a larger-scale example of feeds.

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@ -22,7 +22,7 @@ to:
* Optionally, write a function to compute gradients for the Op. * Optionally, write a function to compute gradients for the Op.
* Optionally, write a function that describes the input and output shapes * Optionally, write a function that describes the input and output shapes
for the Op. This allows shape inference to work with your Op. for the Op. This allows shape inference to work with your Op.
* Test the Op, typically in Python. If you define gradients, you can verify them with the Python [`GradientChecker`](https://www.tensorflow.org/code/tensorflow/python/kernel_tests/gradient_checker.py). * Test the Op, typically in Python. If you define gradients, you can verify them with the Python [`GradientChecker`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/kernel_tests/gradient_checker.py).
[TOC] [TOC]
@ -131,7 +131,7 @@ from tensorflow.python.ops.gen_user_ops import *
You may optionally use your own function instead. To do this, you first hide You may optionally use your own function instead. To do this, you first hide
the generated code for that op by adding its name to the `hidden` list in the the generated code for that op by adding its name to the `hidden` list in the
`"user_ops"` rule in `"user_ops"` rule in
[`tensorflow/python/BUILD`](https://www.tensorflow.org/code/tensorflow/python/BUILD): [`tensorflow/python/BUILD`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/BUILD):
```python ```python
tf_gen_op_wrapper_py( tf_gen_op_wrapper_py(
@ -144,7 +144,7 @@ tf_gen_op_wrapper_py(
``` ```
List your op next to `"Fact"`. Next you add your replacement function to List your op next to `"Fact"`. Next you add your replacement function to
[`tensorflow/python/user_ops/user_ops.py`](https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py). [`tensorflow/python/user_ops/user_ops.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/user_ops/user_ops.py).
Typically your function will call the generated function to actually add the op Typically your function will call the generated function to actually add the op
to the graph. The hidden version of the generated function will be in the to the graph. The hidden version of the generated function will be in the
`gen_user_ops` package and start with an underscore ("`_`"). For example: `gen_user_ops` package and start with an underscore ("`_`"). For example:
@ -216,13 +216,13 @@ This asserts that the input is a vector, and returns having set the
* The `context`, which can either be an `OpKernelContext` or * The `context`, which can either be an `OpKernelContext` or
`OpKernelConstruction` pointer (see `OpKernelConstruction` pointer (see
[`tensorflow/core/framework/op_kernel.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)), [`tensorflow/core/framework/op_kernel.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op_kernel.h)),
for its `SetStatus()` method. for its `SetStatus()` method.
* The condition. For example, there are functions for validating the shape * The condition. For example, there are functions for validating the shape
of a tensor in of a tensor in
[`tensorflow/core/public/tensor_shape.h`](https://www.tensorflow.org/code/tensorflow/core/public/tensor_shape.h) [`tensorflow/core/public/tensor_shape.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/public/tensor_shape.h)
* The error itself, which is represented by a `Status` object, see * The error itself, which is represented by a `Status` object, see
[`tensorflow/core/public/status.h`](https://www.tensorflow.org/code/tensorflow/core/public/status.h). A [`tensorflow/core/public/status.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/public/status.h). A
`Status` has both a type (frequently `InvalidArgument`, but see the list of `Status` has both a type (frequently `InvalidArgument`, but see the list of
types) and a message. Functions for constructing an error may be found in types) and a message. Functions for constructing an error may be found in
[`tensorflow/core/lib/core/errors.h`][validation-macros]. [`tensorflow/core/lib/core/errors.h`][validation-macros].
@ -368,7 +368,7 @@ define an attr with constraints, you can use the following `<attr-type-expr>`s:
The specific lists of types allowed by these are defined by the functions The specific lists of types allowed by these are defined by the functions
(like `NumberTypes()`) in (like `NumberTypes()`) in
[`tensorflow/core/framework/types.h`](https://www.tensorflow.org/code/tensorflow/core/framework/types.h). [`tensorflow/core/framework/types.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/types.h).
In this example the attr `t` must be one of the numeric types: In this example the attr `t` must be one of the numeric types:
```c++ ```c++
@ -889,7 +889,7 @@ There are several ways to preserve backwards-compatibility.
type into a list of varying types). type into a list of varying types).
The full list of safe and unsafe changes can be found in The full list of safe and unsafe changes can be found in
[`tensorflow/core/framework/op_compatibility_test.cc`](https://www.tensorflow.org/code/tensorflow/core/framework/op_compatibility_test.cc). [`tensorflow/core/framework/op_compatibility_test.cc`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op_compatibility_test.cc).
If you cannot make your change to an operation backwards compatible, then create If you cannot make your change to an operation backwards compatible, then create
a new operation with a new name with the new semantics. a new operation with a new name with the new semantics.
@ -906,16 +906,16 @@ made when TensorFlow's changes major versions, and must conform to the
You can implement different OpKernels and register one for CPU and another for You can implement different OpKernels and register one for CPU and another for
GPU, just like you can [register kernels for different types](#polymorphism). GPU, just like you can [register kernels for different types](#polymorphism).
There are several examples of kernels with GPU support in There are several examples of kernels with GPU support in
[`tensorflow/core/kernels/`](https://www.tensorflow.org/code/tensorflow/core/kernels/). [`tensorflow/core/kernels/`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/).
Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file
ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file. ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file.
For example, the [`pad` op](../../api_docs/python/array_ops.md#pad) has For example, the [`pad` op](../../api_docs/python/array_ops.md#pad) has
everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op]. everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op].
The GPU kernel is in The GPU kernel is in
[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op_gpu.cu.cc), [`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/pad_op_gpu.cu.cc),
and the shared code is a templated class defined in and the shared code is a templated class defined in
[`tensorflow/core/kernels/pad_op.h`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.h). [`tensorflow/core/kernels/pad_op.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/pad_op.h).
One thing to note, even when the GPU kernel version of `pad` is used, it still One thing to note, even when the GPU kernel version of `pad` is used, it still
needs its `"paddings"` input in CPU memory. To mark that inputs or outputs are needs its `"paddings"` input in CPU memory. To mark that inputs or outputs are
kept on the CPU, add a `HostMemory()` call to the kernel registration, e.g.: kept on the CPU, add a `HostMemory()` call to the kernel registration, e.g.:
@ -1072,23 +1072,23 @@ any of the inputs. The [`merge_with`](../../api_docs/python/framework.md)
method allows the caller to assert that two shapes are the same, even if either method allows the caller to assert that two shapes are the same, even if either
or both of them do not have complete information. Shape functions are defined or both of them do not have complete information. Shape functions are defined
for all of the for all of the
[standard Python ops](https://www.tensorflow.org/code/tensorflow/python/ops/), [standard Python ops](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/ops/),
and provide many different usage examples. and provide many different usage examples.
[core-array_ops]:https://www.tensorflow.org/code/tensorflow/core/ops/array_ops.cc [core-array_ops]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/ops/array_ops.cc
[python-user_ops]:https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py [python-user_ops]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/user_ops/user_ops.py
[tf-kernels]:https://www.tensorflow.org/code/tensorflow/core/kernels/ [tf-kernels]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/
[user_ops]:https://www.tensorflow.org/code/tensorflow/core/user_ops/ [user_ops]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/user_ops/
[pad_op]:https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.cc [pad_op]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/pad_op.cc
[standard_ops-py]:https://www.tensorflow.org/code/tensorflow/python/ops/standard_ops.py [standard_ops-py]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/ops/standard_ops.py
[standard_ops-cc]:https://www.tensorflow.org/code/tensorflow/cc/ops/standard_ops.h [standard_ops-cc]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/cc/ops/standard_ops.h
[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD [python-BUILD]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/BUILD
[validation-macros]:https://www.tensorflow.org/code/tensorflow/core/lib/core/errors.h [validation-macros]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/lib/core/errors.h
[op_def_builder]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.h [op_def_builder]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op_def_builder.h
[register_types]:https://www.tensorflow.org/code/tensorflow/core/framework/register_types.h [register_types]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/register_types.h
[FinalizeAttr]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.cc#FinalizeAttr [FinalizeAttr]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op_def_builder.cc#FinalizeAttr
[DataTypeString]:https://www.tensorflow.org/code/tensorflow/core/framework/types.cc#DataTypeString [DataTypeString]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/types.cc#DataTypeString
[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD [python-BUILD]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/BUILD
[types-proto]:https://www.tensorflow.org/code/tensorflow/core/framework/types.proto [types-proto]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/types.proto
[TensorShapeProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.proto [TensorShapeProto]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/tensor_shape.proto
[TensorProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor.proto [TensorProto]:https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/tensor.proto

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@ -28,11 +28,11 @@ A `Reader` is something that reads records from a file. There are some examples
of Reader Ops already built into TensorFlow: of Reader Ops already built into TensorFlow:
* [`tf.TFRecordReader`](../../api_docs/python/io_ops.md#TFRecordReader) * [`tf.TFRecordReader`](../../api_docs/python/io_ops.md#TFRecordReader)
([source in `kernels/tf_record_reader_op.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/tf_record_reader_op.cc)) ([source in `kernels/tf_record_reader_op.cc`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/tf_record_reader_op.cc))
* [`tf.FixedLengthRecordReader`](../../api_docs/python/io_ops.md#FixedLengthRecordReader) * [`tf.FixedLengthRecordReader`](../../api_docs/python/io_ops.md#FixedLengthRecordReader)
([source in `kernels/fixed_length_record_reader_op.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/fixed_length_record_reader_op.cc)) ([source in `kernels/fixed_length_record_reader_op.cc`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/fixed_length_record_reader_op.cc))
* [`tf.TextLineReader`](../../api_docs/python/io_ops.md#TextLineReader) * [`tf.TextLineReader`](../../api_docs/python/io_ops.md#TextLineReader)
([source in `kernels/text_line_reader_op.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/text_line_reader_op.cc)) ([source in `kernels/text_line_reader_op.cc`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/text_line_reader_op.cc))
You can see these all expose the same interface, the only differences You can see these all expose the same interface, the only differences
are in their constructors. The most important method is `read`. are in their constructors. The most important method is `read`.
@ -44,15 +44,15 @@ two scalar tensors: a string key and and a string value.
To create a new reader called `SomeReader`, you will need to: To create a new reader called `SomeReader`, you will need to:
1. In C++, define a subclass of 1. In C++, define a subclass of
[`tensorflow::ReaderBase`](https://www.tensorflow.org/code/tensorflow/core/kernels/reader_base.h) [`tensorflow::ReaderBase`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/reader_base.h)
called `SomeReader`. called `SomeReader`.
2. In C++, register a new reader op and kernel with the name `"SomeReader"`. 2. In C++, register a new reader op and kernel with the name `"SomeReader"`.
3. In Python, define a subclass of [`tf.ReaderBase`](https://www.tensorflow.org/code/tensorflow/python/ops/io_ops.py) called `SomeReader`. 3. In Python, define a subclass of [`tf.ReaderBase`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/ops/io_ops.py) called `SomeReader`.
You can put all the C++ code in a file in You can put all the C++ code in a file in
`tensorflow/core/user_ops/some_reader_op.cc`. The code to read a file will live `tensorflow/core/user_ops/some_reader_op.cc`. The code to read a file will live
in a descendant of the C++ `ReaderBase` class, which is defined in in a descendant of the C++ `ReaderBase` class, which is defined in
[`tensorflow/core/kernels/reader_base.h`](https://www.tensorflow.org/code/tensorflow/core/kernels/reader_base.h). [`tensorflow/core/kernels/reader_base.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/kernels/reader_base.h).
You will need to implement the following methods: You will need to implement the following methods:
* `OnWorkStartedLocked`: open the next file * `OnWorkStartedLocked`: open the next file
@ -83,7 +83,7 @@ If `ReadLocked` successfully reads a record from the file, it should fill in:
If you hit the end of a file (EOF), set `*at_end` to `true`. In either case, If you hit the end of a file (EOF), set `*at_end` to `true`. In either case,
return `Status::OK()`. If there is an error, simply return it using one of the return `Status::OK()`. If there is an error, simply return it using one of the
helper functions from helper functions from
[`tensorflow/core/lib/core/errors.h`](https://www.tensorflow.org/code/tensorflow/core/lib/core/errors.h) [`tensorflow/core/lib/core/errors.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/lib/core/errors.h)
without modifying any arguments. without modifying any arguments.
Next you will create the actual Reader op. It will help if you are familiar Next you will create the actual Reader op. It will help if you are familiar
@ -94,12 +94,12 @@ are:
* Define and register an `OpKernel`. * Define and register an `OpKernel`.
To register the op, you will use a `REGISTER_OP` call defined in To register the op, you will use a `REGISTER_OP` call defined in
[`tensorflow/core/framework/op.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op.h). [`tensorflow/core/framework/op.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op.h).
Reader ops never take any input and always have a single output with type Reader ops never take any input and always have a single output with type
`Ref(string)`. They should always call `SetIsStateful()`, and have a string `Ref(string)`. They should always call `SetIsStateful()`, and have a string
`container` and `shared_name` attrs. You may optionally define additional attrs `container` and `shared_name` attrs. You may optionally define additional attrs
for configuration or include documentation in a `Doc`. For examples, see for configuration or include documentation in a `Doc`. For examples, see
[`tensorflow/core/ops/io_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/ops/io_ops.cc), [`tensorflow/core/ops/io_ops.cc`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/ops/io_ops.cc),
e.g.: e.g.:
```c++ ```c++
@ -118,7 +118,7 @@ A Reader that outputs the lines of a file delimited by '\n'.
To define an `OpKernel`, Readers can use the shortcut of descending from To define an `OpKernel`, Readers can use the shortcut of descending from
`ReaderOpKernel`, defined in `ReaderOpKernel`, defined in
[`tensorflow/core/framework/reader_op_kernel.h`](https://www.tensorflow.org/code/tensorflow/core/framework/reader_op_kernel.h), [`tensorflow/core/framework/reader_op_kernel.h`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/reader_op_kernel.h),
and implement a constructor that calls `SetReaderFactory`. After defining and implement a constructor that calls `SetReaderFactory`. After defining
your class, you will need to register it using `REGISTER_KERNEL_BUILDER(...)`. your class, you will need to register it using `REGISTER_KERNEL_BUILDER(...)`.
An example with no attrs: An example with no attrs:
@ -167,8 +167,8 @@ REGISTER_KERNEL_BUILDER(Name("TextLineReader").Device(DEVICE_CPU),
The last step is to add the Python wrapper. You will import The last step is to add the Python wrapper. You will import
`tensorflow.python.ops.io_ops` in `tensorflow.python.ops.io_ops` in
[`tensorflow/python/user_ops/user_ops.py`](https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py) [`tensorflow/python/user_ops/user_ops.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/user_ops/user_ops.py)
and add a descendant of [`io_ops.ReaderBase`](https://www.tensorflow.org/code/tensorflow/python/ops/io_ops.py). and add a descendant of [`io_ops.ReaderBase`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/ops/io_ops.py).
```python ```python
from tensorflow.python.framework import ops from tensorflow.python.framework import ops
@ -187,7 +187,7 @@ ops.RegisterShape("SomeReader")(common_shapes.scalar_shape)
``` ```
You can see some examples in You can see some examples in
[`tensorflow/python/ops/io_ops.py`](https://www.tensorflow.org/code/tensorflow/python/ops/io_ops.py). [`tensorflow/python/ops/io_ops.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/python/ops/io_ops.py).
## Writing an Op for a record format ## Writing an Op for a record format
@ -207,7 +207,7 @@ Examples of Ops useful for decoding records:
Note that it can be useful to use multiple Ops to decode a particular record Note that it can be useful to use multiple Ops to decode a particular record
format. For example, you may have an image saved as a string in format. For example, you may have an image saved as a string in
[a `tf.train.Example` protocol buffer](https://www.tensorflow.org/code/tensorflow/core/example/example.proto). [a `tf.train.Example` protocol buffer](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/example.proto).
Depending on the format of that image, you might take the corresponding output Depending on the format of that image, you might take the corresponding output
from a from a
[`tf.parse_single_example`](../../api_docs/python/io_ops.md#parse_single_example) [`tf.parse_single_example`](../../api_docs/python/io_ops.md#parse_single_example)

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@ -35,7 +35,7 @@ it is executed without a feed, so you won't forget to feed it.
An example using `placeholder` and feeding to train on MNIST data can be found An example using `placeholder` and feeding to train on MNIST data can be found
in in
[`tensorflow/examples/tutorials/mnist/fully_connected_feed.py`](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/fully_connected_feed.py), [`tensorflow/examples/tutorials/mnist/fully_connected_feed.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/fully_connected_feed.py),
and is described in the [MNIST tutorial](../../tutorials/mnist/tf/index.md). and is described in the [MNIST tutorial](../../tutorials/mnist/tf/index.md).
## Reading from files ## Reading from files
@ -135,7 +135,7 @@ uses a file format where each record is represented using a fixed number of
bytes: 1 byte for the label followed by 3072 bytes of image data. Once you have bytes: 1 byte for the label followed by 3072 bytes of image data. Once you have
a uint8 tensor, standard operations can slice out each piece and reformat as a uint8 tensor, standard operations can slice out each piece and reformat as
needed. For CIFAR-10, you can see how to do the reading and decoding in needed. For CIFAR-10, you can see how to do the reading and decoding in
[`tensorflow/models/image/cifar10/cifar10_input.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10_input.py) [`tensorflow/models/image/cifar10/cifar10_input.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_input.py)
and described in and described in
[this tutorial](../../tutorials/deep_cnn/index.md#prepare-the-data). [this tutorial](../../tutorials/deep_cnn/index.md#prepare-the-data).
@ -146,15 +146,15 @@ This approach makes it easier to mix and match data sets and network
architectures. The recommended format for TensorFlow is a architectures. The recommended format for TensorFlow is a
[TFRecords file](../../api_docs/python/python_io.md#tfrecords-format-details) [TFRecords file](../../api_docs/python/python_io.md#tfrecords-format-details)
containing containing
[`tf.train.Example` protocol buffers](https://www.tensorflow.org/code/tensorflow/core/example/example.proto) [`tf.train.Example` protocol buffers](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/example.proto)
(which contain (which contain
[`Features`](https://www.tensorflow.org/code/tensorflow/core/example/feature.proto) [`Features`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/feature.proto)
as a field). You write a little program that gets your data, stuffs it in an as a field). You write a little program that gets your data, stuffs it in an
`Example` protocol buffer, serializes the protocol buffer to a string, and then `Example` protocol buffer, serializes the protocol buffer to a string, and then
writes the string to a TFRecords file using the writes the string to a TFRecords file using the
[`tf.python_io.TFRecordWriter` class](../../api_docs/python/python_io.md#TFRecordWriter). [`tf.python_io.TFRecordWriter` class](../../api_docs/python/python_io.md#TFRecordWriter).
For example, For example,
[`tensorflow/examples/how_tos/reading_data/convert_to_records.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/convert_to_records.py) [`tensorflow/examples/how_tos/reading_data/convert_to_records.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/how_tos/reading_data/convert_to_records.py)
converts MNIST data to this format. converts MNIST data to this format.
To read a file of TFRecords, use To read a file of TFRecords, use
@ -163,7 +163,7 @@ the [`tf.parse_single_example`](../../api_docs/python/io_ops.md#parse_single_exa
decoder. The `parse_single_example` op decodes the example protocol buffers into decoder. The `parse_single_example` op decodes the example protocol buffers into
tensors. An MNIST example using the data produced by `convert_to_records` can be tensors. An MNIST example using the data produced by `convert_to_records` can be
found in found in
[`tensorflow/examples/how_tos/reading_data/fully_connected_reader.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py), [`tensorflow/examples/how_tos/reading_data/fully_connected_reader.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py),
which you can compare with the `fully_connected_feed` version. which you can compare with the `fully_connected_feed` version.
### Preprocessing ### Preprocessing
@ -172,7 +172,7 @@ You can then do any preprocessing of these examples you want. This would be any
processing that doesn't depend on trainable parameters. Examples include processing that doesn't depend on trainable parameters. Examples include
normalization of your data, picking a random slice, adding noise or distortions, normalization of your data, picking a random slice, adding noise or distortions,
etc. See etc. See
[`tensorflow/models/image/cifar10/cifar10.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10.py) [`tensorflow/models/image/cifar10/cifar10.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10.py)
for an example. for an example.
### Batching ### Batching
@ -455,8 +455,8 @@ multiple preprocessing threads, set the `num_threads` parameter to a number
bigger than 1. bigger than 1.
An MNIST example that preloads the data using constants can be found in An MNIST example that preloads the data using constants can be found in
[`tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py), and one that preloads the data using variables can be found in [`tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py), and one that preloads the data using variables can be found in
[`tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py), [`tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py),
You can compare these with the `fully_connected_feed` and You can compare these with the `fully_connected_feed` and
`fully_connected_reader` versions above. `fully_connected_reader` versions above.

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@ -69,7 +69,7 @@ The code example below is a modification of the [simple MNIST tutorial]
added some summary ops, and run them every ten steps. If you run this and then added some summary ops, and run them every ten steps. If you run this and then
launch `tensorboard --logdir=/tmp/mnist_logs`, you'll be able to visualize launch `tensorboard --logdir=/tmp/mnist_logs`, you'll be able to visualize
statistics, such as how the weights or accuracy varied during training. statistics, such as how the weights or accuracy varied during training.
The code below is an excerpt; full source is [here](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py). The code below is an excerpt; full source is [here](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py).
```python ```python
# Create the model # Create the model

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@ -142,11 +142,11 @@ TensorFlow is designed to support multiple client languages. Currently, the
best-supported client language is [Python](../api_docs/python/index.md). The best-supported client language is [Python](../api_docs/python/index.md). The
[C++ client API](../api_docs/cc/index.md) provides an interface for launching [C++ client API](../api_docs/cc/index.md) provides an interface for launching
graphs and running steps; we also have an experimental API for graphs and running steps; we also have an experimental API for
[building graphs in C++](https://www.tensorflow.org/code/tensorflow/cc/tutorials/example_trainer.cc). [building graphs in C++](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/cc/tutorials/example_trainer.cc).
We would like to support more client languages, as determined by community We would like to support more client languages, as determined by community
interest. TensorFlow has a interest. TensorFlow has a
[C-based client API](https://www.tensorflow.org/code/tensorflow/core/public/tensor_c_api.h) [C-based client API](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/public/tensor_c_api.h)
that makes it easy to build a client in many different languages. We invite that makes it easy to build a client in many different languages. We invite
contributions of new language bindings. contributions of new language bindings.

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@ -77,21 +77,21 @@ for details. It consists of 1,068,298 learnable parameters and requires about
## Code Organization ## Code Organization
The code for this tutorial resides in The code for this tutorial resides in
[`tensorflow/models/image/cifar10/`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/). [`tensorflow/models/image/cifar10/`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/).
File | Purpose File | Purpose
--- | --- --- | ---
[`cifar10_input.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10_input.py) | Reads the native CIFAR-10 binary file format. [`cifar10_input.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_input.py) | Reads the native CIFAR-10 binary file format.
[`cifar10.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10.py) | Builds the CIFAR-10 model. [`cifar10.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10.py) | Builds the CIFAR-10 model.
[`cifar10_train.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU. [`cifar10_train.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU.
[`cifar10_multi_gpu_train.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs. [`cifar10_multi_gpu_train.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs.
[`cifar10_eval.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model. [`cifar10_eval.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model.
## CIFAR-10 Model ## CIFAR-10 Model
The CIFAR-10 network is largely contained in The CIFAR-10 network is largely contained in
[`cifar10.py`](https://www.tensorflow.org/code/tensorflow/models/image/cifar10/cifar10.py). [`cifar10.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10.py).
The complete training The complete training
graph contains roughly 765 operations. We find that we can make the code most graph contains roughly 765 operations. We find that we can make the code most
reusable by constructing the graph with the following modules: reusable by constructing the graph with the following modules:

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@ -39,7 +39,7 @@ The MNIST data is hosted on
[Yann LeCun's website](http://yann.lecun.com/exdb/mnist/). For your [Yann LeCun's website](http://yann.lecun.com/exdb/mnist/). For your
convenience, we've included some python code to download and install the data convenience, we've included some python code to download and install the data
automatically. You can either download automatically. You can either download
[the code](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/input_data.py) [the code](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/input_data.py)
and import it as below, or simply copy and paste it in. and import it as below, or simply copy and paste it in.
```python ```python

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@ -1,6 +1,6 @@
# MNIST Data Download # MNIST Data Download
Code: [tensorflow/examples/tutorials/mnist/](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/) Code: [tensorflow/examples/tutorials/mnist/](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/)
The goal of this tutorial is to show how to download the dataset files required The goal of this tutorial is to show how to download the dataset files required
for handwritten digit classification using the (classic) MNIST data set. for handwritten digit classification using the (classic) MNIST data set.
@ -11,7 +11,7 @@ This tutorial references the following files:
File | Purpose File | Purpose
--- | --- --- | ---
[`input_data.py`](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/input_data.py) | The code to download the MNIST dataset for training and evaluation. [`input_data.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/input_data.py) | The code to download the MNIST dataset for training and evaluation.
## Prepare the Data ## Prepare the Data

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@ -20,7 +20,7 @@ TensorFlow session.
### Load MNIST Data ### Load MNIST Data
For your convenience, we've included For your convenience, we've included
[a script](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/input_data.py) [a script](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/input_data.py)
which automatically downloads and imports the MNIST dataset. It will create a which automatically downloads and imports the MNIST dataset. It will create a
directory `'MNIST_data'` in which to store the data files. directory `'MNIST_data'` in which to store the data files.

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@ -1,6 +1,6 @@
# TensorFlow Mechanics 101 # TensorFlow Mechanics 101
Code: [tensorflow/examples/tutorials/mnist/](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/) Code: [tensorflow/examples/tutorials/mnist/](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/)
The goal of this tutorial is to show how to use TensorFlow to train and The goal of this tutorial is to show how to use TensorFlow to train and
evaluate a simple feed-forward neural network for handwritten digit evaluate a simple feed-forward neural network for handwritten digit
@ -18,8 +18,8 @@ This tutorial references the following files:
File | Purpose File | Purpose
--- | --- --- | ---
[`mnist.py`](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/mnist.py) | The code to build a fully-connected MNIST model. [`mnist.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/mnist.py) | The code to build a fully-connected MNIST model.
[`fully_connected_feed.py`](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/fully_connected_feed.py) | The main code to train the built MNIST model against the downloaded dataset using a feed dictionary. [`fully_connected_feed.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/mnist/fully_connected_feed.py) | The main code to train the built MNIST model against the downloaded dataset using a feed dictionary.
Simply run the `fully_connected_feed.py` file directly to start training: Simply run the `fully_connected_feed.py` file directly to start training:

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@ -19,11 +19,11 @@ represent words as vectors.
We walk through the code later during the tutorial, but if you'd prefer to dive We walk through the code later during the tutorial, but if you'd prefer to dive
straight in, feel free to look at the minimalistic implementation in straight in, feel free to look at the minimalistic implementation in
[tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://www.tensorflow.org/code/tensorflow/examples/tutorials/word2vec/word2vec_basic.py) [tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py)
This basic example contains the code needed to download some data, train on it a This basic example contains the code needed to download some data, train on it a
bit and visualize the result. Once you get comfortable with reading and running bit and visualize the result. Once you get comfortable with reading and running
the basic version, you can graduate to the basic version, you can graduate to
[tensorflow/models/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow/models/embedding/word2vec.py) [tensorflow/models/embedding/word2vec.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/embedding/word2vec.py)
which is a more serious implementation that showcases some more advanced which is a more serious implementation that showcases some more advanced
TensorFlow principles about how to efficiently use threads to move data into a TensorFlow principles about how to efficiently use threads to move data into a
text model, how to checkpoint during training, etc. text model, how to checkpoint during training, etc.
@ -269,7 +269,7 @@ nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
Now that we have the parameters in place, we can define our skip-gram model Now that we have the parameters in place, we can define our skip-gram model
graph. For simplicity, let's suppose we've already integerized our text corpus graph. For simplicity, let's suppose we've already integerized our text corpus
with a vocabulary so that each word is represented as an integer (see with a vocabulary so that each word is represented as an integer (see
[tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://www.tensorflow.org/code/tensorflow/examples/tutorials/word2vec/word2vec_basic.py) [tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py)
for the details). The skip-gram model takes two inputs. One is a batch full of for the details). The skip-gram model takes two inputs. One is a batch full of
integers representing the source context words, the other is for the target integers representing the source context words, the other is for the target
words. Let's create placeholder nodes for these inputs, so that we can feed in words. Let's create placeholder nodes for these inputs, so that we can feed in
@ -321,7 +321,7 @@ for inputs, labels in generate_batch(...):
``` ```
See the full example code in See the full example code in
[tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://www.tensorflow.org/code/tensorflow/examples/tutorials/word2vec/word2vec_basic.py). [tensorflow/examples/tutorials/word2vec/word2vec_basic.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py).
## Visualizing the Learned Embeddings ## Visualizing the Learned Embeddings
@ -335,7 +335,7 @@ t-SNE.
Et voila! As expected, words that are similar end up clustering nearby each Et voila! As expected, words that are similar end up clustering nearby each
other. For a more heavyweight implementation of word2vec that showcases more of other. For a more heavyweight implementation of word2vec that showcases more of
the advanced features of TensorFlow, see the implementation in the advanced features of TensorFlow, see the implementation in
[tensorflow/models/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow/models/embedding/word2vec.py). [tensorflow/models/embedding/word2vec.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/embedding/word2vec.py).
## Evaluating Embeddings: Analogical Reasoning ## Evaluating Embeddings: Analogical Reasoning
@ -350,7 +350,7 @@ https://word2vec.googlecode.com/svn/trunk/questions-words.txt.
To see how we do this evaluation, have a look at the `build_eval_graph()` and To see how we do this evaluation, have a look at the `build_eval_graph()` and
`eval()` functions in `eval()` functions in
[tensorflow/models/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow/models/embedding/word2vec.py). [tensorflow/models/embedding/word2vec.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/embedding/word2vec.py).
The choice of hyperparameters can strongly influence the accuracy on this task. The choice of hyperparameters can strongly influence the accuracy on this task.
To achieve state-of-the-art performance on this task requires training over a To achieve state-of-the-art performance on this task requires training over a
@ -378,13 +378,13 @@ your model is seriously bottlenecked on input data, you may want to implement a
custom data reader for your problem, as described in custom data reader for your problem, as described in
[New Data Formats](../../how_tos/new_data_formats/index.md). For the case of Skip-Gram [New Data Formats](../../how_tos/new_data_formats/index.md). For the case of Skip-Gram
modeling, we've actually already done this for you as an example in modeling, we've actually already done this for you as an example in
[tensorflow/models/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow/models/embedding/word2vec.py). [tensorflow/models/embedding/word2vec.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/embedding/word2vec.py).
If your model is no longer I/O bound but you want still more performance, you If your model is no longer I/O bound but you want still more performance, you
can take things further by writing your own TensorFlow Ops, as described in can take things further by writing your own TensorFlow Ops, as described in
[Adding a New Op](../../how_tos/adding_an_op/index.md). Again we've provided an [Adding a New Op](../../how_tos/adding_an_op/index.md). Again we've provided an
example of this for the Skip-Gram case example of this for the Skip-Gram case
[tensorflow/models/embedding/word2vec_optimized.py](https://www.tensorflow.org/code/tensorflow/models/embedding/word2vec_optimized.py). [tensorflow/models/embedding/word2vec_optimized.py](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/embedding/word2vec_optimized.py).
Feel free to benchmark these against each other to measure performance Feel free to benchmark these against each other to measure performance
improvements at each stage. improvements at each stage.

4
tensorflow/python/client/session.py
View File

@ -488,7 +488,7 @@ class Session(BaseSession):
``` ```
The [`ConfigProto`] The [`ConfigProto`]
(https://www.tensorflow.org/code/tensorflow/core/framework/config.proto) (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/config.proto)
protocol buffer exposes various configuration options for a protocol buffer exposes various configuration options for a
session. For example, to create a session that uses soft constraints session. For example, to create a session that uses soft constraints
for device placement, and log the resulting placement decisions, for device placement, and log the resulting placement decisions,
@ -527,7 +527,7 @@ class Session(BaseSession):
Defaults to using an in-process engine. At present, no value Defaults to using an in-process engine. At present, no value
other than the empty string is supported. other than the empty string is supported.
graph: (Optional.) The `Graph` to be launched (described above). graph: (Optional.) The `Graph` to be launched (described above).
config: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/framework/config.proto) config: (Optional.) A [`ConfigProto`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/config.proto)
protocol buffer with configuration options for the session. protocol buffer with configuration options for the session.
""" """

2
tensorflow/python/framework/dtypes.py
View File

@ -447,7 +447,7 @@ def as_dtype(type_value):
Args: Args:
type_value: A value that can be converted to a `tf.DType` type_value: A value that can be converted to a `tf.DType`
object. This may currently be a `tf.DType` object, a object. This may currently be a `tf.DType` object, a
[`DataType` enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto), [`DataType` enum](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/types.proto),
a string type name, or a `numpy.dtype`. a string type name, or a `numpy.dtype`.
Returns: Returns:

4
tensorflow/python/framework/function.py
View File

@ -144,7 +144,7 @@ def graph_to_function_def(graph, name, inputs, outputs):
"""Returns `graph` as a `FunctionDef` protocol buffer. """Returns `graph` as a `FunctionDef` protocol buffer.
This method creates a [`FunctionDef`]( This method creates a [`FunctionDef`](
https://www.tensorflow.org/code/tensorflow/core/framework/function.proto) https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/function.proto)
protocol buffer that contains all the ops present in the graph. The protocol buffer that contains all the ops present in the graph. The
graph effectively becomes the body of the function. graph effectively becomes the body of the function.
@ -189,7 +189,7 @@ def call_function(func_def, *inputs, **kwargs):
`func_def` is a `func_def` is a
[`FunctionDef`]( [`FunctionDef`](
https://www.tensorflow.org/code/tensorflow/core/framework/function.proto) https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/function.proto)
protcol buffer describing a protcol buffer describing a
TensorFlow function. See [`define_function()`](#define_function) for an TensorFlow function. See [`define_function()`](#define_function) for an
easy way to create one from a Python function. easy way to create one from a Python function.

2
tensorflow/python/framework/importer.py
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@ -147,7 +147,7 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
"""Imports the TensorFlow graph in `graph_def` into the Python `Graph`. """Imports the TensorFlow graph in `graph_def` into the Python `Graph`.
This function provides a way to import a serialized TensorFlow This function provides a way to import a serialized TensorFlow
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) [`GraphDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto)
protocol buffer, and extract individual objects in the `GraphDef` as protocol buffer, and extract individual objects in the `GraphDef` as
[`Tensor`](#Tensor) and [`Operation`](#Operation) objects. See [`Tensor`](#Tensor) and [`Operation`](#Operation) objects. See
[`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a [`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a

10
tensorflow/python/framework/ops.py
View File

@ -1297,7 +1297,7 @@ class Operation(object):
Returns: Returns:
A A
[`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) [`NodeDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto)
protocol buffer. protocol buffer.
""" """
return self._node_def return self._node_def
@ -1308,7 +1308,7 @@ class Operation(object):
Returns: Returns:
An An
[`OpDef`](https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto) [`OpDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op_def.proto)
protocol buffer. protocol buffer.
""" """
return self._op_def return self._op_def
@ -1683,7 +1683,7 @@ class Graph(object):
"""The GraphDef version of this graph. """The GraphDef version of this graph.
For details on the meaning of each version, see [`GraphDef`] For details on the meaning of each version, see [`GraphDef`]
(https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto). (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto).
""" """
return self._graph_def_version return self._graph_def_version
@ -1755,7 +1755,7 @@ class Graph(object):
its `version` property had the given value. its `version` property had the given value.
Returns: Returns:
A [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) A [`GraphDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto)
protocol buffer. protocol buffer.
Raises: Raises:
@ -1783,7 +1783,7 @@ class Graph(object):
"""Adds a function to the graph. """Adds a function to the graph.
The function is specified as a [`FunctionDef`] The function is specified as a [`FunctionDef`]
(https://www.tensorflow.org/code/tensorflow/core/framework/function.proto) (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/function.proto)
protocol buffer. protocol buffer.
After the function has been added, you can call to the function by After the function has been added, you can call to the function by

4
tensorflow/python/ops/io_ops.py
View File

@ -50,9 +50,9 @@ formats into tensors.
TensorFlow's [recommended format for training TensorFlow's [recommended format for training
examples](../../how_tos/reading_data/index.md#standard-tensorflow-format) examples](../../how_tos/reading_data/index.md#standard-tensorflow-format)
is serialized `Example` protocol buffers, [described is serialized `Example` protocol buffers, [described
here](https://www.tensorflow.org/code/tensorflow/core/example/example.proto). here](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/example.proto).
They contain `Features`, [described They contain `Features`, [described
here](https://www.tensorflow.org/code/tensorflow/core/example/feature.proto). here](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/feature.proto).
@@VarLenFeature @@VarLenFeature
@@FixedLenFeature @@FixedLenFeature

4
tensorflow/python/ops/parsing_ops.py
View File

@ -154,7 +154,7 @@ def parse_example(serialized, features, name=None, example_names=None):
"""Parses `Example` protos into a `dict` of tensors. """Parses `Example` protos into a `dict` of tensors.
Parses a number of serialized [`Example`] Parses a number of serialized [`Example`]
(https://www.tensorflow.org/code/tensorflow/core/example/example.proto) (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/example.proto)
protos given in `serialized`. protos given in `serialized`.
`example_names` may contain descriptive names for the corresponding serialized `example_names` may contain descriptive names for the corresponding serialized
@ -566,7 +566,7 @@ def parse_single_sequence_example(
"""Parses a single `SequenceExample` proto. """Parses a single `SequenceExample` proto.
Parses a single serialized [`SequenceExample`] Parses a single serialized [`SequenceExample`]
(https://www.tensorflow.org/code/tensorflow/core/example/example.proto) (https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/example/example.proto)
proto given in `serialized`. proto given in `serialized`.
This op parses a serialize sequence example into a tuple of dictionaries This op parses a serialize sequence example into a tuple of dictionaries

4
tensorflow/python/ops/summary_ops.py
View File

@ -36,7 +36,7 @@ def histogram_summary(tag, values, collections=None, name=None):
"""Outputs a `Summary` protocol buffer with a histogram. """Outputs a `Summary` protocol buffer with a histogram.
The generated The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`. has one summary value containing a histogram for `values`.
This op reports an `OutOfRange` error if any value is not finite. This op reports an `OutOfRange` error if any value is not finite.
@ -115,7 +115,7 @@ def merge_summary(inputs, collections=None, name=None):
"""Merges summaries. """Merges summaries.
This op creates a This op creates a
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input protocol buffer that contains the union of all the values in the input
summaries. summaries.

4
tensorflow/python/training/summary_io.py
View File

@ -234,9 +234,9 @@ def summary_iterator(path):
``` ```
See the protocol buffer definitions of See the protocol buffer definitions of
[Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto) [Event](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/util/event.proto)
and and
[Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [Summary](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
for more information about their attributes. for more information about their attributes.
Args: Args:

4
tensorflow/python/training/training.py
View File

@ -86,13 +86,13 @@ see [Queues](../../api_docs/python/io_ops.md#queues).
## Summary Operations ## Summary Operations
The following ops output The following ops output
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) [`Summary`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto)
protocol buffers as serialized string tensors. protocol buffers as serialized string tensors.
You can fetch the output of a summary op in a session, and pass it to You can fetch the output of a summary op in a session, and pass it to
a [SummaryWriter](../../api_docs/python/train.md#SummaryWriter) to append it a [SummaryWriter](../../api_docs/python/train.md#SummaryWriter) to append it
to an event file. Event files contain to an event file. Event files contain
[`Event`](https://www.tensorflow.org/code/tensorflow/core/util/event.proto) [`Event`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/util/event.proto)
protos that can contain `Summary` protos along with the timestamp and protos that can contain `Summary` protos along with the timestamp and
step. You can then use TensorBoard to visualize the contents of the step. You can then use TensorBoard to visualize the contents of the
event files. See [TensorBoard and event files. See [TensorBoard and