Fixed typos in documentation & READMEs (#10365)

tensorflow/compiler/xla/service/llvm_ir/README.md

@@ -1,2 +1,2 @@
-Common utilites and abstractions for handling and emitting LLVM IR for XLA
+Common utilities and abstractions for handling and emitting LLVM IR for XLA
 backends.

tensorflow/contrib/kernel_methods/g3doc/tutorial.md

@@ -273,7 +273,7 @@ features.
 * The parameters of the kernel mapping are often data-dependent. Model quality
 can be very sensitive to these parameters. Use hyperparameter tuning to find the
 optimal values.
-* If you have multiple numerical features, concatinate them into a single
+* If you have multiple numerical features, concatenate them into a single
 multi-dimensional feature and apply the kernel mapping to the concatenated
 vector.
 

tensorflow/contrib/linear_optimizer/kernels/g3doc/readme.md

@@ -159,7 +159,7 @@ expected.
 
 On criteo dataset, the usual Newton method goes out of range for a small (but
 non negligible) fraction of the examples. The returned dual in these cases will
-be $$0$$ or $$\pm 1$$. The modified Newton algorihm always find the true zero
+be $$0$$ or $$\pm 1$$. The modified Newton algorithm always find the true zero
 and achieves a better log loss.
 
 The blue lines represent the modified Newton (evaluation and training) and the

tensorflow/contrib/losses/README.md

@@ -12,10 +12,10 @@ All loss functions take a pair of tensors, `predictions` and ground truth
 `[batch_size, d1, ... dN]` where `batch_size` is the number
 of samples in the batch and `d1` ... `dN` are the remaining dimensions.
 
-THe `weight` parameter can be used to adjust the relative weight samples within
+The `weight` parameter can be used to adjust the relative weight samples within
 the batch. The result of each loss is a scalar average of all sample losses with
 non-zero weights.
 
 Any parameter named `logit` should be the raw model outputs, not a normalized
-probablility distribution (i.e., `[0.0, 1.0]`). `target` for losses taking
+probability distribution (i.e., `[0.0, 1.0]`). `target` for losses taking
 `logit` _should_ be a normalized probability distribution.

tensorflow/contrib/tfprof/README.md

@@ -1,6 +1,6 @@
 # tfprof: A Profiling Tool for TensorFlow Models
 
-# Full Docment in tensorflow/tools/tfprof/README.md
+# Full Document in tensorflow/tools/tfprof/README.md
 
 Author: Xin Pan (xpan@google.com, github: panyx0718), Jon Shlens, Yao Zhang
 

tensorflow/docs_src/api_guides/cc/guide.md

@@ -111,7 +111,7 @@ Here are some of the properties controlled by a `Scope` object:
 Please refer to @{tensorflow::Scope} for the complete list of member functions
 that let you create child scopes with new properties.
 
-### Operation Construtors
+### Operation Constructors
 
 You can create graph operations with operation constructors, one C++ class per
 TensorFlow operation. Unlike the Python API which uses snake-case to name the

tensorflow/docs_src/api_guides/python/contrib.linalg.md

@@ -9,7 +9,7 @@ Subclasses of `LinearOperator` provide a access to common methods on a
 (batch) matrix, without the need to materialize the matrix.  This allows:
 
 * Matrix free computations
-* Different operators to take advantage of special strcture, while providing a
+* Different operators to take advantage of special structure, while providing a
   consistent API to users.
 
 ### Base class

tensorflow/docs_src/deploy/distributed.md

@@ -54,7 +54,7 @@ the following:
 ### Create a `tf.train.ClusterSpec` to describe the cluster
 
 The cluster specification dictionary maps job names to lists of network
-adresses. Pass this dictionary to
+addresses. Pass this dictionary to
 the @{tf.train.ClusterSpec}
 constructor.  For example:
 

tensorflow/docs_src/extend/estimators.md

@@ -303,7 +303,7 @@ The `model_fn` must accept three arguments:
 `model_fn` may also accept a `params` argument containing a dict of
 hyperparameters used for training (as shown in the skeleton above).
 
-The body of the function perfoms the following tasks (described in detail in the
+The body of the function performs the following tasks (described in detail in the
 sections that follow):
 
 *   Configuring the model—here, for the abalone predictor, this will be a neural
@@ -371,7 +371,7 @@ layer.
 
 The input layer is a series of nodes (one for each feature in the model) that
 will accept the feature data that is passed to the `model_fn` in the `features`
-argument. If `features` contains an n-dimenional `Tensor` with all your feature
+argument. If `features` contains an n-dimensional `Tensor` with all your feature
 data (which is the case if `x` and `y` `Dataset`s are passed to `fit()`,
 `evaluate()`, and `predict()` directly), then it can serve as the input layer.
 If `features` contains a dict of @{$linear#feature-columns-and-transformations$feature columns} passed to

tensorflow/docs_src/extend/language_bindings.md

@@ -29,7 +29,7 @@ into broad categories:
     are modified.
 -   *Gradients (AKA automatic differentiation)*: Given a graph and a list of
     input and output operations, add operations to the graph that compute the
-    partial deriviatives (gradients) of the inputs with respect to the outputs.
+    partial derivatives (gradients) of the inputs with respect to the outputs.
     Allows for customization of the gradient function for a particular operation
     in the graph.
 -   *Functions*: Define a subgraph that may be called in multiple places in the

tensorflow/docs_src/get_started/mnist/pros.md

@@ -392,7 +392,7 @@ The differences are that:
 - We will add logging to every 100th iteration in the training process.
 
 We will also use tf.Session rather than tf.InteractiveSession. This better
-separates the process of creating the graph (model sepecification) and the
+separates the process of creating the graph (model specification) and the
 process of evaluating the graph (model fitting). It generally makes for cleaner
 code. The tf.Session is created within a [`with` block](https://docs.python.org/3/whatsnew/2.6.html#pep-343-the-with-statement)
 so that it is automatically destroyed once the block is exited.

tensorflow/docs_src/install/install_java.md

@@ -216,7 +216,7 @@ and Mac OS X:
 
 <pre><b>java -cp libtensorflow-1.2.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
-And the following comand line executes the `HelloTF` program on Windows:
+And the following command line executes the `HelloTF` program on Windows:
 
 <pre><b>java -cp libtensorflow-1.2.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre>
 

tensorflow/docs_src/performance/benchmarks.md

@@ -92,7 +92,7 @@ addition to the batch sizes listed in the table, InceptionV3, ResNet-50,
 ResNet-152, and VGG16 were tested with a batch size of 32. Those results are in
 the *other results* section.
 
-Options            | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+Options            | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ------------------ | ----------- | --------- | ---------- | ------- | -----
 Batch size per GPU | 64          | 64        | 64         | 512     | 64
 Optimizer          | sgd         | sgd       | sgd        | sgd     | sgd
@@ -120,7 +120,7 @@ VGG16       | replicated (with NCCL) | n/a
 
 **Training synthetic data**
 
-GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+GPUs | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ---- | ----------- | --------- | ---------- | ------- | -----
 1    | 142         | 219       | 91.8       | 2987    | 154
 2    | 284         | 422       | 181        | 5658    | 295
@@ -129,7 +129,7 @@ GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
 
 **Training real data**
 
-GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+GPUs | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ---- | ----------- | --------- | ---------- | ------- | -----
 1    | 142         | 218       | 91.4       | 2890    | 154
 2    | 278         | 425       | 179        | 4448    | 284
@@ -182,7 +182,7 @@ addition to the batch sizes listed in the table, InceptionV3 and ResNet-50 were
 tested with a batch size of 32. Those results are in the *other results*
 section.
 
-Options            | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+Options            | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ------------------ | ----------- | --------- | ---------- | ------- | -----
 Batch size per GPU | 64          | 64        | 32         | 512     | 32
 Optimizer          | sgd         | sgd       | sgd        | sgd     | sgd
@@ -199,7 +199,7 @@ The configuration used for each model was `variable_update` equal to
 
 **Training synthetic data**
 
-GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+GPUs | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ---- | ----------- | --------- | ---------- | ------- | -----
 1    | 30.5        | 51.9      | 20.0       | 656     | 35.4
 2    | 57.8        | 99.0      | 38.2       | 1209    | 64.8
@@ -208,7 +208,7 @@ GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
 
 **Training real data**
 
-GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+GPUs | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ---- | ----------- | --------- | ---------- | ------- | -----
 1    | 30.6        | 51.2      | 20.0       | 639     | 34.2
 2    | 58.4        | 98.8      | 38.3       | 1136    | 62.9
@@ -257,7 +257,7 @@ addition to the batch sizes listed in the table, InceptionV3 and ResNet-50 were
 tested with a batch size of 32. Those results are in the *other results*
 section.
 
-Options            | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+Options            | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ------------------ | ----------- | --------- | ---------- | ------- | -----
 Batch size per GPU | 64          | 64        | 32         | 512     | 32
 Optimizer          | sgd         | sgd       | sgd        | sgd     | sgd
@@ -281,7 +281,7 @@ VGG16       | parameter_server          | gpu
 
 **Training synthetic data**
 
-GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+GPUs | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ---- | ----------- | --------- | ---------- | ------- | -----
 1    | 30.8        | 51.5      | 19.7       | 684     | 36.3
 2    | 58.7        | 98.0      | 37.6       | 1244    | 69.4
@@ -290,7 +290,7 @@ GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
 
 **Training real data**
 
-GPUs | InceptionV3 | ResNet-50 | ResNet-152 | Alexnet | VGG16
+GPUs | InceptionV3 | ResNet-50 | ResNet-152 | AlexNet | VGG16
 ---- | ----------- | --------- | ---------- | ------- | -----
 1    | 30.5        | 51.3      | 19.7       | 674     | 36.3
 2    | 59.0        | 94.9      | 38.2       | 1227    | 67.5

tensorflow/docs_src/performance/performance_models.md

@@ -133,7 +133,7 @@ Benefits of using this scheme:
 ## Best Practices in Building High-Performance Models
 
 Collected below are a couple of additional best practices that can improve
-performance and increase the flexiblity of models.
+performance and increase the flexibility of models.
 
 ### Build the model with both NHWC and NCHW
 

tensorflow/docs_src/performance/quantization.md

@@ -153,7 +153,7 @@ bit.
 
 The min and max operations actually look at the values in the input float
 tensor, and then feeds them into the Dequantize operation that converts the
-tensor into eight-bits. There're more details on how the quantized representation
+tensor into eight-bits. There are more details on how the quantized representation
 works later on.
 
 Once the individual operations have been converted, the next stage is to remove

tensorflow/docs_src/programmers_guide/faq.md

@@ -189,7 +189,7 @@ operation for that variable in a session. It is destroyed when that
 
 Variables allow concurrent read and write operations. The value read from a
 variable may change if it is concurrently updated. By default, concurrent
-assigment operations to a variable are allowed to run with no mutual exclusion.
+assignment operations to a variable are allowed to run with no mutual exclusion.
 To acquire a lock when assigning to a variable, pass `use_locking=True` to
 @{tf.Variable.assign}.
 

tensorflow/docs_src/programmers_guide/saved_model_cli.md

@@ -189,7 +189,7 @@ inputs that match the dtype and shape of the model signature.
 
 By default, SavedModel CLI will print outputs to console. If a directory is
 passed to `--outdir` option, the outputs will be saved as npy files named after
-output tensor keys under the given directory. Use `--overwite` to overwrite
+output tensor keys under the given directory. Use `--overwrite` to overwrite
 existing output files.
 
 #### TensorFlow Debugger (tfdbg) Integration

tensorflow/docs_src/programmers_guide/supervisor.md

@@ -203,15 +203,15 @@ Example: Call `my_additional_summaries()` every 20mn:
 
 ```python
 
-def my_additional_sumaries(sv, sess):
+def my_additional_summaries(sv, sess):
  ...fetch and write summaries, see below...
 
 ...
   sv = tf.train.Supervisor(logdir="/my/training/directory")
   with sv.managed_session() as sess:
-    # Call my_additional_sumaries() every 1200s, or 20mn,
+    # Call my_additional_summaries() every 1200s, or 20mn,
     # passing (sv, sess) as arguments.
-    sv.loop(1200, my_additional_sumaries, args=(sv, sess))
+    sv.loop(1200, my_additional_summaries, args=(sv, sess))
     ...main training loop...
 ```
 
@@ -226,11 +226,11 @@ better when only one events file in a directory is being actively appended to.
 The supervisor provides a helper function to append summaries:
 @{tf.train.Supervisor.summary_computed}.
 Just pass to the function the output returned by a summary op.  Here is an
-example of using that function to implement `my_additional_sumaries()` from the
+example of using that function to implement `my_additional_summaries()` from the
 previous example:
 
 ```python
-def my_additional_sumaries(sv, sess):
+def my_additional_summaries(sv, sess):
   summaries = sess.run(my_additional_summary_op)
   sv.summary_computed(sess, summaries)
 ```

tensorflow/docs_src/programmers_guide/tfdbg-tflearn.md

@@ -106,7 +106,7 @@ hooks = [tf_debug.DumpingDebugHook("/shared/storage/location/tfdbg_dumps_1")]
 ```
 
 Then this `hook` can be used in the same way as the `LocalCLIDebugHook` examples
-above. As the training and/or evalution of `Estimator` or `Experiment`
+above. As the training and/or evaluation of `Estimator` or `Experiment`
 happens, directories of the naming pattern
 `/shared/storage/location/tfdbg_dumps_1/run_<epoch_timestamp_microsec>_<uuid>`
 will appear. Each directory corresponds to a `Session.run()` call that underlies

tensorflow/docs_src/programmers_guide/threading_and_queues.md

@@ -121,7 +121,7 @@ example = ...ops to create one example...
 # Create a queue, and an op that enqueues examples one at a time in the queue.
 queue = tf.RandomShuffleQueue(...)
 enqueue_op = queue.enqueue(example)
-# Create a training graph that starts by dequeuing a batch of examples.
+# Create a training graph that starts by dequeueing a batch of examples.
 inputs = queue.dequeue_many(batch_size)
 train_op = ...use 'inputs' to build the training part of the graph...
 ```

tensorflow/docs_src/tutorials/layers.md

@@ -585,7 +585,7 @@ hand-drawn digits) and training labels (the corresponding value from 0–9 for
 each image) as [numpy
 arrays](https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html)
 in `train_data` and `train_labels`, respectively. Similarly, we store the
-evalulation feature data (10,000 images) and evaluation labels in `eval_data`
+evaluation feature data (10,000 images) and evaluation labels in `eval_data`
 and `eval_labels`, respectively.
 
 ### Create the Estimator {#create-the-estimator}

tensorflow/tools/graph_transforms/README.md

@@ -586,7 +586,7 @@ equivalent, followed by a float conversion op so that the result is usable by
 subsequent nodes. This is mostly useful for [shrinking file
 sizes](#shrinking-file-size), but also helps with the more advanced
 [quantize_nodes](#quantize_nodes) transform. Even though there are no
-prerequesites, it is advisable to run [fold_batch_norms](#fold_batch_norms) or
+prerequisites, it is advisable to run [fold_batch_norms](#fold_batch_norms) or
 [fold_old_batch_norms](#fold_old_batch_norms), because rounding variances down
 to zero may cause significant loss of precision.
 
@@ -674,7 +674,7 @@ number of steps. The unique values are chosen per buffer by linearly allocating
 between the largest and smallest values present. This is useful when you'll be
 deploying on mobile, and you want a model that will compress effectively. See
 [shrinking file size](#shrinking-file-size) for more details. Even though there
-are no prerequesites, it is advisable to run
+are no prerequisites, it is advisable to run
 [fold_batch_norms](#fold_batch_norms) or
 [fold_old_batch_norms](#fold_old_batch_norms), because rounding variances down
 to zero may cause significant loss of precision.

tensorflow/tools/tfprof/README.md

@@ -603,15 +603,15 @@ provides checkpointed tensors' values.
 
 `-order_by`: Order the results by [name|depth|bytes|micros|params|float_ops|occurrence]
 
-`-account_type_regexes`: Account and display the ops whose types match one of the type regexes specified. tfprof allow user to define extra op types for ops through tensorflow.tfprof.OpLog proto. regexes are comma-sperated.
+`-account_type_regexes`: Account and display the ops whose types match one of the type regexes specified. tfprof allow user to define extra op types for ops through tensorflow.tfprof.OpLog proto. regexes are comma-separated.
 
 `-start_name_regexes`: Show ops starting from the ops that matches the regexes, recursively. regexes are comma-separated.
 
-`-trim_name_regexes`: Hide ops starting from the ops that matches the regexes, recursively, regexes are comma-seprated.
+`-trim_name_regexes`: Hide ops starting from the ops that matches the regexes, recursively, regexes are comma-separated.
 
-`-show_name_regexes`: Show ops that match the regexes. regexes are comma-seprated.
+`-show_name_regexes`: Show ops that match the regexes. regexes are comma-separated.
 
-`-hide_name_regexes`: Hide ops that match the regexes. regexes are comma-seprated.
+`-hide_name_regexes`: Hide ops that match the regexes. regexes are comma-separated.
 
 Notes: For each op, `-account_type_regexes` is first evaluated, only ops with
 types matching the specified regexes are accounted and selected for displayed.