Fix typos

Examples/Evaluation/CSEvalClient/Program.cs

@@ -333,10 +333,10 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
             // Specifies the number of times to iterate through the test file (epochs)
             int numRounds = 1;
 
-            // Counts the number of evaluations accross all models
+            // Counts the number of evaluations across all models
             int count = 0;
 
-            // Counts the number of failed evaluations (output != expected) accross all models
+            // Counts the number of failed evaluations (output != expected) across all models
             int errorCount = 0;
 
             // The examples assume the executable is running from the data folder

Examples/SequenceToSequence/PennTreebank/README.md

@@ -10,7 +10,7 @@ See License.md in the root level folder of the CNTK repository for full license
 
 ## Overview
 
-|Data      |The Penn Treebank Project (https://www.cis.upenn.edu/~treebank/) annotates naturally-occuring text for linguistic structure .
+|Data      |The Penn Treebank Project (https://www.cis.upenn.edu/~treebank/) annotates naturally-occurring text for linguistic structure .
 |:---------|:---|
 |Purpose   |Showcase how to train a recurrent network for text data.
 |Network   |SimpleNetworkBuilder for recurrent network with two hidden layers.

Manual/How_to_train_using_declarative_and_imperative_API.ipynb

@@ -473,7 +473,7 @@
     "In order to report progress, please provide an instance of the [ProgressWriter](https://www.cntk.ai/pythondocs/cntk.logging.progress_print.html#module-cntk.logging.progress_print). It has its own set of parameters to control how often to print the loss value. If you need to have a custom logic for retrieving current status, please consider implementing your own ProgressWriter.\n",
     "\n",
     "### Checkpointing\n",
-    "[Checkpoint configuration](https://www.cntk.ai/pythondocs/cntk.train.training_session.html#cntk.train.training_session.CheckpointConfig) specifies how often to save a checkpoint to the given file. The checkpointing frequency is specified in samples. When given, the method takes care of saving/restoring the state accross the trainer/learners/minibatch source and propagating this information among distributed workers. If you need to preserve all checkpoints that were taken during training, please set `preserveAll` to true. \n",
+    "[Checkpoint configuration](https://www.cntk.ai/pythondocs/cntk.train.training_session.html#cntk.train.training_session.CheckpointConfig) specifies how often to save a checkpoint to the given file. The checkpointing frequency is specified in samples. When given, the method takes care of saving/restoring the state across the trainer/learners/minibatch source and propagating this information among distributed workers. If you need to preserve all checkpoints that were taken during training, please set `preserveAll` to true. \n",
     "\n",
     "### Validation\n",
     "When [cross validation](https://www.cntk.ai/pythondocs/cntk.train.training_session.html#cntk.train.training_session.CrossValidationConfig) config is given, the training session runs the validation on the specified minibatch source with the specified frequency and reports average metric error. The user can also provide a cross validation callback, that will be called with the specified frequency. It is up to the user to perform validation in the callback and return back `True` if the training should be continued, or `False` otherwise. \n",

Source/CNTK/BrainScript/BrainScriptEvaluator.cpp

@@ -920,7 +920,7 @@ static wstring FormatConfigValue(ConfigValuePtr arg, const wstring &how);
 // StringFunction implements
 //  - Format
 //  - Chr(c) -- gives a string of one character with Unicode value 'c'
-//  - Replace(s,what,withwhat) -- replace all occurences of 'what' with 'withwhat'
+//  - Replace(s,what,withwhat) -- replace all occurrences of 'what' with 'withwhat'
 //  - Substr(s,begin,num) -- get a substring
 // TODO: RegexReplace()
 class StringFunction : public String

Source/CNTKv2LibraryDll/API/CNTKLibrary.h

@@ -35,7 +35,7 @@ namespace CNTK
 {
     ///
     /// Checked mode enables additional runtime verification such as:
-    /// - Tracking NaN occurences in sequence gaps.
+    /// - Tracking NaN occurrences in sequence gaps.
     /// - Function graph verification after binding of free static axes to actual values at runtime
     /// 
     /// Enabling checked mode incurs additional runtime costs and is meant to be used as a debugging aid.

Source/CNTKv2LibraryDll/Common.cpp

@@ -75,7 +75,7 @@ namespace CNTK
 
         // This is used to generate a default seed value for random parameter initializer and also 
         // for stateful nodes (dropout, and both flavors of random sample). The 'perWorkerLocalValue' flag
-        // indicates if the generated value should be identical accross individual workers in distributed 
+        // indicates if the generated value should be identical across individual workers in distributed 
         // setting or if each worker should get a different seed value.        
         size_t GenerateRandomSeed(bool perWorkerLocalValue /*= false*/)
         {

Source/Common/Include/RandomOrdering.h

@@ -27,7 +27,7 @@ static inline size_t rand(const size_t begin, const size_t end)
 
 // Rand based on Mersenne Twister.
 // We use our own distribution in order to match baselines between different operating systems,
-// because uniform_distribution is not guranteed to provide the same numbers on different platforms.
+// because uniform_distribution is not guaranteed to provide the same numbers on different platforms.
 // TODO: Switching to Boost would eliminate this problem.
 static inline size_t RandMT(const size_t begin, const size_t end, std::mt19937_64& rng)
 {

Source/ComputationNetworkLib/TrainingNodes.cpp

@@ -247,7 +247,7 @@ double RandomSampleInclusionFrequencyNode<ElemType>::EstimateNumberOfTries()
     return totalTries / (double)numExperiments;
 }
 
-// Estimates the expected number of occurences of each class in the sampled set.
+// Estimates the expected number of occurrences of each class in the sampled set.
 // For sampling without replacement we use estimate using average number of tries. (Inspired by TensorFlow)
 // BUGBUG: Consider to reimplement using a less biased estimate as proposed by Nikos.
 template<class ElemType>
@@ -340,4 +340,4 @@ template class DropoutNode<double>;
 template class BatchNormalizationNode<float>;
 template class BatchNormalizationNode<double>;
 
-}}}
+}}}

Source/ComputationNetworkLib/TrainingNodes.h

@@ -1447,7 +1447,7 @@ public:
     virtual void /*ComputationNode::*/ ForwardPropNonLooping() override;
     virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override;
 private:
-    // Approximates the expected number of occurences of a class in the sampled set.
+    // Approximates the expected number of occurrences of a class in the sampled set.
     // Assuming (falsely) that the number of tries to get a sampled set with the requested number of distinct values is always estimatedNumTries
     // the probability that a specific class in the sampled set is (1 - (1-p)^estimatedNumTries), where p is the probablity to pick the clas in one draw.
     // The estimate can be quite a bit off but should be better than nothing. Better alternatives?

Source/SGDLib/SGD.cpp

@@ -1283,7 +1283,7 @@ size_t SGD<ElemType>::TrainOneEpoch(ComputationNetworkPtr net,
         // independent of their actual content (which is considered outdated).
 
         // Sum of actualMBSize across all nodes when using parallel training
-        // 'aggregate' here means accross-worker aggregate for this one minibatch.
+        // 'aggregate' here means across-worker aggregate for this one minibatch.
         size_t aggregateNumSamples = actualMBSize; // (0 for empty MB)
         size_t aggregateNumSamplesWithLabel = CriterionAccumulator<ElemType>::GetNumSamples(criterionNodes[0], numSamplesWithLabelOfNetwork); // (0 for empty MB)
 

Tests/EndToEndTests/UnitTests/MathTests/baseline.txt

@@ -412,7 +412,7 @@ Test module "MathTests" has passed with:
     Test case "GPUMatrixSuite/GPUBlasInnerProduct" has passed with:
       2 assertions out of 2 passed
 
-    Test case "GPUMatrixSuite/MatrixCopyAssignAccrossDevices" has passed
+    Test case "GPUMatrixSuite/MatrixCopyAssignAcrossDevices" has passed
 
     Test case "GPUMatrixSuite/GPUMatrixConstructorNoFlag" has passed with:
       6 assertions out of 6 passed

Tests/UnitTests/MathTests/GPUMatrixTests.cpp

@@ -15,7 +15,7 @@ namespace Microsoft { namespace MSR { namespace CNTK { namespace Test {
 
 BOOST_AUTO_TEST_SUITE(GPUMatrixSuite)
 
-BOOST_FIXTURE_TEST_CASE(MatrixCopyAssignAccrossDevices, RandomSeedFixture)
+BOOST_FIXTURE_TEST_CASE(MatrixCopyAssignAcrossDevices, RandomSeedFixture)
 {
     bool hasTwoGpus = false;
 #ifndef CPUONLY

bindings/python/cntk/debugging/debug.py

@@ -107,7 +107,7 @@ class _DebugState(object):
 def set_checked_mode(enable):
     '''
      Checked mode enables additional runtime verification such as:
-        - Tracking NaN occurences in sequence gaps.
+        - Tracking NaN occurrences in sequence gaps.
         - Function graph verification after binding of free static axes to actual values at runtime
 
      Enabling checked mode incurs additional runtime costs and is meant to be used as a debugging aid.

bindings/python/cntk/ops/__init__.py

@@ -2467,7 +2467,7 @@ def random_sample_inclusion_frequency(
     name=''):
     '''
     For weighted sampling with the specifed sample size (`num_samples`)
-    this operation computes the expected number of occurences of each class
+    this operation computes the expected number of occurrences of each class
     in the sampled set. In case of sampling without replacement
     the result is only an estimate which might be quite rough in the
     case of small sample sizes.