minor spelling tweaks

Author: brettkoonce

examples/DistributedTraining.cpp

@@ -37,7 +37,7 @@ int main() {
 
   const int inputIdx = 0, targetIdx = 1;
 
-  // Model defintion - 2-layer Perceptron with ReLU activation
+  // Model definition - 2-layer Perceptron with ReLU activation
   auto model = std::make_shared<Sequential>();
   model->add(Linear(nFeat, 100));
   model->add(ReLU());

examples/Perceptron.cpp

@@ -24,7 +24,7 @@ int main() {
   TensorDataset data({X, Y});
   const int inputIdx = 0, targetIdx = 1;
 
-  // Model defintion - 2-layer Perceptron with ReLU activation
+  // Model definition - 2-layer Perceptron with ReLU activation
   Sequential model;
   model.add(Linear(nFeat, 100));
   model.add(ReLU());

examples/README.md

@@ -7,7 +7,7 @@ In the `make install` step, examples are placed in `<prefix>/share/flashlight/ex
 ## Building In-Source
 Building in-source is simple; examples are built by default. Binaries are placed in `build/examples`.
 
-To disable building examples in source, simply make sure `FL_BUILD_EXAMPLES` is `OFF`. Even though examples won't be built, the install step will still place source files in `examples/` in the isntall target directory mentioned above.
+To disable building examples in source, simply make sure `FL_BUILD_EXAMPLES` is `OFF`. Even though examples won't be built, the install step will still place source files in `examples/` in the install target directory mentioned above.
 
 ## Building as a Standalone Project
 Examples can also be built as standalone projects outside of the `build` directory. After the installation step, simply copy the example source path to a suitable directory, and build:

flashlight/autograd/Functions.h

@@ -181,7 +181,7 @@ Variable operator<=(const Variable& lhs, const double& rhs);
 Variable operator&&(const Variable& lhs, const Variable& rhs);
 
 /**
- * [Non-differentiable] Element-wise lodical not of a Variable.
+ * [Non-differentiable] Element-wise logical not of a Variable.
  * \f[ out_i = !var_i \f]
  */
 Variable operator!(const Variable& input);
@@ -296,28 +296,28 @@ Variable transpose(const Variable& input);
 /**
  * Repeats the tensor `input` along certain dimensions so as to match the shape
  * of `reference`. The dimensions to be repeated along are automatically
- * infered.
+ * inferred.
  */
 Variable tileAs(const Variable& input, const Variable& reference);
 
 /**
  * Repeats the tensor `input` along certain dimensions so as to match the shape
  * in the descriptor `rdims`. The dimensions to be repeated along are
- * automatically infered.
+ * automatically inferred.
  */
 Variable tileAs(const Variable& input, const af::dim4& rdims);
 
 /**
  * Sums up the tensor `input` along certain dimensions so as to match the shape
- * of `reference`. The dimensions to be summed along are automatically infered.
+ * of `reference`. The dimensions to be summed along are automatically inferred.
  * Note that after summation, the shape of those dimensions will be 1.
  */
 Variable sumAs(const Variable& input, const Variable& reference);
 
 /**
  * Sums up the tensor `input` along certain dimensions so as to match the shape
  * in the descriptor `rdims`. The dimensions to be summed along are
- * automatically infered. Note that after summation, the shape of those
+ * automatically inferred. Note that after summation, the shape of those
  * dimensions will be 1.
  */
 Variable sumAs(const Variable& input, const af::dim4& rdims);
@@ -549,15 +549,15 @@ Variable pool2d(
     PoolingMode mode = PoolingMode::MAX);
 
 /**
- * Applies a softmax function on Variable `input` along dimention `dim`, so that
+ * Applies a softmax function on Variable `input` along dimension `dim`, so that
  * the elements of the dimensional `dim` in output lie in the range (0,1) and
  * sum to 1.
  * \f[ out(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)} \f]
  */
 Variable softmax(const Variable& input, const int dim);
 
 /**
- * Applies a log(softmax(x)) function on Variable `input` along dimention `dim`
+ * Applies a log(softmax(x)) function on Variable `input` along dimension `dim`
  * \f[
  *    out(x_{i}) = log \Big( \frac{exp(x_i)}{\sum_j exp(x_j)} \Big)
  * \f]

flashlight/autograd/Variable.h

@@ -91,7 +91,7 @@ class Variable {
    * Creates a Variable which wraps the array and inputs specified
    * @param[in] data array to the stored in the Variable
    * @param[in] inputs a vector specifying inputs for this Variable
-   * @param[in] gradFunc function specifyign how to calculate gradient of the
+   * @param[in] gradFunc function specifying how to calculate gradient of the
    * input Variables
    */
   Variable(af::array data, std::vector<Variable> inputs, GradFunc gradFunc);
@@ -131,7 +131,7 @@ class Variable {
   bool isGradAvailable() const;
 
   /**
-   * Returns the dimesion of the array wrapped by the Variable
+   * Returns the dimension of the array wrapped by the Variable
    */
   af::dim4 dims() const;
 
@@ -222,7 +222,7 @@ class Variable {
 
   /**
    * Run backward pass on the Variable.  Gradient of all the inputs
-   * in the computation graph leading upto the Variable on which the function is
+   * in the computation graph leading up to the Variable on which the function is
    * computed.
    * @param[in] grad gradient w.r.t to the Variable
    * @param[in] retainGraph If False, clears the input Variables stored
@@ -232,7 +232,7 @@ class Variable {
 
   /**
    * Run backward pass on the Variable. Gradient of all the inputs
-   * in the computation graph leading upto the Variable on which the function is
+   * in the computation graph leading up to the Variable on which the function is
    * computed. Gradient w.r.t the all the elements in the variable is set to 1.0
    * @param[in] retainGraph If False, clears the input Variables stored
    * by the Variable

flashlight/common/DevicePtr.h

@@ -13,11 +13,11 @@ namespace fl {
 
 /**
  * DevicePtr provides a wrapper for accessing device pointer of an Arrayfire
- * array in a safer way.  After calliing `device()` on arrayfire array to get
+ * array in a safer way.  After calling `device()` on arrayfire array to get
  * device pointer, the memory is not free until `unlock()` is called -
  * 'http://arrayfire.org/docs/group__device__func__device.htm'.
  * DevicePtr provides a std::lock_guard style API which calls the `unlock()`
- * function in its desctructor after getting device pointer.
+ * function in its destructor after getting device pointer.
  * Example Usage :
  * \code{.cpp}
  * auto A = af::array(10, 10);
@@ -63,7 +63,7 @@ class DevicePtr {
   DevicePtr& operator=(DevicePtr&& other) = delete;
 
   /**
-    `.unlock()` is called on the underlying array in desctructor
+    `.unlock()` is called on the underlying array in destructor
   */
   ~DevicePtr();
 

flashlight/dataset/PrefetchDataset.h

@@ -17,7 +17,7 @@ namespace fl {
  * A view into a dataset, where a given number of samples are prefetched in
  * advance in a ThreadPool. PrefetchDataset should be used when there is a
  * sequential access to the underlying dataset. Otherwise, there will a lot of
- * cache misses leading to a degarded performance.
+ * cache misses leading to a degraded performance.
  *
  * Example:
   \code{.cpp}

flashlight/distributed/DistributedApi.h

@@ -19,7 +19,7 @@ namespace fl {
  * Initialize the distributed environment. Note that `worldSize`, `worldRank`
  * are ignored if DistributedInit::MPI is used.
  *
- * @param initMethod Intialization method used for setting up the rendezvous
+ * @param initMethod Initialization method used for setting up the rendezvous
  * @param worldSize Total number of processes in the communication group
  *`@param worldRank 0-indexed rank of the current process
  * @param params Additional parameters (if any) needed for initialization

flashlight/nn/modules/AdaptiveSoftMaxLoss.h

@@ -65,7 +65,7 @@ class AdaptiveSoftMaxLoss : public BinaryModule {
    * first tail bucket will contain `50 - 5 = 45` targets (subtracting the size
    * of the head bucket), the second tail bucket will contain `100 - 50 = 50`
    * targets (subtracting the size of the first tail bucket). Cutoffs must be
-   * specified to accomadate all targets: any remaining targets are not assigned
+   * specified to accommodate all targets: any remaining targets are not assigned
    * to an 'overflow' bucket.
    * @param div_value determines the number of hidden units in the intermediate
    * layer for each tail bucket:

flashlight/nn/modules/Loss.h

@@ -86,7 +86,7 @@ class BinaryCrossEntropy : public BinaryModule {
    * Perform forward loss computation with an additional weight tensor.
    *
    * @param inputs a tensor with the predicted values
-   * @param targets a tensor with the taret values
+   * @param targets a tensor with the target values
    * @param weights a rescaling weight given to the loss of each element.
    */
   Variable forward(

flashlight/nn/modules/Module.h

@@ -77,7 +77,7 @@ class Module {
   virtual void train();
 
   /**
-   * Switches the module to evalulation mode. Changes all parameters so that
+   * Switches the module to evaluation mode. Changes all parameters so that
    * gradient calculation will be disabled for any calls to `forward`.
    */
   virtual void eval();