several small documentation fixes (#107)

Some documentation syntax, some spelling.

Author: visr

docs/src/custom_behaviour.md

@@ -1,6 +1,6 @@
 # Specifying a backend
 
-By default TensorBoardLogger follows those rules to chose what backend a certain
+By default TensorBoardLogger follows these rules to choose what backend a certain
 value is sent to:
 
   - `::AbstractVector{<:Real}` -> [Histogram backend](https://www.tensorflow.org/guide/tensorboard_histograms) as a vector

docs/src/deserialization.md

@@ -27,7 +27,7 @@ If you want to read data that have been saved previously, you can also construct
 Alternatively, you can use the method [`map_summaries`](@ref) to iterate a function of your choice among the data contained in a logger. This function
 takes two arguments: a function and the path/`TBLogger` to the data.
 
-The function is mapped across all logged summaries. It's signature should be
+The function is mapped across all logged summaries. Its signature should be
 `fun(tag, iteration, value)`, where tag is a `String` with the tag used to serialize the data, `iteration` is an `Int` corresponding to the iteration at which the data was serialized, and `value` is the serialized value.
 
 An attempt is made to reconstruct as much as possible the original data, but in the serialization process data is usually converted to lower precision and compressed, so sometimes it's not the case.

docs/src/examples/flux.md

@@ -1,5 +1,5 @@
 # Using TensorBoard with [Flux](https://fluxml.ai)
-For a Google Colab version of this example, go [here](https://colab.research.google.com/drive/1xfUsBn9GEqbRjBF-UX_jnGjHZNtNsMae)
+For a Google Colab version of this example, go [here](https://colab.research.google.com/drive/1xfUsBn9GEqbRjBF-UX_jnGjHZNtNsMae).
 The most important part of the example is the callback `TBCallback` which
 handles logging to TensorBoard.
 

docs/src/explicit_interface.md

@@ -2,7 +2,7 @@
 
 in addition to the standard logging interface, it is possible to log
 data to TensorBoard using the functions documented below.
-All the functions accept take as first argument a `TBLogger` object
+All the functions take as first argument a `TBLogger` object
 and as the second argument a `String` as the tag under which the
 data will be logged.
 

docs/src/index.md

@@ -91,8 +91,8 @@ end
 
 When you log data to tensorboard, it is processed to convert it to the format
 used by TensorBoard.
-In particoular, fields in structures are logged individually individually and complex
-numbers/arrays are split into their real and imaginary parts. Depending on it's type, an
+In particular, fields in structures are logged individually and complex
+numbers/arrays are split into their real and imaginary parts. Depending on its type, an
 object is serialized to the relevant backend, with the text backend being the
 default fallback.
 
@@ -116,6 +116,6 @@ We also support logging custom types from a the following third-party libraries:
 
 ## Explicit logging
 
-In alternative, you can also log data to TensorBoard through it's functional interface,
+In alternative, you can also log data to TensorBoard through its functional interface,
 by calling the relevant method with a tag string and the data. For information
-on this interface refer to [Explicit interface]@ref()...
+on this interface refer to [Explicit interface](@ref)...

src/Deserialization/histograms.jl

@@ -29,7 +29,7 @@ function lookahead_deserialize_histogram_summary(old_tag, old_val, evs::Summary,
     res = iterate(evs, state + 1)
     res == nothing && return result
 
-    # if the next event is identified, check it's type
+    # if the next event is identified, check its type
     (new_tag, summary), i_state = res
     typ = summary_type(summary)
 

src/Deserialization/lookahead.jl

@@ -54,7 +54,7 @@ function lookahead_deserialize_simple_value_summary(old_tag, old_val, evs::Summa
     res = iterate(evs, state + 1)
     res == nothing && return result
 
-    # if the next event is identified, check it's type
+    # if the next event is identified, check its type
     (new_tag, summary), i_state = res
     typ = summary_type(summary)
 

src/Loggers/LogCustomScalar.jl

@@ -2,15 +2,15 @@
 @enum tb_chart_type tb_multiline=1 tb_margin=2
 
 """
-log_custom_scalar(logger, layout::AbstractDict; step = step(logger))
+    log_custom_scalar(logger, layout::AbstractDict; step = step(logger))
 
 Groups multiple scalars in the same plot to be visualized by the CUSTOM_SCALARS
 plugin. Note that this function sets the metadata: the actual values must be
 logged separately with `log_value` and referenced with the correct tag.
 
 The `layout` argument is structured as follows:
 
-layout = Dict(category => Dict(name => (chart_type, [tag1, tag2, ...])))
+    layout = Dict(category => Dict(name => (chart_type, [tag1, tag2, ...])))
 
 where `category` is the main tag for the plot, `name` is the plot's name,
 `chart_type` is one between `tb_multiline` and `tb_margin` and the array of tags

src/Loggers/LogValue.jl

@@ -1,7 +1,7 @@
 """
     log_value(logger, name, value; step=step(logger))
 
-Logs a Floating-point variable with name `name` at step `step`.
+Logs a floating-point variable with name `name` at step `step`.
 If `value` is a complex number, the real and imaginary part are logged
 separately.
 """

src/PNG.jl

@@ -12,7 +12,7 @@ include("FileIO_workaround.jl")
 """
     PngImage
 
-A wrapper around the binary encoding of a PNG image, holding it's attributes
+A wrapper around the binary encoding of a PNG image, holding its attributes
 """
 struct PngImage
     attr::Dict