1 files changed, 9 insertions, 4 deletions
diff --git a/nerv/nn/network.lua b/nerv/nn/network.lua
index 5a6abb6..bf69ccc 100644
--- a/nerv/nn/network.lua
+++ b/nerv/nn/network.lua
@@ -1,5 +1,5 @@
 --- Implements the concept of computable but opaque networks built ("compiled")
--- from nested layers
+-- from nested layers.
 -- @author Qi Liu <liuq901@163.com>
 -- @author Ted Yin <ted.sybil@gmail.com>
 
@@ -190,7 +190,7 @@ end
 
 --- Initialize the network for training.
 -- To be called before all the epochs, will resolve the structure of the
--- network and allocate the memory for storing temporary values
+-- network and allocate the memory for storing temporary values.
 -- @param batch_size The size of a batch matrix
 -- @param chunk_size The size of a BPTT chunk
 function network:init(batch_size, chunk_size)
@@ -211,7 +211,8 @@ function network:init(batch_size, chunk_size)
 end
 
 --- Initialize the internal state of the network for the new epoch.
--- To be called before each new epoch
+-- To be called before each new epoch.
+
 function network:epoch_init()
     self.timestamp = 0
     for i = 1, #self.layers do
@@ -503,7 +504,7 @@ function network:set_err_output(err_output)
 end
 
 --- Initialize the internal state of the network for the new mini-batch (a BPTT chunk).
--- To be called before each propagation/back-propagation
+-- To be called before each propagation/back-propagation.
 -- @param info a table containing information needed for the current mini-batch computation. The following fields must be supplied:
 --
 -- * `input`: an array containing `chunk_size` number of row-major batch
@@ -526,6 +527,7 @@ end
 --  * `err_output`: an array with the same structure as `input`. Although we
 --    are mostly not interested in its values, just allocate this to unify
 --    the computation and ease the implementation
+
 function network:mini_batch_init(info)
     self.info = info
     self:set_input(self.info.input)
@@ -634,6 +636,7 @@ function network:mini_batch_init(info)
 end
 
 --- Perform a propagation.
+
 function network:propagate()
     for i = 1, #self.queue do
         local t, id = self.queue[i].chunk, self.queue[i].id
@@ -652,6 +655,7 @@ function network:propagate()
 end
 
 --- Perform a backward propagation to calculate gradients used for update.
+
 function network:back_propagate()
     for i = #self.queue, 1, -1 do
         local t, id = self.queue[i].chunk, self.queue[i].id
@@ -677,6 +681,7 @@ function network:back_propagate()
 end
 
 --- Update the parameters bound to each layer.
+
 function network:update()
     for i = 1, #self.layers do
         self.layers[i]:update()