Merge pull request #31 from liuq901/master

modfiy param generate & rewrite LSTM layer

1 files changed, 5 insertions, 6 deletions

diff --git a/nerv/layer/dropout.lua b/nerv/layer/dropout.lua
index 1a379c9..de0fb64 100644
--- a/nerv/layer/dropout.lua
+++ b/nerv/layer/dropout.lua
@@ -2,8 +2,7 @@ local DropoutLayer = nerv.class("nerv.DropoutLayer", "nerv.Layer")
 
 function DropoutLayer:__init(id, global_conf, layer_conf)
     nerv.Layer.__init(self, id, global_conf, layer_conf)
-    self.rate = layer_conf.dropout_rate or global_conf.dropout_rate
-    if self.rate == nil then
+    if self.gconf.dropout_rate == nil then
         nerv.warning("[DropoutLayer:propagate] dropout rate is not set")
     end
     self:check_dim_len(1, 1) -- two inputs: nn output and label
@@ -41,12 +40,12 @@ function DropoutLayer:propagate(input, output, t)
     if t == nil then
         t = 1
     end
-    if self.rate then
+    if self.gconf.dropout_rate then
         self.mask[t]:rand_uniform()
         -- since we will lose a portion of the actvations, we multiply the
         -- activations by 1 / (1 - rate) to compensate
-        self.mask[t]:thres_mask(self.mask[t], self.rate,
-                                0, 1 / (1.0 - self.rate))
+        self.mask[t]:thres_mask(self.mask[t], self.gconf.dropout_rate,
+                                0, 1 / (1.0 - self.gconf.dropout_rate))
         output[1]:mul_elem(input[1], self.mask[t])
     else
         output[1]:copy_fromd(input[1])
@@ -61,7 +60,7 @@ function DropoutLayer:back_propagate(bp_err, next_bp_err, input, output, t)
     if t == nil then
         t = 1
     end
-    if self.rate then
+    if self.gconf.dropout_rate then
         next_bp_err[1]:mul_elem(bp_err[1], self.mask[t])
     else
         next_bp_err[1]:copy_fromd(bp_err[1])