Updated simple.md Dropout doc to v2 of Dropout.

1 files changed, 25 insertions, 18 deletions

diff --git a/doc/simple.md b/doc/simple.md
index a708da5..9a5543d 100644
--- a/doc/simple.md
+++ b/doc/simple.md
@@ -92,21 +92,24 @@ commensurate output element be zero. This has proven an effective technique for
 regularization and preventing the co-adaptation of neurons 
 (see [Hinton et al. 2012](http://arxiv.org/abs/1207.0580)). 
 
+Furthermore, the ouputs are scaled by a factor of `1/(1-p)` during training. This allows the 
+`input` to be simply forwarded as-is during evaluation.
+
 In this example, we demonstrate how the call to [forward](module.md#output-forwardinput) samples 
-different `outputs` given the same `input`:
+different `outputs` to dropout (the zeros) given the same `input`:
 ```lua
 module = nn.Dropout()
 
 > x=torch.Tensor{{1,2,3,4},{5,6,7,8}}
 
 > =module:forward(x)
- 0  2  0  4
- 5  0  0  0
+  2   0   0   8
+ 10   0  14   0
 [torch.DoubleTensor of dimension 2x4]
 
 > =module:forward(x)
- 0  2  3  4
- 5  6  7  8
+  0   0   6   0
+ 10   0   0   0
 [torch.DoubleTensor of dimension 2x4]
 
 ```
@@ -114,35 +117,39 @@ module = nn.Dropout()
 [Backward](module.md#gradinput-backwardinput-gradoutput) drops out the gradients at the same location:
 ```lua
 > =module:forward(x)
- 1  2  0  0
- 5  6  7  0
+  0   4   0   0
+ 10  12   0  16
 [torch.DoubleTensor of dimension 2x4]
 
-> return module:backward(x,x:clone():fill(1))
- 1  1  0  0
- 1  1  1  0
+> =module:backward(x,x:clone():fill(1))
+ 0  2  0  0
+ 2  2  0  2
 [torch.DoubleTensor of dimension 2x4]
+
 ```
+In both cases the `gradOutput` and `input` are scaled by `1/(1-p)`, which in this case is `2`.
 
-During [evaluation](module.md#evaluate), `Dropout` does nothing more than scales the input by `1-p` such that 
-all elements of the input are considered.
+During [evaluation](module.md#evaluate), `Dropout` does nothing more than 
+forward the input such that all elements of the input are considered.
 ```lua
 > module:evaluate()
 
-> return module:forward(x)
- 0.5000  1.0000  1.5000  2.0000
- 2.5000  3.0000  3.5000  4.0000
+> module:forward(x)
+ 1  2  3  4
+ 5  6  7  8
 [torch.DoubleTensor of dimension 2x4]
+
 ```
 
 We can return to training our model by first calling [Module:training()](module.md#training):
 ```lua
 > module:training()
 
-> module:forward(x)
- 0  0  0  4
- 5  6  0  0
+> return module:forward(x)
+  2   4   6   0
+  0   0   0  16
 [torch.DoubleTensor of dimension 2x4]
+
 ```
 
 When used, `Dropout` should normally be applied to the input of parameterized