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| author | cloudygoose <[email protected]> | 2015-06-12 13:06:27 +0800 |
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| committer | cloudygoose <[email protected]> | 2015-06-21 10:25:03 +0800 |
| commit | 839d938df0d83ec311c5d1299923c667adff6a87 (patch) | |
| tree | 5e774230b9a9fd1c99a3f0a0dff0a776ec628d2f /doc/nerv_layer.md | |
| parent | a55769787d1b3ec2d1db519cd5efb3b5b2e75404 (diff) | |
git rebase
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git rebase
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doc change
doc change
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added nerv.Matrix:randomize()
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doc change for DAGLayer
bug fix in nerv.Matrix:random()
doc change
Diffstat (limited to 'doc/nerv_layer.md')
| -rw-r--r-- | doc/nerv_layer.md | 13 |
1 files changed, 11 insertions, 2 deletions
diff --git a/doc/nerv_layer.md b/doc/nerv_layer.md index ac6480c..de2fb12 100644 --- a/doc/nerv_layer.md +++ b/doc/nerv_layer.md @@ -15,7 +15,7 @@ __nerv.Layer__ is the base class and most of its methods are abstract. * __nerv.BiasLayer__ inherits __nerv.Layer__, both `#dim_in` nad `#dim_out` are 1. * `BiasParam bias` The bias parameter. * __nerv.SigmoidLayer__ inherits __nerv.Layer__, both `#dim_in` and `#dim_out` are 1. -* __nerv.SoftmaxCELayer__ inherits __nerv.Layer__, `#dim_in` is 2 and `#dim_out` is 0. `input[1]` is the input to the softmax layer, `input[2]` is the reference distribution. +* __nerv.SoftmaxCELayer__ inherits __nerv.Layer__, `#dim_in` is 2 and `#dim_out` is -1(optional). `input[1]` is the input to the softmax layer, `input[2]` is the reference distribution. In its `propagate(input, output)` method, if `output[1] ~= nil`, cross\_entropy value will outputed. * `float total_ce` Records the accumlated cross entropy value. * `int total_frams` Records how many frames have passed. * `bool compressed` The reference distribution can be a one-hot format. This feature is enabled by `layer_conf.compressed`. @@ -43,6 +43,15 @@ Check whether `#self.dim_in == len_in` and `#self.dim_out == len_out`, if violat Abstract method. The layer should return a list containing its parameters. +####nerv.Layer.get\_dim(self)#### +* Returns: + `dim_in`: __table__. + `dim_out`: __table__. +* Parameters: + `self`: __nerv.Layer__. +* Description: + Returns `self.dim_in, self.dim_out`. + ##Examples## * a basic example using __Nerv__ layers to a linear classification. @@ -168,4 +177,4 @@ for l = 0, 10, 1 do end end --[[end training]]-- -```
\ No newline at end of file +``` |