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authorDeterminant <[email protected]>2015-06-21 23:01:20 +0800
committerDeterminant <[email protected]>2015-06-21 23:01:20 +0800
commit196e9b48a3541caccdffc5743001cced70667091 (patch)
treea639873e282727c01746319169a08766c086965b /doc/nerv_layer.md
parentf3f14b85d3b9c1ccebb1faea37b7b57012fba347 (diff)
parent9a8ef8091806c004bc53c529d0b9f514eb6ac3da (diff)
Merge branch 'master' of github.com:Determinant/nerv
Diffstat (limited to 'doc/nerv_layer.md')
-rw-r--r--doc/nerv_layer.md23
1 files changed, 16 insertions, 7 deletions
diff --git a/doc/nerv_layer.md b/doc/nerv_layer.md
index 0425d5f..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.
@@ -141,7 +150,8 @@ print('network input&output&error space allocation...')
affineI = {dataM} --input to the network is data
affineO = {nerv.CuMatrixFloat(data_num, 2)}
softmaxI = {affineO[1], labelM}
-softmaxO = {nerv.CuMatrixFloat(data_num, 2)}
+softmaxO = {}
+output = nerv.CuMatrixFloat(data_num, 2)
affineE = {nerv.CuMatrixFloat(data_num, 2)}
--[[space allocation end]]--
@@ -152,9 +162,9 @@ ce_last = 0
for l = 0, 10, 1 do
affineL:propagate(affineI, affineO)
softmaxL:propagate(softmaxI, softmaxO)
- softmaxO[1]:softmax(softmaxI[1])
+ output:softmax(softmaxI[1])
- softmaxL:back_propagate(affineE, nil, softmaxI, softmaxO)
+ softmaxL:back_propagate(affineE, {}, softmaxI, softmaxO)
affineL:update(affineE, affineI, affineO)
@@ -162,10 +172,9 @@ for l = 0, 10, 1 do
nerv.utils.printf("training iteration %d finished\n", l)
nerv.utils.printf("cross entropy: %.8f\n", softmaxL.total_ce - ce_last)
ce_last = softmaxL.total_ce
- nerv.utils.printf("accurate labels: %d\n", calculate_accurate(softmaxO[1], labelM))
+ nerv.utils.printf("accurate labels: %d\n", calculate_accurate(output, labelM))
nerv.utils.printf("total frames processed: %.8f\n", softmaxL.total_frames)
end
end
--[[end training]]--
-
-``` \ No newline at end of file
+```