#The Nerv Layer Package#
Part of the [Nerv](../README.md) toolkit.
##Description##
__nerv.Layer__ is the base class and most of its methods are abstract.
###Class hierarchy and their members###
* __nerv.Layer__.
* `table dim_in` It specifies the dimensions of the inputs.
* `table dim_out` It specifies the dimensions of the outputs.
* `string id` ID of this layer.
* `table gconf` Stores the `global_conf`.
* __nerv.AffineLayer__ inherits __nerv.Layer__, both `#dim_in` and `#dim_out` are 1.
* `MatrixParam ltp` The liner transform parameter.
* `BiasParam bp` The bias parameter.
* __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.
* `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`.
##Methods##
* __void Layer.\_\_init(Layer self, string id, table global_conf, table layer_conf)__
Abstract method.
The constructing method should assign `id` to `self.id` and `global_conf` to `self.gconf`, `layer_conf.dim_in` to `self.dim_in`, `layer_conf.dim_out` to `self.dim_out`. `dim_in` and `dim_out` are a list specifies the dimensions of the inputs and outputs. Also, `layer_conf` will include the parameters, which should also be properly saved.
* __void Layer.init(Layer self)__
Abstract method.
Initialization method, in this method the layer should do some self-checking and allocate space for intermediate results.
* __void Layer.update(Layer self, table bp_err, table input, table output)__
Abstract method.
`bp_err[i]` should be the error on `output[i]`. In this method the parameters of `self` is updated.
* __void Layer.propagate(Layer self, table input, table output)__
Abstract method.
Given `input` and the current parameters, propagate and store the result in `output`.
* __void Layer.back_propagate(Layer self, Matrix next_bp_err, Matrix bp_err, Matrix input, Matrix output)__
Abstract method.
Calculate the error on the inputs and store them in `next_bp_err`.
* __void Layer.check_dim_len(int len_in, int len_out)__
Check whether `#self.dim_in == len_in` and `#self.dim_out == len_out`, if violated, an error will be posted.
* __void Layer.get_params(Layer self)__
Abstract method.
The layer should return a list containing its parameters.
##Examples##
* a basic example using __Nerv__ layers to a linear classification.
```
require 'math'
require 'layer.affine'
require 'layer.softmax_ce'
--[[Example using layers, a simple two-classification problem]]--
function calculate_accurate(networkO, labelM)
sum = 0
for i = 0, networkO:nrow() - 1, 1 do
if (labelM[i][0] == 1 and networkO[i][0] >= 0.5) then
sum = sum + 1
end
if (labelM[i][1] == 1 and networkO[i][1] >= 0.5) then
sum = sum + 1
end
end
return sum
end
--[[begin global setting and data generation]]--
global_conf = {lrate = 10,
wcost = 1e-6,
momentum = 0.9,
cumat_type = nerv.CuMatrixFloat}
input_dim = 5
data_num = 100
ansV = nerv.CuMatrixFloat(input_dim, 1)
for i = 0, input_dim - 1, 1 do
ansV[i][0] = math.random() - 0.5
end
ansB = math.random() - 0.5
print('displaying ansV')
print(ansV)
print('displaying ansB(bias)')
print(ansB)
dataM = nerv.CuMatrixFloat(data_num, input_dim)
for i = 0, data_num - 1, 1 do
for j = 0, input_dim - 1, 1 do
dataM[i][j] = math.random() * 2 - 1
end
end
refM = nerv.CuMatrixFloat(data_num, 1)
refM:fill(ansB)
refM:mul(dataM, ansV, 1, 1) --refM = dataM * ansV + ansB
labelM = nerv.CuMatrixFloat(data_num, 2)
for i = 0, data_num - 1, 1 do
if (refM[i][0] > 0) then
labelM[i][0] = 1
labelM[i][1] = 0
else
labelM[i][0] = 0
labelM[i][1] = 1
end
end
--[[global setting and data generation end]]--
--[[begin network building]]--
--parameters
affineL_ltp = nerv.LinearTransParam('AffineL_ltp', global_conf)
affineL_ltp.trans = nerv.CuMatrixFloat(input_dim, 2)
for i = 0, input_dim - 1, 1 do
for j = 0, 1, 1 do
affineL_ltp.trans[i][j] = math.random() - 0.5
end
end
affineL_bp = nerv.BiasParam('AffineL_bp', global_conf)
affineL_bp.trans = nerv.CuMatrixFloat(1, 2)
for j = 0, 1, 1 do
affineL_bp.trans[j] = math.random() - 0.5
end
--layers
affineL = nerv.AffineLayer('AffineL', global_conf, {['ltp'] = affineL_ltp,
['bp'] = affineL_bp,
dim_in = {input_dim},
dim_out = {2}})
softmaxL = nerv.SoftmaxCELayer('softmaxL', global_conf, {dim_in = {2, 2},
dim_out = {}})
print('layers initializing...')
affineL:init()
softmaxL:init()
--[[network building end]]--
--[[begin space allocation]]--
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 = {}
output = nerv.CuMatrixFloat(data_num, 2)
affineE = {nerv.CuMatrixFloat(data_num, 2)}
--[[space allocation end]]--
--[[begin training]]--
ce_last = 0
for l = 0, 10, 1 do
affineL:propagate(affineI, affineO)
softmaxL:propagate(softmaxI, softmaxO)
output:softmax(softmaxI[1])
softmaxL:back_propagate(affineE, {}, softmaxI, softmaxO)
affineL:update(affineE, affineI, affineO)
if (l % 5 == 0) then
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(output, labelM))
nerv.utils.printf("total frames processed: %.8f\n", softmaxL.total_frames)
end
end
--[[end training]]--
```