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diff --git a/nerv/doc/nerv_layer.md b/nerv/doc/nerv_layer.md new file mode 100644 index 0000000..de2fb12 --- /dev/null +++ b/nerv/doc/nerv_layer.md @@ -0,0 +1,180 @@ +#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 -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`. + +##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. + +####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. + +``` +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]]-- +``` |