1 files changed, 0 insertions, 106 deletions
diff --git a/lua/network.lua b/lua/network.lua
deleted file mode 100644
index d106ba1..0000000
--- a/lua/network.lua
+++ /dev/null
@@ -1,106 +0,0 @@
-nerv.include('select_linear.lua')
-
-local nn = nerv.class('nerv.NN')
-
-function nn:__init(global_conf, train_data, val_data, layers, connections)
-    self.gconf = global_conf
-    self.network = self:get_network(layers, connections)
-    self.train_data = self:get_data(train_data)
-    self.val_data = self:get_data(val_data)
-end
-
-function nn:get_network(layers, connections)
-    local layer_repo = nerv.LayerRepo(layers, self.gconf.pr, self.gconf)
-    local graph = nerv.GraphLayer('graph', self.gconf, 
-        {dim_in = {1, self.gconf.vocab_size}, dim_out = {1}, 
-        layer_repo = layer_repo, connections = connections})
-    local network = nerv.Network('network', self.gconf, 
-        {network = graph, clip = self.gconf.clip})
-    network:init(self.gconf.batch_size, self.gconf.chunk_size)
-    return network
-end
-
-function nn:get_data(data)
-    local err_output = {}
-    local softmax_output = {}
-    local output = {}
-    for i = 1, self.gconf.chunk_size do
-        err_output[i] = self.gconf.cumat_type(self.gconf.batch_size, 1)
-        softmax_output[i] = self.gconf.cumat_type(self.gconf.batch_size, self.gconf.vocab_size)
-        output[i] = self.gconf.cumat_type(self.gconf.batch_size, 1)
-    end
-    local ret = {}
-    for i = 1, #data do
-        ret[i] = {}
-        ret[i].input = {}
-        ret[i].output = {}
-        ret[i].err_input = {}
-        ret[i].err_output = {}
-        for t = 1, self.gconf.chunk_size do
-            ret[i].input[t] = {}
-            ret[i].output[t] = {}
-            ret[i].err_input[t] = {}
-            ret[i].err_output[t] = {}
-            ret[i].input[t][1] = data[i].input[t]
-            ret[i].input[t][2] = data[i].output[t]
-            ret[i].output[t][1] = output[t]
-            local err_input = self.gconf.mmat_type(self.gconf.batch_size, 1)
-            for j = 1, self.gconf.batch_size do
-                if t <= data[i].seq_len[j] then
-                    err_input[j - 1][0] = 1
-                else
-                    err_input[j - 1][0] = 0
-                end
-            end
-            ret[i].err_input[t][1] = self.gconf.cumat_type.new_from_host(err_input)
-            ret[i].err_output[t][1] = err_output[t]
-            ret[i].err_output[t][2] = softmax_output[t]
-        end
-        ret[i].seq_length = data[i].seq_len
-        ret[i].new_seq = {}
-        for j = 1, self.gconf.batch_size do
-            if data[i].seq_start[j] then
-                table.insert(ret[i].new_seq, j)
-            end
-        end
-    end
-    return ret
-end
-
-function nn:process(data, do_train)
-    local timer = self.gconf.timer
-    local total_err = 0
-    local total_frame = 0
-    for id = 1, #data do
-        data[id].do_train = do_train
-        timer:tic('network')
-        self.network:mini_batch_init(data[id])
-        self.network:propagate()
-        timer:toc('network')
-        for t = 1, self.gconf.chunk_size do
-            local tmp = data[id].output[t][1]:new_to_host()
-            for i = 1, self.gconf.batch_size do
-                if t <= data[id].seq_length[i] then
-                    total_err = total_err + math.log10(math.exp(tmp[i - 1][0]))
-                    total_frame = total_frame + 1
-                end
-            end
-        end
-        if do_train then
-            timer:tic('network')
-            self.network:back_propagate()
-            self.network:update()
-            timer:toc('network')
-        end
-        timer:tic('gc')
-        collectgarbage('collect')
-        timer:toc('gc')
-    end
-    return math.pow(10, - total_err / total_frame)
-end
-
-function nn:epoch()
-    local train_error = self:process(self.train_data, true)
-    local val_error = self:process(self.val_data, false)
-    return train_error, val_error
-end