1 files changed, 0 insertions, 136 deletions
diff --git a/lua/tnn.lua b/lua/tnn.lua
deleted file mode 100644
index bf9f118..0000000
--- a/lua/tnn.lua
+++ /dev/null
@@ -1,136 +0,0 @@
-nerv.include('select_linear.lua')
-
-local reader = nerv.class('nerv.TNNReader')
-
-function reader:__init(global_conf, data)
-    self.gconf = global_conf
-    self.offset = 0
-    self.data = data
-end
-
-function reader:get_batch(feeds)
-    self.offset = self.offset + 1
-    if self.offset > #self.data then
-        return false
-    end
-    for i = 1, self.gconf.chunk_size do
-        feeds.inputs_m[i][1]:copy_from(self.data[self.offset].input[i])
-        feeds.inputs_m[i][2]:copy_from(self.data[self.offset].output[i]:decompress(self.gconf.vocab_size))
-    end
-    feeds.flags_now = self.data[self.offset].flags
-    feeds.flagsPack_now = self.data[self.offset].flagsPack
-    return true
-end
-
-function reader:has_data(t, i)
-    return t <= self.data[self.offset].seq_len[i]
-end
-
-function reader:get_err_input()
-    return self.data[self.offset].err_input
-end
-
-local nn = nerv.class('nerv.NN')
-
-function nn:__init(global_conf, train_data, val_data, layers, connections)
-    self.gconf = global_conf
-    self.tnn = self:get_tnn(layers, connections)
-    self.train_data = self:get_data(train_data)
-    self.val_data = self:get_data(val_data)
-end
-
-function nn:get_tnn(layers, connections)
-    self.gconf.dropout_rate = 0
-    local layer_repo = nerv.LayerRepo(layers, self.gconf.pr, self.gconf)
-    local tnn = nerv.TNN('TNN', self.gconf, {dim_in = {1, self.gconf.vocab_size}, 
-        dim_out = {1}, sub_layers = layer_repo, connections = connections, 
-        clip = self.gconf.clip})
-    tnn:init(self.gconf.batch_size, self.gconf.chunk_size)
-    return tnn
-end
-
-function nn:get_data(data)
-    local ret = {}
-    for i = 1, #data do
-        ret[i] = {}
-        ret[i].input = data[i].input
-        ret[i].output = data[i].output
-        ret[i].flags = {}
-        ret[i].err_input = {}
-        for t = 1, self.gconf.chunk_size do
-            ret[i].flags[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
-                    ret[i].flags[t][j] = nerv.TNN.FC.SEQ_NORM
-                    err_input[j - 1][0] = 1
-                else
-                    ret[i].flags[t][j] = 0
-                    err_input[j - 1][0] = 0
-                end
-            end
-            ret[i].err_input[t] = self.gconf.cumat_type.new_from_host(err_input)
-        end
-        for j = 1, self.gconf.batch_size do
-            if data[i].seq_start[j] then
-                ret[i].flags[1][j] = bit.bor(ret[i].flags[1][j], nerv.TNN.FC.SEQ_START)
-            end
-            if data[i].seq_end[j] then
-                local t = data[i].seq_len[j]
-                ret[i].flags[t][j] = bit.bor(ret[i].flags[t][j], nerv.TNN.FC.SEQ_END)
-            end
-        end
-        ret[i].flagsPack = {}
-        for t = 1, self.gconf.chunk_size do
-            ret[i].flagsPack[t] = 0
-            for j = 1, self.gconf.batch_size do
-                ret[i].flagsPack[t] = bit.bor(ret[i].flagsPack[t], ret[i].flags[t][j])
-            end
-        end
-        ret[i].seq_len = data[i].seq_len
-    end
-    return ret
-end
-
-function nn:process(data, do_train)
-    local total_err = 0
-    local total_frame = 0
-    local reader = nerv.TNNReader(self.gconf, data)
-    while true do
-        local r, _ = self.tnn:getfeed_from_reader(reader)
-        if not r then
-            break
-        end
-        if do_train then
-            self.gconf.dropout_rate = self.gconf.dropout
-        else
-            self.gconf.dropout_rate = 0
-        end
-        self.tnn:net_propagate()
-        for t = 1, self.gconf.chunk_size do
-            local tmp = self.tnn.outputs_m[t][1]:new_to_host()
-            for i = 1, self.gconf.batch_size do
-                if reader:has_data(t, 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
-            local err_input = reader:get_err_input()
-            for i = 1, self.gconf.chunk_size do
-                self.tnn.err_inputs_m[i][1]:copy_from(err_input[i])
-            end
-            self.tnn:net_backpropagate(false)
-            self.tnn:net_backpropagate(true)
-        end
-        collectgarbage('collect')
-    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