From 2f46a5e2b37a054f482f76f4ac3d26b144cf988f Mon Sep 17 00:00:00 2001
From: Qi Liu <liuq901@163.com>
Date: Fri, 11 Mar 2016 18:18:59 +0800
Subject: add lua

---
 lua/tnn.lua | 136 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 136 insertions(+)
 create mode 100644 lua/tnn.lua

(limited to 'lua/tnn.lua')

diff --git a/lua/tnn.lua b/lua/tnn.lua
new file mode 100644
index 0000000..bf9f118
--- /dev/null
+++ b/lua/tnn.lua
@@ -0,0 +1,136 @@
+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
-- 
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