From 05fcde5bf0caa1ceb70fef02fc88eda6f00c5ed5 Mon Sep 17 00:00:00 2001
From: Qi Liu <liuq901@163.com>
Date: Wed, 9 Mar 2016 11:58:13 +0800
Subject: add recipe

---
 lua/network.lua | 109 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 lua/network.lua

(limited to 'lua/network.lua')

diff --git a/lua/network.lua b/lua/network.lua
new file mode 100644
index 0000000..6280f24
--- /dev/null
+++ b/lua/network.lua
@@ -0,0 +1,109 @@
+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)
+    self.gconf.dropout_rate = 0
+    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].info = {}
+        ret[i].info.seq_length = data[i].seq_len
+        ret[i].info.new_seq = {}
+        for j = 1, self.gconf.batch_size do
+            if data[i].seq_start[j] then
+                table.insert(ret[i].info.new_seq, j)
+            end
+        end
+    end
+    return ret
+end
+
+function nn:process(data, do_train)
+    local total_err = 0
+    local total_frame = 0
+    for id = 1, #data do
+        if do_train then
+            self.gconf.dropout_rate = self.gconf.dropout
+        else
+            self.gconf.dropout_rate = 0
+        end
+        self.network:mini_batch_init(data[id].info)
+        local input = {}
+        for t = 1, self.gconf.chunk_size do
+            input[t] = {data[id].input[t][1], data[id].input[t][2]:decompress(self.gconf.vocab_size)}
+        end
+        self.network:propagate(input, data[id].output)
+        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].info.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
+            self.network:back_propagate(data[id].err_input, data[id].err_output, input, data[id].output)
+            self.network:update(data[id].err_input, input, data[id].output)
+        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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