fix duplicate bug on <input> & <output>

3 files changed, 48 insertions, 6 deletions

diff --git a/nerv/examples/network_debug/config.lua b/nerv/examples/network_debug/config.lua
index e20d5a9..0429e9a 100644
--- a/nerv/examples/network_debug/config.lua
+++ b/nerv/examples/network_debug/config.lua
@@ -35,6 +35,10 @@ function get_layers(global_conf)
         ['nerv.SoftmaxCELayer'] = {
             softmax = {dim_in = {global_conf.vocab_size, global_conf.vocab_size}, dim_out = {1}, compressed = true},
         },
+        ['nerv.DuplicateLayer'] = {
+            dup1 = {dim_in = {1}, dim_out = {1}},
+            dup2 = {dim_in = {1}, dim_out = {1}},
+        },
     }
     for i = 1, global_conf.layer_num do
         layers['nerv.LSTMLayer']['lstm' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size}, pr = pr}
@@ -45,12 +49,14 @@ end
 
 function get_connections(global_conf)
     local connections = {
-        {'<input>[1]', 'select[1]', 0},
+        {'<input>[1]', 'dup1[1]', 0},
+        {'dup1[1]', 'select[1]', 0},
         {'select[1]', 'lstm1[1]', 0},
         {'dropout' .. global_conf.layer_num .. '[1]', 'output[1]', 0},
         {'output[1]', 'softmax[1]', 0},
         {'<input>[2]', 'softmax[2]', 0},
-        {'softmax[1]', '<output>[1]', 0},
+        {'softmax[1]', 'dup2[1]', 0},
+        {'dup2[1]', '<output>[1]', 0},
     }
     for i = 1, global_conf.layer_num do
         table.insert(connections, {'lstm' .. i .. '[1]', 'dropout' .. i .. '[1]', 0})
diff --git a/nerv/examples/network_debug/reader.lua b/nerv/examples/network_debug/reader.lua
index 76a78cf..70c0c97 100644
--- a/nerv/examples/network_debug/reader.lua
+++ b/nerv/examples/network_debug/reader.lua
@@ -32,8 +32,8 @@ end
 function Reader:get_seq(input_file)
     local f = io.open(input_file, 'r')
     self.seq = {}
-    while true do
-    -- for i = 1, 26 do
+    -- while true do
+    for i = 1, 26 do
         local seq = f:read()
         if seq == nil then
             break
diff --git a/nerv/nn/network.lua b/nerv/nn/network.lua
index b06028e..6f7fe10 100644
--- a/nerv/nn/network.lua
+++ b/nerv/nn/network.lua
@@ -27,7 +27,17 @@ function network:__init(id, global_conf, network_conf)
         if self.input_conn[id][port] ~= nil then
             nerv.error('duplicate edge')
         end
-        self.input_conn[id][port] = {0, i, time}
+        if nerv.is_type(self.layers[id], 'nerv.DuplicateLayer') then
+            local tmp = nerv.IdentityLayer('', self.gconf, {dim_in = {self.dim_in[i]}, dim_out = {self.dim_in[i]}})
+            table.insert(self.layers, tmp)
+            local new_id = #self.layers
+            self.input_conn[new_id] = {{0, i, time}}
+            self.output_conn[new_id] = {{id, port, 0}}
+            self.input_conn[id][port] = {new_id, 1, 0}
+            self.socket.inputs[i] = {new_id, 1, time}
+        else
+            self.input_conn[id][port] = {0, i, time}
+        end
     end
     for i = 1, #self.dim_out do
         local edge = self.socket.outputs[i]
@@ -35,7 +45,17 @@ function network:__init(id, global_conf, network_conf)
         if self.output_conn[id][port] ~= nil then
             nerv.error('duplicate edge')
         end
-        self.output_conn[id][port] = {0, i, time}
+        if nerv.is_type(self.layers[id], 'nerv.DuplicateLayer') then
+            local tmp = nerv.IdentityLayer('', self.gconf, {dim_in = {self.dim_out[i]}, dim_out = {self.dim_out[i]}})
+            table.insert(self.layers, tmp)
+            local new_id = #self.layers
+            self.input_conn[new_id] = {{id, port, 0}}
+            self.output_conn[new_id] = {{0, i, time}}
+            self.output_conn[id][port] = {new_id, 1, 0}
+            self.socket.outputs[i] = {new_id, 1, time}
+        else
+            self.output_conn[id][port] = {0, i, time}
+        end
     end
 
     self.delay = 0
@@ -140,8 +160,10 @@ function network:init(batch_size, chunk_size)
     collectgarbage('collect')
 
     self.flush = {}
+    self.gconf.mask = {}
     for t = 1, self.chunk_size do
         self.flush[t] = {}
+        self.gconf.mask[t] = self.mat_type(self.batch_size, 1)
     end
 end
 
@@ -348,6 +370,7 @@ function network:make_initial_store()
             local dim_in, dim_out = self.layers[i]:get_dim()
             for j = 1, #dim_in do
                 if self.input[t][i][j] == nil then
+                    print(t,i,j,self.layers[i].id)
                     nerv.error('input reference dangling')
                 end
                 if self.err_output[t][i][j] == nil then
@@ -450,6 +473,19 @@ function network:mini_batch_init(info)
         self:set_err_output(self.info.err_output)
     end
 
+    -- calculate mask
+    for t = 1, self.chunk_size do
+        local tmp = self.gconf.mmat_type(self.batch_size, 1)
+        for i = 1, self.batch_size do
+            if t <= self.info.seq_length[i] then
+                tmp[i - 1][0] = 1
+            else
+                tmp[i - 1][0] = 0
+            end
+        end
+        self.gconf.mask[t]:copy_fromh(tmp)
+    end
+
     -- calculate border
     self.max_length = 0
     self.timestamp = self.timestamp + 1