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-rw-r--r--nerv/examples/network_debug/config.lua10
-rw-r--r--nerv/examples/network_debug/reader.lua4
-rw-r--r--nerv/nn/network.lua40
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