1 files changed, 30 insertions, 6 deletions
diff --git a/nerv/main.lua b/nerv/main.lua
index 5cb7d07..865aba0 100644
--- a/nerv/main.lua
+++ b/nerv/main.lua
@@ -1,8 +1,10 @@
-print 'Hello'
-
 local global_conf = {
     cumat_type = nerv.CuMatrixFloat,
     param_random = function() return 0 end,
+    lrate = 0.1,
+    wcost = 0,
+    momentum = 0.9,
+    batch_size = 2,
 }
 
 local layer_repo = nerv.LayerRepo(
@@ -11,13 +13,13 @@ local layer_repo = nerv.LayerRepo(
             rnn = {dim_in = {23}, dim_out = {26}},
         },
         ['nerv.AffineLayer'] = {
-            input = {dim_in = {20}, dim_out = {23}},
+            input = {dim_in = {62}, dim_out = {23}},
             output = {dim_in = {26, 79}, dim_out = {79}},
         },
         ['nerv.SigmoidLayer'] = {
             sigmoid = {dim_in = {23}, dim_out = {23}},
         },
-        ['nerv.SoftmaxLayer'] = {
+        ['nerv.IdentityLayer'] = {
             softmax = {dim_in = {79}, dim_out = {79}},
         },
         ['nerv.DuplicateLayer'] = {
@@ -36,8 +38,30 @@ local connections = {
     {'softmax[1]', '<output>[1]', 0},
 }
 
-local graph = nerv.GraphLayer('graph', global_conf, {dim_in = {20}, dim_out = {79}, layer_repo = layer_repo, connections = connections})
+local graph = nerv.GraphLayer('graph', global_conf, {dim_in = {62}, dim_out = {79}, layer_repo = layer_repo, connections = connections})
 
 local network = nerv.Network('network', global_conf, {network = graph})
 
-network:init(2,5)
+local batch = global_conf.batch_size
+local chunk = 5
+network:init(batch, chunk)
+
+local input = {}
+local output = {}
+local err_input = {}
+local err_output = {}
+local input_size = 62
+local output_size = 79
+for i = 1, chunk do
+    input[i] = {global_conf.cumat_type(batch, input_size)}
+    output[i] = {global_conf.cumat_type(batch, output_size)}
+    err_input[i] = {global_conf.cumat_type(batch, output_size)}
+    err_output[i] = {global_conf.cumat_type(batch, input_size)}
+end
+
+for i = 1, 100 do
+    network:mini_batch_init({seq_length = {5, 3}, new_seq = {2}})
+    network:propagate(input, output)
+    network:back_propagate(err_input, err_output, input, output)
+    network:update(err_input, input, output)
+end