1 files changed, 0 insertions, 73 deletions
diff --git a/nerv/main.lua b/nerv/main.lua
deleted file mode 100644
index 7c82ebf..0000000
--- a/nerv/main.lua
+++ /dev/null
@@ -1,73 +0,0 @@
-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(
-    {
-        ['nerv.RNNLayer'] = {
-            rnn1 = {dim_in = {23}, dim_out = {26}},
-            rnn2 = {dim_in = {26}, dim_out = {26}},
-        },
-        ['nerv.AffineLayer'] = {
-            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.IdentityLayer'] = {
-            softmax = {dim_in = {79}, dim_out = {79}},
-        },
-        ['nerv.DuplicateLayer'] = {
-            dup = {dim_in = {79}, dim_out = {79, 79}},
-        },
-    }, nerv.ParamRepo(), global_conf)
-
-local connections = {
-    {'<input>[1]', 'input[1]', 0},
-    {'input[1]', 'sigmoid[1]', 0},
-    {'sigmoid[1]', 'rnn1[1]', 0},
-    {'rnn1[1]', 'rnn2[1]', 0},
-    {'rnn2[1]', 'output[1]', 0},
-    {'output[1]', 'dup[1]', 0},
-    {'dup[1]', 'output[2]', -1},
-    {'dup[2]', 'softmax[1]', 0},
-    {'softmax[1]', '<output>[1]', 0},
-}
-
-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})
-
-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
-
-local tmp = network:get_params()
-
-tmp:export('../../workspace/test.param')