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authorDeterminant <[email protected]>2015-06-02 23:07:15 +0800
committerDeterminant <[email protected]>2015-06-02 23:07:15 +0800
commit08a52c03a77ce13ae4f6a4deb06ab0ae274d399a (patch)
tree1cf0ac8c7d05a83c9a7246d6b6c56eb113124385 /examples/test_nn_lib.lua
parent74d9e9e7371c80394698fb9805cbf0cbde67a8f3 (diff)
fix a bug: input[1] should be input[2] (since Lua arrays are 1-based)
Diffstat (limited to 'examples/test_nn_lib.lua')
-rw-r--r--examples/test_nn_lib.lua60
1 files changed, 47 insertions, 13 deletions
diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
index fd7167a..ec338fe 100644
--- a/examples/test_nn_lib.lua
+++ b/examples/test_nn_lib.lua
@@ -1,25 +1,46 @@
-require 'layer.affine'
-require 'layer.sigmoid'
-require 'layer.softmax_ce'
+-- require 'layer.affine'
+-- require 'layer.sigmoid'
+-- require 'layer.softmax_ce'
gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
mat_type = nerv.CuMatrixFloat,
batch_size = 10}
-param_repo = nerv.ParamRepo({"affine.param"})
+param_repo = nerv.ParamRepo({"converted.nerv"})
sublayer_repo = nerv.LayerRepo(
{
["nerv.AffineLayer"] =
{
- affine1 = {{ltp = "a", bp = "b"}, {dim_in = {429}, dim_out = {2048}}}
+ affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
+ {dim_in = {429}, dim_out = {2048}}},
+ affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"},
+ {dim_in = {2048}, dim_out = {2048}}},
+ affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"},
+ {dim_in = {2048}, dim_out = {2048}}},
+ affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"},
+ {dim_in = {2048}, dim_out = {2048}}},
+ affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"},
+ {dim_in = {2048}, dim_out = {2048}}},
+ affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"},
+ {dim_in = {2048}, dim_out = {2048}}},
+ affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"},
+ {dim_in = {2048}, dim_out = {2048}}},
+ affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"},
+ {dim_in = {2048}, dim_out = {3001}}}
},
["nerv.SigmoidLayer"] =
{
- sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}}
+ sigmoid0 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+ sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+ sigmoid2 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+ sigmoid3 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+ sigmoid4 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+ sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+ sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}
},
["nerv.SoftmaxCELayer"] =
{
- softmax_ce1 = {{}, {dim_in = {2048, 2048}, dim_out = {}}}
+ softmax_ce0 = {{}, {dim_in = {3001, 3001}, dim_out = {}}}
}
}, param_repo, gconf)
@@ -28,20 +49,33 @@ layer_repo = nerv.LayerRepo(
["nerv.DAGLayer"] =
{
main = {{}, {
- dim_in = {429, 2048}, dim_out = {},
+ dim_in = {429, 3001}, dim_out = {},
sub_layers = sublayer_repo,
connections = {
- ["<input>[1]"] = "affine1[1]",
+ ["<input>[1]"] = "affine0[1]",
+ ["affine0[1]"] = "sigmoid0[1]",
+ ["sigmoid0[1]"] = "affine1[1]",
["affine1[1]"] = "sigmoid1[1]",
- ["sigmoid1[1]"] = "softmax_ce1[1]",
- ["<input>[2]"] = "softmax_ce1[2]"
+ ["sigmoid1[1]"] = "affine2[1]",
+ ["affine2[1]"] = "sigmoid2[1]",
+ ["sigmoid2[1]"] = "affine3[1]",
+ ["affine3[1]"] = "sigmoid3[1]",
+ ["sigmoid3[1]"] = "affine4[1]",
+ ["affine4[1]"] = "sigmoid4[1]",
+ ["sigmoid4[1]"] = "affine5[1]",
+ ["affine5[1]"] = "sigmoid5[1]",
+ ["sigmoid5[1]"] = "affine6[1]",
+ ["affine6[1]"] = "sigmoid6[1]",
+ ["sigmoid6[1]"] = "affine7[1]",
+ ["affine7[1]"] = "softmax_ce0[1]",
+ ["<input>[2]"] = "softmax_ce0[2]"
}
}}
}
}, param_repo, gconf)
df = nerv.ChunkFile("input.param", "r")
-label = nerv.CuMatrixFloat(10, 2048)
+label = nerv.CuMatrixFloat(10, 3001)
label:fill(0)
for i = 0, 9 do
label[i][i] = 1.0
@@ -51,7 +85,7 @@ input = {df:read_chunk("input", gconf).trans, label}
output = {}
err_input = {}
err_output = {input[1]:create()}
-sm = sublayer_repo:get_layer("softmax_ce1")
+sm = sublayer_repo:get_layer("softmax_ce0")
main = layer_repo:get_layer("main")
main:init()
for i = 0, 3 do