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-- 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({"converted.nerv"})
sublayer_repo = nerv.LayerRepo(
{
["nerv.AffineLayer"] =
{
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"] =
{
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_ce0 = {{}, {dim_in = {3001, 3001}, dim_out = {}}}
}
}, param_repo, gconf)
layer_repo = nerv.LayerRepo(
{
["nerv.DAGLayer"] =
{
main = {{}, {
dim_in = {429, 3001}, dim_out = {},
sub_layers = sublayer_repo,
connections = {
["<input>[1]"] = "affine0[1]",
["affine0[1]"] = "sigmoid0[1]",
["sigmoid0[1]"] = "affine1[1]",
["affine1[1]"] = "sigmoid1[1]",
["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, 3001)
label:fill(0)
for i = 0, 9 do
label[i][i] = 1.0
end
input = {df:read_chunk("input", gconf).trans, label}
output = {}
err_input = {}
err_output = {input[1]:create()}
sm = sublayer_repo:get_layer("softmax_ce0")
main = layer_repo:get_layer("main")
main:init()
for i = 0, 3 do
main:propagate(input, output)
main:back_propagate(err_output, err_input, input, output)
main:update(err_input, input, output)
nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
nerv.utils.printf("frames: %.8f\n", sm.total_frames)
end
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