diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/test_dnn_layers.lua | 43 | ||||
| -rw-r--r-- | examples/test_nn_lib.lua | 97 |
2 files changed, 120 insertions, 20 deletions
diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua index 866e685..6e4d98d 100644 --- a/examples/test_dnn_layers.lua +++ b/examples/test_dnn_layers.lua @@ -11,10 +11,14 @@ bp = pf:read_chunk("b", global_conf) -- print(bp.trans) -af = nerv.AffineLayer("test", global_conf, ltp, bp) -sg = nerv.SigmoidLayer("test2", global_conf) -sm = nerv.SoftmaxCELayer("test3", global_conf) - +af = nerv.AffineLayer("test", global_conf, {["ltp"] = ltp, + ["bp"] = bp, + dim_in = {429}, + dim_out = {2048}}) +sg = nerv.SigmoidLayer("test2", global_conf, {dim_in = {2048}, + dim_out = {2048}}) +sm = nerv.SoftmaxCELayer("test3", global_conf, {dim_in = {2048, 2048}, + dim_out = {}}) af:init() sg:init() sm:init() @@ -27,18 +31,18 @@ for i = 0, 9 do label[i][i] = 1.0 end -input1 = {[0] = df:read_chunk("input", global_conf).trans} -output1 = {[0] = nerv.CuMatrixFloat(10, 2048)} +input1 = {df:read_chunk("input", global_conf).trans} +output1 = {nerv.CuMatrixFloat(10, 2048)} input2 = output1 -output2 = {[0] = nerv.CuMatrixFloat(10, 2048)} -input3 = {[0] = output2[0], [1] = label} +output2 = {nerv.CuMatrixFloat(10, 2048)} +input3 = {output2[1], label} output3 = nil err_input1 = nil -err_output1 = {[0] = nerv.CuMatrixFloat(10, 2048)} +err_output1 = {nerv.CuMatrixFloat(10, 2048)} err_input2 = err_output1 -err_output2 = {[0] = nerv.CuMatrixFloat(10, 2048)} +err_output2 = {nerv.CuMatrixFloat(10, 2048)} err_input3 = err_output2 -err_output3 = {[0] = input1[0]:create()} +err_output3 = {input1[1]:create()} for i = 0, 3 do -- propagate @@ -46,26 +50,25 @@ for i = 0, 3 do sg:propagate(input2, output2) sm:propagate(input3, output3) - -- back_propagate sm:back_propagate(err_output1, err_input1, input3, output3) - sm:update(err_input1, input3, output3) - sg:back_propagate(err_output2, err_input2, input2, output2) - sg:update(err_input2, input2, output2) - af:back_propagate(err_output3, err_input3, input1, output1) + + -- update + sm:update(err_input1, input3, output3) + sg:update(err_input2, input2, output2) af:update(err_input3, input1, output1) print("output1") - print(output1[0]) + print(output1[1]) print("output2") - print(output2[0]) + print(output2[1]) print("err_output1") - print(err_output1[0]) + print(err_output1[1]) print("err_output2") - print(err_output2[0]) + print(err_output2[1]) nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce) nerv.utils.printf("frames: %.8f\n", sm.total_frames) end diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua new file mode 100644 index 0000000..ec338fe --- /dev/null +++ b/examples/test_nn_lib.lua @@ -0,0 +1,97 @@ +-- 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 |