diff options
Diffstat (limited to 'examples/test_dnn_layers.lua')
-rw-r--r-- | examples/test_dnn_layers.lua | 74 |
1 files changed, 74 insertions, 0 deletions
diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua new file mode 100644 index 0000000..c57de6d --- /dev/null +++ b/examples/test_dnn_layers.lua @@ -0,0 +1,74 @@ +require 'layer.affine' +require 'layer.sigmoid' +require 'layer.softmax_ce' + +global_conf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9} + +pf = nerv.ParamFile("affine.param", "r") +ltp = pf:read_param("a") +bp = pf:read_param("b") + +-- print(bp.trans) + +af = nerv.AffineLayer("test", global_conf, ltp, bp) +sg = nerv.SigmoidLayer("test2", global_conf) +sm = nerv.SoftmaxCELayer("test3", global_conf) + +af:init() +sg:init() +sm:init() + +df = nerv.ParamFile("input.param", "r") + +label = nerv.CuMatrixFloat(10, 2048) +label:fill(0) +for i = 0, 9 do + label[i][i] = 1.0 +end + +input1 = {[0] = df:read_param("input").trans} +output1 = {[0] = nerv.CuMatrixFloat(10, 2048)} +input2 = output1 +output2 = {[0] = nerv.CuMatrixFloat(10, 2048)} +input3 = {[0] = output2[0], [1] = label} +output3 = nil +err_input1 = nil +err_output1 = {[0] = nerv.CuMatrixFloat(10, 2048)} +err_input2 = err_output1 +err_output2 = {[0] = nerv.CuMatrixFloat(10, 2048)} +err_input3 = err_output2 +err_output3 = {[0] = input1[0]:create()} + +for i = 0, 3 do + -- propagate + af:propagate(input1, output1) + 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) + af:update(err_input3, input1, output1) + + + print("output1") + print(output1[0]) + print("output2") + print(output2[0]) + print("err_output1") + print(err_output1[0]) + print("err_output2") + print(err_output2[0]) + nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce) + nerv.utils.printf("frames: %.8f\n", sm.total_frames) +end +print("linear") +print(af.ltp.trans) +print("linear2") +print(af.bp.trans) |