add softmax + ce layer; test_dnn_layers produces the same result as TNet

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)