[common][mltrain] decrease dataset size and batch size

 - To prevent timeout blocking decrease dataset size and batch size

Change-Id: Ic700718b912451c03bc3b3ef432ec4337c81687f
Signed-off-by: hyeonseok lee <hs89.lee@samsung.com>

diff --git a/common/tct-mltrain-tizen-tests/media/testSet.dat b/common/tct-mltrain-tizen-tests/media/testSet.dat
index 105708cfe9ded15a1c703fb869287e906e487db3..3ab1addb08d0c1cd7c1debd7224e7a370a392f85 100755 (executable)
Binary files a/common/tct-mltrain-tizen-tests/media/testSet.dat and b/common/tct-mltrain-tizen-tests/media/testSet.dat differ

diff --git a/common/tct-mltrain-tizen-tests/media/test_model.ini b/common/tct-mltrain-tizen-tests/media/test_model.ini
index 3faa9feb05e961f870d3595de868ccc3460b64af..901786993343f885be9706538fdef70498f5bf65 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/media/test_model.ini
+++ b/common/tct-mltrain-tizen-tests/media/test_model.ini
@@ -1,5 +1,5 @@
 [model]
-batch_size = 16
+batch_size = 2
 continue_train = false
 epochs = 2
 loss = cross
@@ -7,13 +7,8 @@ save_path = /home/owner/share/Documents/webapi_tizen_model.bin
 type = NeuralNetwork
 
 [optimizer]
-beta1 = 0.002
-beta2 = 0.001
-decay_rate = 0.960000
-decay_steps = 1000
-epsilon = 1e-07
 learning_rate = 0.000100
-type = adam
+type = sgd
 
 [train_set]
 buffer_size = 100
@@ -26,27 +21,13 @@ path = /opt/usr/home/owner/share/Documents/valSet.dat
 type = file
 
 [inputlayer]
-input_shape = 16:1:1:62720
-name = inputlayer
-normalization = true
-standardization = false
-trainable = true
+input_shape = 1:1:1024
 type = input
 
 [fc1]
 bias_initializer = zeros
 input_layers = inputlayer
-name = fc1
 trainable = true
 type = fully_connected
+activation=softmax
 unit = 10
-weight_initializer = xavier_uniform
-weight_regularizer = l2norm
-weight_regularizer_constant = 0.005000
-
-[cross_softmax1]
-input_layers = fc1
-name = cross_softmax1
-trainable = true
-type = cross_softmax
-

diff --git a/common/tct-mltrain-tizen-tests/media/trainingSet.dat b/common/tct-mltrain-tizen-tests/media/trainingSet.dat
index a8d45f60078a7328b1e269514590a2a4e6480cc7..bbe4b63c7d20d3faaa9d84acc6074405f3d35b85 100755 (executable)
Binary files a/common/tct-mltrain-tizen-tests/media/trainingSet.dat and b/common/tct-mltrain-tizen-tests/media/trainingSet.dat differ

diff --git a/common/tct-mltrain-tizen-tests/media/valSet.dat b/common/tct-mltrain-tizen-tests/media/valSet.dat
index 546d093155eca21d7e764fb604f87f8c321a2de2..4b3be065f496effcc45eb380950ffadb0dbc9a86 100755 (executable)
Binary files a/common/tct-mltrain-tizen-tests/media/valSet.dat and b/common/tct-mltrain-tizen-tests/media/valSet.dat differ

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Layer_setProperty.html b/common/tct-mltrain-tizen-tests/mltrain/Layer_setProperty.html
index 93dc444a303a5c23fc9c255257fda50622f4bc89..02cbe904c37c6ac996bca685510e308eaf3085eb 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Layer_setProperty.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Layer_setProperty.html
@@ -38,7 +38,7 @@ Authors:
 test(function () {
     var model = tizen.ml.trainer.createModel();
     var layer = tizen.ml.trainer.createLayer("LAYER_INPUT");
-    layer.setProperty("input_shape", "1:1:62720");
+    layer.setProperty("input_shape", "1:1:1024");
     assert_equals(layer.type, "LAYER_INPUT", "layer setProperty should work properly");
 }, document.title);
 

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Model_addLayer.html b/common/tct-mltrain-tizen-tests/mltrain/Model_addLayer.html
index 913aac9657ea6d6b278892ea1ea7dd74f3fed162..3c083228cf4c52407dcf401b4e86363b491afac3 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Model_addLayer.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Model_addLayer.html
@@ -38,7 +38,7 @@ Authors:
 test(function () {
     var model = tizen.ml.trainer.createModel();
     var layer = tizen.ml.trainer.createLayer("LAYER_INPUT")
-    layer.setProperty("input_shape", "1:1:62720")
+    layer.setProperty("input_shape", "1:1:1024")
     layer.setProperty("normalization", "true")
     layer.setProperty("name", "inputlayer")
     model.addLayer(layer)

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Model_compile_with_options.html b/common/tct-mltrain-tizen-tests/mltrain/Model_compile_with_options.html
index 6a70af331fcd0d1885debaddbad6a4901b80e6d2..856a8e413998650bfac0456b786c8e6e59edbb71 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Model_compile_with_options.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Model_compile_with_options.html
@@ -40,7 +40,7 @@ test(function () {
     model.load(modelFile, "FORMAT_INI");
 
     var compileOpts = {
-        loss: "cross", batch_size: "16"
+        loss: "cross", batch_size: "2"
     }
     model.compile(compileOpts);
 }, document.title);

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Model_run.html b/common/tct-mltrain-tizen-tests/mltrain/Model_run.html
index 74a878918b6d06bb7fa57600ba39b0e7d3029813..17d2eeedbb8899525e05183b10020c3e3b0c9351 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Model_run.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Model_run.html
@@ -44,7 +44,7 @@ t.step(function () {
     Model.load(modelFile, "FORMAT_INI");
 
     var compileOpts = {
-        loss: "cross", batch_size: "16"
+        loss: "cross", batch_size: "2"
     }
     Model.compile(compileOpts);
 

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Model_run_exist.html b/common/tct-mltrain-tizen-tests/mltrain/Model_run_exist.html
index 9c37073f7eb95f9a8444971f20310b256f7679a5..cf135b2f34c5d44a1c50e8c76f9f00dc2072547b 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Model_run_exist.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Model_run_exist.html
@@ -40,7 +40,7 @@ test(function () {
     model.load(modelFile, "FORMAT_INI");
 
     var compileOpts = {
-        loss: "cross", batch_size: "16"
+        loss: "cross", batch_size: "2"
     }
     model.compile(compileOpts);
     check_method_exists(model, "run");

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Model_run_options_TypeMismatch.html b/common/tct-mltrain-tizen-tests/mltrain/Model_run_options_TypeMismatch.html
index 50da446516cb11b8d6b1e7f0598b8621d6659849..e19b5fd9aa06710b6950a5597e958a723b5f8083 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Model_run_options_TypeMismatch.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Model_run_options_TypeMismatch.html
@@ -44,7 +44,7 @@ t.step(function () {
     Model.load(modelFile, "FORMAT_INI");
 
     var compileOpts = {
-        loss: "cross", batch_size: "16"
+        loss: "cross", batch_size: "2"
     }
     Model.compile(compileOpts);
     successCallback = t.step_func(function () {

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_TypeMismatch.html b/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_TypeMismatch.html
index 2ab5bd858c8a7e97cb7c3f9ea3c7d911008b7c4e..bbc8a7db4386601b36b909882585f9230bae075e 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_TypeMismatch.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_TypeMismatch.html
@@ -44,7 +44,7 @@ t.step(function () {
     Model.load(modelFile, "FORMAT_INI");
 
     var compileOpts = {
-        loss: "cross", batch_size: "16"
+        loss: "cross", batch_size: "2"
     }
     Model.compile(compileOpts);
     runOpts = {epochs: "2", save_path: outputFile};

diff --git a/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_invalid_cb.html b/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_invalid_cb.html
index 84653b971325ed65885e380696c2482132ad267d..18d28214718d0e743852836b317f73a84a40aa10 100755 (executable)
--- a/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_invalid_cb.html
+++ b/common/tct-mltrain-tizen-tests/mltrain/Model_run_successCallback_invalid_cb.html
@@ -43,7 +43,7 @@ t.step(function () {
     Model.load(modelFile, "FORMAT_INI");
 
     var compileOpts = {
-        loss: "cross", batch_size: "16"
+        loss: "cross", batch_size: "2"
     }
     Model.compile(compileOpts);
     successCallback = {