[UTC][nntrainer][Non-ACR][Fix model tc]

**Changes proposed in this PR:**
- Fix rare cases that tct failes due to ANR issue

related issue: TSIX-6702
Related Feature: http://tizen.org/feature/machine_learning.training

Change-Id: I53380085d5e0344a0c1e30636ceaa51bf6fa0a51
Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

diff --git a/src/utc/nntrainer/res/model.ini b/src/utc/nntrainer/res/model.ini
index 794a33366b8eb6e20eec1db44944f5d692855a1d..bb87381e1319fac8e06232c46941282095b89723 100644 (file)
--- a/src/utc/nntrainer/res/model.ini
+++ b/src/utc/nntrainer/res/model.ini
@@ -4,19 +4,19 @@ Type = NeuralNetwork  # Model Type : Regression, KNN, NeuralNetwork
 Learning_rate = 0.0001         # Learning Rate
 Decay_rate = 0.96      # for the decay_rate for the decayed learning rate
 Decay_steps = 1000       # decay step for the exponential decayed learning rate
-Epochs = 3             # Epoch
+Epochs = 2             # Epoch
 Optimizer = adam       # Optimizer : sgd (stochastic gradien decent),
                        #             adam (Adamtive Moment Estimation)
 loss = cross           # Cost(loss) function : mse (mean squared error)
                         #                       cross (cross entropy)
-batch_size = 3
+batch_size = 2
 save_path = "model.bin"        # model path to save / read
 beta1 = 0.9            # beta 1 for adam
 beta2 = 0.9999 # beta 2 for adam
 epsilon = 1e-7 # epsilon for adam
 
 [DataSet]
-BufferSize=3
+BufferSize=2
 TrainData="trainingSet.dat"
 ValidData="trainingSet.dat"
 LabelData="label.dat"
@@ -24,7 +24,7 @@ LabelData="label.dat"
 # Layer Section : Name
 [inputlayer]
 Type = input
-Input_Shape = 1:1:62720        # Input Layer Dimensions
+Input_Shape = 1:1:1280 # Input Layer Dimensions
 bias_initializer = zeros       # Zero Bias
 Normalization = true