|
@@ -35,13 +35,12 @@ const printout_interval = 5
|
|
|
const save_interval = 25
|
|
|
const time_format = "HH:MM:SS"
|
|
|
const date_format = "dd_mm_yyyy"
|
|
|
-data_size = (50, 1)
|
|
|
+data_size = (48, 6) # resulting in a 240ms frame
|
|
|
|
|
|
# ARCHITECTURE
|
|
|
-inputDense1
|
|
|
-inputDense2
|
|
|
-inputDense3
|
|
|
-classes = 2
|
|
|
+inputDense1 =
|
|
|
+inputDense2 =
|
|
|
+inputDense3 =
|
|
|
# enter the datasets and models you want to train
|
|
|
|
|
|
dataset_folderpath = "../MATLAB/TrainingData/"
|
|
@@ -67,9 +66,10 @@ function adapt_learnrate(epoch_idx)
|
|
|
end
|
|
|
|
|
|
function load_dataset(dataset_name)
|
|
|
-end
|
|
|
|
|
|
|
|
|
+end
|
|
|
+
|
|
|
|
|
|
|
|
|
model = Chain(
|
|
@@ -82,6 +82,6 @@ model = Chain(
|
|
|
flatten,
|
|
|
Dense(inputDense1, inputDense2, σ),
|
|
|
Dense(inputDense2, inputDense3, σ),
|
|
|
- Dense(inputDense3, classes) # identity to output coordinates!
|
|
|
+ Dense(inputDense3, 2) # identity to output coordinates!
|
|
|
)
|
|
|
|