Added hyperparamteroptimization (run did not finish) and commit to add stuff for presentation

1-first-project/tdt/README.md

@@ -45,19 +45,7 @@ Since information has different scale (i.e. Acc1: [-32768;32768] and Acc2 [-8192
 
 - Could be used for identifying each data entry -> needs to be normalized to the first entry of the data set to see the comlete timeline of the data
 
-### Acc1
-
-todo
-
-### Acc2
-
-todo
-
-### Gyro 
-
-todo
-
-### Mag
+### Acc1/Acc2/Gyro/Mag
 
 todo
 
@@ -81,7 +69,12 @@ This segment are notes dedicated to the neural network itself.
 
 ### Results
 
+#### Test1 (72.99%)
+
 ```python
+thresh      = 100
+leeway      = 5
+
 model = Sequential()
 
 model.add(Flatten(input_shape=X_filter[0].shape))
@@ -104,4 +97,88 @@ Evaluate on test data
 82/82 [==============================] - 0s 3ms/step - loss: 1.4249 - acc: 0.7299
 ```
 
-todo: more testing
+#### Test2 (75.21%)
+
+```python
+thresh      = 75
+leeway      = 3
+
+model = Sequential()
+
+model.add(Flatten(input_shape=X_filter[0].shape))
+
+model.add(BatchNormalization())
+
+model.add(Dense(1560, activation='relu'))
+
+model.add(Dense(750, activation='relu'))
+
+model.add(Dense(300, activation='relu'))
+
+model.add(Dense(156, activation='relu'))
+
+model.add(Dense(26, activation='softmax'))
+```
+
+```
+Evaluate on test data
+82/82 [==============================] - 0s 2ms/step - loss: 1.5145 - acc: 0.7521
+```
+
+#### Test3 (36.96%)
+
+```python
+thresh      = 10
+leeway      = 3
+
+model = Sequential()
+
+model.add(Flatten(input_shape=X_filter[0].shape))
+
+model.add(BatchNormalization())
+
+model.add(Dense(1560, activation='relu'))
+
+model.add(Dense(750, activation='relu'))
+
+model.add(Dense(300, activation='relu'))
+
+model.add(Dense(156, activation='relu'))
+
+model.add(Dense(26, activation='softmax'))
+```
+
+```
+Evaluate on test data
+82/82 [==============================] - 0s 2ms/step - loss: 2.5231 - acc: 0.3696
+```
+
+#### Test4 (71.51%)
+
+```python
+thresh      = 50
+leeway      = 3
+
+model = Sequential()
+
+model.add(Flatten(input_shape=X_filter[0].shape))
+
+model.add(BatchNormalization())
+
+model.add(Dense(1560, activation='relu'))
+
+model.add(Dense(750, activation='relu'))
+
+model.add(Dense(300, activation='relu'))
+
+model.add(Dense(156, activation='relu'))
+
+model.add(Dense(26, activation='softmax'))
+```
+
+```
+Evaluate on test data
+82/82 [==============================] - 0s 2ms/step - loss: 1.5005 - acc: 0.7151
+```
+
+#### Test5 ()