Added temp results from README, added more hyperparameter runs and added temporal results to presentation
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 1200\n"
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.40\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 78.09\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 1800\n",
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" Dense Count 2: 3\n",
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" Dense Neurons 2: 2400\n",
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"Accuracy: 77.56\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 600\n",
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"Accuracy: 78.10\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1200\n",
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"Accuracy: 78.49\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 1800\n",
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"Accuracy: 78.24\n",
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"Testing with: Threshold: 70\n",
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" Leeway: 0\n",
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" Epoch: 20\n",
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" Dense Count 1: 2\n",
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" Dense Neurons 1: 2400\n",
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" Dense Count 2: 1\n",
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" Dense Neurons 2: 2400\n"
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]
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}
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],
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@ -74,6 +74,7 @@ This segment are notes dedicated to the neural network itself.
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```python
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thresh = 100
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leeway = 5
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epoch = 20
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model = Sequential()
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```python
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thresh = 75
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leeway = 3
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epoch = 20
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model = Sequential()
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```python
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thresh = 10
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leeway = 3
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epoch = 20
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model = Sequential()
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82/82 [==============================] - 0s 2ms/step - loss: 2.5231 - acc: 0.3696
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```
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#### Test4 (71.51%)
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#### Test4 (69.66%)
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```python
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thresh = 50
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leeway = 3
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epoch = 20
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model = Sequential()
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```
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Evaluate on test data
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82/82 [==============================] - 0s 2ms/step - loss: 1.5005 - acc: 0.7151
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82/82 [==============================] - 0s 2ms/step - loss: 1.6965 - acc: 0.6966
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```
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#### Test5 ()
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#### Test5 (73.13%)
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```python
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thresh = 60
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leeway = 3
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epoch = 20
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model = Sequential()
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model.add(Flatten(input_shape=X_filter[0].shape))
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model.add(BatchNormalization())
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model.add(Dense(1560, activation='relu'))
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model.add(Dense(750, activation='relu'))
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model.add(Dense(300, activation='relu'))
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model.add(Dense(156, activation='relu'))
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model.add(Dense(26, activation='softmax'))
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```
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```
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Evaluate on test data
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82/82 [==============================] - 0s 2ms/step - loss: 1.4886 - acc: 0.7313
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```
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#### Test6 (75.68%)
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```python
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thresh = 68
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leeway = 3
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epoch = 20
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model = Sequential()
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model.add(Flatten(input_shape=X_filter[0].shape))
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model.add(BatchNormalization())
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model.add(Dense(1560, activation='relu'))
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model.add(Dense(750, activation='relu'))
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model.add(Dense(300, activation='relu'))
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model.add(Dense(156, activation='relu'))
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model.add(Dense(26, activation='softmax'))
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```
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```
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Evaluate on test data
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82/82 [==============================] - 0s 2ms/step - loss: 1.4227 - acc: 0.7568
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```
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#### Test7 (76.07%)
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```python
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thresh = 68
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leeway = 3
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epoch = 30
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model = Sequential()
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model.add(Flatten(input_shape=X_filter[0].shape))
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model.add(BatchNormalization())
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model.add(Dense(1560, activation='relu'))
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model.add(Dense(750, activation='relu'))
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model.add(Dense(300, activation='relu'))
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model.add(Dense(156, activation='relu'))
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model.add(Dense(26, activation='softmax'))
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```
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```
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Evaluate on test data
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82/82 [==============================] - 0s 2ms/step - loss: 1.5863 - acc: 0.7607
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```
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#### Test8 (75.49%)
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```python
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THRESH = 70
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LEEWAY = 3
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1800
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```
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```
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Evaluate on test data
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21/21 [==============================] - 0s 2ms/step - loss: 1.5598 - acc: 0.7684
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```
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#### Test9 (78.15%)
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```python
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THRESH = 70
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LEEWAY = 1
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1800
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```
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```
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Evaluate on test data
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21/21 [==============================] - 0s 2ms/step - loss: 1.4677 - acc: 0.7815
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```
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#### Test10 (77.64%)
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1900
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```
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#### Test11 (77.41%)
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1800
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```
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#### Test12 (73.90%)
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1800
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DENSE_COUNT = 5
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DENSE_NEURONS = 900
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```
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#### Test13 (78.89%)
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NORM->FLAT->DENSE
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1900
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```
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#### Test14 (79.04%)
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NORM->FLAT->DENSE
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1800
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```
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#### Test15 (79.00%)
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NORM->FLAT->DENSE
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1700
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```
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#### Test16 (78.69%)
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NORM->FLAT->DENSE
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 5
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DENSE_NEURONS = 1600
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```
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#### Test17 (78.57%)
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NORM->FLAT->DENSE
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 4
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DENSE_NEURONS = 1800
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```
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#### Test18 (78.12%)
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NORM->FLAT->DENSE
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 6
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DENSE_NEURONS = 1800
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```
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#### Test19 (79.13%)
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NORM->FLAT->DENSE
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```python
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THRESH = 70
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LEEWAY = 0
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EPOCH = 30
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DENSE_COUNT = 3
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DENSE_NEURONS = 1800
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DENSE2_COUNT = 2
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DENSE2_NEURONS = 1200
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```
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Loading…
Reference in New Issue