---
# System prepended metadata

title: 'GA2: Exploration Jason'

---

# GA2: Exploration Jason

## Original

![](https://i.imgur.com/Dx2rVGJ.png)

## Batch size = 256
![](https://i.imgur.com/4PuSlcJ.png)


## Epochs 50
![](https://i.imgur.com/15BQp8c.png)

## Add 128 conv layer
![](https://i.imgur.com/qIacdjm.png)
Deeper networks require batchnorm.
![](https://i.imgur.com/lg5J1CT.png)


## Add batchnorm to every conv layer
![](https://i.imgur.com/yGVpTC0.png)

## Add 256 conv layer and batchnorm
![](https://i.imgur.com/y3GQdZg.png)
This did not make it better, so lets revert this change.

## Decrease learning rate to 0.0001
![](https://i.imgur.com/VDSU7a3.png)

Toch nog eens een layer toevoegen.
![](https://i.imgur.com/lG2P5R8.png)

## Misschien vooraan eens een layer toevoegen van 16 en de 256 opnieuw verwijderen.
![](https://i.imgur.com/9qUW6bn.png)

-----------------------------------------------------------
# Vince 67 accuracy als base model nu
Model misschien te complex want regularization werkt niet

  model = Sequential()
  model.add(Conv2D(32, (3, 3), padding='same', input_shape=(32, 32, 3)))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(64, (3, 3), padding='same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D((2, 2)))

  model.add(Conv2D(128, (3, 3), padding='same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(128, (3, 3), padding='same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D((2, 2)))

  model.add(Conv2D(256, (3, 3), padding='same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv2D(256, (3, 3), padding='same'))
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(MaxPooling2D((2, 2)))

  model.add(Flatten())


  model.add(Dense(128))
  model.add(BatchNormalization())
  model.add(Activation('relu'))

  model.add(Dense(64))
  model.add(BatchNormalization())
  model.add(Activation('relu'))


  model.add(Dense(num_classes))
  model.add(Activation('softmax'))

![](https://i.imgur.com/a3VSFbJ.png)

Dit geeft relatief cleane curve (behalve de insane drop in de laatste epochs, maar dat is normaal zeiden ze.)

## Minder epochs (25)
![](https://i.imgur.com/6OlKz6R.png)

# Verwijder gewoon alle dense layers

![](https://i.imgur.com/0QkHs0q.png)

# add 2x512 conv
![](https://i.imgur.com/0LNnu0t.png)


# add 2x1024 conv
![](https://i.imgur.com/N09pfBg.png)

# Start wider and less deep
![](https://i.imgur.com/hIEsymD.png)

# Weer naar zonder dense layers, 2x256, dropout 0.1 op 3 laatste conv layers
not good mb

## Vince model
![](https://i.imgur.com/GzGdUmS.png)

## Add maxnorm 4 in dropout layers