# Presentation

```
model = Sequential([
    Conv2D(32, (3, 3), input_shape=input_shape),
    Activation('relu'),
    MaxPooling2D(pool_size=(2, 2)),

    Conv2D(32, (3, 3)),
    Activation('relu'),
    MaxPooling2D(pool_size=(2, 2)),

    Conv2D(64, (3, 3)),
    Activation('relu'),
    MaxPooling2D(pool_size=(2, 2)),

    Flatten(),
    Dense(64),
    Activation('relu'),
    Dropout(0.5),
    Dense(5),
    Activation('sigmoid')
    ])
```

```
VGG16_model = VGG16(include_top=False, 
                    weights='imagenet')
                    
model = Sequential([
    VGG16_model,
    Flatten(input_shape=train_data.shape[1:]),
    Dense(256, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='sigmoid')
    ])
```

