Adding dropout to your network
Dropout is a form of regularization that removes a different random subset of the units in a layer in each round of training. In this exercise, we will add dropout to the convolutional neural network that we have used in previous exercises:
- Convolution (15 units, kernel size 2, 'relu' activation)
- Dropout (20%)
- Convolution (5 units, kernel size 2, 'relu' activation)
- Flatten
- Dense (3 units, 'softmax' activation)
A Sequential model along with Dense, Conv2D, Flatten, and Dropout objects are available in your workspace.
This exercise is part of the course
Image Modeling with Keras
Exercise instructions
- Add dropout applied to the first layer with 20%.
- Add a flattening layer.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Add a convolutional layer
model.add(Conv2D(15, kernel_size=2, activation='relu',
input_shape=(img_rows, img_cols, 1)))
# Add a dropout layer
____
# Add another convolutional layer
model.add(Conv2D(5, kernel_size=2, activation='relu'))
# Flatten and feed to output layer
____
model.add(Dense(3, activation='softmax'))