10. Data Preprocessing

Lambda Layers

In Keras, lambda layers can be used to create arbitrary functions that operate on each image as it passes through the layer.

In this project, a lambda layer is a convenient way to parallelize image normalization. The lambda layer will also ensure that the model will normalize input images when making predictions in drive.py .

That lambda layer could take each pixel in an image and run it through the formulas:

pixel_normalized = pixel / 255

pixel_mean_centered = pixel_normalized - 0.5

A lambda layer will look something like:

Lambda(lambda x: (x / 255.0) - 0.5)

Below is some example code for how a lambda layer can be used.

from keras.models import Sequential, Model
from keras.layers import Lambda

# set up lambda layer
model = Sequential()
model.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=(160,320,3)))
...