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Convolutional Neural Networks
So, we have an improvement of 15.14% with respect to the previous simpler deeper
network. For sake of completeness, let's also report the accuracy and loss during
training.
Improving the CIFAR-10 performance with
data augmentation
Another way to improve the performance is to generate more images for our
training. The idea here is that we can take the standard CIFAR training set and
augment this set with multiple types of transformation, including rotation, rescaling,
horizontal or vertical flip, zooming, channel shift, and many more. Let's see the code
applied on the same network defined in the previous section:
from tensorflow.keras.preprocessing.image import ImageDataGenerator
#image augmentation
datagen = ImageDataGenerator(
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
)
datagen.fit(x_train)
rotation_range is a value in degrees (0-180) for randomly rotating pictures; width_
shift and height_shift are ranges for randomly translating pictures vertically or
horizontally; zoom_range is for randomly zooming pictures; horizontal_flip is
for randomly flipping half of the images horizontally; and fill_mode is the strategy
used for filling in new pixels that can appear after a rotation or a shift.
After augmentation we have generated many more training images starting from the
standard CIFAR-10 set, as shown in Figure 15:
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