def cifar_standardization(x, mode='FEATURE_NORMALIZE', data_samples=None):
mode = mode.upper()
assert mode in ['FEATURE_NORMALIZE', 'PIXEL_MEAN_SUBTRACT']
if mode == 'PIXEL_MEAN_SUBTRACT' and not data_samples:
raise ValueError('`data_samples` argument should not be `None`, '
'when `mode="PIXEL_MEAN_SUBTRACT"`.')
if mode == 'FEATURE_NORMALIZE':
cifar_mean = tf.cast(CIFAR_MEAN, tf.float32).numpy()
cifar_std = tf.cast(CIFAR_STD, tf.float32).numpy()
x = Rescaling(scale=1. / cifar_std,
offset=-(cifar_mean / cifar_std),
name='mean_normalization')(x)
elif mode == 'PIXEL_MEAN_SUBTRACT':
mean_subtraction_layer = Normalization(axis=[1, 2, 3],
name='pixel_mean_subtraction')
mean_subtraction_layer.adapt(data_samples)
# set values of variance = 1. and keep mean values as is
mean_pixels = mean_subtraction_layer.get_weights()[0]
mean_subtraction_layer.set_weights(
[mean_pixels, tf.ones_like(mean_pixels)])
x = mean_subtraction_layer(x)
x = Rescaling(scale=1 / 255., name='rescaling')(x)
return x
already-preprocessed data. The reason being that, if your
model expects preprocessed data, any time you export
your model to use it elsewhere (in a web browser, in a
mobile app), you'll need to reimplement the same exact
preprocessing pipeline. This can be a bit tricky to do.
"""
# normalize in range [0, 1]
scaling_layer = Rescaling(1.0 / 255)
# normalize in range [-1, 1]
input_ = tf.keras.Input(shape=(32, 32, 3))
norm_neg_one_to_one = Normalization()
x = norm_neg_one_to_one(input_)
import numpy as np
mean = [127.5]*3
var = mean ** 2
norm_neg_one_to_one.set_weights([mean, var])
norm_neg_one_to_one.get_weights()
# normalize with mean 0 and std 1
norm_mean_std = Normalization()
norm_mean_std.adapt(x_train[0])
model_ = Sequential([
tf.keras.Input(shape=(32, 32, 3)),
norm_mean_std,
model
])
model_.compile(
optimizer="Adam",
loss="sparse_categorical_crossentropy",