CIFAR10 Encode

cifar10_encode.py

import tensorflow as tf

def normalize(image, label):
  return tf.cast(image, dtype=tf.float32) / 255.0, label

train = ds['train'].map(normalize).batch(32)
test = ds['test'].map(normalize).batch(32)

encoding_model = tf.keras.Model(inputs=model.input, outputs=model.get_layer('flatten').output)

def csv_dump2(dataset, filename):
  with open(filename, 'w') as f:
    for images, labels in dataset:
      encodings = encoding_model.predict(images)
      labels = labels.numpy()
      for i in range(len(labels)):
        f.write('{},'.format(labels[i]))
        f.write(','.join(str(x) for x in list(encodings[i])))
        f.write('\n')

csv_dump2(train, 'train_encoded.csv')
csv_dump2(test, 'test_encoded.csv')