CIFAR10 Data Preparation

cifar10_data.py

import tensorflow as tf

(train_images, train_labels), (test_images, test_labels) = tf.keras.dataset.cifar10.load_data()

def preprocess(filename, images, labels):
  with tf.io.TFRecordWriter(filename) as writer:
    for image, label in zip(images, labels):
      # Encode the image and label in tf.train.Example.
      feature = {
        # Normalize the image to range [0, 1].
        'image': tf.train.Feature(float_list=tf.train.FloatList(value=(image/255.0).reshape(-1))),
        'label': tf.train.Feature(int64_list=tf.train.Int64List(value=label))
      }
      example = tf.train.Example(features=tf.train.Features(feature=feature))
      writer.write(example.SerializeToString())

preprocess('train.tfrecord', train_images[:40000], train_labels[:40000])
preprocess('val.tfrecord', train_images[40000:], train_labels[40000:])
preprocess('test.tfrecord', test_images, test_labels)

should be 'datasets' instead of 'dataset'
tf.keras.datasets.cifar10.load_data()