eileen-code4fun

model.fit(train_dataset, epochs=5, validation_data=val_dataset)
model.evaluate(test_dataset, verbose=2)
# Output
# loss: 1.0985 - accuracy: 0.6060

eileen-code4fun

from tensorflow.keras import layers, models, losses

def create_model():
  model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D(2, 2),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D(2, 2),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),

eileen-code4fun

def extract(example):
  data = tf.io.parse_example(
    example,
    # Schema of the example.
    {
      'image': tf.io.FixedLenFeature(shape=(32, 32, 3), dtype=tf.float32),
      'label': tf.io.FixedLenFeature(shape=(), dtype=tf.int64)
    }
  )
  return data['image'], data['label']

eileen-code4fun

import matplotlib.pyplot as plt

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

dataset = tf.data.TFRecordDataset([GCS_PATH_FOR_DATA + 'train.tfrecord'])
plt.figure(figsize=(10, 10))
for i, example in enumerate(dataset.take(16)):
  data = tf.train.Example()
  data.ParseFromString(example.numpy())
  image = tf.constant(data.features.feature['image'].float_list.value, shape=[32, 32, 3])

eileen-code4fun

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].