Super Agents of AI

What is Transfer Learning

Four Steps:

• Load data
• Build model
• Train and Test
• Transfer Learning

Load Data

• Images and labels
  • X = [1.png, 2.png, 3.png]
  • Y = [4, 9, 1]
• data = tf.data.Dataset.from_tensor_slices((x,y))
• data.shuffle().map(func).batch()

Preprocessing

• Read and resize
  • 224*224 for ResNet
• Data Augmentation
  • Rotate/Flip
  • Crop
• Normalize
  • Mean, std

def preprocess(x, y):
    # x: 图片的路径，y：图片的数字编码
    x = tf.io.read_file(x)
    x = tf.image.decode_jpeg(x, channels=3)  # RGBA
    x = tf.image.resize(x, [244, 244])

    # x = tf.image.random_flip_left_right(x)
    x = tf.image.random_flip_up_down(x)
    x = tf.image.random_crop(x, [224, 224, 3])

    # x: [0,255]=> -1~1
    x = tf.cast(x, dtype=tf.float32) / 255.
    x = normalize(x)
    y = tf.convert_to_tensor(y)
    y = tf.one_hot(y, depth=5)

    return x, y

Build Model

• Inherit from Model
• Define forward graph
• Add optimizer

Train and Test

• Train, validation, test
• Early stopping

详见Code

Reference

1. TensorFlow-2.x-Tutorials
2. Transfer learning

Note: Cover Picture