2.9-tf2-数据增强-tf_flowers

tf_flowers数据集

1.导入包

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds

from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

2.加载数据

(train_ds, val_ds, test_ds), metadata = tfds.load(
    'tf_flowers',
    split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
    with_info=True,
    as_supervised=True,
)

#The flowers dataset has five classes.

num_classes = metadata.features['label'].num_classes
print(num_classes)
5

复现数据:

#Let's retrieve an image from the dataset and use it to demonstrate data augmentation.

get_label_name = metadata.features['label'].int2str

image, label = next(iter(train_ds))
_ = plt.imshow(image)
_ = plt.title(get_label_name(label))

3.数据预处理

Use Keras preprocessing layers

#Resizing and rescaling
#You can use preprocessing layers to resize your images to a consistent shape, and to rescale pixel values.

IMG_SIZE = 180

resize_and_rescale = tf.keras.Sequential([
  layers.experimental.preprocessing.Resizing(IMG_SIZE, IMG_SIZE),
  layers.experimental.preprocessing.Rescaling(1./255)
])
#Note: the rescaling layer above standardizes pixel values to [0,1]. If instead you wanted [-1,1], you would write Rescaling(1./127.5, offset=-1).

result = resize_and_rescale(image)
_ = plt.imshow(result)

#You can verify the pixels are in [0-1].

print("Min and max pixel values:", result.numpy().min(), result.numpy().max())

Min and max pixel values: 0.0 1.0

4.数据增强

#Data augmentation
#You can use preprocessing layers for data augmentation as well.

#Let's create a few preprocessing layers and apply them repeatedly to the same image.

data_augmentation = tf.keras.Sequential([
  layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
  layers.experimental.preprocessing.RandomRotation(0.2),
])

# Add the image to a batch
image = tf.expand_dims(image, 0)

plt.figure(figsize=(10, 10))
for i in range(9):
  augmented_image = data_augmentation(image)
  ax = plt.subplot(3, 3, i + 1)
  plt.imshow(augmented_image[0])
  plt.axis("off")

#There are a variety of preprocessing layers you can use for data augmentation including layers.RandomContrast, layers.RandomCrop, layers.RandomZoom, and others.

5.预处理层的两种方法

There are two ways you can use these preprocessing layers, with important tradeoffs.

1. 第一种方法

Option 1: Make the preprocessing layers part of your model
model = tf.