数据加载

 

目录

• Outline
• keras.datasets
  • MNIST
  • CIFAR10/100
• tf.data.Dataset
  • .shuffle
  • .map
  • .batch
  • .repeat()
• For example

 

Outline

• keras.datasets

• tf.data.Dataset.from_tensor_slices

  • shuffle
  • map
  • batch
  • repeat

• will display Input Pipeline later（大数据集）

keras.datasets

• boston housing

  • Boston housing price regression dataset

• mnist/fashion mnist

  • MNIST/Fashion-MNIST dataset

• cifar10/100

  • small images classification dataset

• imdb

  • sentiment classification dataset

MNIST

import tensorflow as tf
from tensorflow import keras

# train: 60k | test: 10k
(x, y), (x_test, y_test) = keras.datasets.mnist.load_data()

x.shape

(60000, 28, 28)

y.shape

(60000,)

# 0纯黑、255纯白
x.min(), x.max(), x.mean()

(0, 255, 33.318421449829934)

x_test.shape, y_test.shape

((10000, 28, 28), (10000,))

y[:4]

array([5, 0, 4, 1], dtype=uint8)

# 0-9有10种分类结果
y_onehot = tf.one_hot(y, depth=10)
y_onehot[:2]

<tf.Tensor: id=13, shape=(2, 10), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)>

CIFAR10/100

• 10个大类中有100个小类

# train: 50k | test: 10k
(x, y), (x_test, y_test) = keras.datasets.cifar10.load_data()

Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 63s 0us/step

x.shape, y.shape, x_test.shape, y_test.shape

((50000, 32, 32, 3), (50000, 1), (10000, 32, 32, 3), (10000, 1))

x.min(), x.max()

(0, 255)

y[:4]

array([[6],
       [9],
       [9],
       [4]], dtype=uint8)

tf.data.Dataset

• from_tensor_slices()

db = tf.data.Dataset.from_tensor_slices(x_test)
next(iter(db)).shape

TensorShape([32, 32, 3])

db = tf.data.Dataset.from_tensor_slices((x_test, y_test))
next(iter(db))[0].shape

TensorShape([32, 32, 3])

.shuffle

• 打乱数据

db = tf.data.Dataset.from_tensor_slices((x_test, y_test))
db = db.shuffle(10000)

.map

• 数据预处理

def preprocess(x, y):
    x = tf.cast(x, dtype=tf.float32) / 255.
    y = tf.cast(y, dtype=tf.int32)
    y = tf.one_hot(y, depth=10)
    return x, y

db2 = db.map(preprocess)

res = next(iter(db2))
res[0].shape, res[1].shape

(TensorShape([32, 32, 3]), TensorShape([1, 10]))

res[1][:2]

<tf.Tensor: id=84, shape=(1, 10), dtype=float32, numpy=array([[0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]], dtype=float32)>

.batch

• 一次性得到多张照片

db3 = db2.batch(32)
res = next(iter(db3))
res[0].shape, res[1].shape

(TensorShape([32, 32, 32, 3]), TensorShape([32, 1, 10]))

db_iter = iter(db3)
while True:
    next(db_iter)

.repeat()

# 迭代不退出
db4 = db3.repeat()
# 迭代两次退出
db3 = db3.repeat(2)

For example

def prepare_mnist_features_and_labels(x, y):
    x = tf.cast(x, tf.float32) / 255.
    y = tf.cast(y, tf.int64)
    return x, y


def mnist_dataset():
    (x, y), (x_val, y_val) = datasets.fashion_mnist.load_data()
    y = tf.one_hot(y, depth=10)
    y_val = tf.one_hot(y_val, depth=10)

    ds = tf.data.Dataset.from_tensor_slices((x, y))
    ds = ds.map(prepare_mnist_features_and_labels)
    ds = ds.shffle(60000).batch(100)
    ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
    ds_val = ds_val.map(prepare_mnist_features_and_labels)
    ds_val = ds_val.shuffle(10000).batch(100)
    return ds, ds_val