python - 正确使用来自 tfds.load() 的 Cifar-10 数据集

Question

我正在尝试使用 Cifar-10 数据集来练习我的 CNN 技能。

如果我这样做没问题:

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

但我正在尝试使用 tfds.load() 但我不知道该怎么做。

有了这个我就下载了，

train_ds, test_ds = tfds.load('cifar10', split=['train','test'])

现在我试过了，但是没有用，

assert isinstance(train_ds, tf.data.Dataset)
assert isinstance(test_ds, tf.data.Dataset)
(train_images, train_labels) = tuple(zip(*train_ds))
(test_images, test_labels) = tuple(zip(*test_ds))

谁能告诉我实现它的方法？

谢谢!

Answer 1

您可以按如下方式执行此操作。

import tensorflow as tf 
import tensorflow_datasets as tfds

train_ds, test_ds = tfds.load('cifar10', split=['train','test'], as_supervised=True)

这些 train_ds 和 test_ds 是 tf.data.Dataset 对象，所以你可以使用 map，batch，以及与它们中的每一个类似的功能。

def normalize_resize(image, label):
    image = tf.cast(image, tf.float32)
    image = tf.divide(image, 255)
    image = tf.image.resize(image, (28, 28))
    return image, label

def augment(image, label):
    image = tf.image.random_flip_left_right(image)
    image = tf.image.random_saturation(image, 0.7, 1.3)
    image = tf.image.random_contrast(image, 0.8, 1.2)
    image = tf.image.random_brightness(image, 0.1)
    return image, label 

train = train_ds.map(normalize_resize).cache().map(augment).shuffle(100).batch(64).repeat()
test = test_ds.map(normalize_resize).cache().batch(64)

现在，我们可以将 train 和 test 直接传递给 model.fit。

model = tf.keras.models.Sequential(
        [
            tf.keras.layers.Flatten(input_shape=(28, 28, 3)),
            tf.keras.layers.Dense(128, activation="relu"),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.Dense(10, activation="softmax"),
        ]
    )

model.compile(
    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
)
model.fit(
    train,
    epochs=5,
    steps_per_epoch=60000 // 64,
    validation_data=test, verbose=2
)

Epoch 1/5
17s 17ms/step - loss: 2.0848 - accuracy: 0.2318 - val_loss: 1.8175 - val_accuracy: 0.3411
Epoch 2/5
11s 12ms/step - loss: 1.8827 - accuracy: 0.3144 - val_loss: 1.7800 - val_accuracy: 0.3595
Epoch 3/5
11s 12ms/step - loss: 1.8383 - accuracy: 0.3272 - val_loss: 1.7152 - val_accuracy: 0.3904
Epoch 4/5
11s 11ms/step - loss: 1.8129 - accuracy: 0.3397 - val_loss: 1.6908 - val_accuracy: 0.4060
Epoch 5/5
11s 11ms/step - loss: 1.8022 - accuracy: 0.3461 - val_loss: 1.6801 - val_accuracy: 0.4081