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)
These train_ds
and test_ds
are tf.data.Dataset
对象,所以你可以使用map
, batch
,以及与其中每一个类似的功能。
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,我们可以通过train
and test
直接到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