取出數(shù)據(jù)集方法一，模型直取

mnist.load.data()

用mnist.load.data()讀取numpy數(shù)據(jù)直接送入model
喂入numpy
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

定義輸入維數(shù)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
])

numpy降維輸入，張量轉(zhuǎn)化為numpy
predictions = model(x_train[:1])
predictions = model(x_train[:1]).numpy()
predictions

cifar10.load_data()

第一個例子是 mnist.load_data()讀出的numpy, 容易限定為一張圖片，next(iter(train_ds))[0]只能是一批batch圖片，張量可用序號

用train_ds.take(1)方式取出的是批量圖片張量，張量可用序號

輸入可以是張量或numpy
train_ds = tf.data.Dataset.from_tensor_slices(
(x_train, y_train)).shuffle(10000).batch(32)

從中取出圖片張量
for images, labels in train_ds:
train_step(images, labels)

GradientTape方法需要model輸入圖片張量

with tf.GradientTape() as tape:
# training=True is only needed if there are layers with different
# behavior during training versus inference (e.g. Dropout).
predictions = model(images, training=True)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))

取出數(shù)據(jù)集方法二 image_data_from_directory

import pathlib
dataset_url = “https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz”
data_dir = tf.keras.utils.get_file(‘flower_photos’, origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset=“training”, seed=123, image_size=(img_height, img_width), batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)

取出圖片方法：
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype(“uint8”))
plt.title(class_names[labels[i]])
plt.axis(“off”)

train_ds放緩存
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

model.fit直接用train_ds
history = model.fit( train_ds, validation_data=val_ds, epochs=epochs)

train validation已經(jīng)在不同目錄
_URL = ‘https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip’
path_to_zip = tf.keras.utils.get_file(‘cats_and_dogs.zip’, origin=_URL, extract=True)
PATH = os.path.join(os.path.dirname(path_to_zip), ‘cats_and_dogs_filtered’)
train_dir = os.path.join(PATH, ‘train’)
validation_dir = os.path.join(PATH, ‘validation’)
BATCH_SIZE = 32
IMG_SIZE = (160, 160)
train_dataset = image_dataset_from_directory(train_dir, shuffle=True, batch_size=BATCH_SIZE, image_size=IMG_SIZE)

分出test_dataset數(shù)據(jù)集
val_batches = tf.data.experimental.cardinality(validation_dataset)
test_dataset = validation_dataset.take(val_batches // 5)
validation_dataset = validation_dataset.skip(val_batches // 5)

取出數(shù)據(jù)集方法三使用昵稱直接tfds下載

此方法可下載大部分數(shù)據(jù)集
(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,)

特有的獲得圖片名稱的方法
get_label_name = metadata.features[‘label’].int2str
image, label = next(iter(train_ds))
print(image.shape)
_ = plt.imshow(image)
_ = plt.title(get_label_name(label))

num_classes = metadata.features[‘label’].num_classes

數(shù)據(jù)加強層及擴維方式一加層

data_augmentation = tf.keras.Sequential([tf.keras.layers.experimental.preprocessing.RandomFlip(‘horizontal’),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),])

for image, _ in train_dataset.take(1):
plt.figure(figsize=(10, 10))
first_image = image[0]
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
augmented_image = data_augmentation(tf.expand_dims(first_image, 0))
plt.imshow(augmented_image[0] / 255)
plt.axis(‘off’)

數(shù)據(jù)加強層方法二數(shù)據(jù)集函數(shù)

batch_size = 32 AUTOTUNE = tf.data.experimental.AUTOTUNEdata_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),layers.experimental.preprocessing.RandomRotation(0.2), ])def prepare(ds, shuffle=False, augment=False):# Resize and rescale all datasetsds = ds.map(lambda x, y: (resize_and_rescale(x), y), num_parallel_calls=AUTOTUNE)if shuffle:ds = ds.shuffle(1000)# Batch all datasetsds = ds.batch(batch_size)# Use data augmentation only on the training setif augment:ds = ds.map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=AUTOTUNE)# Use buffered prefecting on all datasetsreturn ds.prefetch(buffer_size=AUTOTUNE)train_ds = prepare(train_ds, shuffle=True, augment=True) val_ds = prepare(val_ds) test_ds = prepare(test_ds)

數(shù)據(jù)加強方法三數(shù)據(jù)集方法tf.image

數(shù)據(jù)加強方法四加子類

class RandomInvert(layers.Layer):
def init(self, factor=0.5, **kwargs):
super().init(**kwargs)
self.factor = factor

def call(self, x):
return random_invert_img(x)

_ = plt.imshow(RandomInvert()(image)[0])

生成子類后，用以上所有方式調(diào)用類方法或直接map
aug_ds = train_ds.map(lambda x, y: (resize_and_rescale(x, training=True), y))

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