使用 TensorFlow-Keras API 进行数据增强

Question

以下代码允许训练集的图像在每个时期结束时旋转 90°。from skimage.io import imreadfrom skimage.transform import resize, rotateimport numpy as npimport tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import layersfrom keras.utils import Sequence from keras.models import Sequentialfrom keras.layers import Conv2D, Activation, Flatten, Dense# Model architecture  (dummy)model = Sequential()model.add(Conv2D(32, (3, 3), input_shape=(15, 15, 4)))model.add(Activation('relu'))model.add(Flatten())modl.add(Dense(1))model.add(Activation('sigmoid'))model.compile(loss='binary_crossentropy',              optimizer='rmsprop',              metrics=['accuracy'])# Data iterator class CIFAR10Sequence(Sequence):    def __init__(self, filenames, labels, batch_size):        self.filenames, self.labels = filenames, labels        self.batch_size = batch_size        self.angles = [0,90,180,270]        self.current_angle_idx = 0    # Method to loop throught the available angles    def change_angle(self):      self.current_angle_idx += 1      if self.current_angle_idx >= len(self.angles):        self.current_angle_idx = 0      def __len__(self):        return int(np.ceil(len(self.filenames) / float(self.batch_size)))    # read, resize and rotate the image and return a batch of images    def __getitem__(self, idx):        angle = self.angles[self.current_angle_idx]        print (f"Rotating Angle: {angle}")        batch_x = self.filenames[idx * self.batch_size:(idx + 1) * self.batch_size]        batch_y = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]        return np.array([            rotate(resize(imread(filename), (15, 15)), angle)               for filename in batch_x]), np.array(batch_y)如何修改代码以便在每个纪元期间发生图像的旋转？换句话说，我如何编写一个在纪元“结束”时运行的回调，该回调会更改角度值并继续在同一纪元上进行训练（而不更改到下一个纪元）？

Answer 1

由于您有一个自定义序列生成器，您可以创建一个在纪元开始或结束时运行的函数。您可以在其中放置代码来修改图像。文档位于[此处。][1]

Epoch-level methods (training only)

on_epoch_begin(self, epoch, logs=None)

Called at the beginning of an epoch during training.

on_epoch_end(self, epoch, logs=None)

Called at the end of an epoch during training.

  [1]: https://keras.io/guides/writing_your_own_callbacks/

Answer 2

没有必要CustomCallback为此目的创建一个；最后，您希望在训练期间进行增强。

解决方案是应用旋转操作的概率

# read, resize and rotate the image and return a batch of images

def __getitem__(self, idx):

    angle = self.angles[self.current_angle_idx]

    print(f"Rotating Angle: {angle}")

    batch_x = self.filenames[idx * self.batch_size:(idx + 1) * self.batch_size]

    batch_y = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]

    #These new lines (say we augment with probability > 0.5)

    #Number between 0 and 1

    images = []

    for filename in batch_x:

        probability = random.random()

        apply_rotate = probability > 0.5

        if apply_rotate:

            images.append(rotate(resize(imread(filename), (15, 15)), angle))

        else:

            images.append(resize(imread(filename), (15, 15)))

    return np.array(images), np.array(batch_y)