使用 TensorFlow-Keras API 进行数据增强

Question

以下代码允许在每个 epoch 结束时将训练集的图像旋转 90º。

from skimage.io import imread
from skimage.transform import resize, rotate
import numpy as np

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.utils import Sequence 
from keras.models import Sequential
from 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())
model.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)

# Custom call back to hook into on epoch end
class CustomCallback(keras.callbacks.Callback):
    def __init__(self, sequence):
      self.sequence = sequence

    # after end of each epoch change the rotation for next epoch
    def on_epoch_end(self, epoch, logs=None):
      self.sequence.change_angle()               


# Create data reader
sequence = CIFAR10Sequence(["f1.PNG"]*10, [0, 1]*5, 8)
# fit the model and hook in the custom call back
model.fit(sequence, epochs=10, callbacks=[CustomCallback(sequence)])

如何修改代码以便在每个时期发生图像的旋转？

期望的输出：

Epoch 1/10
Rotating Angle: 0
Rotating Angle: 90
Rotating Angle: 180
Rotating Angle: 270

Epoch 2/10
Rotating Angle: 0
Rotating Angle: 90
Rotating Angle: 180
Rotating Angle: 270

(...)

Epoch 10/10
Rotating Angle: 0
Rotating Angle: 90
Rotating Angle: 180
Rotating Angle: 270

换句话说，我如何编写一个回调，该回调在一个时代的“结束”上运行，该回调改变了角度值并在同一时代继续训练（不更改到下一个）？

提前致谢

注意：代码来源来自“mujjiga”。

Answer 1

由于您有一个自定义序列生成器，因此您可以创建一个在 epoch 开始或结束时运行的函数。 这是您可以放置​​代码来修改图像的地方。 文档在[这里][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

无需为此创建CustomCallback ； 最后，您希望在训练期间进行扩充。

解决方案是以概率应用旋转操作

# 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)