TensorFlow 文檔有以下示例，可以說明當訓練集太大而無法放入內存時，如何創建批量生成器以將訓練集批量提供給模型：from skimage.io import imreadfrom skimage.transform import resizeimport tensorflow as tfimport numpy as npimport math# Here, `x_set` is list of path to the images# and `y_set` are the associated classes.class CIFAR10Sequence(tf.keras.utils.Sequence): def __init__(self, x_set, y_set, batch_size): self.x, self.y = x_set, y_set self.batch_size = batch_size def __len__(self): return math.ceil(len(self.x) / self.batch_size) def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] return np.array([ resize(imread(file_name), (200, 200)) for file_name in batch_x]), np.array(batch_y)我的目的是通過將每個圖像旋轉 3 倍 90° 來進一步增加訓練集的多樣性。在訓練過程的每個 Epoch 中，模型將首先輸入“0° 訓練集”，然后分別輸入 90°、180° 和 270° 旋轉集。如何修改前面的代碼以在CIFAR10Sequence()數據生成器中執行此操作？請不要使用tf.keras.preprocessing.image.ImageDataGenerator()，以免答案失去對其他類型不同性質的類似問題的普遍性。注意：這個想法是在模型被輸入時“實時”創建新數據，而不是（提前）創建并在磁盤上存儲一個比稍后使用的原始訓練集更大的新的增強訓練集（也在批次）在模型的訓練過程中。
1 回答
米脂
TA貢獻1836條經驗 獲得超3個贊
使用自定義Callback并掛鉤到on_epoch_end. 每個紀元結束后更改數據迭代器對象的角度。
示例（內聯記錄）
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)])
輸出：
Rotating Angle: 0
Epoch 1/10
Rotating Angle: 0
Rotating Angle: 0
2/2 [==============================] - 2s 755ms/step - loss: 1.0153 - accuracy: 0.5000
Epoch 2/10
Rotating Angle: 90
Rotating Angle: 90
2/2 [==============================] - 0s 190ms/step - loss: 0.6975 - accuracy: 0.5000
Epoch 3/10
Rotating Angle: 180
Rotating Angle: 180
2/2 [==============================] - 2s 772ms/step - loss: 0.6931 - accuracy: 0.5000
Epoch 4/10
Rotating Angle: 270
Rotating Angle: 270
2/2 [==============================] - 0s 197ms/step - loss: 0.6931 - accuracy: 0.5000
Epoch 5/10
Rotating Angle: 0
Rotating Angle: 0
2/2 [==============================] - 0s 189ms/step - loss: 0.6931 - accuracy: 0.5000
Epoch 6/10
Rotating Angle: 90
Rotating Angle: 90
2/2 [==============================] - 2s 757ms/step - loss: 0.6932 - accuracy: 0.5000
Epoch 7/10
Rotating Angle: 180
Rotating Angle: 180
2/2 [==============================] - 2s 757ms/step - loss: 0.6931 - accuracy: 0.5000
Epoch 8/10
Rotating Angle: 270
Rotating Angle: 270
2/2 [==============================] - 2s 761ms/step - loss: 0.6932 - accuracy: 0.5000
Epoch 9/10
Rotating Angle: 0
Rotating Angle: 0
2/2 [==============================] - 1s 744ms/step - loss: 0.6932 - accuracy: 0.5000
Epoch 10/10
Rotating Angle: 90
Rotating Angle: 90
2/2 [==============================] - 0s 192ms/step - loss: 0.6931 - accuracy: 0.5000
<tensorflow.python.keras.callbacks.History at 0x7fcbdf8bcdd8>
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