google colab TPU上的自定义图像数据生成器(具有增强功能)的示例(keras)

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rescale=1./255,  # 图像归一化
    rotation_range=20,  # 随机旋转角度范围
    width_shift_range=0.2,  # 随机水平平移范围
    height_shift_range=0.2,  # 随机垂直平移范围
    shear_range=0.2,  # 随机错切变换范围
    zoom_range=0.2,  # 随机缩放范围
    horizontal_flip=True,  # 随机水平翻转
    fill_mode='nearest'  # 填充像素的策略
)

train_generator = datagen.flow_from_directory(
    'path_to_train_directory',
    target_size=(224, 224),  # 图像尺寸
    batch_size=32,
    class_mode='binary'  # 分类模式
)

model = tf.keras.models.Sequential()
# 添加模型层

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)

with strategy.scope():
    model.fit(train_generator, epochs=10)