在TensorFlow Jupyter笔记本中加载图像文件数据集可以通过以下步骤完成:
import tensorflow as tf
import os
import glob
dataset_path = '/path/to/dataset'
image_paths = glob.glob(os.path.join(dataset_path, '*.jpg'))
def preprocess_image(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.resize(image, [224, 224])
image = image / 255.0 # 归一化到 [0, 1] 范围
return image
tf.data.Dataset.from_tensor_slices
方法创建一个数据集对象,并将图像文件路径列表作为输入:dataset = tf.data.Dataset.from_tensor_slices(image_paths)
map
方法将图像文件路径转换为图像数据:dataset = dataset.map(preprocess_image)
labels.txt
的文件中,每行对应一个图像的标签,可以使用以下代码加载标签数据:labels_path = '/path/to/labels.txt'
def load_labels(labels_path):
with open(labels_path, 'r') as f:
labels = f.read().splitlines()
return labels
labels = load_labels(labels_path)
dataset = dataset.map(lambda x: (x, labels))
dataset = dataset.shuffle(buffer_size=len(image_paths))
dataset = dataset.batch(batch_size)
最后,你可以在训练模型之前对数据集进行迭代,例如:
for image, label in dataset:
# 在这里执行你的训练代码
pass
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