Tengo la misma pregunta: la entrada para remodelar es un tensor con 37632 valores, pero la forma solicitada tiene 150528.

 writer = tf.python_io.TFRecordWriter("/home/henson/Desktop/vgg/test.tfrecords")  # 要生成的文件

for index, name in enumerate(classes):
    class_path = cwd + name +'/'
    for img_name in os.listdir(class_path):
        img_path = class_path + img_name  # 每一个图片的地址
    img = Image.open(img_path)
    img = img.resize((224, 224))
    img_raw = img.tobytes()  # 将图片转化为二进制格式
    example = tf.train.Example(features=tf.train.Features(feature={
        "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
        'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
    }))  # example对象对label和image数据进行封装
    writer.write(example.SerializeToString())  # 序列化为字符串

writer.close()


def read_and_decode(filename):  # 读入dog_train.tfrecords
    filename_queue = tf.train.string_input_producer([filename])  # 生成一个queue队列

reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)  # 返回文件名和文件
features = tf.parse_single_example(serialized_example,
                                   features={
                                       'label': tf.FixedLenFeature([], tf.int64),
                                       'img_raw': tf.FixedLenFeature([], tf.string),
                                   })  # 将image数据和label取出来

img = tf.decode_raw(features['img_raw'], tf.uint8)
img = tf.reshape(img, [224, 224, 3])  # reshape为128*128的3通道图片
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5  # 在流中抛出img张量
label = tf.cast(features['label'], tf.int32)  # 在流中抛出label张量
print(img,label)
return img, label

images, labels = read_and_decode("/home/henson/Desktop/vgg/TFrecord.tfrecords")
print(images,labels)
images, labels = tf.train.shuffle_batch([images, labels], batch_size=20, capacity=16*20, min_after_dequeue=8*20)

Creo que tengo que cambiar el tamaño de img a 224 * 224, y cambiar la forma a [224,224,3], pero no funciona. ¿Cómo podría hacerlo?