如何在Tensorflow中显示数据集对象中的类分布

Question

我正在使用自己的图像进行多标签分类任务。

filenames = [] # a list of filenames
labels = [] # a list of labels corresponding to the filenames
full_ds = tf.data.Dataset.from_tensor_slices((filenames, labels))

此完整数据集将被重新整理，分为训练，有效和测试数据集

full_ds_size = len(filenames)
full_ds = full_ds.shuffle(buffer_size=full_ds_size*2, seed=128) # seed is used for reproducibility

train_ds_size = int(0.64 * full_ds_size)
valid_ds_size = int(0.16 * full_ds_size)

train_ds = full_ds.take(train_ds_size)
remaining = full_ds.skip(train_ds_size)  
valid_ds = remaining.take(valid_ds_size)
test_ds = remaining.skip(valid_ds_size)

现在，我正在努力了解如何在train_ds，valid_ds和test_ds中分配每个类。一个丑陋的解决方案是迭代数据集中的所有元素并计算每个类的出现次数。有没有更好的解决方法？

我的丑陋解决方案：

def get_class_distribution(dataset):
    class_distribution = {}
    for element in dataset.as_numpy_iterator():
        label = element[1]

        if label in class_distribution.keys():
            class_distribution[label] += 1
        else:
            class_distribution[label] = 0

    # sort dict by key
    class_distribution = collections.OrderedDict(sorted(class_distribution.items())) 
    return class_distribution


train_ds_class_dist = get_class_distribution(train_ds)
valid_ds_class_dist = get_class_distribution(valid_ds)
test_ds_class_dist = get_class_distribution(test_ds)

print(train_ds_class_dist)
print(valid_ds_class_dist)
print(test_ds_class_dist)

Answer 1

下面的答案假定：

• 有五个班级。
• 标签是从0到4的整数。

可以根据您的需要进行修改。

定义计数器功能：

def count_class(counts, batch, num_classes=5):
    labels = batch['label']
    for i in range(num_classes):
        cc = tf.cast(labels == i, tf.int32)
        counts[i] += tf.reduce_sum(cc)
    return counts

使用reduce操作：

initial_state = dict((i, 0) for i in range(5))
counts = train_ds.reduce(initial_state=initial_state,
                         reduce_func=count_class)

print([(k, v.numpy()) for k, v in counts.items()])