如何在Tensorflow中显示Dataset对象中的类分布

Question

我正在使用我自己的图像进行多类分类任务。

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

这个完整的数据集将被打乱并分为训练数据集、有效数据集和测试数据集

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)

现在我正在努力理解每个类在train_ds、valid_ds和test_ds中是如何分布的。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。有没有更好的办法解决呢？

我的丑陋的解决方案：

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

下面的答案假设：

• 有五个班。
• 标签是 0 到 4 之间的整数。

可以对其进行修改以满足您的需求。

定义一个计数器函数：

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()])