tensorflow - 如何在 Tensorflow 中的 Dataset 对象中显示类分布

Question

我正在使用我自己的图像处理多类分类任务。

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成训练数据集、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 中的分布情况。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。有没有更好的方法来解决它？

我丑陋的解决方案:

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:

有五个类(class)。

标签是从 0 到 4 的整数。

它可以根据您的需要进行修改。

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手术:

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()])