python - 如何循环张量对象直到满足条件

Question

我有一个像这样的张量:

masked_bad_col = [[False  True  True False  True  True  True  True  True  True  True False]]

我想循环遍历这个张量，直到所有元素都为True。所以我有另一个函数，它将更新这个张量，我们称之为唯一性。

def uniqueness():

   'blah blah blha'
   return tensor1, updated_masked_bad_col

我查看了文档并了解到我可以使用 tf.while_loop 来做到这一点。尽管如此，我找不到任何关于 bool 值的例子。这是我到目前为止所做的:

tensor1, _ = tf.while_loop(masked_bad_col != True, uniqueness)

这显然是不正确的，但不知道如何使用masked_bad_col的每个元素作为继续循环uniqueness函数的条件。

更新 1这是我试图在循环中调用的方法:

corpus = load_corpus('path_to_corpus/train.corpus')
topics = []
vocab, docs = corpus['vocab'], corpus['docs']
number_of_topics = 0
encoder_model = load_keras_model(
    'path_to_model/encoder_model',
    custom_objects={"KCompetitive": KCompetitive})
weights = encoder_model.get_weights()[0]
for idx in range(encoder_model.output_shape[1]):
    token_idx = np.argsort(weights[:, idx])[::-1][:20]
    topics.append([(revdict(vocab)[x]) for x in token_idx])
    number_of_topics += 1

nparr = np.asarray(topics)
# print nparr.shape

unique, indices, count = np.unique(nparr, return_inverse=True, return_counts=True)

tensor1 = (np.sum(count[indices].reshape(nparr.shape), axis=1).reshape(1, nparr.shape[0]) / (
        number_of_topics * 20))

def uniqueness_score():
    corpus = load_corpus('path_to_corpus/train.corpus')
    topics = []
    vocab, docs = corpus['vocab'], corpus['docs']
    number_of_topics = 0
    encoder_model = load_keras_model(
        'path_to_model/encoder_model',
        custom_objects={"KCompetitive": KCompetitive})
    weights = encoder_model.get_weights()[0]
    for idx in range(encoder_model.output_shape[1]):
        token_idx = np.argsort(weights[:, idx])[::-1][:20]
        topics.append([(revdict(vocab)[x]) for x in token_idx])
        number_of_topics += 1

    nparr = np.asarray(topics)

    unique, indices, count = np.unique(nparr, return_inverse=True, return_counts=True)

    tensor1 = (np.sum(count[indices].reshape(nparr.shape), axis=1).reshape(1, nparr.shape[0]) / (
            number_of_topics * 20))
    return tensor1

这就是我在 while_loop 中调用此方法的方式

with tf.Session() as sess:

        tensor2, _ = tf.while_loop(
            # Loop condition (negated goal condition)
            lambda tensor1: ~tf.math.reduce_all(tensor1 > tf.reduce_mean(tensor1)),
            # Loop body
            lambda tensor1: uniqueness_score(),
            # Loop variables
            [tensor1])
        # Returned loop value
        print(tensor2.eval())

Answer 1

我想我或多或少知道你想要什么，但我不确定我是否需要 bool 数组。如果您想要执行一些迭代过程，计算或检索某些值直到它们满足某些条件，则无需额外的数组即可实现。例如，请参阅此循环对一些随机值进行采样，直到所有值都满足条件:

import tensorflow as tf

# Draw five random numbers until all are > 0.5
with tf.Graph().as_default(), tf.Session() as sess:
    tf.random.set_random_seed(0)
    # Initial values, here simply initialized to zero
    tensor1 = tf.zeros([5], dtype=tf.float32)
    # Loop
    tensor1 = tf.while_loop(
        # Loop condition (negated goal condition)
        lambda tensor1: ~tf.math.reduce_all(tensor1 > 0.5),
        # Loop body
        lambda tensor1: tf.random.uniform(tf.shape(tensor1), dtype=tensor1.dtype),
        # Loop variables
        [tensor1])
    # Returned loop value
    print(tensor1.eval())
    # [0.7778928  0.9396961  0.572209   0.6187117  0.89615726]

看看这是否有帮助，如果您仍然不确定如何将其应用于您的特定案例，请留下评论。

<小时/>

编辑:再次看到你的问题，你的唯一性函数计算了tensor1和掩码，所以也许更相似的类似代码是这样的:

import tensorflow as tf

def sample_numbers(shape, dtype):
    tensor1 = tf.random.uniform(shape, dtype=dtype)
    mask = tensor1 > 0.5
    return tensor1, mask

# Draw five random numbers until all are > 0.5
with tf.Graph().as_default(), tf.Session() as sess:
    tf.random.set_random_seed(0)
    # Initial values, here simply initialized to zero
    tensor1 = tf.zeros([5], dtype=tf.float32)
    mask = tf.zeros(tf.shape(tensor1), dtype=tf.bool)
    # Loop
    tensor1, _ = tf.while_loop(
        # Loop condition (negated goal condition)
        lambda tensor1, mask: ~tf.math.reduce_all(mask),
        # Loop body
        lambda tensor1, mask: sample_numbers(tf.shape(tensor1), tensor1.dtype),
        # Loop variables
        [tensor1, mask])
    # Returned loop value
    print(tensor1.eval())
    # [0.95553064 0.5170193  0.69573617 0.9501506  0.99776053]