TensorFlow 张量排序

Reference


sort、argsort

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arr = tf.range(10)
arr1 = tf.random.shuffle(arr)
arr21 = tf.sort(arr1, direction='DESCENDING')
arr22 = tf.argsort(arr1, direction='DESCENDING')
arr31 = tf.sort(arr1, direction='ASCENDING')
arr32 = tf.argsort(arr1, direction='ASCENDING')
print(arr1) # tf.Tensor([8 5 3 2 4 0 7 1 9 6], shape=(10,), dtype=int32)
print(arr21) # tf.Tensor([9 8 7 6 5 4 3 2 1 0], shape=(10,), dtype=int32)
print(arr22) # tf.Tensor([8 0 6 9 1 4 2 3 7 5], shape=(10,), dtype=int32)
print(arr31) # tf.Tensor([0 1 2 3 4 5 6 7 8 9], shape=(10,), dtype=int32)
print(arr32) # tf.Tensor([5 7 3 2 4 1 9 6 0 8], shape=(10,), dtype=int32)

top_k

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arr = tf.range(10)
arr1 = tf.random.shuffle(arr)
res = tf.math.top_k(arr1, 2)
print(arr1) # tf.Tensor([8 7 3 2 9 5 0 6 4 1], shape=(10,), dtype=int32)
print(res.values) # tf.Tensor([9 8], shape=(2,), dtype=int32)
print(res.indices) # tf.Tensor([4 0], shape=(2,), dtype=int32)

计算 top_k 概率

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def accuracy(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.shape[0]
pred = tf.math.top_k(output, maxk).indices
print(pred) # tf.Tensor(
# [[1 0 2]
# [0 1 2]
# [1 0 2]
# [1 0 2]], shape=(4, 3), dtype=int32)
# 转置
pred = tf.transpose(pred, perm=[1, 0])
print(pred) # tf.Tensor(
# [[1 0 1 1]
# [0 1 0 0]
# [2 2 2 2]], shape=(3, 4), dtype=int32)
target_ = tf.broadcast_to(target, pred.shape)
print(target_) # tf.Tensor(
# [[0 1 0 2]
# [0 1 0 2]
# [0 1 0 2]], shape=(3, 4), dtype=int32)
correct = tf.equal(pred, target_)
print(correct) # tf.Tensor(
# [[False False False False]
# [ True True True False]
# [False False False True]], shape=(3, 4), dtype=bool)
res = []
for k in topk:
correct_k = tf.cast(tf.reshape(correct[:k], [-1]), dtype=tf.float32)
correct_k = tf.reduce_sum(correct_k)
acc = float(correct_k * (100.0 / batch_size))
res.append(acc)
return res
output = tf.random.normal([4, 3])
output = tf.math.softmax(output, axis=1)
print('output:', output.numpy()) # output: [[0.16469155 0.7675324 0.06777601]
# [0.50406045 0.31256208 0.18337746]
# [0.24097656 0.5227547 0.23626871]
# [0.2440812 0.5844257 0.17149314]]
target = tf.random.uniform([4], maxval=3, dtype=tf.int32)
print('target:', target.numpy()) # target: [0 1 0 2]
acc = accuracy(output, target, topk=(1, 2, 3))
print('top1-6 acc:', acc) # top1-6 acc: [0.0, 75.0, 100.0]

本文标题:TensorFlow 张量排序

文章作者:魏超

发布时间:2019年05月28日 - 09:05

最后更新:2019年05月29日 - 09:05

原始链接:http://www.weichao.io/2019/05/28/TensorFlow-张量排序/

许可协议: 署名-非商业性使用-禁止演绎 4.0 国际 转载请保留原文链接及作者。

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