目錄python
Sort/argsortapp
Topkdom
Top-5 Acc.ide
import tensorflow as tf
a = tf.random.shuffle(tf.range(5)) a
<tf.Tensor: id=59, shape=(5,), dtype=int32, numpy=array([4, 0, 3, 2, 1], dtype=int32)>
tf.sort(a, direction='DESCENDING')
<tf.Tensor: id=69, shape=(5,), dtype=int32, numpy=array([4, 3, 2, 1, 0], dtype=int32)>
# 返回索引 tf.argsort(a, direction='DESCENDING')
<tf.Tensor: id=81, shape=(5,), dtype=int32, numpy=array([0, 2, 3, 4, 1], dtype=int32)>
idx = tf.argsort(a, direction='DESCENDING') tf.gather(a, idx)
<tf.Tensor: id=94, shape=(5,), dtype=int32, numpy=array([4, 3, 2, 1, 0], dtype=int32)>
a = tf.random.uniform([3, 3], maxval=10, dtype=tf.int32) a
<tf.Tensor: id=99, shape=(3, 3), dtype=int32, numpy= array([[1, 9, 4], [2, 1, 4], [3, 6, 0]], dtype=int32)>
tf.sort(a)
<tf.Tensor: id=112, shape=(3, 3), dtype=int32, numpy= array([[1, 4, 9], [1, 2, 4], [0, 3, 6]], dtype=int32)>
tf.sort(a, direction='DESCENDING')
<tf.Tensor: id=122, shape=(3, 3), dtype=int32, numpy= array([[9, 4, 1], [4, 2, 1], [6, 3, 0]], dtype=int32)>
idx = tf.argsort(a) idx
<tf.Tensor: id=146, shape=(3, 3), dtype=int32, numpy= array([[0, 2, 1], [1, 0, 2], [2, 0, 1]], dtype=int32)>
a
<tf.Tensor: id=99, shape=(3, 3), dtype=int32, numpy= array([[1, 9, 4], [2, 1, 4], [3, 6, 0]], dtype=int32)>
# 返回前2個值 res = tf.math.top_k(a, 2) res
TopKV2(values=<tf.Tensor: id=160, shape=(3, 2), dtype=int32, numpy= array([[9, 4], [4, 2], [6, 3]], dtype=int32)>, indices=<tf.Tensor: id=161, shape=(3, 2), dtype=int32, numpy= array([[1, 2], [2, 0], [1, 0]], dtype=int32)>)
res.values
<tf.Tensor: id=160, shape=(3, 2), dtype=int32, numpy= array([[9, 4], [4, 2], [6, 3]], dtype=int32)>
res.indices
<tf.Tensor: id=161, shape=(3, 2), dtype=int32, numpy= array([[1, 2], [2, 0], [1, 0]], dtype=int32)>
Prob:[0.1,0.2,0.3,0.4]code
Lable:[2]orm
Only consider top-3 prediction:[3,2,1]索引
prob = tf.constant([[0.1, 0.2, 0.7], [0.2, 0.7, 0.1]]) target = tf.constant([2, 0])
# 機率最大的索引在最前面 k_b = tf.math.top_k(prob, 3).indices k_b
<tf.Tensor: id=190, shape=(2, 3), dtype=int32, numpy= array([[2, 1, 0], [1, 0, 2]], dtype=int32)>
k_b = tf.transpose(k_b, [1, 0]) k_b
<tf.Tensor: id=193, shape=(3, 2), dtype=int32, numpy= array([[2, 1], [1, 0], [0, 2]], dtype=int32)>
# 對真實值broadcast,與prod比較 target = tf.broadcast_to(target, [3, 2]) target
<tf.Tensor: id=196, shape=(3, 2), dtype=int32, numpy= array([[2, 0], [2, 0], [2, 0]], dtype=int32)>
def accuracy(output, target, topk=(1, )): maxk = max(topk) batch_size = target.shape[0] pred = tf.math.top_k(output, maxk).indices pred = tf.transpose(pred, perm=[1, 0]) target_ = tf.broadcast_to(target, pred.shape) correct = tf.equal(pred, target_) 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 / batch_size) res.append(acc) return res
# 10個樣本6類 output = tf.random.normal([10, 6]) # 使得全部樣本的機率加起來爲1 output = tf.math.softmax(output, axis=1) # 10個樣本對應的標記 target = tf.random.uniform([10], maxval=6, dtype=tf.int32) print(f'prob: {output.numpy()}') pred = tf.argmax(output, axis=1) print(f'pred: {pred.numpy()}') print(f'label: {target.numpy()}') acc = accuracy(output, target, topk=(1, 2, 3, 4, 5, 6)) print(f'top-1-6 acc: {acc}')
prob: [[0.12232917 0.18645659 0.27771464 0.17322136 0.14854735 0.09173083] [0.02338449 0.01026637 0.11773597 0.69083494 0.03814701 0.11963127] [0.05774692 0.1926369 0.49359822 0.10262781 0.10738047 0.0460096 ] [0.21298195 0.02826484 0.1813868 0.06380058 0.06848615 0.44507968] [0.01364106 0.16782394 0.08621352 0.22500433 0.19081964 0.31649753] [0.02917767 0.15526605 0.6310118 0.11471876 0.05473462 0.0150911 ] [0.03684716 0.15286008 0.11792535 0.47401306 0.05833342 0.160021 ] [0.32859987 0.17415446 0.07394216 0.22221863 0.07559296 0.12549189] [0.02662764 0.5529567 0.06995299 0.02131662 0.08664025 0.2425058 ] [0.10253917 0.10178788 0.21553555 0.12878521 0.3788466 0.07250563]] pred: [2 3 2 5 5 2 3 0 1 4] label: [3 4 3 0 4 0 3 2 1 4] top-1-6 acc: [0.30000001192092896, 0.4000000059604645, 0.6000000238418579, 0.800000011920929, 0.8999999761581421, 1.0]