Tensorflow：分类模型评估

系统管理员 2022-06-12 02:49 472阅读 0赞

## tf.keras.metrics.AUC ##

tensorflow2.\*常用

tf.keras.metrics.AUC(  
    num\_thresholds=200, curve='ROC',  
    summation\_method='interpolation', name=None, dtype=None,  
    thresholds=None, multi\_label=False, num\_labels=None, label\_weights=None,  
    from\_logits=False  
)

此度量标准创建用于计算AUC的四个局部变量 true\_positives ， true\_negatives ， false\_positives 和 false\_negatives 。为了离散化AUC曲线，使用一组线性间隔的阈值来计算成对的召回率和精度值。因此，ROC曲线下方的面积是通过假阳性率使用召回值的高度来计算的，而PR曲线下方的面积是通过召回率使用精度值的高度来计算的。

该值最终以 auc 形式返回，这是一个幂等运算，用于计算精度与召回值（使用上述变量计算）的离散曲线下的面积。

num\_thresholds 变量控制离散化具有较大的阈值更紧密地逼近真实AUC的数量程度。近似的质量可能会因 num\_thresholds 而有很大差异。

thresholds 参数可用于手动指定阈值，这些阈值可以更均匀地划分预测。

sample\_weight 为 None ，则权重默认为1。使用 sample\_weight 为0掩盖值。

为了获得最佳结果， predictions 应该在\[0，1\]范围内大致均匀分布，并且不要在0或1附近达到峰值。如果不是这种情况，则AUC近似值的质量可能很差。将 summation\_method 设置为“ minoring”或“ majoring”可以通过提供AUC的下限或上限估算值来帮助量化近似值中的误差。

\[[tf.keras.metrics.AUC][]\]

\[[https://runebook.dev/zh-CN/docs/tensorflow/keras/metrics/auc][https_runebook.dev_zh-CN_docs_tensorflow_keras_metrics_auc]\]

## tf.metrics.AUC ##

tensorflow1.\*常用

tf.metrics.auc(  
    labels, predictions, weights=None, num\_thresholds=200, metrics\_collections=None,  
    updates\_collections=None, curve='ROC', name=None,  
    summation\_method='trapezoidal', thresholds=None  
)

\[[Module: tf.keras.metrics][Module_ tf.keras.metrics]\]

使用tf.estimator时，如果调用 Estimator 的 evaluate 方法，则 model\_fn 会收到 mode = ModeKeys.EVAL。在这种情况下，模型函数必须返回一个包含模型损失和一个或多个指标（可选）的 tf.estimator.EstimatorSpec。虽然返回指标是可选的，但大多数自定义 Estimator 至少会返回一个指标。TensorFlow 提供一个指标模块 tf.metrics 来计算常用指标。

## 几个常用的指标 ##

### 这些可能只针对二分类 ###

文档表示标签和预测都将转换为bool，因此它只涉及二进制分类。也许有可能对这些例子进行热门编码，它会起作用吗？但不确定这一点。\[[Tensorflow中多类分类的类精度和召回率？][Tensorflow]\]

[`accuracy(...)`][accuracy_...]: Calculates how often `predictions` matches `labels`.

The `accuracy` function creates two local variables, `total` and `count` that are used to compute the frequency with which `predictions` matches `labels`. This frequency is ultimately returned as `accuracy`: an idempotent operation that simply divides `total` by `count`.

[`auc(...)`][auc_...]: Computes the approximate AUC via a Riemann sum.

[`average_precision_at_k(...)`][average_precision_at_k_...]: Computes average precision@k of predictions with respect to sparse labels.

[`precision(...)`][precision_...]: Computes the precision of the predictions with respect to the labels. 准确率。[`tf.metrics.accuracy`][accuracy_...] 函数会将我们的预测值与真实值进行比较，即与输入函数提供的标签进行比较。[`tf.metrics.accuracy`][accuracy_...] 函数要求标签和预测具有相同的形状。

[`precision_at_k(...)`][precision_at_k_...]: Computes precision@k of the predictions with respect to sparse labels.

[`recall(...)`][recall_...]: Computes the recall of the predictions with respect to the labels.

[`recall_at_k(...)`][recall_at_k_...]: Computes recall@k of the predictions with respect to sparse labels.

\[[Module: tf.metrics][Module_ tf.metrics]\]

\[[评估][Link 1]\]

### 初始化 ###

这些函数创建的都是local variables，直接初始化时需要使用sess.run(tf.local\_variables\_initializer())而不是tf.global\_variables\_initializer()。不初始化可能出错：Attempting to use uninitialized value total\_confusion\_matrix。

### 参数 ###

1 如果输出的是序列label（如ner模型），则一般需要使用mask。\[[Tensorflow：tensor变换][Tensorflow_tensor]\]

2 对于分类模型，

2.1 计算precission、recall时，pred\_ids需要是one-hot形式，如

labels = \[\[0, 1, 0\],  
          \[1, 0, 0\],  
          \[0, 0, 1\]\]，

\[[tensorflow – 如何正确使用tf.metrics.accuracy？][tensorflow _ _tf.metrics.accuracy]\]

\[[Tensorflow踩坑记之tf.metrics][Tensorflow_tf.metrics]\]

note:

1 当然对比的pred\_ids不能是有负值的logits，否则出错\[\`predictions\` contains negative values\] \# \[Condition x >= 0 did not hold element-wise:\] \[x (Reshape\_2:0) = \] \[0 -6 3...\]。

2 非要改成非one-hot形式，如果argmax维度搞错没写或0，输入(batch\_size, num\_labels)，输出本应是(batch\_size,)，变成了输出(num\_labels,)，一般如果num\_labels>batch\_size不会报错，<则报错“(batch\_size, num\_labels) tf\_metircs \[\`labels\` out of bound\] \[Condition x < y did not hold element-wise:\]”，但是两者都是错误的。

2.2 计算acc、auc（这个不清楚原理）时则不需要这种转换，直接输入即可。

### 多类分类的测试 ###

计算precission、recall时，pred\_ids需要是one-hot形式，如

labels = \[\[0, 1, 0\],  
          \[1, 0, 0\],  
          \[0, 0, 1\]\]，

经大规模测试，发现其计算实际上是micro平均，即precission=recall=acc；同时自带的这种等价于使用下面提到的多分类指标评价tf.metrics.accuracy(labels=labels,  predictions=pred\_ids)等价于tf\_metrics.accuracy(labels=tf.argmax(labels, 1),  predictions=tf.[argmax][](pred\_ids,1))。

### 返回值 ###

以accuracy的返回值为例：

accuracy: A Tensor representing the accuracy, the value of total divided by count. 准确性调用不会使用新输入更新度量标准，它只使用两个局部变量返回值。（具体意思看示例1就ok了）  
        update\_op: An operation that increments the total and count variables appropriately and whose value matches accuracy.

## Multi-class metrics for Tensorflow: tf\_metrics ##

precision(labels, predictions, num\_classes, pos\_indices=None, weights=None, average='micro'):  
参数：  
    labels : Tensor of tf.int32 or tf.int64  
        The true labels 输入为shape=(batch,)的非one-hot的labels列表。  
    predictions : Tensor of tf.int32 or tf.int64  
        The predictions, same shape as labels  
    num\_classes : int  
        The number of classes  
    pos\_indices : list of int, optional  
        The indices of the positive classes, default is all  
    weights : Tensor of tf.int32, optional  
        Mask, must be of compatible shape with labels  
    average : str, optional  
        'micro': counts the total number of true positives, false  
            positives, and false negatives for the classes in  
            \`pos\_indices\` and infer the metric from it.  
        'macro': will compute the metric separately for each class in  
            \`pos\_indices\` and average. Will not account for class  
            imbalance.  
        'weighted': will compute the metric separately for each class in  
            \`pos\_indices\` and perform a weighted average by the total  
            number of true labels for each class.

recall(labels, predictions, num\_classes, pos\_indices=None, weights=None, average='micro')  
f1(labels, predictions, num\_classes, pos\_indices=None, weights=None, average='micro')

输入如果是one-hot形式，需要转换成预测标签类别

acc, acc\_op = tf\_metrics.accuracy(labels=tf.argmax(labels, 1),  predictions=tf.[argmax][](logits,1))

## 示例 ##

**示例1**

label\_ids = tf.constant(\[\[3, 1, 5\]\])  
pred\_ids = tf.constant(\[\[3, 2, 5\]\])  
acc, acc\_op = tf.metrics.accuracy(label\_ids, pred\_ids)  
stream\_vars = \[i for i in tf.local\_variables()\]  
print(stream\_vars)

with tf.Session() as sess:  
    sess.run(tf.local\_variables\_initializer())  
    print('\[total, count\]:', sess.run(stream\_vars))  
    print(acc.eval())  \# 只使用两个局部变量（此时未更新为0）返回值  
    print(acc\_op.eval())  
    print('\[total, count\]:', sess.run(stream\_vars))  
    print(acc.eval())  \# 只使用两个局部变量（此时已更新非0）返回值\[<tf.Variable 'accuracy/total:0' shape=() dtype=float32\_ref>, <tf.Variable 'accuracy/count:0' shape=() dtype=float32\_ref>\]

\[total, count\]: \[0.0, 0.0\]  
0.0  
0.6666667  
\[total, count\]: \[2.0, 3.0\]  
0.6666667

\[[tensorflow – 如何正确使用tf.metrics.accuracy？][tensorflow _ _tf.metrics.accuracy]\]

\[[深入理解TensorFlow中的tf.metrics算子][TensorFlow_tf.metrics]\]

\[[Tensorflow踩坑记之tf.metrics][Tensorflow_tf.metrics]\]

**示例2**

\# Compute evaluation metrics.  
acc, acc\_op = tf.metrics.accuracy(labels=tf.argmax(labels, 1),  predictions=tf.argmax(logits,1))

**示例3：多分类**

label_ids = tf.constant([[0, 0, 0, 1],
                             [0, 0, 1, 0],
                             [1, 0, 0, 0],
                             [0, 1, 0, 0],
                             [0, 1, 0, 0]])
    pred_ids = tf.constant([[0, 0, 0, 1],
                            [0, 1, 0, 0],
                            [1, 0, 0, 0],
                            [1, 0, 0, 0],
                            [1, 0, 0, 0]])
    num_labels = label_ids.shape[1]
    label_arg_ids = tf.argmax(label_ids, 1)
    pred_arg_ids = tf.argmax(pred_ids, 1)
    # _, tp_op = tf.metrics.true_positives(label_ids, pred_ids)
    # _, fp_op = tf.metrics.false_positives(label_ids, pred_ids)
    
    _, acc_op = tf.metrics.precision(label_ids, pred_ids)
    _, acc_op1 = tf.metrics.accuracy(label_arg_ids, pred_arg_ids)
    _, pre_op = tf.metrics.precision(label_ids, pred_ids)
    # _, pre_op1 = tf.metrics.precision(label_arg_ids, pred_arg_ids)
    _, rec_op = tf.metrics.recall(label_ids, pred_ids)
    # _, rec_op1 = tf.metrics.recall(label_arg_ids, pred_arg_ids)
    
    # _, pre_op_ = tf_metrics.precision(label_ids, pred_ids, num_labels)
    _, pre_op1_ = tf_metrics.precision(label_arg_ids, pred_arg_ids, num_labels, average='macro')
    # _, rec_op_ = tf_metrics.recall(label_ids, pred_ids, num_labels)
    _, rec_op1_ = tf_metrics.recall(label_arg_ids, pred_arg_ids, num_labels, average='macro')
    _, f1_op1_ = tf_metrics.f1(label_arg_ids, pred_arg_ids, num_labels, average='macro')
    
    stream_vars = [i for i in tf.local_variables()]
    print(stream_vars)
    
    with tf.Session() as sess:
        sess.run(tf.local_variables_initializer())
        print(label_arg_ids.eval())
        print(pred_arg_ids.eval())
        # print(tp_op.eval())  # 2
        # print(fp_op.eval())  # 3
    
        print('acc_op:', acc_op.eval())
        print('acc_op1:', acc_op1.eval())
        print('pre_op:', pre_op.eval())
        # print('pre_op1:', pre_op1.eval())  # 1.0
        print('rec_op:', rec_op.eval())
        # print('rec_op1:', rec_op1.eval())  # 0.5
    
        # print(pre_op_.eval()) # 0.7
        print('pre_op1_:', pre_op1_.eval())
        # print(rec_op_.eval()) # 0.7
        print('rec_op1_:', rec_op1_.eval())
        print('f1_op1_:', f1_op1_.eval())

\[3 2 0 1 1\]  
\[3 1 0 0 0\]  
2.0  
3.0  
acc\_op: 0.4  
acc\_op1: 0.4  
pre\_op: 0.4  
pre\_op1: 1.0  
rec\_op: 0.4  
rec\_op1: 0.5  
0.7  
pre\_op1\_: 0.33333334  
0.7  
rec\_op1\_: 0.5  
f1\_op1\_: 0.375

### average\_precision\_at\_k示例 ###

在以后的tf版本里，将tf.metrics.average\_precision\_at\_k替代tf.metrics.sparse\_average\_precision\_at\_k。

y\_true = tf.constant(\[\[2\], \[1\], \[0\], \[3\], \[0\]\])  
y\_true = tf.cast(y\_true, tf.int64)

y\_pred = tf.constant(\[\[0.1, 0.2, 0.6, 0.1\],  
                      \[0.8, 0.05, 0.1, 0.05\],  
                      \[0.3, 0.4, 0.1, 0.2\],  
                      \[0.6, 0.25, 0.1, 0.05\],  
                      \[0.1, 0.2, 0.6, 0.1\]  
                      \])

\_, m\_ap = tf.metrics.average\_precision\_at\_k(y\_true, y\_pred, 3)  
stream\_vars = \[i for i in tf.local\_variables()\]  
tmp\_rank = tf.nn.top\_k(y\_pred, 3)

with tf.Session() as sess:  
    sess.run(tf.local\_variables\_initializer())  
    print("TF\_MAP", sess.run(m\_ap))  
    print("STREAM\_VARS", sess.run(stream\_vars))  
    print("TMP\_RANK", sess.run(tmp\_rank))

输出

TF\_MAP 0.4333333333333333  
STREAM\_VARS \[5.0, 2.1666666666666665\]  
TMP\_RANK TopKV2(values=array(\[\[0.6 , 0.2 , 0.1 \],  
       \[0.8 , 0.1 , 0.05\],  
       \[0.4 , 0.3 , 0.2 \],  
       \[0.6 , 0.25, 0.1 \],  
       \[0.6 , 0.2 , 0.1 \]\], dtype=float32),

indices=array(

\[\[2, 1, 0\],  
       \[0, 2, 1\],  
       \[1, 0, 3\],  
       \[0, 1, 2\],  
       \[2, 1, 0\]\], dtype=int32))

计算逻辑是：第一个2命中\[2 1 0\]的top1，则是1；第二个1命中\[0 2 1\]的top3，则是1/3；类似第三个1/2；第4个在top3中都没命中，为0；第5个1/3；平均一下。即(1+1/3+1/2+0+1/3)/5=13/30=0.433

\[[搜索排序评估方法][Link 2]\]

### precision\_at\_k示例 ###

上面代码中average\_precision\_at\_k改成precision\_at\_k，其计算逻辑则是：命中则是1/k，如第一个命中\[2 1 0\]，则是1/k = 1/3；第二个1命中也是1/3；类似第三个1/3；第4个没命中，为0；第5个1/3；平均一下。即(1/3+1/3+1/3+0+1/3)/5=4/15=0.2667

输出TF\_MAP 0.26666666666666666

\[[Tensorflow踩坑记之tf.metrics][Tensorflow_tf.metrics]\]

[\-柚子皮-][-_-]

## 其它方法及示例 ##

计算softmax输出的准确度

import tensorflow as tf
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    
    def evaluation(sess, outputs, labels):
        correct = tf.nn.in_top_k(outputs, labels, 1)
        print(sess.run(correct))
        return tf.reduce_sum(tf.cast(correct, tf.int32))
    
    
    with tf.Graph().as_default():
        sess = tf.Session()
        sess.run(tf.global_variables_initializer())
        a = evaluation(sess, [[0.8, 0.1, 0.1], [0.2, 0.6, 0.2], [0.7, 0.1, 0.2]], [0, 1, 2])
        print(sess.run(a))

from: [\-柚子皮-][-_-]

ref:

[tf.keras.metrics.AUC]: https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC
[https_runebook.dev_zh-CN_docs_tensorflow_keras_metrics_auc]: https://runebook.dev/zh-CN/docs/tensorflow/keras/metrics/auc
[Module_ tf.keras.metrics]: https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/metrics
[Tensorflow]: http://cn.voidcc.com/question/p-ywvdprht-tp.html
[accuracy_...]: https://www.tensorflow.org/api_docs/python/tf/metrics/accuracy?hl=zh_cn
[auc_...]: https://www.tensorflow.org/api_docs/python/tf/metrics/auc?hl=zh_cn
[average_precision_at_k_...]: https://www.tensorflow.org/api_docs/python/tf/metrics/average_precision_at_k?hl=zh_cn
[precision_...]: https://www.tensorflow.org/api_docs/python/tf/metrics/precision?hl=zh_cn
[precision_at_k_...]: https://www.tensorflow.org/api_docs/python/tf/metrics/precision_at_k?hl=zh_cn
[recall_...]: https://www.tensorflow.org/api_docs/python/tf/metrics/recall?hl=zh_cn
[recall_at_k_...]: https://www.tensorflow.org/api_docs/python/tf/metrics/recall_at_k?hl=zh_cn
[Module_ tf.metrics]: https://www.tensorflow.org/api_docs/python/tf/metrics?hl=zh_cn
[Link 1]: https://www.tensorflow.org/guide/custom_estimators?hl=zh_cn#evaluate
[Tensorflow_tensor]: https://blog.csdn.net/pipisorry/article/details/76021045
[tensorflow _ _tf.metrics.accuracy]: https://codeday.me/bug/20180901/236600.html
[Tensorflow_tf.metrics]: https://www.jianshu.com/p/b091bd274f95
[argmax]: https://youzipi.blog.csdn.net/article/details/76021045
[TensorFlow_tf.metrics]: https://zhuanlan.zhihu.com/p/42438077
[Link 2]: http://www.woshipm.com/pmd/232014.html
[-_-]: https://me.csdn.net/pipisorry