【tf.keras】实现 F1 score、precision、recall 等 metric

tf.keras.metric 里面竟然没有实现 F1 score、recall、precision 等指标,一开始觉得真不可思议。但这是有原因的,这些指标在 batch-wise 上计算都没有意义,需要在整个验证集上计算,而 tf.keras 在训练过程(包括验证集)中计算 acc、loss 都是一个 batch 计算一次的,最后再平均起来。Keras 2.0 版本将 precision, recall, fbeta_score, fmeasure 等 metrics 移除了。

虽然 tf.keras.metric 中没有实现 f1 socre、precision、recall,但我们可以通过 tf.keras.callbacks.Callback 实现。即在每个 epoch 末尾,在整个 val 上计算 f1、precision、recall。

一些博客实现了二分类下的 f1 socre、precision、recall,如下所示:

以下代码实现了多分类下对验证集 F1 值、precision、recall 的计算,并且保存 val_f1 值最好的模型:

import tensorflow as tf

from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, recall_score, precision_score
import numpy as np
import os


class Metrics(tf.keras.callbacks.Callback):
    def __init__(self, valid_data):
        super(Metrics, self).__init__()
        self.validation_data = valid_data

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        val_predict = np.argmax(self.model.predict(self.validation_data[0]), -1)
        val_targ = self.validation_data[1]
        if len(val_targ.shape) == 2 and val_targ.shape[1] != 1:
            val_targ = np.argmax(val_targ, -1)

        _val_f1 = f1_score(val_targ, val_predict, average='macro')
        _val_recall = recall_score(val_targ, val_predict, average='macro')
        _val_precision = precision_score(val_targ, val_predict, average='macro')

        logs['val_f1'] = _val_f1
        logs['val_recall'] = _val_recall
        logs['val_precision'] = _val_precision
        print(" — val_f1: %f — val_precision: %f — val_recall: %f" % (_val_f1, _val_precision, _val_recall))
        return


(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=10000, random_state=32)

# LeNet-5
model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(32, 32, 3)),
    tf.keras.layers.Conv2D(6, 5, activation='relu'),
    tf.keras.layers.AveragePooling2D(),
    tf.keras.layers.Conv2D(16, 5, activation='relu'),
    tf.keras.layers.AveragePooling2D(),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(120, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(84, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

if not os.path.exists('./checkpoints'):
    os.makedirs('./checkpoints')

# 按照 val_f1 保存模型
ck_callback = tf.keras.callbacks.ModelCheckpoint('./checkpoints/weights.{epoch:02d}-{val_f1:.4f}.hdf5',
                                                 monitor='val_f1', 
                                                 mode='max', verbose=2,
                                                 save_best_only=True,
                                                 save_weights_only=True)
tb_callback = tf.keras.callbacks.TensorBoard(log_dir='./logs', profile_batch=0)
model.fit(x_train, y_train,
          validation_data=(x_val, y_val),
          epochs=100,
          callbacks=[Metrics(valid_data=(x_val, y_val)),
                     ck_callback,
                     tb_callback])

注意 Metrics()ck_callback 两个 callback 的顺序,互换之后将报错。

References

How to calculate F1 Macro in Keras? -- StackOverflow
How to compute f1 score for each epoch in Keras -- Thong Nguyen
keras如何求分类问题中的准确率和召回率? - 鱼塘邓少的回答 - 知乎
Keras 2.0 release notes -- keras-team/keras

posted @ 2019-12-05 22:21  wuliytTaotao  阅读(3211)  评论(0编辑  收藏