tensorflow - tensorflow 中的自定义 f1_score 指标

Question

我想为 tf.keras 实现 f1_score 指标。

from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.metrics import Accuracy, BinaryAccuracy
from sklearn.metrics import accuracy_score
import numpy as np
import tensorflow as tf

class F1_Score(tf.keras.metrics.Metric):

    def __init__(self, name='f1_score', **kwargs):
        super().__init__(name=name, **kwargs)
        self.f1 = self.add_weight(name='f1', initializer='zeros')

    def update_state(self, y_true, y_pred, sample_weight=None):
        p = Precision(thresholds=0.5)(y_true, y_pred)
        r = Recall(thresholds=0.5)(y_true, y_pred)
        self.f1 = 2 * ((p * r) / (p + r + 1e-6))

    def result(self):
        return self.f1

    def reset_states(self):
        self.f1.assign(0)
        
model = Sequential([
  Dense(64, activation='relu', input_shape=(784,)),
  Dense(64, activation='relu'),
  Dense(4, activation='sigmoid'),
])
x = np.random.normal(size=(10, 784))
y = np.random.choice(2, size=(10, 4))
model.compile(optimizer=Adam(0.001), loss='binary_crossentropy',
                  metrics=['accuracy', , F1_Score()])
model.fit(x[:1], y[:1], batch_size=1, epochs=1, verbose=1)

我有一个错误:

ValueError: tf.function-decorated function tried to create variableson non-first call.

Answer 1

你得到这个错误是因为你想实例化一些 tf.Variable s 在 update_state 函数期间。从 Precision 和 Recall 类实例化对象时，您正在创建一些 tf.Variable s。
在构造函数中实例化对象，并在 update_state 函数中调用它们:

class F1_Score(tf.keras.metrics.Metric):

    def __init__(self, name='f1_score', **kwargs):
        super().__init__(name=name, **kwargs)
        self.f1 = self.add_weight(name='f1', initializer='zeros')
        self.precision_fn = Precision(thresholds=0.5)
        self.recall_fn = Recall(thresholds=0.5)

    def update_state(self, y_true, y_pred, sample_weight=None):
        p = self.precision_fn(y_true, y_pred)
        r = self.recall_fn(y_true, y_pred)
        # since f1 is a variable, we use assign
        self.f1.assign(2 * ((p * r) / (p + r + 1e-6)))

    def result(self):
        return self.f1

    def reset_states(self):
        # we also need to reset the state of the precision and recall objects
        self.precision_fn.reset_states()
        self.recall_fn.reset_states()
        self.f1.assign(0)

行为说明:
Tensorflow 允许仅在 tf.function 的第一次调用时创建变量，见 documentation :

tf.function only allows creating new tf.Variable objects when it is called for the first time

Keras 指标包含在 tf.function 中以允许与 tensorflow v1 兼容。您可以在 code 中找到此评论

If update_state is not in eager/tf.function and it is not from abuilt-in metric, wrap it in tf.function. This is so that users writingcustom metrics in v1 need not worry about control dependencies andreturn ops.

您的类(class)中还有另一个错误，就是您覆盖了 f1 tf.Variable您通过计算 f1 分数创建的。要更新变量的值，您需要使用 assign .我们一定不要忘记重置正在使用的 Precision 和 Recall Metrics 对象的状态!