在Keras/Tensorflow中，如何制作truepostitives的自定义指标减去假阳性（TP-FP）？

Question

我知道有tf.keras.metrics.precision（），tf.keras.metrics.truepositives（）， tf.keras.metrics.metrics.falsepositives（） 。但是如何在自定义度量功能中实现这些内置指标的输出？ 这是我的工作代码：

    import tensorflow_addons as tfa
    import tensorflow as tf
    import autokeras as ak
    
    def f1_loss(y_true, y_pred): # not coded by me
        tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)
        tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=0)
        fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=0)
        fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)
        p = tp / (tp + fp + K.epsilon())
        r = tp / (tp + fn + K.epsilon())
        f1 = 2*p*r / (p+r+K.epsilon())
        f1 = tf.where(tf.math.is_nan(f1), tf.zeros_like(f1), f1)
        return  1- K.mean(f1)
    
    length=100000;WIDTH = 3; HEIGHT=3;CLASSES=2 
    X=np.random.random((length,HEIGHT,WIDTH)).astype(np.float32)
    Y_float=np.ones((length,CLASSES)).astype(np.float32)
    for i in range (length):
        Y_float[i]=np.array([np.mean( X[i]),np.mean( X[i])/2])
    Y_binary= (Y_float>=0.5).astype(np.int32)   
    
    input_node = ak.Input()
    output_node=ak.DenseBlock()(input_node)
    Classification_output = ak.ClassificationHead(loss=f1_loss,metrics=[tfa.metrics.F1Score(num_classes=2),
tf.keras.metrics.TruePositives(), tf.keras.metrics.FalsePositives()],multi_label=True)(output_node)
    auto_model= ak.AutoModel(  inputs=[input_node], outputs=[Classification_output], max_trials=1,overwrite=True)
    ak_history=auto_model.fit(x=[X],y=Y_binary,validation_split=0.2 )

尽管f1_loss从不等于tfa.metrics.f1score.f1score或1-tfa.metrics .f1score。真正的问题是，我需要添加一个度量标准，该指标以后可以用于搜索最佳模型。

def diff(y_true, y_pred): # the new custom metric I would like to add
    d=tf.keras.metrics.TruePositives()- tf.keras.metrics.FalsePositives()
    return d

现在，如果更新指标为

metrics=[diff,tfa.metrics.F1Score(num_classes=2),tf.keras.metrics.TruePositives(), tf.keras.metrics.FalsePositives()]

我有错误：

TypeError: in user code:

    /opt/conda/lib/python3.7/site-packages/keras/engine/training.py:853 train_function  *
        return step_function(self, iterator)
    /tmp/ipykernel_33/1113116857.py:16 diff  *
        d=tf.keras.metrics.TruePositives()- tf.keras.metrics.FalsePositives()

    TypeError: unsupported operand type(s) for -: 'TruePositives' and 'FalsePositives'

Answer 1

自定义度量在这里适应我的要求。

class diff(keras.metrics.Metric):
    def __init__(self, name = 'diff', **kwargs):
        super(diff, self).__init__(**kwargs)
        self.tp = self.add_weight('tp', initializer = 'zeros')
        self.fp = self.add_weight('fp', initializer = 'zeros')

    def update_state(self, y_true, y_pred,sample_weight=None):
        y_true = tf.cast(y_true, tf.bool)
        y_pred = tf.math.greater(y_pred, 0.5)      
        true_p = tf.logical_and(tf.equal(y_true, True), tf.equal(y_pred, True))
        false_p = tf.logical_and(tf.equal(y_true, False), tf.equal(y_pred, True))
        self.tp.assign_add(tf.reduce_sum(tf.cast(true_p, self.dtype)))
        self.fp.assign_add(tf.reduce_sum(tf.cast(false_p, self.dtype)))
    def reset_state(self):
        self.tp.assign(0)
        self.fp.assign(0)

    def result(self):
        return self.tp - self.fp

Answer 2

您可以编写函数用于计算自定义损失tp -fp -fp如下：

import tensorflow_addons as tfa
import tensorflow as tf
import numpy as np

x_train = np.random.rand(1000,10)
y_train = np.random.randint(0, 2, 1000)
y_train = tf.keras.utils.to_categorical(y_train, 2)


tp = tf.keras.metrics.TruePositives()
fp = tf.keras.metrics.FalsePositives()
def diff(y_true, y_pred): 
    a = tp(y_true, y_pred) 
    b = fp(y_true, y_pred)
    return a - b

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(units=64,input_shape=(10,), activation='relu'))
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(2, activation='softmax'))
model.compile(optimizer='adam',
              loss = 'categorical_crossentropy',
              metrics=[diff, tf.keras.metrics.TruePositives(), 
                       tf.keras.metrics.FalsePositives(), tfa.metrics.F1Score(num_classes=2)])

model.fit(x_train,y_train,epochs=2, batch_size=64, validation_split=0.2)

输出：输出：

Epoch 1/2
13/13 [==============================] - 3s 85ms/step - loss: 0.6942 - diff: 8.4615 - true_positives_24: 401.0000 - false_positives_24: 399.0000 - f1_score: 0.5001 - val_loss: 0.6979 - val_diff: -10.0000 - val_true_positives_24: 89.0000 - val_false_positives_24: 111.0000 - val_f1_score: 0.4433
Epoch 2/2
13/13 [==============================] - 0s 10ms/step - loss: 0.6916 - diff: -8.4615 - true_positives_24: 399.0000 - false_positives_24: 401.0000 - f1_score: 0.4807 - val_loss: 0.6990 - val_diff: -34.0000 - val_true_positives_24: 95.0000 - val_false_positives_24: 105.0000 - val_f1_score: 0.4385