训练期间每类验证的准确性

Question

Keras 在训练时给出了整体training和validation准确率。有没有办法在培训期间获得a per-class validation accuracy？更新：来自 Pycharm 的错误日志File "C:/Users/wj96hq/PycharmProjects/PedestrianClassification/Awareness.py", line 82, in <module>shuffle=True, callbacks=callbacks)File "C:\Users\wj96hq\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapperreturn method(self, *args, **kwargs)File "C:\Users\wj96hq\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py", line 876, in fitcallbacks.on_epoch_end(epoch, epoch_logs)File "C:\Users\wj96hq\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\callbacks.py", line 365, in on_epoch_endcallback.on_epoch_end(epoch, logs)File "C:/Users/wj96hq/PycharmProjects/PedestrianClassification/Awareness.py", line 36, in on_epoch_endx_test, y_test = self.validation_data[0], self.validation_data[1]TypeError: 'NoneType' object is not subscriptable

Answer 1

使用它来获得每类准确性：

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

class Metrics(keras.callbacks.Callback):

    def on_train_begin(self, logs={}):

        self._data = []

    def on_epoch_end(self, batch, logs={}):

        x_test, y_test = self.validation_data[0], self.validation_data[1]

        y_predict = np.asarray(model.predict(x_test))

        true = np.argmax(y_test, axis=1)

        pred = np.argmax(y_predict, axis=1)

        

        cm = confusion_matrix(true, pred)

        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

        self._data.append({

            'classLevelaccuracy':cm.diagonal() ,

        })

        return

    def get_data(self):

        return self._data

metrics = Metrics()

history = model.fit(x_train, y_train, epochs=100, validation_data=(x_test, y_test), callbacks=[metrics])

metrics.get_data()

您可以在指标类中更改代码。随心所欲..并且这个工作。你只是用来metrics.get_data()获取所有信息..

Answer 2

好吧，准确性是一个global指标，没有per-class accuracy. 也许你的意思是，这就是orproportion of the class correctly identified的确切定义。TPRrecall

Answer 3

如果您想获得某个类别或一组特定类别的准确性，掩码可能是一个很好的解决方案。看这段代码：

def cus_accuracy(real, pred):

    score = accuracy(real, pred)

    mask = tf.math.greater_equal(real, 5)

    mask = tf.cast(mask, dtype=real.dtype)

    score *= mask

    mask2 = tf.math.less_equal(real, 10)

    mask2 = tf.cast(mask2, dtype=real.dtype)

    score *= mask2

return tf.reduce_mean(score)

这个指标给出了 5 到 10 类的准确度。我用它来测量 seq2seq 模型中某些单词的准确度。