Keras 在訓練時給出了整體training和validation準確率。有沒有辦法在培訓期間獲得a per-class validation accuracy？更新：來自 Pycharm 的錯誤日志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
3 回答
慕斯709654
TA貢獻1840條經驗 獲得超5個贊
使用它來獲得每類準確性：
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()
您可以在指標類中更改代碼。隨心所欲..并且這個工作。你只是用來metrics.get_data()獲取所有信息..
猛跑小豬
TA貢獻1858條經驗 獲得超8個贊
好吧，準確性是一個global指標，沒有per-class accuracy.?也許你的意思是，這就是orproportion of the class correctly identified的確切定義。TPRrecall
倚天杖
TA貢獻1828條經驗 獲得超3個贊
如果您想獲得某個類別或一組特定類別的準確性，掩碼可能是一個很好的解決方案。看這段代碼：
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)
這個指標給出了 5 到 10 類的準確度。我用它來測量 seq2seq 模型中某些單詞的準確度。
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