Sklearn指标值与Keras值有很大不同

Question

我需要一些帮助，以了解在Keras中拟合模型时如何计算准确性。这是训练模型的示例历史记录：

Train on 340 samples, validate on 60 samples
Epoch 1/100
340/340 [==============================] - 5s 13ms/step - loss: 0.8081 - acc: 0.7559 - val_loss: 0.1393 - val_acc: 1.0000
Epoch 2/100
340/340 [==============================] - 3s 9ms/step - loss: 0.7815 - acc: 0.7647 - val_loss: 0.1367 - val_acc: 1.0000
Epoch 3/100
340/340 [==============================] - 3s 10ms/step - loss: 0.8042 - acc: 0.7706 - val_loss: 0.1370 - val_acc: 1.0000
...
Epoch 25/100
340/340 [==============================] - 3s 9ms/step - loss: 0.6006 - acc: 0.8029 - val_loss: 0.2418 - val_acc: 0.9333
Epoch 26/100
340/340 [==============================] - 3s 9ms/step - loss: 0.5799 - acc: 0.8235 - val_loss: 0.3004 - val_acc: 0.8833

因此，验证精度在最初几个时期中为1？验证精度如何比训练精度更好？

这些数字显示了准确性和损失的所有值：

然后我使用sklearn指标评估最终结果：

def evaluate(predicted_outcome, expected_outcome):
    f1_score = metrics.f1_score(expected_outcome, predicted_outcome, average='weighted')
    balanced_accuracy_score = metrics.balanced_accuracy_score(expected_outcome, predicted_outcome)
    print('****************************')
    print('| MODEL PERFORMANCE REPORT |')
    print('****************************')
    print('Average F1 score = {:0.2f}.'.format(f1_score))
    print('Balanced accuracy score = {:0.2f}.'.format(balanced_accuracy_score))
    print('Confusion matrix')
    print(metrics.confusion_matrix(expected_outcome, predicted_outcome))
    print('Other metrics')
    print(metrics.classification_report(expected_outcome, predicted_outcome))

我得到此输出（如您所见，结果很糟糕）：

****************************
| MODEL PERFORMANCE REPORT |
****************************
Average F1 score = 0.25.
Balanced accuracy score = 0.32.
Confusion matrix
[[  7  24   2  40]
 [ 11  70   4 269]
 [  0   0   0  48]
 [  0   0   0   6]]
Other metrics
              precision    recall  f1-score   support

           0       0.39      0.10      0.15        73
           1       0.74      0.20      0.31       354
           2       0.00      0.00      0.00        48
           3       0.02      1.00      0.03         6

   micro avg       0.17      0.17      0.17       481
   macro avg       0.29      0.32      0.12       481
weighted avg       0.61      0.17      0.25       481

为什么Keras拟合函数的准确性和损失值与sklearn指标的值如此不同？

这是我的模型，以防万一：

model = Sequential()

model.add(LSTM(
                units=100, # the number of hidden states
                return_sequences=True, 
                input_shape=(timestamps,nb_features),
                dropout=0.2, 
                recurrent_dropout=0.2
              )
         )

model.add(Dropout(0.2))

model.add(Flatten())

model.add(Dense(units=nb_classes,
               activation='softmax'))

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

输入数据尺寸：

400 train sequences
481 test sequences
X_train shape: (400, 20, 17)
X_test shape: (481, 20, 17)
y_train shape: (400, 4)
y_test shape: (481, 4)

这是我应用sklearn指标的方式：

testPredict = model.predict(np.array(X_test))
y_test = np.argmax(y_test.values, axis=1)
y_pred = np.argmax(testPredict, axis=1)
evaluate(y_pred, y_test)

似乎我错过了一些东西。

Answer 1

您听起来有点困惑。