我已经在Keras上实现并训练了一个多类卷积神经网络。最终的测试精度为0.9522。但是,当我使用scikit-learn中的precision_score计算准确性时,我得到0.6224。这是我所做的:
X_train = X[:60000, :, :, :]
X_test = X[60000:, :, :, :]
y_train = y[:60000, :]
y_test = y[60000:, :]
print ('Size of the arrays:')
print ('X_train: ' + str(X_train.shape))
print ('X_test: ' + str(X_test.shape))
print ('y_train: ' + str(y_train.shape))
print ('y_test: ' + str(y_test.shape))
结果:
Size of the arrays:
X_train: (60000, 64, 64, 3)
X_test: (40000, 64, 64, 3)
y_train: (60000, 14)
y_test: (40000, 14)
拟合Keras模型(为了简化代码,我在这里不添加整个模型):
model = Sequential()
model.add(Conv2D(10, (5,5), padding='same', input_shape=(64, 64, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(14))
model.add(Activation('softmax'))
model.compile(optimizer='rmsprop', loss='mean_squared_error', metrics['accuracy'])
model.fit(X_train, y_train, batch_size=100, epochs=5, verbose=1, validation_data=(X_test, y_test))
Scikit-Learn的准确性:
y_pred = model.predict(X_test, batch_size=100)
y_pred1D = y_pred.argmax(1)
y_pred = model.predict(X_test, batch_size=100)
y_test1D = y_test.argmax(1)
print ('Accuracy on validation data: ' + str(accuracy_score(y_test1D, y_pred1D)))
得分:
Accuracy on validation data: 0.6224
Keras的准确性:
score_Keras = model.evaluate(X_test, y_test, batch_size=200)
print('Accuracy on validation data with Keras: ' + str(score_Keras[1]))
结果:
Accuracy on validation data with Keras: 0.95219109267
我的问题是:为什么这两种精度不同,应该使用哪一种来评估我的多分类器的性能?
提前致谢!
最佳答案
您的代码中有一个错字,为什么要两次定义y_pred
?
y_pred = model.predict(X_test, batch_size=100)
y_pred1D = y_pred.argmax(1)
y_pred = model.predict(X_test, batch_size=100)
y_test1D = y_test.argmax(1)
print ('Accuracy on validation data: ' + str(accuracy_score(y_test1D, y_pred1D)))
应该 :
y_pred = model.predict(X_test, batch_size=100)
y_pred1D = y_pred.argmax(1)
y_test1D = y_test.argmax(1)
print ('Accuracy on validation data: ' + str(accuracy_score(y_test1D, y_pred1D)))
尽管如此,您仍应提供
y_pred1D
和y_test1D
的值和形状,但在使用和y_pred1D = y_pred.argmax(1)
来使用scikit学习度量时,错误就出在这里。我的猜测是,这与您的想法不同,否则这两个指标将是相同的。