Kerasのメトリックをsklearn.classification_reportのメトリックと比較する

Question

私はニューラルネットワークを評価する際にさまざまな測定基準に苦しんでいます。 私の調査によると、Keras（バージョン1.2.2）は、sklearn.classificationレポートと比較して、特定のメトリック（関数評価を使用）に対して異なる値を計算することが示されました。

具体的には、「精密度」（すなわち、SkurarnのKeras！= 'precision'の精度）または 'recall'（SklearnのKeras！= 'recall'の 'recall'）の値が異なります。 次の作業例では、違いはランダムであるように見えますが、大きなネットワークを評価すると、Kerasの「精度」はSklearnの（ほぼ）「リコール」と同じですが、両方の「リコール」メトリクスは明確に異なります。

私はあなたの助けに感謝します！

from __future__ import print_function 
import numpy as np 
np.random.seed(1337) # for reproducibility 

from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers import Dense, Dropout, Activation, Flatten 
from keras.layers import Convolution2D, MaxPooling2D 
from keras.utils import np_utils # numpy utils for to_categorical() 
from keras import backend as K # abstract backend API (in order to generate compatible code for Theano and Tf) 
from sklearn.metrics import classification_report 

batch_size = 128 
nb_classes = 10 
nb_epoch = 30 

# input image dimensions 
img_rows, img_cols = 28, 28 
# number of convolutional filters to use 
nb_filters = 32 
# size of pooling area for max pooling 
pool_size = (2, 2) 
# convolution kernel size 
kernel_size = (3, 3) 

# the data, shuffled and split between train and test sets 
(X_train, y_train), (X_test, y_test) = mnist.load_data() 

if K.image_dim_ordering() == 'th': 
    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols) 
    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols) 
    input_shape = (1, img_rows, img_cols) 
else: 
    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) 
    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) 
    input_shape = (img_rows, img_cols, 1) 

X_train = X_train.astype('float32') 
X_test = X_test.astype('float32') 
X_train /= 255 # range [0,1] 
X_test /= 255 # range [0,1] 
print('X_train shape:', X_train.shape) 
print(X_train.shape[0], 'train samples') 
print(X_test.shape[0], 'test samples') 

# convert class vectors to binary class matrices 
Y_train = np_utils.to_categorical(y_train, nb_classes) # necessary for use of categorical_crossentropy 
Y_test = np_utils.to_categorical(y_test, nb_classes) # necessary for use of categorical_crossentropy 

# create model 
model = Sequential() 

model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], 
         border_mode='valid', 
         input_shape=input_shape)) 
model.add(Activation('relu')) 
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=pool_size)) 
model.add(Dropout(0.25)) 

model.add(Flatten()) 
model.add(Dense(128)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(nb_classes)) 
model.add(Activation('softmax')) 

# configure model 
model.compile(loss='categorical_crossentropy', 
       optimizer='adadelta', 
       metrics=['accuracy', 'precision', 'recall']) 

# train model 
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, 
      verbose=1, validation_data=(X_test, Y_test)) 

# evaluate model with keras 
score = model.evaluate(X_test, Y_test, verbose=0) 
print('Test score:', score[0]) 
print('Test accuracy:', score[1]) 
print('Test precision:', score[2]) 
print('Test recall:', score[3]) 

# evaluate model with sklearn 
predictions_last_epoch = model.predict(X_test, batch_size=batch_size, verbose=1) 
target_names = ['class 0', 'class 1', 'class 2', 'class 3', 'class 4', 
        'class 5', 'class 6', 'class 7', 'class 8', 'class 9'] 

predicted_classes = np.argmax(predictions_last_epoch, axis=1) 
print('\n') 
print(classification_report(y_test, predicted_classes, 
     target_names=target_names, digits = 6)) 

EDIT

上記で与えられたスクリプトの出力：別のモデルについて

Test score: 0.0271549037314 
Test accuracy: 0.9916 
Test precision: 0.992290322304 
Test recall: 0.9908 


9728/10000 [============================>.] - ETA: 0s 

     precision recall f1-score support 

class 0 0.987867 0.996939 0.992382  980 
class 1 0.993860 0.998238 0.996044  1135 
class 2 0.990329 0.992248 0.991288  1032 
class 3 0.991115 0.994059 0.992585  1010 
class 4 0.994882 0.989817 0.992343  982 
class 5 0.991041 0.992152 0.991597  892 
class 6 0.993678 0.984342 0.988988  958 
class 7 0.992180 0.987354 0.989761  1028 
class 8 0.989754 0.991786 0.990769  974 
class 9 0.991054 0.988107 0.989578  1009 

avg/total 0.991607 0.991600 0.991597  10000 

：（model.compile用）の所望のメトリックの

val/test loss: 0.231304548573 
val/test categorical_accuracy: **0.978500002956** 
val/test precision: *0.995103668976* 
val/test recall: 0.941900001907 
val/test fbeta_score: 0.967675107574 
val/test mean_squared_error: 0.0064611148566 
10000/10000 [==============================] - 0s  


     precision recall f1-score support 

class 0 0.989605 0.971429 0.980433  980 
class 1 0.985153 0.993833 0.989474  1135 
class 2 0.988154 0.969961 0.978973  1032 
class 3 0.981373 0.991089 0.986207  1010 
class 4 0.968907 0.983707 0.976251  982 
class 5 0.997633 0.945067 0.970639  892 
class 6 0.995690 0.964509 0.979852  958 
class 7 0.987230 0.977626 0.982405  1028 
class 8 0.945205 0.991786 0.967936  974 
class 9 0.951429 0.990089 0.970374  1009 

avg/total *0.978964* **0.978500** 0.978522  10000 

定義：

metrics=['categorical_accuracy', 'precision', 'recall', 'fbeta_score', 'mean_squared_error'] 

model.compile(loss='categorical_crossentropy', 
      optimizer='sgd', 
      metrics=metrics) 

model.metrics_namesの出力：

['loss', 'categorical_accuracy', 'precision', 'recall', 'fbeta_score', 'mean_squared_error'] 

Answer 1

はい、それが原因sklearn分類レポートはあなたのサポートに基づいて加重平均を与えるという事実に異なっています。

実験で：（1 + 0 + 0.33）/ 3 = 0.44（3）、しかしとして

class 0  0.33  1.00  0.50   1 
    class 1  0.00  0.00  0.00   2 
    class 2  1.00  1.00  1.00   1 

avg/total  0.33  0.50  0.38   **4** 

しかし、 精密リコールF1-スコアのサポート：

from sklearn.metrics import classification_report 
y_true = [0, 1,2,1] 
y_pred = [0, 0,2,0] 
target_names = ['class 0', 'class 1', 'class 2'] 
print(classification_report(y_true, y_pred, target_names=target_names)) 

があなたに与えますそれはサポートの列sklearnから返されます（1 * 1 + 0 * 2 + 0.33 * 1）/4=0.3325