不能建立混淆矩阵

Question

I want to build a confusion matrix for my CNN model there is the code:

classifier = Sequential()


classifier.add(Conv2D(32, (3, 3), input_shape=(64,64, 3), 
activation='relu'))


classifier.add(MaxPooling2D(pool_size=(2, 2)))  

classifier.add(Flatten())
classifier.add(Dense(units=1, activation='sigmoid'))



classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics= 
['accuracy'])


batch_size = 32

train_datagen = ImageDataGenerator(rescale=1. / 255,
                               shear_range=0.2,
                               zoom_range=0.2,
                               horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

training_set = train_datagen.flow_from_directory('x1' ,
                                             target_size=(64,64),
                                             batch_size=64,
                                             class_mode='binary')

test_set = test_datagen.flow_from_directory('x2' ,
                                        target_size=(64,64),
                                        batch_size=64,
                                        class_mode='binary')



ep=50

H=classifier.fit_generator(training_set,
                     steps_per_epoch=1204/batch_size,
                     epochs=ep,
                     validation_data=test_set,
                     validation_steps=408/batch_size,
                                     )

validation_steps=408

confusion matrix:

from sklearn.metrics import confusion_matrix
Y_pred = classifier.predict_generator(test_set,validation_steps//batch_size+1)


y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(test_set.classes, y_pred))

I got this error :

ValueError: Found input variables with inconsistent numbers of samples: [408, 792]

What should I do?

Answer 1

test_set and y_pred don't have the same number of observations. Likely the number of steps you are passing to predict_generator is incorrect.

Not sure which version of Keras you are using, but try Y_pred =classifier.predict_generator(test_set) . In newer versions leaving out number of steps will return predictions on all images.

Answer 2

You have

batch_size = 32

but inside test_set,

batch_size=64

so when you run predict_generator with validation_steps//batch_size+1 steps you are using 32 to create your steps but 64 for your generator.

You must set both to 32 or 64