[英]Keras evaluate_generator accuracy high, but accuracy of each class is low
I am using Keras ImageDataGenerator to load and train a model on 101-food-dataset.我正在使用 Keras ImageDataGenerator在 101-food-dataset 上加载和训练模型。 I am using the same split for validation and test .我使用相同的拆分进行验证和测试。 I also use sklearn.metrics.confusion_matrix to calculate accuracy for each class.我还使用sklearn.metrics.confusion_matrix来计算每个类的准确度。
After training, when I use evaluate_generator , I get high accuracy.训练后,当我使用evaluate_generator 时,我获得了很高的准确性。 But the accuracy of individual classes is very low.但是个别类的准确率很低。 What mistake am I making?我犯了什么错误?
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical',
subset='training')
validation_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical',
subset='validation')
history = model.fit_generator(train_generator,
steps_per_epoch = train_generator.samples // batch_size,
validation_data=validation_generator,
validation_steps=validation_generator.samples // batch_size,
epochs=epochs,
verbose=1,
callbacks=[csv_logger, checkpointer])
loss, acc = model.evaluate_generator(validation_generator, steps=3, verbose=0)
print('loss: ', loss, 'accuracy: ', acc) # loss: 0.4534463981787364 accuracy: 0.8333333
y_pred = model.predict_generator(validation_generator)
y_pred = np.array([np.argmax(x) for x in y_pred])
y_test = validation_generator.classes
cm = confusion_matrix(y_test, y_pred)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cm.diagonal()
acc_each_class = cm.diagonal()
print('accuracy of each class: \n')
for i in range(len(labels)):
print(labels[i], ' : ', acc_each_class[i])
print('\n')
'''
accuracy of each class:
cannoli : 0.085
dumplings : 0.065
edamame : 0.1
falafel : 0.125
french_fries : 0.12
grilled_cheese_sandwich : 0.13
hot_dog : 0.075
seaweed_salad : 0.085
tacos : 0.105
takoyaki : 0.135
'''
I investigated your issue I think this is due to that you have more than one worker opening the files numerous of times, to fix this issue make sure to set :我调查了您的问题,我认为这是由于您有多个工作人员多次打开文件,要解决此问题,请确保设置:
shuffle = false洗牌 = 假
pickle_safe = True pickle_safe = 真
workers = 1工人 = 1
or only evaluate your model via evaluate_generator or predict_generator :或仅通过evaluate_generator 或predict_generator 评估您的模型:
validation_generator2 = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary', shuffle=False)
score = model.evaluate_generator(validation_generator, nb_validation_samples/batch_size, workers=12)
scores = model.predict_generator(validation_generator2, nb_validation_samples/batch_size, workers=12)
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