如何在用 Keras 构建和训练的神经网络中处理错误预测？

我对神经网络非常陌生，我在一些互联网博客的帮助下尝试了一个典型的第一个例子：猫或狗的图像分类。在训练下面的神经网络后，我试图识别一些我在谷歌上发现的猫/狗的随机图片，它们既不在我的训练集也不在我的测试集......我发现，有时网络会给出正确的预测（识别狗当展示一只狗时），不幸的是有时会出现错误的预测，即我展示了一张猫的图片，而网络预测的是一只“狗”。我该如何处理此类错误？

将所有错误的图片添加到 training_set 或 test_set 并重新进行整个训练过程？或者有没有其他选项可以告诉网络它做出了错误的预测并应该调整其权重？

#Part 1 - Import
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
#Part 2 – Build Network
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
#Part 3 - Training
from keras.preprocessing.image import ImageDataGenerator
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('C:/…/KNNDaten/training_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')
test_set = test_datagen.flow_from_directory('C:/…/KNNDaten/test_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')

classifier.fit_generator(training_set, steps_per_epoch = 8000, epochs = 25, validation_data = test_set, validation_steps = 2000)

#Part 4 – Saving Model and weights 
model_json = classifier.to_json()
with open("model1.json", "w") as json_file:
 json_file.write(model_json)
 classifier.save_weights("model1.h5")

# Part 5 - Making new predictions
import numpy as np
from keras.preprocessing import image
test_image = image.load_img('C:/… /KNNDaten/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
    prediction = 'dog'
else:
    prediction = 'cat'

print("Image contains: " + prediction);

目前我的训练过程看起来像：训练过程的结果：准确性，...

非常感谢您的帮助！