我目前正在开发一个CNN，用于预测两类图像的分类：武器和非武器。这个项目的目的是能够检测图像中是否存在武器（手枪/步枪）。
我的问题是：无论我尝试什么，分类器都预测图像中没有武器。你们能找出我的代码中可能导致此问题的缺陷吗？
我是一名计算机科学的高年级学生，但在机器学习领域几乎没有背景知识。
任何帮助都将不胜感激！
# Initializing the CNNclassifier = Sequential()# Step 1 - Convolutionclassifier.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))# Step 2 - Poolingclassifier.add(MaxPooling2D(pool_size=(2, 2)))# Adding a second convolutional layerclassifier.add(Conv2D(32, (3, 3), activation='relu'))classifier.add(MaxPooling2D(pool_size=(2, 2)))# Step 3 - Flatteningclassifier.add(Flatten())# Step 4 - Full connectionclassifier.add(Dense(units=128, activation='relu'))classifier.add(Dense(units=1, activation='sigmoid'))# Compiling the CNNclassifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])# Part 2 - Fitting the CNN to the imagesfrom keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale=1. / 255)test_datagen = ImageDataGenerator(rescale=1. / 255)valid_datagen = ImageDataGenerator(rescale=1. / 255)training_set = train_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/TrainDataSet/', target_size=(64, 64), batch_size=29, class_mode='binary')test_set = test_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/', target_size=(64, 64), batch_size=7, class_mode='binary')valid_set = valid_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/ValidationDataSet/', target_size=(64, 64), batch_size=7, class_mode='binary')classifier.fit_generator(training_set, steps_per_epoch=348, epochs=1, validation_data=valid_set, validation_steps=100)# Part 3 - Making new predictionsimport numpy as npfrom keras.preprocessing import image# test_image = image.load_img('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/ProbablySoap/P1030135.jpg',# target_size=(64, 64))test_image = image.load_img('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/Guns/301.jpeg', target_size=(64, 64))test_image = image.img_to_array(test_image)test_image = np.expand_dims(test_image, axis=0)result = classifier.predict_classes(test_image)print(result[0][0])var = training_set.class_indicesif result[0][0] == 1: prediction = 1 print("Gun!")else: prediction = 0 print("Not.")免责声明：“ProbablySoap”只是不包含武器的图像集。
更新
在这种情况下，输入图像包含武器。每次输出都预测为“不是”。
更新2这是代码的输出：
Found 348 images belonging to 2 classes.Found 42 images belonging to 2 classes.Found 42 images belonging to 2 classes.Epoch 1/1 1/348 [..............................] - ETA: 1:15 - loss: 0.6915 - accuracy: 0.5517 2/348 [..............................] - ETA: 47s - loss: 0.6994 - accuracy: 0.6724 3/348 [..............................] - ETA: 38s - loss: 0.7130 - accuracy: 0.6897 4/348 [..............................] - ETA: 33s - loss: 0.6565 - accuracy: 0.7155 5/348 [..............................] - ETA: 30s - loss: 0.6496 - accuracy: 0.7103 6/348 [..............................] - ETA: 28s - loss: 0.6384 - accuracy: 0.7241 7/348 [..............................] - ETA: 27s - loss: 0.6301 - accuracy: 0.7340…
346/348 [============================>.] - ETA: 0s - loss: 0.0940 - accuracy: 0.9628347/348 [============================>.] - ETA: 0s - loss: 0.0937 - accuracy: 0.9629348/348 [==============================] - 34s 98ms/step - loss: 0.0935 - accuracy: 0.9630 - val_loss: 0.2081 - val_accuracy: 0.97570Not.Process finished with exit code 0回答：
我认为你的问题是：
你生成了一个带有缩放的test_set：
test_datagen = ImageDataGenerator(rescale=1. / 255)test_set = test_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/', target_size=(64, 64), batch_size=7, class_mode='binary')但你从未使用过它，你从测试目录导入了一个文件，并在没有缩放的情况下使用它：
test_image = image.load_img('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/Guns/301.jpeg', target_size=(64, 64))test_image = image.img_to_array(test_image)test_image = np.expand_dims(test_image, axis=0)这就是为什么分类器无法正确分类后续导入的图像的原因。
希望这对你有帮助。
