CNN 使用 predict_classes 给出不正确的预测

Question

我仍处于 keras 和 CNN 的学习模式。我觉得我理解了基本点，但我的执行很困难。我创建了一个用于狗/猫图像数据集的Sequential 分类器。我使用fit_generator 得到以下信息（代码如下）：

Epoch 1/5
8000/8000 [==============================] - 1942s 243ms/step - loss: 0.3658 - acc: 0.8299 - val_loss: 0.6998 - val_acc: 0.7785] - ETA: 24:40 - loss: 0.6010 - acc: 0.6705
Epoch 2/5
8000/8000 [==============================] - 1829s 229ms/step - loss: 0.1266 - acc: 0.9522 - val_loss: 0.9218 - val_acc: 0.7731
Epoch 3/5
8000/8000 [==============================] - 1806s 226ms/step - loss: 0.0689 - acc: 0.9759 - val_loss: 1.2006 - val_acc: 0.7813
Epoch 4/5
8000/8000 [==============================] - 1936s 242ms/step - loss: 0.0504 - acc: 0.9830 - val_loss: 1.2396 - val_acc: 0.7748- ETA: 18:07 - loss: 0.0548 - acc: 0.9817
Epoch 5/5
8000/8000 [==============================] - 2259s 282ms/step - loss: 0.0393 - acc: 0.9870 - val_loss: 1.3916 - val_acc: 0.7818

用于产生上述结果的代码：

# Importing the Keras libraries and packages
import os
from keras.models import Sequential, load_model
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator

#create classifying sequential neural 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(Flatten())
classifier.add(Dense(units = 128, activation = 'relu')) #ReLU stands for Rectified Linear Unit. It takes a real-valued input and thresholds it at zero (replaces negative values with zero)

#initialise our output layer, which should contain only one node, as it is 
#binary classification. Single node gives us a binary output of either a Cat or Dog.
classifier.add(Dense(units = 1, activation = 'sigmoid')) 

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

#CREATE IMAGE DATA GENERATORS---
#perform image augmentations, essentially synthesising training data 
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('training_set', 
                                                 target_size = (64, 64), 
                                                 batch_size = 32, 
                                                 class_mode = 'binary')

#create test set for training, by feeding generated data from images using the test directory
test_set = test_datagen.flow_from_directory('test_set', 
                                            target_size = (64, 64), 
                                            batch_size = 32, 
                                            class_mode = 'binary')


#fit the training set to model        
classifier.fit_generator(training_set, 
                         steps_per_epoch = 8000, 
                         epochs = 5, 
                         validation_data = test_set, 
                         validation_steps = 2000)

#save the model for further use
classifier.save('classifier_v1.h5')

从这里开始，然后我使用load_model 函数来调用我的分类器，并尝试在 5 张图片的测试集上预测是狗还是猫。不管我做什么，分类器只会得到 1。

from keras.models import load_model
from keras.preprocessing import image
import numpy as np

classifier = load_model('classifier_v1.h5')
data_path = r'C:\Users\aneja\Documents\Python Scripts\CNN\Cats-Dogs\test_2'
image_list = [x for x in os.listdir(data_path) if '.jpg' in x]

#loop to test through test images
for jpeg in image_list:
    #load a test image
    img = image.load_img(os.path.join(data_path, jpeg), target_size=(64,64))

    #process image to extract numpy arrays
    y = image.img_to_array(img)
    x = np.expand_dims(y, axis=0)

    #predict!   1 = dog   0 = cat
    images = np.vstack([x])
    classes = classifier.predict_classes(images, batch_size=10)
    if classes[0][0]==1:
        print('The {} file is predicted to be a dog'.format(jpeg))
    elif classes[0][0]==0:
        print('The {} file is prediected to be a cat'.format(jpeg))
    else:
        print('Yea, I did something wrong')

当使用predict 而不是predict_classes 时，我也会得到相同的结果。我希望我离解决方案不会太远，但我担心我从根本上误解了概念。谁能提供任何帮助来解释为什么我的分类器似乎总是将类分类为 1？

Answer 1

以下是一些建议：

首先，检查以确保您的“steps_per_epoch = 8000”和“validation_steps=2000”实际上是正确的值。根据我的经验，不正确的数字会极大地损害模型的性能。大多数人通过执行以下操作来确保这些值是正确的： generator_train.samples/generator_train.batch_size

其次，增加Conv2D层数。通常，对于一个强大的模型来说，一层是不够的。如果您发现您的计算机无法快速训练您的模型，请使用 Google Colaboratory，这是一个免费的 GPU 和基于云的站点，可以让您执行此操作。

第三，增加测试规模。 5 张图片不足以让您自信地评估模型的性能。

第四，脱离 S.Mohsen sh 所说的，如果你发现“predict”给出的只是返回“1s”，那么这意味着你的模型没有经过适当的训练，所以试试我的前两个建议。

希望这有帮助！