"NaN" 运行 多 class class 化时的结果

Question

当我 运行 这些代码行用于二进制 class 化时 运行ning 很好，没有任何问题并得到了很好的结果，但是当我尝试将其用于许多 classes 例如 3 classes 它在预测结果

中给出 "NaN"

# Importing the Keras libraries and packages

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


# Initialising the CNN
classifier = Sequential()

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

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

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2enter code hereD(32, (3, 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)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 3, activation = 'sigmoid'))

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

# Part 2 - Fitting the CNN to the images

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('data/train',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('data/test',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'categorical')

classifier.fit_generator(training_set,
                         steps_per_epoch = 240 ,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 30)



import numpy as np
from keras.preprocessing import image
test_image = image.load_img('2.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(test_image)
training_set.class_indices

我用损失函数 "binary" 和 2 classes 尝试了这些代码行，它运行良好，没有任何问题，并得到了一个很好的结果，帮助我完成了我的工作，准确度大约为 '93 %'.
但是我的项目基于multi class class化，所以我尝试将损失函数更改为'categorical_crossentropy'和class mod in fit_generator到 'categorical' 使其成为多 class，准确度从 60% 开始增长到 99，然后突然下降到 33%。

预期结果 classes 的标签 实际结果是 "NaN".

提前致谢。

Answer 1

对于多class class化，通常softmax应用于最后一个密集层而不是sigmoid。将其更改为 softmax 以查看问题是否仍然存在。