tensorflow - 运行多类分类时的“NaN”结果

问题描述

当我为二进制分类运行这些代码行时，它运行良好，没有任何问题并获得了良好的结果，但是当我尝试为许多类（例如 3 个类）制作它时，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

我尝试了这些带有损失函数“二进制”的代码行和 2 个类，它运行良好，没有任何问题，并获得了一个很好的结果，帮助我完成了工作，准确度约为 '93%'。
但是我的项目基于多类分类，所以我尝试将损失函数更改为'categorical_crossentropy'和类 modfit_generator以'categorical'使其成为多类，准确率从 60% 开始增长到 99，然后突然下降到 33%。

预期结果类的标签实际结果是“NaN”。

提前致谢。

标签： tensorflowkerasdeep-learningmultilabel-classificationmulticlass-classification

tensorflow - 运行多类分类时的“NaN”结果

问题描述

解决方案

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