首先我的假设可能是错误的:
- 损失是每个训练示例与正确答案的距离(然后除以示例数量 - 一种平均损失)。
- 准确性是有多少训练样例是正确的(如果将最高输出作为正确答案，那么它是否为 0.7 并不重要，这会导致 0.3 的损失，它仍然会输出正确答案)。这是以百分比形式给出的。
在我看来，这意味着准确度通常会接近 100%，而不是损失接近 0。这不是我所看到的:
10000/10000 [==============================] - 1067s - loss: 0.0408 - acc: 0.9577 - val_loss: 0.0029 - val_acc: 0.9995
Epoch 2/5
10000/10000 [==============================] - 991s - loss: 0.0021 - acc: 0.9997 - val_loss: 1.9070e-07 - val_acc: 1.0000
Epoch 3/5
10000/10000 [==============================] - 990s - loss: 0.0011 - acc: 0.4531 - val_loss: 1.1921e-07 - val_acc: 0.2440
那是在 3 个时期，第二次尝试让它工作。这是 train_dategen 具有 shuffle=True。我得到了 shuffle=False 的结果(我最初认为这可能是问题)，在这里:
10000/10000 [==============================] - 1168s - loss: 0.0079 - acc: 0.9975 - val_loss: 0.0031 - val_acc: 0.9995
Epoch 2/5
10000/10000 [==============================] - 1053s - loss: 0.0032 - acc: 0.9614 - val_loss: 1.1921e-07 - val_acc: 0.2439
Epoch 3/5
10000/10000 [==============================] - 1029s - loss: 1.1921e-07 - acc: 0.2443 - val_loss: 1.1921e-07 - val_acc: 0.2438
Epoch 4/5
10000/10000 [==============================] - 1017s - loss: 1.1921e-07 - acc: 0.2439 - val_loss: 1.1921e-07 - val_acc: 0.2438
Epoch 5/5
10000/10000 [==============================] - 1041s - loss: 1.1921e-07 - acc: 0.2445 - val_loss: 1.1921e-07 - val_acc: 0.2435
我使用 categorical_crossentropy 作为损失函数，因为我有 3 个类。我有比需要更多的数据(大约 178,000 张图像，全部分为 3 类中的 1 类)。
我是误会了什么，还是出了什么问题？
这是我的完整代码:
# 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 = (200, 200, 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)))
# 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)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (200, 200),
batch_size = 64,
class_mode = 'categorical',
shuffle=True)
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (200, 200),
batch_size = 62,
class_mode = 'categorical',
shuffle=True)
classifier.fit_generator(training_set,
steps_per_epoch = 10000,
epochs = 5,
validation_data = test_set,
validation_steps=1000)
classifier.save("CSGOHeads.h5")
# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image
test_image = image.load_img('dataset/single_prediction/1.bmp', target_size = (200, 200))
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
if result[0][0] == 1:
prediction = 'head'
else:
prediction = 'not'
请您参考如下方法:
由于您将图像分类为 3 类之一(即称为单标签多类分类:有多个类，但每个图像只有一个标签)，您应该使用 softmax 作为最后一层的激活函数而不是使用 sigmoid:
classifier.add(Dense(units = 3, activation = 'softmax')) # don't use sigmoid here
如果您想让我解释更多，请告诉我，我会更新我的答案。
