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在vgg、keras中打印所有分类类。索引器

pre-trained-model index-error keras printing python

1

Jürgen K. · 技术社区 · 7 年前

我试图打印出所有已知的类及其概率值。第一个值是概率最高的类。

以下是我目前的代码:

from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions

model = VGG16()

print(model.summary())


# load an image from file
image = load_img('./pictures/door.jpg', target_size=(224, 224))
image = img_to_array(image)  #output Numpy-array

#4-dimensional: samples, rows, columns, and channels.
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))


# prepare the image for the VGG model. 
image = preprocess_input(image)


# predict the probability across all output classes. 
yhat = model.predict(image)


# convert the probabilities to class labels
label = decode_predictions(yhat)
# retrieve the most likely result, e.g. highest probability
for i in range(0,5):
    label = label[i][i]
    print('%s (%.2f%%)' % (label[1], label[2] * 100))

我得到以下错误:

Traceback (most recent call last):
  File path, line 38, in <module>
    print('%s (%.2f%%)' % (label[1], label[2] * 100))
IndexError: string index out of range

你知道怎么处理吗? 提前感谢^^

1 回复 | 直到 6 年前

1

3

Ioannis Nasios 7 年前

您的代码中有一个错误。试试这个:

labels = decode_predictions(yhat)[0]
# retrieve the most likely result, e.g. highest probability
for i in range(0,5):
    label = labels[i]
    #print('%s (%.2f%%)' % (label[1], label[2] * 100)) 
    print('%s (%.2f%%)' % (label[1], float(label[2]) * 100))