Question

我是一名新的ML程序员，正在研究代码以显示所有类的准确性。该代码仅显示最可能的类。

而且，我在结果变量上打印了一个图，以查看其中的内容，除了一个类之外，其他所有内容都为0是否正常？所有班级应该有某种权重和概率？

整个项目都是关于迁移学习的，我在keras和cifar10中使用VGG16，model_weights.h5具有从cifar10中提取的功能，并且model_structure文件是具有模型结构的JSON文件，而vgg16具有密集结构修改的图层

from keras.models import model_from_json
from pathlib import Path
from keras.preprocessing import image
import numpy as np

# These are the CIFAR10 class labels from the training data (in order from 0 to 9)
class_labels = [
    "Plane",
    "Car",
    "Bird",
    "Cat",
    "Deer",
    "Dog",
    "Frog",
    "Horse",
    "Boat",
    "Truck"
]

# Load the json file that contains the model's structure
f = Path("model_structure.json")
model_structure = f.read_text()

# Recreate the Keras model object from the json data
model = model_from_json(model_structure)

# Re-load the model's trained weights
model.load_weights("model_weights.h5")

# Load an image file to test, resizing it to 32x32 pixels (as required by this model)
img = image.load_img("catdog11.jpg", target_size=(32, 32))

# Convert the image to a numpy array
image_to_test = image.img_to_array(img)

# Add a fourth dimension to the image (since Keras expects a list of images, not a single image)
list_of_images = np.expand_dims(image_to_test, axis=0)

# Make a prediction using the model
results = model.predict(list_of_images)
print("what is in results?: ", results)

# Since we are only testing one image, we only need to check the first result
single_result = results[0]

# We will get a likelihood score for all 10 possible classes. Find out which class had the highest score.


most_likely_class_index = int(np.argmax(single_result))
class_likelihood = single_result[most_likely_class_index]


# Get the name of the most likely class
class_label = class_labels[most_likely_class_index]

# Print the result
print("This is image is a {} - Likelihood: {:2f}".format(class_label, class_likelihood))

此刻正在显示

Using TensorFlow backend.
2019-04-15 17:56:05.082617: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] 
Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

结果如何？：[[0。 0. 0. 0. 0. 1. 1. 0. 0. 0. 0。]]

这是一只狗的图像-可能性：1.000000

以退出代码0结束的过程

我想要获得的结果是：

CIFAR 10具有10个类别，因此当我输入图像时，它应该显示为：

青蛙：0.4％

卡车：0.002％

以此类推

Answer 1

您只需要更改最后一条输出行，即可将class_likelihood乘以百分比：

print("This is image is a {} - Likelihood: {}".format(class_label, class_likelihood*100 ) )

简而言之，您只需将class_likelihood乘以100。

percent = class_likelihood * 100

如何在python中显示所有10个CIFAR10类的准确性？

1 个答案: