KERAS：使用INCEPTIONV3、RESNET50预训练模

最近在了解关于CNN的网络结构，找几个实例操作一下。

预训练，第一次接触~~~，不用训练，直接从网上下载模型来搞。
一，代码

from keras.applications.inception_v3 import InceptionV3
from keras.applications.resnet import ResNet50
from keras.applications.resnet import preprocess_input,decode_predictions
from keras.applications.inception_v3 import decode_predictions
from keras.preprocessing import image
import numpy as np
import cv2

model = InceptionV3(weights='imagenet', include_top=True)
img_path = 'dog1.jpg'

img = image.image_utils.load_img(img_path, target_size=(299, 299))
img = image.image_utils.img_to_array(img) / 255.0
img = np.expand_dims(img, axis=0)

predictions = model.predict(img)
print('Predicted:', decode_predictions(predictions, top=3))

descripton = decode_predictions(predictions, top=3)[0][0][1]

src = cv2.imread(img_path)
cv2.putText(src, descripton, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
cv2.imshow('Predicted', src)
cv2.waitKey()

print('=========================')

model = ResNet50(weights='imagenet')
img_path = 'bird.jpg'

img = image.image_utils.load_img(img_path, target_size=(224, 224))
img = image.image_utils.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)

predictions = model.predict(img)
print('Predicted:', decode_predictions(predictions, top=3))

descripton = decode_predictions(predictions, top=3)[0][0][1]

src = cv2.imread(img_path)
cv2.putText(src, descripton, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
cv2.imshow('Predicted', src)
cv2.waitKey()

二，输出

C:\Users\ccc\AppData\Local\Programs\Python\Python38\python.exe D:/tmp/tup_ai/codes/2.clustering/kmeans/tf_test.py
2022-06-27 10:12:10.303860: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
1/1 [==============================] - 2s 2s/step
Predicted: [[('n02110185', 'Siberian_husky', 0.8130109), ('n02109961', 'Eskimo_dog', 0.14220986), ('n02110063', 'malamute', 0.0025941634)]]
=========================
1/1 [==============================] - 1s 1s/step
Predicted: [[('n01531178', 'goldfinch', 0.82197833), ('n01560419', 'bulbul', 0.062186603), ('n01537544', 'indigo_bunting', 0.05275042)]]

Process finished with exit code 0