一、安装
必要：tensorflow，Keras
首次运行需要安装：
1）下载模型权重 inception_v3_weights_tf_dim_ordering_tf_kernels.h5
路径见前一篇
2）安装h5py
pip install h5py
3）安装PIL
遇到pip无法安装，以pillow替代，见Stack Overflow
二、参数说明
分类结果：
ImageNet的1000种object，对应模型分类结果的1000 classes：
text: imagenet 1000 class id to human readable labels
https://github.com/cjyanyi/keras_deep_learning_tutorial/blob/master/imagenet1000_clsid_to_human.txt
三、代码示例
import numpy as np
from keras.preprocessing import image
from keras.applications import inception_v3 img = image.load_img("xxx.jpg", target_size=(299, 299))
input_image = image.img_to_array(img)
input_image /= 255.
input_image -= 0.5
input_image *= 2.
# Add a 4th dimension for batch size (Keras)
input_image = np.expand_dims(input_image, axis=0) # Run the image through the NN
predictions = model.predict(input_image) # Convert the predictions into text
predicted_classes = inception_v3.decode_predictions(predictions, top=1)
imagenet_id, name, confidence = predicted_classes[0][0]
print("This is a {} with {:-4}% confidence!".format(name, confidence * 100))
input_image 是一个默认大小：1*299*299*3 的4维向量（列表）