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)
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