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

Keras之inception_v3使用-LMLPHP

二、参数说明

Keras之inception_v3使用-LMLPHP

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) 

05-01 23:32