無巴澤爾

Question

Tensorflow以來我試圖從tensorflow網站上的這個圖像識別教程： https://www.tensorflow.org/tutorials/image_retraining 並將其與巴澤爾BU命令行 succefully工作是否有可能把這種以來模型編程方式使用巴澤勒或通過例如Python腳本，所以我可以用圖片給它容易

Answer 1

您可以使用tmp目錄下生成文件，寫一個python腳本加載模型，並生成預測。

而且，最好是保持文件比TMP文件夾以外的目錄文件夾中的內容可以被沖走。

import tensorflow as tf 
import sys 


image_path = sys.argv[1] 
image_data = tf.gfile.FastGFile(image_path, 'rb').read() 

#loads label file, strips off carriage return 
label_lines = [line.strip() for line in tf.gfile.GFile("/tmp/output_labels.txt")] 

# Unpersists graph from file 
with tf.gfile.FastGFile("/tmp/output_graph.pb", 'rb') as f: 
    graph_def = tf.GraphDef() 
    graph_def.ParseFromString(f.read()) 
    _ = tf.import_graph_def(graph_def, name='') 

with tf.Session() as sess: 
    # Feed the image data as input to the graph an get first prediction 
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0') 
    predictions = sess.run(softmax_tensor, \ 
      {'DecodeJpeg/contents:0':image_data}) 
    # Sort to show labels of first prediction in order of confidence 
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1] 

    for node_id in top_k: 
     human_string = label_lines[node_id] 
     score = predictions[0][node_id] 
     print('%s (score = %.2f)' % (human_string, score))