上有許多圖像tensorflow預測

我已經使用重新訓練圖像，例如再培訓培養了tensorflow模型：https://www.tensorflow.org/versions/master/how_tos/image_retraining/index.html 上有許多圖像tensorflow預測

現在我想用它來預測上有許多圖像，我已經修改了這條巨蟒script對許多運行圖片：

import numpy as np 
import tensorflow as tf 
import glob 
import os 
modelFullPath = 'output_graph.pb' 


def create_graph(): 
    """Creates a graph from saved GraphDef file and returns a saver.""" 
    # Creates graph from saved graph_def.pb.                                          
    with tf.gfile.FastGFile(modelFullPath, 'rb') as f: 
     graph_def = tf.GraphDef() 
     graph_def.ParseFromString(f.read()) 
     _ = tf.import_graph_def(graph_def, name='') 

if __name__ == '__main__': 

    imagePath = 'MYFOLDERWITHIMAGES/*.jpg' 
    testimages=glob.glob(imagePath) 

    ## init numpy array to hold all predictions                                          
    all_predictions = np.zeros(shape=(len(testimages),121)) ## 121 categories                                  


    # Creates graph from saved GraphDef.                                           
    create_graph() 

    with tf.Session() as sess: 
     softmax_tensor = sess.graph.get_tensor_by_name('final_result:0') 
     for i in range(len(testimages)): 
      image_data1 = tf.gfile.FastGFile(testimages[i], 'rb').read() 
      predictions = sess.run(softmax_tensor, 
            {'DecodeJpeg/contents:0': image_data1}) 
      all_predictions[i,:] = np.squeeze(predictions) 
      if i % 100 == 0: 
       print(str(i) +' of a total of '+ str(len(testimages)))

但即使在我的GPU上運行，它是相當慢（約500每500圖像25秒）。我該如何加快速度？

來源

2016-11-29 Angel Lopez

加速張量流的標準方法在這裏可能是一個好主意。例如，使用輸入隊列可以幫助您保持GPU繁忙，如the reading data section of the tensorflow documentation中所述。同時爲了提高GPU利用率，您希望使用更大的批量，而不是一次預測一個圖像。

來源

2016-11-29 20:03:46

上有許多圖像tensorflow預測

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