tf_classify.py

import tensorflow as tf
import sys
 
image_path = sys.argv[1]
 
# Reads in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
 
# Loads label file, strip off carriage return
label_lines = [line.rstrip() for line
                   in tf.gfile.GFile("/tf_files/retrained_labels.txt")]
 
# Unpersists graph from file
with tf.gfile.FastGFile("/tf_files/retrained_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 and get first prediction
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
 
    predictions = sess.run(softmax_tensor, \
            {'DecodeJpeg/contents:0': image_data})
 
    # sorts to show labels of first prediction in order of  confidence
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
 
    # for every prediction that we have, get the label
    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
        print('%s (score = %.5f)' % (human_string, score))