real_label.py

import tensorflow as tf, sys

image_path = sys.argv[1]


image_data = tf.gfile.FastGFile(image_path, 'rb').read()


label_lines = [line.rstrip() for line
					in tf.gfile.GFile("retrained_labels.txt")]


with tf.gfile.FastGFile("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:
	
	softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')

	predictions = sess.run(softmax_tensor, \
			  {'DecodeJpeg/contents:0': image_data})

	
	top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
	comp = 0
	value = "neutral"
	for node_id in top_k:
		human_string = label_lines[node_id]
		score = predictions[0][node_id]
		if(score > comp):
			comp = score
			value = human_string
		print('%s (score = %.5f)' % (human_string, score))
	print('%s' % (value))