import tensorflow as tf import sys import os # Disable tensorflow compilation warnings os.environ['TF_CPP_MIN_LOG_LEVEL']='2' import tensorflow as tf image_path = sys.argv[1] # Read the image_data image_data = tf.gfile.FastGFile(image_path, 'rb').read() # Loads label file, strips off carriage return label_lines = [line.rstrip() for line in tf.gfile.GFile("logs/output_labels.txt")] # Unpersists graph from file with tf.gfile.FastGFile("logs/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 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}) # 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 = %.5f)' % (human_string, score))