curl -O http://download.tensorflow.org/example_images/flower_photos.tgz

curl -O https://raw.githubusercontent.com/tensorflow/tensorflow/10cf65b48e1b2f16eaa826d2793cb67207a085d0/tensorflow/examples/image_retraining/retrain.py

python retrain.py --image_dir flower_photos --output_graph output_graph.pb --output_labels output_labels.txt

import tensorflow as tf, sys
 
image_path = sys.argv[1]
graph_path = 'output_graph.pb'
labels_path = 'output_labels.txt'
 
# Read in 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(labels_path)]
 
# Unpersists graph from file
with tf.gfile.FastGFile(graph_path, 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')
 
# Feed the image_data as input to the graph and get first prediction
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})
    # 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))

tensorboard --logdir /tmp/retrain_logs