input_data_2 = tf.placeholder(tf.float32, shape=(None, 227, 227, 3)) input_data_3 = tf.placeholder(tf.float32, shape=(None, 227, 227, 3)) net = AlexNet({'data': input_data}) print 'Initial Variable List:' print[tt.name for tt in tf.trainable_variables()] image_paths = glob.glob('data/*.JPEG') image_reader = ImageReader(image_paths=image_paths, batch_size=100) with tf.Session() as sesh: # load model weights model_data = 'alexnet_weights.npy' net.load(model_data, sesh) # start image reading coordinator = tf.train.Coordinator() threads = image_reader.start_reader(session=sesh, coordinator=coordinator) # get a batch indices, input_images = image_reader.get_batch(sesh) # get labels probs = sesh.run(net.get_output(), feed_dict={input_data: input_images}) display_results([image_paths[i] for i in indices], probs) coordinator.request_stop() coordinator.join(threads, stop_grace_period_secs=2)