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)
