Retrain a tensorflow model based on Inception v3.

Transfer learning, which means we are starting with a model that has been already trained on another problem. We will then be retraining it on a similar problem. Deep learning from scratch can take days, but transfer learning can be done in short order.

In this demo you need install TensorFlow. You can reference tensorflow Github.

The Demo perform an ability to identify images after simple training.

(accuracy on 500 times training and 300 feature images)

Inception_Tensorflow

bottlenecks Empty folder to cache the training

data Image data folder

inception Empty folder to restore the Inception v3 model

label.py The python program for labeling

retrain.py The python program for training

Before starting the training, launch tensorboard in the background. TensorBoard is a monitoring and inspection tool included with tensorflow. You will use it to monitor the training progress.

tensorboard --logdir training_summaries &

This command will fail with the following error if you already have a tensorboard process running:

ERROR:tensorflow:TensorBoard attempted to bind to port 6006, but it was already in use

You can kill all existing TensorBoard instances with:

pkill -f "tensorboard"

You can run the script using the python command. Take a minute to skim its "help".

python -m retrain -h

As noted in the introduction, ImageNet models are networks with millions of parameters that can differentiate a large number of classes. We're only training the final layer of that network, so training will end in a reasonable amount of time.

Start your retraining with one big command:

python -m retrain \
  --bottleneck_dir=tf_files/bottlenecks \
  --how_many_training_steps=500 \
  --model_dir=tf_files/models/ \
  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  --output_graph=tf_files/retrained_graph.pb \
  --output_labels=tf_files/retrained_labels.txt \
  --image_dir=tf_files/flower_photos

the default value of --how_many_training_steps is 4000.

note the --summaries_dir option, sending training progress reports to the directory that tensorboard is monitoring.

This script downloads the pre-trained model, adds a new final layer, and trains that layer on the feature photos you've downloaded. You can view training progress via TensorBoard.

It may take a while to finish the training After the training 'retrained_graph.pb' and 'retrained_labels.txt' will be generated.

python label.py [image]

Copyright (c) 2015-2017 Wanguy. Released under GPLv3. See LICENSE.txt for details.