TensorFlow setup for image classification for Linux

“TensorFlow for poets” command line setup  in linux


This setup has been tested e.g. with the University of Helsinki’s virtual environment, from which you can find more here.


Create virtual environment

Virtual environment is especially useful if you use virtual desktop – e.g. the tensorboard tries to install itself to common environment, which will not succeed.

virtualenv tepoet

Activate the virtual environment . Note this changes e.g. path so it uses the one under the tepoet/bin -directory

source tepoet/bin/activate


TensorFlow details:

(Skipped tensorboard due to above mentioned environment issue, but within virtualenv using tensorboard should be fine).

Install tensorflow and get the scripts from the tensorflow github.

pip install --upgrade tensorflow

git clone https://github.com/googlecodelabs/tensorflow-for-poets-2

cd tensorflow-for-poets-2/

curl http://download.tensorflow.org/example_images/flower_photos.tgz \
    | tar xz -C tf_files

ls tf_files/flower_photos/

Next thing is to to create the model and run the actual classifier to a image used in this post.

export IMAGE_SIZE=224

export ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"

python -m scripts.retrain -h

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

python -m  scripts.label_image -h

python -m scripts.label_image     --graph=tf_files/retrained_graph.pb \

python -m scripts.label_image     --graph=tf_files/retrained_graph.pb\

And finally run the classifier against your own image

python -m scripts.label_image     --graph=tf_files/retrained_graph.pb \

The results should be :

Evaluation time (1-image): 0.501s

roses 0.4427891
dandelion 0.36607185
sunflowers 0.1426634
tulips 0.0484466
daisy 2.9001703e-05


Remember to register to the DHN18  , and pick “Miniature histories – Digitized newspapers and cultural heritage assets as source for the local history” as your workshop!


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