Install Tensorflow

pip install --upgrade "tensorflow==1.7.*"

Fork the git repository

Clone the git repository

git clone https://github.com/username/build-your-first-imageClassifier.git

cd build-your-first-imageClassifier 

Download the training images

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

ls tf_files/flower_photos

daisy/
dandelion/
roses/
sunflowers/
tulip/
LICENSE.txt

(Re)training the network

In this exercise, we will retrain a MobileNet. MobileNet is a a small efficient convolutional neural network. "Convolutional" just means that the same calculations are performed at each location in the image.

Set those variables in your shell

IMAGE_SIZE=224
ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"

Investigate the retraining script

python -m scripts.retrain -h

Run the training

python -m scripts.retrain \
  --bottleneck_dir=tf_files/bottlenecks \
  --how_many_training_steps=4000\
  --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

Classifying an image

python -m scripts.label_image \
    --graph=tf_files/retrained_graph.pb  \
    --image=tf_files/flower_photos/daisy/21652746_cc379e0eea_m.jpg

Problem #1

** Training on Your Own Categories**