The women-techmakers-intern-meeetup from bowen-jiang

women-techmakers-intern-meeetup's Introduction

TensorFlow Codelab

Step 1 : Let’s check if TensorFlow is correctly installed.

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session() # It will print some warnings here.
print(sess.run(hello))    #Output : Hello, TensorFlow!

Step 2 : Retrieving the images

mkdir tf_files  #Creating a working directory
cd tf_files

curl -O http://download.tensorflow.org/example_images/flower_photos.tgz
tar xzf flower_photos.tgz

ls -l #Checking for flower_photos folder
#Output : A folder named flower_photos inside which there are 5 sub folders namely daisy, tulips, sunflowers, roses and dandelion.

Step 3 : (Re)training Inception

curl -O      https://raw.githubusercontent.com/tensorflow/tensorflow/r1.1/tensorflow/examples/image_retraining/retrain.py

python retrain.py \
--bottleneck_dir=bottlenecks \
--how_many_training_steps=500 \
--model_dir=inception \
--summaries_dir=training_summaries/basic \
--output_graph=retrained_graph.pb \
--output_labels=retrained_labels.txt \
--image_dir=flower_photos

Step 4 : Using the Retrained Model

#Classifying an image
curl -L https://goo.gl/3lTKZs > label_image.py

#Run the python file on a daisy
python label_image.py flower_photos/daisy/21652746_cc379e0eea_m.jpg

#Output : 	daisy (score = 0.99071)
#sunflowers (score = 0.00595)
#dandelion (score = 0.00252)
#roses (score = 0.00049)
#tulips (score = 0.00032)

Step 5 : Going above and beyond!

#Trying Other Hyperparameters --learning_rate = 0.005(more time, high precision)[Default : 0.01]

#Training on Your Own Categories --image_dir=<root folder containing subfolders having folder names as label names, and the images inside each folder should be pictures that correspond to that label>