先來個最基本的 TensorFlow 實作
這個範例是把圖片根據 “目錄名稱” 分類好，透過 retrain.py 來訓練，訓練後的結果會存在 output_graph.pb 跟 output_labels.txt 中，可以隨時調用。
下載範例圖片
curl -O http://download.tensorflow.org/example_images/flower_photos.tgz
下載程式
curl -O https://raw.githubusercontent.com/tensorflow/tensorflow/10cf65b48e1b2f16eaa826d2793cb67207a085d0/tensorflow/examples/image_retraining/retrain.py
開始訓練
python retrain.py --image_dir flower_photos --output_graph output_graph.pb --output_labels output_labels.txt
套用訓練後的資料判斷
將下列程式命名為 classify.py
import tensorflow as tf, sys
image_path = sys.argv[1]
graph_path = 'output_graph.pb'
labels_path = 'output_labels.txt'
# Read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line
in tf.gfile.GFile(labels_path)]
# Unpersists graph from file
with tf.gfile.FastGFile(graph_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
# Feed the image_data as input to the graph and get first prediction
with tf.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))
也可以用 Tensorboard 看看訓練的資料過程
tensorboard --logdir /tmp/retrain_logs
