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retrain.py
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retrain.py
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import tensorflow as tf
import sys
retrain_data_path = sys.argv[1]
#read in the data
retrain_data = tf.gfile.FastGFile(retrain_data_path, 'rb').read()
#loads label data, strips off carriage return
label_lines = [line.rstrip() for line in tf.gfile.GFile("/tf_files/retrained_labels.txt")]
# unpersists graph from file
with tf.gfile.FastGfile("/tf_files/retrained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
#feed the image data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0' : retrain_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=%s.5f)' % (human_string, score))