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import os, sys | |
import tensorflow as tf | |
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | |
# change this as you see fit | |
image_path = sys.argv[1] | |
# 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("retrained_labels-chickens.txt")] | |
# Unpersists graph from file | |
with tf.gfile.FastGFile("retrained_graph-chickens.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': 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)) |
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