使用Inception v3进行图像分类

Google Inception模型简介
Inception为Google开源的CNN模型，至今已经公开四个版本，每一个版本都是基于大型图像数据库ImageNet中的数据训练而成。因此我们可以直接利用Google的Inception模型来实现图像分类。本篇文章主要以Inception_v3模型为基础。Inception v3模型大约有2500万个参数，分类一张图像就用了50亿的乘加指令。在一台没有GPU的现代PC上，分类一张图像转眼就能完成。

已经训练好的Inception_v3模型

import tensorflow as tf
import numpy as np

uid_to_human = {}
for line in tf.gfile.GFile('imagenet_synset_to_human_label_map.txt').readlines():
	items = line.strip().split('\t')
	uid_to_human[items[0]] = items[1]

node_id_to_uid = {}
for line in tf.gfile.GFile('imagenet_2012_challenge_label_map_proto.pbtxt').readlines():
	if line.startswith('  target_class:'):
		target_class = int(line.split(': ')[1])
	if line.startswith('  target_class_string:'):
		target_class_string = line.split(': ')[1].strip('\n').strip('\"')
		node_id_to_uid[target_class] = target_class_string

node_id_to_name = {}
for key, value in node_id_to_uid.items():
	node_id_to_name[key] = uid_to_human[value]

def create_graph():
	with tf.gfile.FastGFile('classify_image_graph_def.pb', 'rb') as f:
		graph_def = tf.GraphDef()
		graph_def.ParseFromString(f.read())
		_ = tf.import_graph_def(graph_def, name='')

def classify_image(image, top_k=1):
	image_data = tf.gfile.FastGFile(image, 'rb').read()

	create_graph()

	with tf.Session() as sess:
		# 'softmax:0': A tensor containing the normalized prediction across 1000 labels
		# 'pool_3:0': A tensor containing the next-to-last layer containing 2048 float description of the image
		# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG encoding of the image
		softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
		predictions = sess.run(softmax_tensor, feed_dict={'DecodeJpeg/contents:0': image_data})
		predictions = np.squeeze(predictions)

		top_k = predictions.argsort()[-top_k:]
		for node_id in top_k:
			human_string = node_id_to_name[node_id]
			score = predictions[node_id]
			print('%s (score = %.5f)' % (human_string, score))

classify_image('test2.png')

test2.png如下：

运行结果如下：
Pekinese, Pekingese, Peke (score = 0.90348)