# 使用 TensorFlow 中的 Inception v3 进行图像分类 图像分类与使用 VGG 16 模型的上一节中说明的相同。 Inception v3 模型的完整代码如下： ```py x_p = tf.placeholder(shape=(None, image_height, image_width, 3 ), dtype=tf.float32, name='x_p') with slim.arg_scope(inception.inception_v3_arg_scope()): logits,_ = inception.inception_v3(x_p, num_classes=inet.n_classes, is_training=False ) probabilities = tf.nn.softmax(logits) init = slim.assign_from_checkpoint_fn( os.path.join(model_home, '{}.ckpt'.format(model_name)), slim.get_variables_to_restore()) with tf.Session() as tfs: init(tfs) probs = tfs.run([probabilities],feed_dict={x_p:images_test}) probs=probs[0] ``` 让我们看看我们的模型如何处理测试图像：  ```py Probability 95.15% of [zebra] Probability 0.07% of [ostrich, Struthio camelus] Probability 0.07% of [hartebeest] Probability 0.03% of [sock] Probability 0.03% of [warthog] ``` ---  ```py Probability 93.09% of [horse cart, horse-cart] Probability 0.47% of [plow, plough] Probability 0.07% of [oxcart] Probability 0.07% of [seashore, coast, seacoast, sea-coast] Probability 0.06% of [military uniform] ``` ---  ```py Probability 18.94% of [Cardigan, Cardigan Welsh corgi] Probability 8.19% of [Pembroke, Pembroke Welsh corgi] Probability 7.86% of [studio couch, day bed] Probability 5.36% of [English springer, English springer spaniel] Probability 4.16% of [Border collie] ``` ---  ```py Probability 27.18% of [water ouzel, dipper] Probability 24.38% of [junco, snowbird] Probability 6.91% of [chickadee] Probability 0.99% of [magpie] Probability 0.73% of [brambling, Fringilla montifringilla] ``` ---  ```py Probability 93.00% of [hog, pig, grunter, squealer, Sus scrofa] Probability 2.23% of [wild boar, boar, Sus scrofa] Probability 0.65% of [ram, tup] Probability 0.43% of [ox] Probability 0.23% of [marmot] ``` ---  ```py Probability 84.27% of [brown bear, bruin, Ursus arctos] Probability 1.57% of [American black bear, black bear, Ursus americanus, Euarctos americanus] Probability 1.34% of [sloth bear, Melursus ursinus, Ursus ursinus] Probability 0.13% of [lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens] Probability 0.12% of [ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus] ``` ---  ```py Probability 20.20% of [honeycomb] Probability 6.52% of [gazelle] Probability 5.14% of [sorrel] Probability 3.72% of [impala, Aepyceros melampus] Probability 2.44% of [Saluki, gazelle hound] ``` ---  ```py Probability 41.17% of [harp] Probability 13.64% of [accordion, piano accordion, squeeze box] Probability 2.97% of [window shade] Probability 1.59% of [chain] Probability 1.55% of [pay-phone, pay-station] ``` 虽然它在与 VGG 模型几乎相同的地方失败了，但并不算太糟糕。现在让我们用 COCO 动物图像和标签再训练这个模型。