Mastering TensorFlow 1.x

Image classification using Inception v3 in TensorFlow

The image classification is the same as explained in the previous section using the VGG 16 model. The full code for the Inception v3 model is as follows:

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]

Let us see how our model ...