<jupyter_start><jupyter_text>** Inception V3 **  **Hands on Tutorial on TensorFlow**   **Outline**   -  Clues in TensorFlow   -  First Model in TensorFlow   -  Convolutional Neural Network (CNN) on Cifar-10   -  Techniques for Deep Learning in TensorFlow -  ** Inception V3 **   **Imagenet in TensorFlow**   One of most succesful Deep Learning Models is ImageNet, it is presented in four version:        -  Inception v1        -  Inception v2        -  Inception v3        -  Inception v4  In particular, this tutorial demonstrates how to train the Inception v3 architecture as specified in:" Rethinking the Inception Architecture for Computer Vision "<NAME>, <NAME>, <NAME>, <NAME>, <NAME>nahttp://arxiv.org/abs/1512.00567This network achieves 21.2% top-1 and 5.6% top-5 error for single frame evaluation with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. Below is a visualization of the model architecture [\[1]][1].[1]: https://github.com/tensorflow/models/tree/master/inception  **TensorFlow-Slim**   In this implementation TF-Slim is used. TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow. Components of tf-slim can be freely mixed with native tensorflow, as well as other frameworks, such as tf.contrib.learn [TensorFlow-Slim][1]  Usage:[1]: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim<jupyter_code>import tensorflow.contrib.slim as slim<jupyter_output><empty_output>
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