# factorization

Tensorflow insights - part 6: Custom model - Inception V3

The VGG block boils down to only a sub-network that contains a sequence of convolutional layers and a max-pooling layer. Each layer is just connected right after another layer in a consecutive manner, which is exactly in the same way as all the networks that we used before part 4. For that reason, you might not have gained the full advantage of using the Tensorflow custom layer/model. In this post, we will get familiar with the idea of parallel paths and implement the Inception module which is used by the variants of the Inception network. To be practical, we will then show you how to implement the Inception-v3 network architecture. Throughout this post, you will see a lot more of the power of the Tensorflow custom layer/model.