Large-scale visual recognition with GoogLeNet/Inception

 In 2014, the paper Going Deeper with Convolutions (https://arxiv.org/abs/1409.4842) was published by Google, introducing the GoogLeNet architecture. Subsequently, newer versions (https://arxiv.org/abs/1512.00567 in 2015) were published under the name Inception. In these GoogLeNet/Inception models, multiple convolutional layers are applied in parallel before being stacked and fed to the next layer. A great benefit of the network architecture is that the computational cost is lower and the file size of the trained weights is much smaller. In this recipe, we will demonstrate how to load the InceptionV3 weights in Keras and apply the model to classify images.

How to do it...

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