How To Extract Feature Vectors From Deep Neural Networks In Python Caffe

Convolutional Neural Networks are great at identifying all the information that makes an image distinct. When we train a deep neural network in Caffe to classify images, we specify a multilayered neural network with different types of layers like convolution, rectified linear unit, softmax loss, and so on. The last layer is the output layer that gives us the output tag with the corresponding confidence value. But sometimes it's useful for us to extract the feature vectors from various layers and use it for other purposes. Let's see how to do it in Python Caffe, shall we?