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? Continue reading “How To Extract Feature Vectors From Deep Neural Networks In Python Caffe”
Tag: Caffe
How To Programmatically Create A Deep Neural Network In Python Caffe
When you are working with Caffe, you need to define your deep neural network architecture in a ‘.prototxt’ file. These prototxt files usually consist of hundreds of lines, defining layers and corresponding parameters. Before you start training your neural network, you need to create these files and define your architecture. One way to do this is manually write all these lines into a file. But sometimes, it’s beneficial to dynamically create this architecture depending on our needs. In such cases, creating a deep neural network programmatically can be very useful. Let’s go ahead and see how to do it in Python Caffe, shall we? Continue reading “How To Programmatically Create A Deep Neural Network In Python Caffe”
Deep Learning With Caffe In Python – Part IV: Classifying An Image
In the previous blog post, we learnt how to train a convolutional neural network (CNN). One of the most popular use cases for a CNN is to classify images. Once the CNN is trained, we need to know how to use it to classify an unknown image. The trained model files will be stored as “caffemodel” files, so we need to load those files, preprocess the input images, and then extract the output tags for those images. In this post, we will see how to load those trained model files and use it to classify an image. Let’s go ahead see how to do it, shall we? Continue reading “Deep Learning With Caffe In Python – Part IV: Classifying An Image”
Deep Learning With Caffe In Python – Part III: Training A CNN
In the previous blog post, we learnt about how to interact with a Caffe model. In this blog post, we will learn how to train a proper CNN. Up until now, we were dealing with a single layer network. We just defined it in a prototxt file and visualized it easily. If we want our CNN to perform any meaningful tasks, we should define a multilayer network and allow it to train on a large amount of data. Caffe makes it very easy for us to train a multilayer network. We can specify all the parameters in a prototxt file, create a training database, and just train the network. Let’s go ahead and see how to do that, shall we? Continue reading “Deep Learning With Caffe In Python – Part III: Training A CNN”
Deep Learning With Caffe In Python – Part II: Interacting With A Model
I know that the title looks slightly misleading. If you are thinking that we will be talking about how to interact with fashion models at a coffee shop, you are in for a big surprise! In the previous blog post, we talked about how to define and visualize a single layer convolutional neural network (CNN). In this post, we will discuss how to interact with a Caffe model. This is a continuation of the previous blog post. So if you haven’t read it, you may want to take a quick glance at it before you proceed. In that post, we defined our CNN architecture in a prototxt file. Now how do we make it do stuff for us? When we load such a network using Caffe, it comes with a bunch of features. Let’s see how to work with our model, shall we? Continue reading “Deep Learning With Caffe In Python – Part II: Interacting With A Model”
Deep Learning With Caffe In Python – Part I: Defining A Layer
Caffe is one the most popular deep learning packages out there. In one of the previous blog posts, we talked about how to install Caffe. In this blog post, we will discuss how to get started with Caffe and use its various features. We will then build a convolutional neural network (CNN) that can be used for image classification. Caffe plays very well with the GPU during the training process, hence we can achieve a lot of speed-up. For the purpose of this discussion, it is assumed that you have already installed Caffe on your machine. Let’s go ahead and see how to interact with Caffe, shall we? Continue reading “Deep Learning With Caffe In Python – Part I: Defining A Layer”
Perpetual Enigma 