Meanwhile, if you’re using pip install tensorflow-gpu, simply download TensorRT files for Ubuntu 14.04 not16.04, no matter what version of Ubuntu you’re running. As an example, 0.67 would allocate 67% of GPU memory for TensorFlow, making the remaining 33% available for TensorRT engines. For example, you can use transfer learning with the Inception image classification model to train an image classifier that uses your specialized image data. Object Detection API generates different Preprocessor sub-graph based on the image resizer type. Click the image to enlarge it. To generate IRs for SSD topologies, the Model Optimizer creates a number of PriorBoxClustered layers instead of a constant node with prior boxes calculated for the particular input image size. Model Optimizer saves part of the model GraphDef into the generated XML. For that, we need an optimizer. We need to add bias into the mix. The objective function, or cost function, used in this approach is simply the sum of squared distances between predicted ratings and actual ratings, so this is what we need to minimize. The ratings matrix is sparse, meaning most of the values are 0, because each user has only rated a small number of items. 1,586,126 values to store in memory while doing computations on them. That means it has the potential to make Google smarter at doing everything from delivering you better search results to recommending what YouTube videos to watch. It’ll also make third-party apps a lot more useful. The concept of a sparse matrix can actually be translated to a different data structure that retains only information about the non-zero values, making it a much more memory-efficient represenation of the same information.
Alternative to TensorFlow
I’ve been working on building a content recommender in TensorFlow using matrix factorization, following the approach described in the article Matrix Factorization Techniques for Recommender Systems (MFTRS). I’ll explain briefly here what matrix factorization is in the context of recommender systems (although I highly cough recommend reading the MFTRS article) and how things needed to be set up to do this in TensorFlow. Then I’ll show the code I wrote to train the model and the resulting TensorFlow computation graph produced by TensorBoard. And here’s the training cost portion of the graph expanded. We do this by multiplying the sum of the squares of the elements of the user and item matrices by a configurable regularization parameter and including this in our cost function. A tensor field is a tensor valued function. This change is performed only if the tensor with prior boxes is not constant (so it is produced by PriorBoxClustered layers during inference). If the shape isn’t passed, this tensor can be fed with any shape. But the overall mean rating can be added on after we do the matrix multiplication. What matrix factorization does is to come up with two smaller matrices, one representing users and one representing items, which when multiplied together will produce roughly this matrix of ratings, ignoring the 0 entries. IDs, so we have users 1 through 943 and items 1 through 1682. Subracting 1 from each ID allows us to use them as matrix indices. This contains 100,000 ratings from 943 users of 1,682 movies.