TFrecord files are TensorFlow’s suggested data format, although they are very difficult to inspect given their binary nature. Inspecting the contents of existing record files and ensuring the data in your input pipeline is as you expect is a good technique to have. Inspecting TFRecord values The first trick is reading in the tfrecord files and inspecting their values in python. As you’d expect, the TensorFlow API allows this (although a little hidden down). The small code snippet below highlights using the tf.python_io.tf_record_iterator to inspect ’examples’ in your record file. Replace the ’label’ or ’text_label’ as appropriate for your features, but it shows you can dot access into the property values ...

I recently completed Stanford Machine Learning on Coursera taught by Andrew Ng. It is a fantastic course for anyone interested, plus it’s free! The course uses MATLAB or Octave for the programming assignments. With the release of Azure Machine Learning, this seemed like a good exercise to reimplement these assignments on Azure ML to learn the tool. Creating an Azure ML Workspace To get started, you will need an Azure subscription. From the Azure Management Portal click ‘+ NEW’ from the lower left and create a workspace, and link to or create an associated storage account for use with the data. ...

An MNIST classifier is the go-to introduction for machine learning. Tensorflow is no different, and evolves to the Deep MNIST for Experts to include convolution, max pooling, dense layers and dropout: a good overview of ML layers for image problems. The downside of this is it doesn’t make use of Tensorflow’s new tf.estimator high level APIs. These provide all sorts of benefits for free than the usual sess.run TensorFlow tutorials you see online. The tf.estimator Quickstart gives a good reason to use it: ...

Azure Batch AI provides us the PaaS opportunity to use GPU resources in the cloud. The basis is to use virtual machines in a managed cluster (i.e. you don’t have to maintain them) and run jobs as you see fit. For my use case, the opportunity of low-priority VMs to reduce the cost of using GPU machines is also particularly promising. What I’ll run through is running our first job on Azure Batch AI. For setup, we’ll use the Azure CLI as I find it easier and quicker than using the UI portal. Saying that, everything can be achieved by point and click at portal.azure.com Assuming you already have the CLI installed and you are already logged in with it. ...

Azure provides low priority VMs that we can use to save some money when utilising GPUs for machine learning. All the major cloud providers offer spare compute for a discount: Google cloud has Preemptible VMs, AWS has spot instances and Azure has low priority VMs. On Azure these low priority VMs can only be provisioned in Azure Batch or virtual machine scale sets and we can use the latter for hosting a jupyter environment. The low priority pricing is 23c/hour vs the normal $1.147/hour…a nice saving ...

Ever since watching the session ‘What’s New in Table and Collection Views’ from WWDC 2014, I have tried to create all of my tables and collections use self-sizing and update for dynamic typing. Straight from the presentation, this sums up a pretty good motivation: This strategy is really going to improve the way that we architect our table views, number 1, because you can encapsulate logic right in the cells, and 2, the fact that you can do that will make it so much easier to have cells that are not a hard coded compile time known height which means you can easily adopt the dynamic font changes. -- Luke Hiesterman ...

For most of my development, I use Jupyter notebooks which are fantastic for iterative development, but running in managed environments such as Google’s ML Engine require python scripts. You can obviously run these locally with python in the terminal or your IDE, but the loop from debug, terminate, change and re-run is rather slow (from what I can tell due to import speed of tensorflow and other imported packages). I wanted to be able to keep these imports in memory (like Jupyter) and just re-run a single function during development. This is my workflow/setup for such a process. ...

Here we’ll look at exporting our previously trained dog and cat classifier and call that with local or remote files to test it out. To do this, I’ll use TensorFlow Serving in a docker container and use a python client to call to the remote host. Update 12th June, 2018: I used the gRPC interface here, but TensorFlow serving now has a REST API that could be beneficial or of more interest ...

TensorFlow serving can handle a variable batch size when doing predictions. I never understood how to configure this and also the shape of the results returned. Finally figuring this out, here’s the changes to our previous serving setup to accept a variable number of images to classify for our model. Serving input function First thing is to update our serving input receiver function placeholder. In the past we had set the placeholder to have a shape of [1], for variable batch size, this is as easy as setting it to [None]. Our updated input receiver function is now: ...

I’ve had a few attempts at getting TensorFlow estimators into a serving host and a client I can use to query them. Finally getting it working, I thought I’d write up the steps for reproduction. Assumptions and Prerequisites The first assumption is that you have already trained your estimator (say the tf.estimator.DNNRegressor) and this is now in the variable estimator. You also have a list of feature columns as is standard in a variable feature_columns. The example here is basically the same as my trained estimator. You will also have the tensorflow serving source code locally. If not, you can grab it and all the submodules by running git clone --recursive https://github.com/tensorflow/serving.git --depth=1. n.b. you will need the path to this later (mine is at ~/developer/serving). Finally, you will also need to have the gRPC tools installed and can be installed by running pip3 install grpcio-tools ...