I love getting to a point with Infrastructure as Code (IaC) where not only are the resources reproducable, but also encoding good security and utilisation of cloud resources into the contents. Firstly, support in Azure Storage for Active Directory access control went GA and utilising this over an access key is one of those security considerations that seems could be automated. Secondly, managed identities are a fantastic way to get the power of Azure Active Directory without the process of keeping secrets and other management secure. ...

Understanding the shape of your model is sometimes non-trivial when it comes to machine learning. Look at convolutional neural nets with the number of filters, padding, kernel sizes etc and it’s quickly evident why understanding what shapes your inputs and outputs are will keep you sane and reduce the time spent digging into strange errors. TensorFlow’s RNN API exposed me to similar frustrations and misunderstandings about what I was expected to give it and what I was getting in return. Extracting these operations out helped me get a simple view of the RNN API and hopefully reduce some headaches in the future. In this post, I’ll outline my findings with a few examples and. Firstly the input data shape: batch size is part of running any graph and you’ll get used to seeing None or ? as the first dimension of your shapes. RNN data expects each sample to have two dimensions of it’s own. This is different to understanding that images have two dimensions, RNN data expects a sequence of samples, each of which has a number of features. Lets make this clearer with an example: ...

Embeddings can be used in machine learning to represent data and take advantage of reducing the dimensionality of the dataset and learning some latent factors between data points. Commonly this is used with words to say, reduce a 400,000 word vector to a 50 dimensional vector, but could equally be used to map post codes or other token encoded data. Another use case might be in recommender systems GloVe (Global Vectors for Word Representation) was developed at Stanford and more information can be found here. There are a few learnt datasets including Wikipedia, web crawl and a Twitter set, each increasing the number of words in its vocabulary with varying embedding dimensions. We will be using the smallest Wikipedia dataset and for this sample will pick the 50 dimensional embedding. ...

The TensorFlow canned estimators got promoted to core in version 1.3 to make training and evaluation of machine learning models very easy. This API allows you to describe your input data (categorical, numeric, embedding etc) through the use of feature columns. The estimator API also allows you to write a custom model for your unique job, and the feature columns capabilities can be utilised here as well to simplify or enhance things. In a custom estimator, you are required to write a model_fn and it’s here you utilise your features from your input_fn with your model architecture. To showcase using the tf.feature_column API, lets compare an embedding column with and without this module. Without the feature column API, a typical embedding of your features can be setup as so: ...

Simple script to bump the build number of my Xcode project on each Archive. The version number I am leaving as a manual change for the moment, but each time I release a build to testers, I want the build to change and a commit message for the change. In Xcode from the menu go to Product -> Scheme -> Edit Scheme On the side, expand the Arhive build and select Pre-actions. Here we will edit the pre-action script to increment the build number. Choose to provide the build settings from your main app target and in the script pane add: ...