TensorFlow

This notebook is available as a codelab TensorFlow Hub was announced at TensorFlow Dev Summit 2018 and promises to reduce the effort required to use existing machine learning models and weights in your own custom model. From the overview page TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a TensorFlow graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning. ...

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. ...

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: ...

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: ...

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: ...