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Category: Machine Learning

Image Classification with Keras and TensorFlow 2 in R

Shirin Glander takes us through the task of image classification using TensorFlow version 2.2.0:

Recently, I have been getting a few comments on my old article on image classification with Keras, saying that they are getting errors with the code. And I have also gotten a few questions about how to use a Keras model to predict on new images (of different size). Instead of replying to them all individually, I decided to write this updated version using recent Keras and TensorFlow versions (all package versions and system information can be found at the bottom of this article, as usual).

Click through for the R code.

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Implementing an LSTM Model with Python

Mrinal Walia takes us through the concept of Long Short Term Memory:

A simple Recurrent Neural Network has a very simple structure, that forms a chain of repeating modules of a neural network, with just a single activation function such as tanh layer, similarly LSTM too have a chain-like structure with repeating modules just like RNN but instead of a single Neural network layer in RNN, LSTM has four layers which are interacting in a very different way each performing its unique function in the network.

Read on for a good amount of theory followed by an example using Keras.

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Generating Predictions with SQL Server ML Services

Jeffin Mathew walks us through SQL Server Machine Learning Services:

The purpose of this blog is to explore the process of running ML predictions on SQL server using Python. We are going to train and test the data to predict information about bike sharing for a specific year. We are going to be using the provided 2011 data and predict what 2012 will result in. The 2012 data already exists inside the dataset, so we will be able to compare the predicted to the actual amount.

For certain use cases—especially when the data already exists in SQL Server, and especially especially when you can use native scoring—Machine Learning Services does a great job.

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MLOPS in R with GitHub Actions

David Smith explains MLOPS and GitHub actions in a talk:

In the talk, I demonstrate the process in action (the demo starts at the 14:30 mark in the video below). I used Visual Studio Code to edit the app.R file in repository, and then pushed the changes to GitHub. That immediately triggered the action to deploy the updated file via SSH to the Shiny Server, running in a remote VM. Similarly, changes to the data file or to the R script files implementing the logistic regression model would trigger the model to be retrained in the cluster, and re-deploy the endpoint to deliver new predictions from the updated model.

Click through for a quick summary, link to the repo, and embedded video of the talk.

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The %tensorboard Magic Command in Databricks Notebooks

Jerry Liang and Hossein Falaki introduce a new magic command in Databricks Runtime 7.2:

In 2017, we released the  dbutils.tensorboard.start() API to manage and use TensorBoard inside Databricks python notebooks. This API only permits one active TensorBoard process on a cluster at any given time – which hinders multi-tenant use-cases. Early last year, TensorBoard released its own API for notebooks via the %tensorboard python magic command. This API not only starts TensorBoard processes but also exposes the TensorBoard’s command line arguments in the notebook environment. In addition, it embeds the TensorBoard UI inside notebooks, whereas the dbutils.tensorboard.start API prints a link to open TensorBoard in a new tab.

Read on to see how you can use it.

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Neural Network Model Deployment with ONNX

Terry McCann gives us a primer on ONNX:

Let me introduce you to ONNX. ONNX or the Open Neural Network eXchange is a runtime which can take a model, that you have trained in PyTorch or Tensorflow and encapsulate it in an ONNX format which is executed on something running the ONNX runtime. This new model can be trained in Python and deployed on an application, with no need for integration coding.

We have spent a huge amount of time creating different Docker containers for different types of models, the Tensorflow container or the PyTorch container or a container running in our model in Spark, the list goes on. That way of working is kind of becoming defunct. ONNX really breaks that down into a simple standard runtime that you can start working with and you can deploy your model into multiple different environments and ensure that is runs on your database, on your website, on your mobile device and also at the edge.

Terry has a video as well. I like the fact that ONNX exists and also that it’s available in Azure SQL Edge (in part because I want it available on-premises as well).

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ML Services and Resource Governor

I have a post on two gotchas you might run into around Resource Governor throttling SQL Server Machine Learning Services:

By default, SQL Server will grant 20% of available memory to any R or Python scripts running. The purpose of this limit is to prevent you from hurting server performance with expensive external scripts (like, say, training large neural networks on a SQL Server).

Here’s the kicker: this affects you even if you don’t have Resource Governor enabled. If you see out-of-memory exceptions in Python or error messages about memory allocation in R, I’d recommend bumping this max memory percent up above 20, and I have scripts to help you with the job. Of course, making this change assumes that your server isn’t stressed to the breaking point; if it is, you might simply want to offload that work somewhere else.

Click through for the other issue.

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Stock Price Predictions with LSTM Models

Thenuja Shanthacumaran walks us through training a Long Short-Term Memory neural network model for predicting stock prices:

LSTM could not process a single data point. it needs a sequence of data for processing and able to store historical information. LSTM is an appropriate algorithm to make prediction and process based-on time-series data. It’s better to work on the regression problem.

The stock market has enormously historical data that varies with trade date, which is time-series data, but the LSTM model predicts future price of stock within a short-time period with higher accuracy when the dataset has a huge amount of data.

Click through for the process and a demo.

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Installing TensorFlow and Keras for R on SQL Server 2019 ML Services

I have a post on using TensorFlow and Keras in R on SQL Server 2019 Machine Learning Services:

What I’m doing is building a new virtual environment named r-reticulate, which is what the reticulate package in R desires. Inside that virtual environment, I’m installing the latest versions of tensorflow-probabilitytensorflow , and keras. I had DLL loading problems with TensorFlow 2.1 on Windows, so if you run into those, the proper solution is to ensure that you have the appropriate Visual C++ redistributables installed on your server.

Then, I switched back to the base virtual environment and installed the same packages. My thinking here is that I’ll probably need them for other stuff as well (and don’t tell anybody, but I’m not very good with Python environments).

Please continue not to tell anybody that I’m not very good with Python environments. I tend to dump things in the base environment, forget which one I’m in, and all kinds of other bad practices. I think I’m secretly undermining myself in Python, but I don’t have enough proof yet.

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Avoiding Overfitting and Underfitting in Neural Networks

Manas Narkar provides some advice on optimizing neural network models:

Adding Dropout

Dropout is considered as one of the most effective regularization methods. Dropout is basically randomly zero-ing or dropping out features from your layer during the training process, or introducing some noise in the samples. The key thing to note is that this is only applied at training time. At test time, no values are dropped out. Instead, they are scaled. The typical dropout rate is between 0.2 to 0.5.

Click through for a demo on dropout, as well as coverage of several other techniques.

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