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Visualizing TensorFlow training jobs with TensorBoard

TensorBoard is an open-source toolkit for TensorFlow users that allows you to visualize a wide range of useful information about your model, from model graphs; to loss, accuracy, or custom metrics; to embedding projections, images, and histograms of weights and biases. This post demonstrates how to use TensorBoard with Amazon SageMaker training jobs, write logs […]

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TensorBoard is an open-source toolkit for TensorFlow users that allows you to visualize a wide range of useful information about your model, from model graphs; to loss, accuracy, or custom metrics; to embedding projections, images, and histograms of weights and biases.

This post demonstrates how to use TensorBoard with Amazon SageMaker training jobs, write logs from TensorFlow training scripts to Amazon Simple Storage Service (Amazon S3), and ways to run TensorBoard: locally, using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate, or inside of an Amazon SageMaker notebook instance.

Generating training logs using tf.summary

TensorFlow comes with a tf.summary module to write summary data, which it uses for monitoring and visualization. The module’s API provides methods to write scalars, audio, histograms, text, and image summaries, and can trace information that’s useful for profiling training jobs. An example command to write the accuracy of the first step of training looks like the following:

tf.summary.scalar('accuracy', 0.45, step=1)

To use the summary data after the training job is complete, it’s important to write the files to a persistent storage. This way, you can visualize your past jobs or compare different runs during the hyperparameter tuning phase. The tf.summary module allows you to use Amazon S3 as the destination for log files, passing the S3 bucket URI directly into the create_file_writer method. See the following code:

tf.summary.create_file_writer('s3://<bucket_name>/<prefix>')

Keras users can use keras.callbacks.TensorBoard as one of the callbacks provided to the Model.fit() method. This callback provides an abstraction of a low-level tf.summary API and collects a lot of the data automatically. With TensorBoard callbacks, you can collect data to visualize training graphs, metrics plots, activation histograms, and run profiling. See the following code:

tb_callback = tf.keras.callbacks.TensorBoard(log_dir='s3://<bucket_name>/<prefix>')
model.fit(x, y, epochs=5, callbacks=[tb_callback])

For a detailed example of how to collect summary data in the training scripts, see the TensorBoard Keras example notebook on the Amazon SageMaker examples GitHub repo or inside a running Amazon SageMaker notebook instance on the Amazon SageMaker Examples tab. This notebook uses TensorFlow 2.2 and Keras to train a Convolutional Neural Network (CNN) to recognize images from the CIFAR-10 dataset. Code in the notebook runs the training job locally inside the notebook instance one time, and then another 10 times during the hyperparameter tuning job. All training jobs write log files under one Amazon S3 prefix, so the log destination path for every run follows the format s3://<bucket_name>/<project_name>/logs/<training_job_name>, where the project name is tensorboard_keras_cifar10.

The notebook also demonstrates how to run TensorBoard inside of the Amazon SageMaker notebook instance. This method has some limitations; for example, the TensorBoard command blocks the run of the notebook and lives as long as the notebook instance is alive, but allows you to quickly access the dashboard and make sure the training is running correctly.

In the following sections, we look at other ways to run TensorBoard.

Running TensorBoard on your local machine

If you want to run TensorBoard locally, the first thing you need to do is to install TensorFlow:

pip3 install tensorflow

An independent distribution of TensorBoard is also available, but it has limited functionality if run without TensorFlow. For this post, we use TensorBoard as part of the TensorFlow distribution.

Assuming your AWS Command Line Interface (AWS CLI) is installed and configured properly, we simply run TensorBoard pointing to the Amazon S3 directory containing the generated summary data:

AWS_REGION=eu-west-1 tensorboard --logdir s3://<bucket_name>/tensorboard_keras_cifar10/logs/

You must specify the region where your S3 bucket is located. You can find the right region in the list of buckets on the Amazon S3 console.

The user you use must have read access to the specified S3 bucket. For more information about securely granting access to S3 buckets to a specific user, see Writing IAM Policies: How to Grant Access to an Amazon S3 Bucket.

You should see something similar to the following screenshot.

Running TensorBoard on Amazon ECS on AWS Fargate

If you prefer to have an instance of TensorBoard permanently running and accessible to your whole team, you can deploy it as an independent application in the cloud. One of the easiest ways to do this without managing servers is AWS Fargate, a serverless compute engine for containers. The following diagram illustrates this architecture.

You can deploy an example TensorBoard container image with all required roles and an Application Load Balancer by using the provided AWS CloudFormation template:

This template has five input parameters:

  • TensorBoard container image – Use tensorflow/tensorflow for a standard distribution or a custom container image if you want to enable the Profiler plugin
  • S3Bucket – Enter the name of the bucket where TensorFlow logs are stored
  • S3Prefix – Enter the path to the TensorFlow logs inside of the bucket; for example, tensorboard_keras_cifar10/logs/
  • VpcId – Select the VPC where you want TensorBoard to be deployed to
  • SubnetId – Select two or more subnets in the selected VPC

This example solution doesn’t include authorization and authentication mechanisms. Remember that if you deploy TensorBoard to a publicly accessible subnet, your TensorBoard instance and training logs are accessible to everyone on the internet. You can secure TensorBoard with the following methods:

After you create the CloudFormation stack, you can find the link to the deployed TensorBoard on the Outputs tab on the AWS CloudFormation console.

Using a custom TensorBoard container image

Because TensorBoard is part of the TensorFlow distribution, we can use the official tensorflow Docker container image hosted on Docker Hub.

Optionally, we can build a custom image with the optional Profiler TensorBoard plugin to visualize profiling data:

#Dockerfile
FROM tensorflow/tensorflow RUN python3 -m pip install --upgrade --no-cache-dir tensorboard_plugin_profile EXPOSE 6006 ENTRYPOINT ["tensorboard"]

You can build and test the container locally:

docker build -t tensorboard . docker run -p 6006:6006 --env AWS_ACCESS_KEY_ID=XXXXX --env AWS_SECRET_ACCESS_KEY=XXXXX --env AWS_REGION=eu-west-1 tensorboard --logdir s3://bucket_name/tensorboard_keras_cifar10/logs/

After testing the container, you need to push it to a container image repository of your choice. Detailed instructions on deploying an application aren’t in the scope of this post. To set up Amazon ECS and Elastic Load Balancer, see Building, deploying, and operating containerized applications with AWS Fargate.

Conclusion

In this post, I showed you how to use TensorBoard to visualize TensorFlow training jobs using Amazon S3 as storage for the logs. You can use this solution and the example notebooks to build and train a model with Amazon SageMaker and run a hyperparameter tuning job. You can use TensorBoard to compare hyperparameters from different training runs, generate and display confusion matrices for the classifier, and profile and visualize the training job’s performance.


About the Author

Yegor Tokmakov is a solutions architect at AWS, working with startups. Before joining AWS, Yegor was Chief Technology Officer at a healthcare startup based in Berlin and was responsible for architecture and operations, as well as product development and growth of the tech team. Yegor is passionate about novel AI applications and data analytics. You can find him at @yegortokmakov on Twitter.

Source: https://aws.amazon.com/blogs/machine-learning/visualizing-tensorflow-training-jobs-with-tensorboard/

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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