Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs). The success of PyTorch is attributed to its simplicity, first-class Python integration, and imperative style of programming. Since the launch of PyTorch in 2017, it has strived for high performance and eager execution. It has provided some of the best abstractions for distributed training, data loading, and automatic differentiation. With continuous innovation from the PyTorch team, PyTorch has moved from version 1.0 to the most recent version, 1.13. However, over all these years, hardware accelerators like GPUs have become 15x and 2x faster in compute and memory access, respectively. Thus, to leverage these resources and deliver high-performance eager execution, the team moved substantial parts of PyTorch internals to C .

Ease of use, expressivity, and debuggability are among the core principles of PyTorch. One of the key drivers for the ease of use is that PyTorch execution is by default "eager, i.e. op by op execution preserves the imperative nature of the program. However, eager execution does not offer the compiler based optimization, for example, the optimizations when the computation can be expressed as a graph. LazyTensor [1], first introduced with PyTorch/XLA, helps combine these seemingly disparate approaches. While PyTorch eager execution is widely used, intuitive, and well understood, lazy execution is not as prevalent yet.

I have been using Pytorch for several years now, and always enjoyed it. It is clear, intuitive, flexible and fast. And then I was confronted with an opportunity to do my new computer vision projects on TensorFlow. This is where this story begins. TensorFlow is a well-established widely used framework. It couldn't be that bad, I said to myself.

Now that we have seen Tensorboard in action, we've finished the core concepts of TensorFlow. However, TensorFlow is not as useful if we do not apply it for machine learning. We will now walk through Keras, TensorFlow's simple API for training neural networks. Simply put, Keras makes it quite simple to train and test your machine learning models. Let's now demonstrate Keras in action.

TensorFlow World, October 28-31, 2019, in Santa Clara, California, is the best place to learn all about TensorFlow 2.0. With the recent release of TensorFlow 2.0 and TensorFlow World coming soon, we talked to Paige Bailey, TensorFlow product manager at Google, to learn how TensorFlow has evolved and where it and machine learning (ML) are heading. She also gave us a rundown on notable updates in TensorFlow 2.0. TensorFlow was open sourced by Google in 2015 to improve the speed, scalability, and usability for machine learning researchers who were interested in prototyping algorithms in Python rather than C . Get a free trial today and find answers on the fly, or master something new and useful.

In 2019, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators for scaled serving, and the two APIs are very convenient to use. The writing of code was divided into two parts: building the computational graph and later creating a session to execute it.

TensorFlow uses Eager execution, which is a more convenient way to execute the code, and also more "Pythonic". It is a default choice in the latest version TensorFlow 2.0. In TensorFlow 1.x, we first need to write a Python program that constructs a graph for our computation, the program then invokes Session.run(), which hands the graph off for execution to the C runtime. This type of programming is called declarative programming (specification of the computation is separated from the execution of it). So, Sessions provide one way to execute these compositions.

TensorFlow 2.0, released in October, has got developers excited about a myriad of features and its ease of use. At the EuroPython Conference 2019, Brad Miro, developer programs engineer at Google talked about the updates being made to TensorFlow 2.0. He also gave an overview of how Google is using TensorFlow, moving on to why Python is important for TensorFlow development and how to migrate from TF 1.x to TF 2.0. EuroPython is one of the most popular Python programming language community conferences. Below are some highlights from Brad's talk at EuroPython.

I started my journey in Machine Learning around 2015 when I was in my late teens. Without any clear vision about the field, I read many articles and watched a ton of YouTube videos. I did not have any clue what the field was or how it works. That was the time when Google's popular Machine Learning library, Tensorflow was released. Tensorflow was released in November 2015 as an'Open Source Software Library for Machine Intelligence'.

In this tutorial you'll discover the difference between Keras and tf.keras, including what's new in TensorFlow 2.0. Today's tutorial is inspired from an email I received last Tuesday from PyImageSearch reader, Jeremiah. Hi Adrian, I saw that TensorFlow 2.0 was released a few days ago. TensorFlow developers seem to be promoting Keras, or rather, something called tf.keras, as the recommended high-level API for TensorFlow 2.0. But I thought Keras was its own separate package?