AI Engineer asks what’s the difference between TensorFlow 1.0 and Tensorflow 2.0

Since the day Google had released TensorFlow 1.0 in 2017, it gained immediate popularity with machine learning engineers as one of the open-source machine learning libraries. However, two years later, when Google launched its updated version – TensorFlow 2.0 on 30^th September 2019 – the entire AI community went into a frenzy.

With AI Engineers around the world debating about the differences between TensorFlow 1.0 and TensorFlow 2.0, it became important to understand the differences between the two.

But before we delve into the differences between the two let’s have a look at some of the TensorFlow facts –

• With TensorFlow, almost all the genres of industries were transformed including banking, healthcare, agriculture, pharma to name a few
• TensorFlow had played a pivotal role in enabling organizations to leverage AI and thus make their services or products better than before
• Talk about being an apple of somebody’s eyes, well TensorFlow quickly became one for almost all the industry biggies including LinkedIn, Twitter, PayPal to name a few
• Tensor Flow’s use cases – sentiment analysis, object, and video detection, including image and speech recognition

These were the reasons why TensorFlow 1.0 became such a hit with deep learning enthusiasts. However, there were some loopholes in this renowned open-source ML library, which usually made AI Engineers turn toward other high-level options like PyTorch and Keras.

So again what is the difference between the two?

According to the TensorFlow team, there is no battle between the two versions as TensorFlow 2.0 is an updated version of TensorFlow 2.0.

The makers of TensorFlow have worked diligently on all the drawbacks of TensorFlow 1.0. They had liaised with AI engineers and deep learning enthusiasts to understand the problem areas and then went to work on them. Result: TensorFlow 2.0 – a smarter, a better, and definitely an easier version of TensorFlow 1.0.

Since TensorFlow 1.0 was significantly difficult to understand thus complex to use, the updated version i.e. TensorFlow 2.0 was comparatively easier and simpler.

The real picture behind the up-gradation of TensorFlow 1.0!

The exhausting code of TensorFlow baffled both AI experts and deep learning beginners.

Reason: the coding logic being entirely different from the other available libraries. Result: Other higher-level packages like Keras and Pytorch gained popularity.

Interestingly, both Keras and TensorFlow are open-source and though in 2017 Keras was incorporated into TensorFlow, TensorFlow was losing its popularity at the top speed.

However, when TensorFlow 2.0 came into the picture in 2019 things begin to change for this open-source ML library. It was the new avatar of old TensorFlow with some of the features intact and others simplified – old wine in a new bottle – was what came to the minds of AI engineers around the world.

So what was changed or tweaked?

Here’s what happened!

As mentioned earlier, the difficulty to understand the coding of TensorFlow was baffling deep learning enthusiasts, so the makers simplified the coding process.

Was it that easy?

Well, not really! There were other aspects as well.

One of the reasons TensorFlow 2.0 is better than its previous version is that the developers instead of creating their own high-level syntax borrowed it from Keras. Yes, the result was – the versatility of Tensorflow 1.0 and Keras’ simplicity.

Now, that it is clear why TensorFlow 2.0 is a better version of its older self, let’s have a look at some of the major differences between the two.

• TensorFlow 2.0 has Keras as its high-level API – easier for beginners to start with TensorFlow
• TensorFlow 2.0 is compatible with the entire TensorFlow ecosystem —  TensorFlow Lite, TensorFlow.js, and TensorFlow Extended to name a few
• In the current version, APIs have either moved or have been replaced with their updated versions
• TensorFlow 2.0 is for both experts and beginners and offers an amazing experience for advanced level users.

With all these upgradation it is not surprising that TensorFlow 2.0 has replaced its previous version and became popular with both beginners and experts as well.