Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Minor Errors #2007

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Minor Errors #2007

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0f2f4f4
Minor Erros
Raheesha Jun 17, 2024
2a4c32c
Updated README.md
Raheesha Jun 17, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 7 additions & 8 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# What's new in TensorFlow 2.x

The following are all the changes coming in TensorFlow 2.x. Let's have a closer look at them:
The following are all the changes coming in TensorFlow 2. x. Let's have a closer look at them:

* Eager Execution / tf.function
* Integration of the Keras API
Expand All @@ -23,9 +23,9 @@ Lack of eager execution was one of the main complaints against TensorFlow. We al

With TensorFlow 2.0, eager execution is activated by default and the very cool thing is that the code nearly doesn't change. Under the hood, you are just working with so-called "EagerTensors" instead of "Tensors" but since they share the same interface, the difference is barely noticeable. Even in execution speed, the difference is hard to see.

This means, from now on, TensorFlow code can be used and debugged as ordinary python code (using numpy for example). This is one aspect of making TensorFlow more pythonic.
This means, from now on, TensorFlow code can be used and debugged as ordinary Python code (using Numpy for example). This is one aspect of making TensorFlow more pythonic.

Below there are two tasks. I highly recommend doing them because while watching me coding and coding yourself you'll definitely internalize the material.
Below there are two tasks. I highly recommend doing them because while watching me coding and coding yourself you'll internalize the material.

### Tasks

Expand All @@ -36,13 +36,13 @@ Below there are two tasks. I highly recommend doing them because while watching

## Integration of the Keras API

Actually, Keras is one of the greatest APIs on the planet for DeepLearning. Now Keras has been eaten up by TensorFlow. A bit sad, but in reality it doesn't make any difference since nearly everyone used Keras on top of TensorFlow anyway. So let's consider Keras to be part of TensorFlow (or TensorFlow to be part of Keras). The cool thing is, that you now can use the straightforward, and easy to use Keras API and still can claim to be a TensorFlow developer. Yeah, Google made Keras the official high level API of TensorFlow.
Keras is one of the greatest APIs on the planet for DeepLearning. Now Keras has been eaten up by TensorFlow. A bit sad, but in reality it doesn't make any difference since nearly everyone used Keras on top of TensorFlow anyway. So let's consider Keras to be part of TensorFlow (or TensorFlow to be part of Keras). The cool thing is, that you now can use the straightforward, and easy-to-use Keras API and still can claim to be a TensorFlow developer. Yeah, Google made Keras the official high-level API of TensorFlow.

So you might think, so what? Just some imports change. But this is only one part of the story. Yes, the imports changed, and as you can see later in the example, you can basically leave your existing Keras code intact most of the times and just change the import and you are done.
So you might think, so what? Just some imports change. But this is only one part of the story. Yes, the imports changed, and as you can see later in the example, you can basically leave your existing Keras code intact most of the time and just change the import and you are done.

But in addition, Keras now can make use of built-in TensorFlow functionality which wasn't possible before. For example, you can take your 1:1 Keras code and TensorFlow will scale it to a large GPU or TPU cluster. We'll have a look at this in the next chapter.
In addition, Keras now can make use of built-in TensorFlow functionality which wasn't possible before. For example, you can take your 1:1 Keras code and TensorFlow will scale it to a large GPU or TPU cluster. We'll have a look at this in the next chapter.

For now, just follow along the video and code exercise below to get an idea how things work:
For now, just follow along with the video and code exercise below to get an idea of how things work:

### Tasks

Expand All @@ -54,4 +54,3 @@ For now, just follow along the video and code exercise below to get an idea how


If you want to learn more, please have a look at our [book](https://learning.oreilly.com/library/view/whats-new-in/9781492073727/)