Tensorflow 1.xvs. Tensorflow 2.x: What’s the Difference?

TensorFlow is an end-to-end open-source machine learning platform that contains comprehensive tools, libraries and community resources. It is meant for developers, data scientists and researchers to build and deploy applications powered by machine learning. TensorFlow was essentially built to scale, developed by Google Brain team, TensorFlow accelerates ML and deep neural network research. It can run on multiple CPUs or GPUs and mobile operating systems. Also, it has several wrappers in languages like Python, C++, or Java.

What is TensorFlow 2.0?

TensorFlow 2.0 is an updated version of TensorFlow that has been designed with a focus on simple execution, ease of use, and developer’s productivity. TensorFlow 2.0 makes the development of data science and machine learning applications even easier. With updated features like tight integration of Keras, default Eager execution, and Pythonic function execution, there have been considerable and successful efforts to make the experience of developing applications more familiar for Python developers.

The TensorFlow team has also invested heavily in low-level API this time. All the ops that are used internally are now exported and inheritable interfaces for concepts such as variables and checkpoints are provided. So, as you build onto the internals of TensorFlow, you won’t have to rebuild TensorFlow.

Key Differences Between TensorFlow 1.x and TensorFlow 2.x

TensorFlow 1.x and TensorFlow 2.x are two major versions of the TensorFlow library, with TensorFlow 2.x being a significant evolution and improvement over TensorFlow 1.x.

Here are some of the key differences between the two versions: –

Parameter	TensorFlow 1.x	TensorFlow 2.x
Eager Execution	TensorFlow 1.x by default used a symbolic graph approach where you first define a computation graph and then execute it within a session.	TensorFlow 2.x, on the other hand, uses eager execution by default, which means operations are executed immediately and you can use Python control flow structures naturally, making it more intuitive and easier to debug.
API Simplification	TensorFlow 1.x contains many redundant or outdated APIs.	TensorFlow 2.x introduces a more simplified and consistent API compared to TensorFlow 1.x. Many redundant or outdated APIs have been removed or consolidated in TensorFlow 2.x, making it easier for users to learn and use the library.
Keras Integration	In TensorFlow 1.x, the high-level Keras API is not tightly integrated as the default API for model building. Keras does not offer a simple and intuitive interface for constructing neural networks within TensorFlow 1.x. Instead, developers are encouraged to use other APIs or frameworks for defining and training models in TensorFlow 1.x.	TensorFlow 2.x tightly integrates the high-level Keras API as the default API for model building. Keras provides a simple and intuitive interface for building neural networks, and in TensorFlow 2.x, Keras is the recommended way to define and train models.
Model Building	In TensorFlow 1.x, model building often involved a mix of low-level TensorFlow operations and high-level APIs like tf.layers and tf.contrib.	TensorFlow 2.x promotes the use of Keras layers and models for building neural networks, providing a higher-level interface that is more user-friendly.
Automatic Differentiation	TensorFlow 1.x lacks a sophisticated and user-friendly automatic differentiation system. The absence of the tf.GradientTape API hinders dynamic computation of gradients, thereby complicating the implementation of custom training loops and advanced optimization algorithms.	TensorFlow 2.x includes a more advanced and user-friendly automatic differentiation system compared to TensorFlow 1.x. The tf.GradientTape API allows for dynamic computation of gradients, making it easier to implement custom training loops and more advanced optimization algorithms.
Compatibility	TensorFlow 1.x does not maintain compatibility with TensorFlow 2.x models through any specific module or mechanism. Users transitioning from TensorFlow 2.x to TensorFlow 1.x may encounter challenges in migrating their existing code due to differences in APIs, functionalities, and features between the two versions.	TensorFlow 2.x maintains compatibility with TensorFlow 1.x models through the tf.compat.v1 module, which allows users to migrate their existing TensorFlow 1.x code to TensorFlow 2.x gradually.
Performance	TensorFlow 1.x lacks the optimizations and improvements present in TensorFlow 2.x, which can lead to inferior performance, particularly in eager execution mode.	TensorFlow 2.x includes optimizations and improvements that may result in better performance in some cases, especially for eager execution mode.
Distribution Strategy	TensorFlow 1.x does not introduce tf.distribute.Strategy, a high-level API for distributed training, which complicates scaling training across multiple GPUs or TPUs.	TensorFlow 2.x introduces tf.distribute.Strategy, a high-level API for distributed training, making it easier to scale training across multiple GPUs or TPUs.
Community and Support	As TensorFlow 1.x is an earlier version of the library, it may not receive as much attention and support from the TensorFlow development team, and the community compared to TensorFlow 2.x.	As TensorFlow 2.x is the latest version of the library, it receives more attention and support from the TensorFlow development team and the community, including bug fixes, new features, and tutorials.
No more globals	TensorFlow relied on implicitly global namespaces. So, what happened is – if you called tf.variable(), it would be put into the default graph. And even if you lose the Python variable pointing to it, it will remain there, nonetheless. You could only recover that tf.variable if you knew the name it was created with. Now, since the user doesn’t control the creation of the variable, it was extremely difficult to recover it.	In TensorFlow 2.0, developers have taken care of this issue. If you lose track of tf.variable in TensorFlow 2.0, it will be garbage collected.

TensorFlow 2.x offers a more streamline and user-friendly experience for developing, training and deploying machine learning models compared to TensorFlow 1.x.

Coding Experience in TensorFlow 1.x and TensorFlow 2.x

TensorFlow 1.x	TensorFlow 2.x
Python3 import tensorflow.compat.v1 as tf tf.disable_v2_behavior() # Define the input placeholders a = tf.placeholder(tf.float32) b = tf.placeholder(tf.float32) # Define the operation sum = tf.add(a, b) # Start a session and run the operation with tf.Session() as sess: result = sess.run(sum, feed_dict={a: 5.0, b: 3.0}) print("Sum of two numbers:", result) Output: Sum of two numbers: 8.0	Python3 import tensorflow as tf #Define the input constants a = tf.constant(5.0) b = tf.constant(3.0) #Define the operation sum = tf.add(a,b) #Print the result print('sum of two numbers', sum.numpy()) Output: sum of two numbers 8.0

In TensorFlow 1.x, placeholders are defined for inputs and operations are executed explicitly within a session. Whereas in TensorFlow 2.x, the user simply provided the constants and eager execution, the operation was evaluated without the need of session. TensorFlow 2.x utilizes the constants or variables directly.

TensorFlow 1.x requires starting a session and running operations within it, while TensorFlow 2.x defaults to eager execution, making the code more concise and intuitive.