PyTorch vs TensorFlow for Your Python Deep Learning Project :

PyTorch vs TensorFlow for Your Python Deep Learning Project
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PyTorch vs TensorFlow: What’s the difference? Both are open-source Python libraries that use graphs to perform numerical computations on data in deep learning applications. Both are used extensively in academic research and commercial code. Both are extended by a variety of APIs, cloud computing platforms, and model repositories.

If they’re so similar, then how do you decide which one is best for your project?

In this tutorial, you’ll learn:

  • What the differences are between PyTorch and TensorFlow
  • What tools and resources are available for each
  • How to choose the best option for your specific use case

You’ll start by taking a close look at both platforms, beginning with the slightly older TensorFlow. Then, you’ll explore PyTorch and some considerations to help you determine which choice is best for your project. Let’s get started!

What Is TensorFlow?

TensorFlow was developed by Google and released as open-source in 2015. It grew out of Google’s homegrown machine learning software, which was refactored and optimized for use in production.

The name “TensorFlow” describes how you organize and perform operations on data. The basic data structure for both TensorFlow and PyTorch is a tensor. When you use TensorFlow, you perform operations on the data in these tensors by building a stateful dataflow graph, kind of like a flowchart that remembers past events.

Who Uses TensorFlow?

TensorFlow has a reputation for being a production-grade deep learning library. It has a large and active user base and a proliferation of official and third-party tools and platforms for training, deploying, and serving models.

After PyTorch was released in 2016, TensorFlow declined in popularity. But in late 2019, Google released TensorFlow 2.0—a major update that simplified the library and made it more user-friendly, leading to renewed interest among the machine learning community.

Code Style and Function

In TensorFlow 2.0, you can use eager execution, which is how Python normally works. Eager execution evaluates operations immediately, so you can write your code using Python control flow rather than graph control flow. To see this in action, you’ll take a look at how you would multiply two tensors using both Tensorflow 1.0 and 2.0.

To start, here’s an example of how to multiply tensors using TensorFlow 2.0. With eager execution, all you need is tf.math.multiply():

>>> import tensorflow as tf

>>> x = [[2., 4., 6.]]
>>> y = [[1.], [3.], [5.]]
>>> m = tf.math.multiply(x, y)

>>> m
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[ 2.,  4.,  6.],
       [ 6., 12., 18.],
       [10., 20., 30.]], dtype=float32)>

In this code, you declare your tensors using Python’s list notation, and tf.math.multiply() executes the element-wise multiplication immediately when you call it.

Before TensorFlow 2.0, you had to manually stitch together an abstract syntax tree by making tf.* API calls. You then had to pass a set of output tensors and input tensors to a call and manually compile the model.

A Session object is a class for running TensorFlow operations. It contains the environment in which Tensor objects are evaluated and Operation objects are executed, and it can own resources like tf.Variable objects. The most common way to use a Session is as a context manager.

To see how a Session is used in this way, here’s an example of how you multiply two tensors using the old TensorFlow 1.0 method:

>>> import tensorflow as tf

>>> tf.compat.v1.disable_eager_execution()

>>> x = tf.compat.v1.placeholder(tf.float32, name = "x")
>>> y = tf.compat.v1.placeholder(tf.float32, name = "y")

>>> multiply = tf.math.multiply(x, y)

>>> with tf.compat.v1.Session() as session:
...     m =
...         multiply, feed_dict={x: [[2., 4., 6.]], y: [[1.], [3.], [5.]]}
...     )
...     print(m)
[[ 2.  4.  6.]
 [ 6. 12. 18.]
 [10. 20. 30.]]

This code uses TensorFlow 2.x’s tf.compat API to access TensorFlow 1.x methods and disable eager execution.

You first declare the input tensors x and y using tf.compat.v1.placeholder tensor objects. Then you define the operation to perform on them. Note that nothing has been calculated at this point.

Next, using the tf.Session object as a context manager, you create a container to encapsulate the runtime environment and do the multiplication by feeding real values into the placeholders with a feed_dict. Finally, still inside the session, you print() the result.

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May 08, 2024 at 07:30PM
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