Machine learning involves lots of numerical data handling and processing. TensorFlow provides a Python API for data handling and processing. For data handling, we can directly use Python API with data files and database connections. Python API is very robust and powerful, but there are situations when it is not flexible enough, especially when we want to use other programming languages. In those cases, TensorFlow supports other languages as well. Some of the supported languages are C, C++, R, Scala, Haskell, Python, Julia, and so on. The supported languages help in many ways. One of the ways they help is by providing a language-native API for data handling. This can be very helpful when the data handling language is not Python, such as Microsoft Excel. Other languages provide a language-native API for data processing, which can be very helpful when the language does not have the ability to process data, such as when we want to use a high-level language.

TensorFlow for Microsoft Excel

Suppose you want to process data in Microsoft Excel with TensorFlow. The first step is to import the necessary libraries from PyPI and data files from your local machine. Then, you need to create an instance of Tensorflow. Next, you need to load the data into the tensorflow_dataset() function as a list of values. Finally, you can run the model on your dataset by passing in your Python list and returning a Keras object.
# Import necessary libraries
import tensorflow as tf
# Create an instance of TensorFlow
tfs = tf.TF()
# Loading data into the tensorflow_dataset(). A list of values needs to be passed here
tfds = tf.data.Dataset({"x": ["1", "2", "3"]})
# Now run on the dataset using Keras object; this will return predictions/results

Python – Language for Data Handling

If we have a data file in Python, we can use the Python API to handle that file. We can also use a Python script.
A simple example of handling a data file with TensorFlow is shown below:
def main():
url = "https://youtube.com"
image = tf.gfile.Open(url)
result = image.read()
print("Processing %s"% result)
You can see that the code is pretty straightforward and easy to understand, because it uses the Python API for handling files, which makes it much easier than using other languages. Similarly, when we want to process data with other languages, such as C and C++, we can simply use their language-native API for data processing:

C++

C++ has data handling and processing APIs that are language-native. This is because C++ can directly use C API to access TensorFlow, which means that we do not need to use Python API in the C++ code.

This is very helpful when we want to use a high-level language.

C++ API

The C++ API supports:
- access from Python, Haskell, or any language supporting the C++ Standard Library.
- a library for data access in C++.
- support for many common machine learning tasks, such as classification and regression.
In addition to the supported languages, TensorFlow also supports a variety of programming languages that can be used with TensorFlow. One of these supported languages is C++. The C++ API isn't natively available to Python or other supported languages, but it's still possible to use it with them because it's actually built on top of these supported languages. The language-native APIs make it possible to seamlessly use TensorFlow with a high-level language that doesn't have native support for processing machine learning data.

C++ - TensorFlow.org

This article provides an example of using C++ with TensorFlow. TensorFlow provides a language-native API for data handling and processing. It is more convenient to use C++ with TensorFlow than the Python API because it offers extended features, such as support for multidimensional arrays and built-in linear algebra operations. In this guide, we will explore how to set up and use the language-native API in C++.

Timeline

Published on: 09/16/2022 23:15:00 UTC
Last modified on: 09/20/2022 14:40:00 UTC

References