Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications, and much more. It is used in research and for production purposes. It has optimization techniques that help in performing complicated mathematical operations quickly.
This is because it uses NumPy and multi-dimensional arrays. These multi-dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural networks. It is highly scalable and comes with many popular datasets. It uses GPU computation and automates the management of resources. It comes with multitude of machine learning libraries and is well-supported and documented. The framework has the ability to run deep neural network models, train them, and create applications that predict relevant characteristics of the respective datasets.
The ‘tensorflow’ package can be installed on Windows using the below line of code −
pip install tensorflow
Keras was developed as a part of the research for the project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). Keras is a deep learning API, which is written in Python. It is a high-level API that has a productive interface that helps solve machine learning problems. It is highly scalable and comes with cross-platform abilities. This means Keras can be run on TPU or clusters of GPUs. Keras models can also be exported to run in a web browser or a mobile phone as well.
Keras is already present within the Tensorflow package. It can be accessed using the below line of code.
import tensorflow from tensorflow import keras
We are using Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook. Following is the code snippet to explore the dataset associated with predicting tag for a StackOverflow question in Python −
print("Downloading tensorflow-text") !pip -q install tensorflow-text import collections import pathlib import re import string import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import losses from tensorflow.keras import preprocessing from tensorflow.keras import utils from tensorflow.keras.layers.experimental.preprocessing import TextVectorization import tensorflow_datasets as tfds import tensorflow_text as tf_text data_url = 'https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz' dataset = utils.get_file( 'stack_overflow_16k.tar.gz', data_url, untar=True, cache_dir='stack_overflow', cache_subdir='') dataset_dir = pathlib.Path(dataset).parent
Code credit − https://www.tensorflow.org/tutorials/load_data/text
Downloading tensorflow-text Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz 6053888/6053168 [==============================] - 0s 0us/step
The required packages are imported.
The data is loaded from the api.