One of the most well-known tools for developing machine learning models is TensorFlow. We use TensorFlow in many applications in various industries.
In this post, we’ll examine some of the TensorFlow AI models. Hence, we can create intelligent systems.
We will also go through frameworks that TensorFlow offers for creating AI models. So let’s get started!
A Brief Introduction to TensorFlow
Google’s TensorFlow is an open-source machine learning software package. It includes tools for training and deploying machine learning models on many platforms. and devices, as well as support for deep learning and neural networks.
TensorFlow enables developers to create models for a variety of applications. This includes image and audio recognition, natural language processing, and computer vision. It’s a strong and adaptable tool with widespread community support.
To install TensorFlow on your computer you can type this in your command window:
pip install tensorflow
How Do AI Models Work?
AI models are computer systems. Therefore, they are meant to do activities that would ordinarily need human intellect. Image and speech recognition and decision-making are examples of such tasks. AI models are developed on massive datasets.
They employ machine learning techniques to generate predictions and perform actions. They have several uses, including self-driving automobiles, personal assistants, and medical diagnostics.
So, what are the popular TensorFlow AI models?
ResNet, or Residual Network, is a form of convolutional neural network. We use it for image categorization and object detection. It was developed by Microsoft researchers in 2015. Also, it is mainly distinguished by the use of residual connections.
These connections allow the network to learn successfully. Hence, it is possible by enabling information to flow more freely between the layers.
ResNet may be implemented in TensorFlow by leveraging the Keras API. It provides a high-level, user-friendly interface for creating and training neural networks.
After installing TensorFlow, you may use the Keras API to create a ResNet model. TensorFlow includes the Keras API, so you don’t need to install it individually.
You may import the ResNet model from tensorflow.keras.applications. And, you can select the ResNet version to use, for example:
from tensorflow.keras.applications import ResNet50
You can also use the following code to load pre-trained weights for ResNet:
model = ResNet50(weights='imagenet')
By selecting the property include_top=False, you may additionally utilize the model for additional training or fine-tuning your custom dataset.
model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
ResNet’s Areas of Usage
ResNet may be used in image classification. So, you can categorize photos into many groups. First, you need to train a ResNet model on a large dataset of labeled photos. Then, ResNet can predict the class of previously unseen images.
ResNet may also be used for object detection tasks like detecting things in photos. We can do this by first training a ResNet model on a collection of photos labeled with object-bounding boxes. Then, we can apply the learned model to recognize objects in fresh images.
We can also use ResNet for semantic segmentation tasks. So, we can assign a semantic label to each pixel in an image.
Inception is a deep learning model capable of recognizing things in images. Google announced it in 2014, and it analyzes images of various sizes using many layers. With Inception, your model can comprehend the image accurately.
TensorFlow is a strong tool for creating and running Inception models. It provides a high-level and user-friendly interface for training neural networks. Hence, Inception is a pretty straightforward model to apply for developers.
You can install Inception by typing out this line of code.
from tensorflow.keras.applications import InceptionV3
Inception’s Areas of Usage
The Inception model may also be used to extract features in deep learning models like Generative Adversarial Networks (GANs) and Autoencoders.
The Inception model may be fine-tuned to identify specific traits. Also, we may be able to diagnose certain disorders in medical imaging applications such as X-ray, CT, or MRI.
The Inception model may be fine-tuned to check image quality. We can evaluate whether an image is fuzzy or crisp.
Inception may be used for video analysis tasks such as object tracking and action detection.
BERT (Bidirectional Encoder Representations from Transformers) is a Google-developed pre-trained neural network model. We may use it for a variety of natural language processing tasks. These tasks can vary from text categorization to answering questions.
BERT is built on transformer architecture. Hence, you can handle vast volumes of text input while comprehending word connections.
BERT is a pre-trained model that you can incorporate into TensorFlow applications.
TensorFlow includes a pre-trained BERT model as well as a collection of utilities for fine-tuning and applying BERT to a variety of tasks. Thus, you can easily integrate BERT’s sophisticated natural language processing capabilities.
Using the pip package manager, you can install BERT in TensorFlow:
pip install tensorflow-gpu==2.2.0 # This installs TensorFlow with GPU support
pip install transformers==3.0.0 # This installs the transformers library, which includes BERT
TensorFlow’s CPU version may easily be installed by substituting tensorflow-gpu with tensorflow.
After installing the library, you may import the BERT model and utilize it for different NLP tasks. Here is some sample code for fine-tuning a BERT model on a text classification problem, for example:
from transformers import BertForSequenceClassification
# Load the pre-trained BERT model
model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
# Fine-tune the model on your text classification task
# Make predictions on new data
predictions = model.predict(test_data)
BERT’s Areas of Usage
You can perform text classification tasks. For example, it is possible to achieve sentiment analysis, topic categorization, and spam detection.
BERT has a Named Entity Recognition (NER) feature. Hence, you can recognize and label entities in text such as persons and organizations.
It can be used to answer queries depending on a particular context, such as in a search engine or chatbot application.
BERT may be useful for Language Translation to increase machine translation accuracy.
BERT may be used for text summarization. Hence, it can provide a brief, useful summaries of lengthy text documents.
Baidu Research created DeepVoice, a text-to-speech synthesis model.
It was created with the TensorFlow framework and trained on a big collection of voice data.
DeepVoice generates voice from text input. DeepVoice makes it possible by using deep learning techniques. It is a neural network-based model.
Hence, it analyzes input data and generates speech using a huge number of layers of connected nodes.
!pip install deepvoice
# Clone the DeepVoice repository
!git clone https://github.com/r9y9/DeepVoice3_pytorch.git
!pip install -r requirements.txt
DeepVoice’s Ares of Usage
You can use DeepVoice to produce speech for personal assistants like Amazon Alexa and Google Assistant.
Also, DeepVoice may be used to produce speech for voice-enabled devices like smart speakers and home automation systems.
DeepVoice can create a voice for speech therapy applications. It can assist patients with speech problems to improve their speech.
DeepVoice may be used to create a speech for educational material like audiobooks and language learning apps.