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https://pyimagesearch.com/2024/04/15/exploring-gan-code-generation-with-gemini-pro-and-chatgpt-3-5-a-comparative-study/ | A significant portion of the tutorial focuses on generating, evaluating, and refining GAN codes for both Vanilla GAN and Deep Convolutional GAN (DCGAN) models. For each model, we discussed the positives, identified any negatives, and provided recommendations for improvement, accompanied by qualitative results to assess the generated images’ realism. Special attention was given to code corrections for DCGAN, ensuring practical functionality and effectiveness. Furthermore, the exploration ventured into understanding GAN architectures through ChatGPT-3.5’s perspective, generating codes for Vanilla GAN and DCGAN. Each generated code’s positives and negatives were meticulously analyzed, followed by a qualitative evaluation of the resultant images, highlighting the sophistication and potential of AI in generating complex neural network models. The emphasis on generative AI capabilities throughout this tutorial reflects a broader interest in exploring the frontiers of AI technology. It illustrates the evolving nature of AI systems, from mere tools of automation to sophisticated partners in creative and technical endeavors. As we concluded our exploration, it became evident that Gemini Pro and ChatGPT-3.5 are not just contributing to advancements in image synthesis but are also pivotal in shaping the future of AI-driven development and innovation. Citation Information
Sharma, A. “Exploring GAN Code Generation with Gemini Pro and ChatGPT-3.5: A Comparative Study,” PyImageSearch, P. Chugh, A. R. Gosthipaty, S. Huot, K. Kidriavsteva, and R. Raha, eds., 2024, https://pyimg.co/0rj4y
@incollection{Sharma_2024_GAN-ChatGPT35,
author = {Aditya Sharma},
title = {Exploring GAN Code Generation with Gemini Pro and ChatGPT-3.5: A Comparative Study,},
booktitle = {PyImageSearch},
editor = {Puneet Chugh and Aritra Roy Gosthipaty and Susan Huot and Kseniia Kidriavsteva and Ritwik Raha},
year = {2024},
url = {https://pyimg.co/0rj4y},
}
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Home » Blog » Integrating Document Embedding in Gemini Pro: An Approach to Retrieval-Augmented Generation
Table of Contents
Integrating Document Embedding in Gemini Pro: An Approach to Retrieval-Augmented Generation
Introduction to Document Embedding with Gemini Pro
The Essential Role of Embeddings
Setting Up Gemini Pro for Document Embedding and Generation
Implementing Document Embedding: Code Integration with Gemini Pro
Preparing Your Development Environment for Gemini Pro
Step 1: Installing the Google Generative AI Library
Step 2: Importing Essential Python Packages
Step 3: Securely Configuring Your API Key
Listing the Generative and Embedding Models
Selecting Models for Embedding and Generation
Initializing the Generation Model
Defining Documents for Embedding
Creating a DataFrame from Documents
Embedding Documents
Displaying Embedding Length
Query Embedding
Finding the Most Relevant Passage
Crafting a Prompt for the Generative Model
Generating a Response
Testing Gemini Pro with an Irrelevant Passage
Reflecting on Our Document Embedding Journey with Gemini Pro
Summary
Citation Information
Integrating Document Embedding in Gemini Pro: An Approach to Retrieval-Augmented Generation
In this tutorial, we will explore the exciting integration of document embedding with Gemini Pro to elevate the capabilities of generative artificial intelligence (AI). Leveraging the Google AI Python SDK (software development kit), this guide introduces a basic proof-of-concept on how to enhance text generation with Gemini Pro. By employing a retrieval-augmented approach, we demonstrate how embedding and dynamically utilizing documents can significantly enrich AI-driven content generation. This process enables Gemini Pro to tap into a wider array of information, paving the way for more informed and detailed outputs. While this tutorial focuses on establishing a foundational integration rather than implementing a full-fledged retrieval-augmented generation (RAG) system, it marks a pivotal first step toward seamlessly combining an embedding model from Google with Gemini Pro to improve generative outcomes. This lesson is the last in a 6-part series on Gemini Pro:
Introduction to Gemini Pro VisionImage Processing with Gemini ProImage Classification with Gemini ProConversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI DialogueExploring GAN Code Generation with Gemini Pro and ChatGPT-3.5: A Comparative StudyIntegrating Document Embedding in Gemini Pro: An Approach to Retrieval-Augmented Generation (this tutorial)
To learn how to enhance generative AI by integrating document embedding with Gemini Pro through the Google AI Python SDK, enabling a retrieval-augmented approach for more informative and detailed discussions, just keep reading. Introduction to Document Embedding with Gemini Pro
Welcome to a unique lesson in our Google Gemini series that ventures into uncharted territories of generative AI. This tutorial diverges from our previous focus on image classification and processing code generation, steering instead toward the innovative integration of document embedding within Gemini Pro, powered by the Google AI Python SDK. Our objective is to unveil how document embedding can significantly enhance Gemini Pro’s generative AI, enabling it to engage in more informed and context-rich text generation. This tutorial introduces a departure from direct code generation to a conceptual demonstration of retrieval-augmented generation. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Here, we illustrate the process of enriching Gemini Pro’s dialogue capabilities by embedding and dynamically utilizing textual documents. Through a practical example involving documents on diverse topics like Microservices with Docker, TensorFlow for Deep Learning, and Internet of Things (IoT) Device Security, we explore how to incorporate this cutting-edge technique into generative AI workflows. By embedding documents into the generative context, Gemini Pro can draw from a vast knowledge base, providing outputs that are not only accurate but deeply rooted in contextual understanding. Our journey entails a hands-on demonstration of creating a Python script that processes document embedding and retrieval in a generative setting. We begin with the preparation of documents on various technical subjects, followed by embedding these documents into a format that Gemini Pro can understand and utilize within generation. The core of this tutorial revolves around leveraging the models/embedding-001 model to generate embeddings for both the documents and user queries, facilitating a seamless retrieval process that matches queries with the most relevant document content. This approach not only showcases Gemini Pro’s versatility beyond code generation but also sets the stage for a new era of generative AI, where discussions are augmented with a depth of knowledge previously unattainable. Join us in this exploration as we demonstrate a basic yet powerful proof-of-concept that merges Google’s embedding model with Gemini Pro, aiming to transform how we interact with generative AI. Whether you’re an AI enthusiast, a developer seeking to enhance AI responses, or a content creator exploring the boundaries of generative AI, this tutorial promises a comprehensive understanding of integrating document embedding into generative models. Through this integration, we not only push the boundaries of what Gemini Pro can achieve but also offer a glimpse into the future of AI-driven generation enriched with unparalleled context and relevance. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Stay tuned as we dive into the technicalities, challenges, and breakthroughs of bringing document embedding into the realm of generative AI with Gemini Pro. The Essential Role of Embeddings
The concept of embeddings stands as a cornerstone in the evolution of artificial intelligence (AI) and machine learning, offering a sophisticated mechanism to encode text, words, documents, or even images into a format that machines can intuitively process. This method transforms complex, high-dimensional data into a more manageable, lower-dimensional space, significantly enhancing AI models’ ability to decipher language, context, and meaning — far surpassing older methods like numeric or one-hot encoding. Embeddings go beyond merely noting the existence of words or phrases; they intricately map out the relationships and contextual similarities between them. In the domain of natural language processing (NLP), this means that words with similar meanings are represented similarly within the vector space. This proximity emerges from learning from real-world data usage. Hence, embeddings serve as a vital instrument for semantic search, text analysis, and notably, in refining generative AI’s comprehension and response generation to mirror human-like interactions more closely. Document embeddings extrapolate the principle from singular words or sentences to entire texts, encapsulating their core themes or contents into dense vectors. Such advancements allow AI models, like Gemini Pro, to parse and extract information from extensive text collections efficiently. Integrating document embeddings into generative AI ushers in the era of retrieval-augmented generation, wherein AI can dynamically tap into an extensive knowledge base, ensuring outputs are not only accurate but contextually rich and relevant. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | By embedding and utilizing these document representations, generative AI transcends traditional chat functionalities; it begins to engage based on a profound understanding of content, marking a leap toward more intelligent, intuitive, and useful AI-driven responses. This capability is highlighted in the visual representations provided, where embeddings are depicted within a vector space, illustrating the semantic or contextual closeness of words or terms. Figure 1 effectively shows how related terms cluster closer together, underscoring how embeddings quantify language nuances beyond simple co-occurrence. Figure 1: Graphical representation of phrases and associated sounds in a vector space, depicting semantic relationships (source: Graphofsimilarembeddings.svg). Figure 2 delves into the multidimensional nature of these embeddings, portraying how words and phrases extend beyond a two-dimensional framework into a complex, multifaceted vector space. This intricate mapping is pivotal for capturing the full spectrum of language’s semantic richness, though we often simplify it to two or three dimensions for visualization purposes. Figure 2: Visualization of a Sentence Transformed into Multidimensional Vector Embeddings (source: vectors-1.svg). Document embeddings further this concept by situating entire documents within this high-dimensional space, facilitating the AI’s ability to discern thematic or content-based linkages across texts. Such a mechanism is invaluable for tasks demanding a deep grasp of document content, including information retrieval, document classification, and, notably, enriching generative AI. In the realm of generative AI, like that provided by Gemini Pro, embeddings revolutionize information access and processing. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Shifting away from mere keyword matching, these models leverage document embeddings to understand texts’ thematic essences, enabling outputs that are contextually apt and semantically coherent. This approach is critical for retrieval-augmented generation, allowing AI to provide informed, nuanced text generation that significantly transcends basic question-and-answer exchanges. Thus, the integration of word and document embeddings represents a pivotal stride in AI’s evolution toward more natural, engaging, and intelligent interaction paradigms. It lays the groundwork for systems like Gemini Pro not only to communicate but to comprehend and interact with users in a manner that closely emulates human understanding and responsiveness, promising a future where AI-driven generation is as rich and informative as those between humans. Setting Up Gemini Pro for Document Embedding and Generation
As we continue our exploration with the Google AI Python SDK, similar to our approach in the last tutorials, we’ll maintain our focus on setting up and utilizing Gemini Pro. This consistent practice ensures a thorough understanding and mastery of the tools at our disposal. To begin accessing Gemini Pro for this session, you’ll first need to secure your API key. You can do this by visiting Google MakerSuite and signing into your Google account. Upon login, you’ll be directed to Google AI Studio, where instructions for creating your API key await. Remember, this key is your gateway to accessing Gemini Pro and other SDK resources for your projects. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Look for the option to generate your API key, which is shown in Figure 3. Figure 3: Snapshot of Google AI Studio showing the process of generating an API key (source: image by the author). Once you’ve generated your API key, it’s important to copy it and keep it in a secure location. If you’re working with Google Colab, you can protect environment variables, file paths, or keys by setting them as private, ensuring they’re only visible to you and the notebooks you specify. This key plays a crucial role in your work with the Gemini Pro model, especially in the development of image processing code. Safely storing your key ensures you have continuous access to the features and functionalities provided by Gemini Pro. Implementing Document Embedding: Code Integration with Gemini Pro
Transitioning to the hands-on segment, we now explore the implementation process. This section bridges our theoretical understanding of document embeddings with their practical application, demonstrating how these concepts empower Gemini Pro’s generative capabilities. We’ll cover the essentials of transforming textual data into meaningful embeddings and integrating these into Gemini Pro. The focus will be on a straightforward, step-by-step guide that brings document embeddings directly into our generative AI framework, enhancing its ability to deliver contextually rich and accurate outputs. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Through concise code examples, we’ll explore how to leverage the models/embedding-001 model by Google for dynamic information retrieval and generation enhancement, showcasing Gemini Pro’s advanced interaction potential. Preparing Your Development Environment for Gemini Pro
Step 1: Installing the Google Generative AI Library
We initiate the process by installing the google-generativeai library through pip. This step allows us to engage with Google’s generative models, such as Gemini Pro and the Embedding model, directly in Python, as illustrated below:
! pip install -q -U google-generativeai
Installs the google-generativeai library, enabling direct interaction with Google’s Gemini Pro and Embedding model. Step 2: Importing Essential Python Packages
import textwrap
import numpy as np
import pandas as pd
from google.colab import userdata
import google.generativeai as genai
We begin by importing several foundational libraries:
textwrap: for text formattingnumpy: for numerical computationspandas: for data handlinguserdata: from google.colab for accessing user-specific data in Colab notebooks
These libraries provide the basic toolkit for data manipulation and preparation, which is crucial for any data science or AI-driven project. More importantly, import google.generativeai as genai connects us to Google’s generative AI capabilities. This library is the key to accessing a wide array of Google’s advanced AI models, including but not limited to Gemini Pro for generative AI applications and the Embedding model for tasks that require understanding and generating text based on semantic meaning. The genai module stands out for its ability to bridge our Python scripts with the cutting-edge AI technology hosted by Google, enabling us to push the boundaries of what’s possible in natural language processing and generation. Step 3: Securely Configuring Your API Key
# Used to securely store your API key
# Or use `os.getenv('GOOGLE_API_KEY')` to fetch an environment variable. GOOGLE_API_KEY=userdata.get("GEMINI_API_KEY")
genai.configure(api_key=GOOGLE_API_KEY)
In the above code block, the userdata module from the google.colab library is leveraged to securely access the stored "GEMINI_API_KEY", which is then assigned to GOOGLE_API_KEY. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Alternatively, the API key could be obtained through os.getenv('GOOGLE_API_KEY'), fetching it as an environment variable. Subsequently, the script configures the GenAI library for use by calling genai.configure(api_key=GOOGLE_API_KEY), effectively enabling authorized access to its functionalities. This approach, particularly within Google Colab notebooks, offers a secure method for managing API keys. Listing the Generative and Embedding Models
for m in genai.list_models():
if "generateContent" in m.supported_generation_methods or 'embedContent' in m.supported_generation_methods:
print(m.name)
To identify models within the genai library that are equipped for either content generation or embedding, we execute a loop through the models fetched by genai.list_models(). This method returns a sequence of model objects, where each object is characterized by several attributes, one of which is supported_generation_methods. This attribute is a collection indicating what operations the model is capable of performing, such as generating content or embedding information. Within this loop, for each model m returned, we examine whether "generateContent" or "embedContent" appears in the model’s supported_generation_methods. This check helps us determine whether a model has the capability to generate content or perform embedding tasks. When a model confirms these criteria, indicating its capability for either of these tasks, we output the model’s name, signaling its readiness for use in content generation or embedding scenarios. models/gemini-1.0-pro
models/gemini-1.0-pro-001
models/gemini-1.0-pro-latest
models/gemini-1.0-pro-vision-latest
models/gemini-pro
models/gemini-pro-vision
models/embedding-001
In our series, we’ve explored the capabilities of various models within Google’s Generative AI suite, with a particular focus on the Gemini Pro and Gemini Pro Vision for their roles in generative AI and content generation. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | The output listed above showcases an expanded set of models available for use, reflecting recent updates and additions to the Gemini lineup. This expanded range includes both familiar models and new entries like models/embedding-001, which introduce embedding capabilities alongside the generative functions of the Gemini models. Today, we will harness the strengths of both models/gemini-pro for generation tasks and models/embedding-001 for embedding, allowing us to diversify our approach to AI-driven projects further. The inclusion of models/embedding-001 marks our first foray into embedding techniques within this series, complementing our ongoing exploration with generative models. As we continue, this blend of generative and embedding capabilities opens new avenues for innovation and application in our projects. Selecting Models for Embedding and Generation
embedding_model = "models/embedding-001"
generation_model = "gemini-pro"
This block specifies which models will be used for embedding documents (embedding_model) and for generating text (generation_model). The embedding_model is selected for its ability to convert textual content into numerical vectors, capturing the essence and semantics of the documents in a form that’s understandable to machines. This process is crucial for allowing the AI to “read” and “understand” the content at a computational level. On the other hand, the generation_model is chosen for its capability to craft outputs that are not only relevant to the input prompts but are also coherent and contextually appropriate, mimicking the generative style and depth you would expect from a human in conversation. Together, these models serve as the twin engines powering our journey through enhanced AI responses. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Initializing the Generation Model
model_generation = genai. GenerativeModel(generation_model)
Here, we initialize the Gemini Pro generative model, which we will later use to generate content based on prompts. By initializing the model with generation_model, we specify that our generation will be powered by Gemini Pro’s advanced capabilities. Defining Documents for Embedding
DOCUMENT1 = {
"title": "Implementing Microservices with Docker",
"content": "Expanding on the details of implementing microservices with Docker requires a deeper dive into the intricacies of the architecture and the role Docker plays in it. Microservices architecture is about breaking down a monolithic application into smaller, independently deployable services, each running its own process and communicating through lightweight mechanisms. This architectural style not only enhances scalability and flexibility but also allows for independent development and deployment, significantly reducing downtime and improving productivity. Docker emerges as a vital tool in this landscape, providing a standardized unit of software packaging, which encapsulates everything a microservice needs to run. This encapsulation includes the application itself, along with its dependencies, environment variables, and configuration files, housed within a container. Dockerfile creation is the first technical step, where each service’s environment is precisely defined, detailing the base image, dependencies, and commands necessary for setting up the microservice. Following this, service orchestration becomes crucial, employing `docker-compose.yml` to manage multi-container applications efficiently, facilitating the definition of services, networking, and volumes within a Dockerized environment. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Deployment strategies evolve with the use of orchestration platforms like Docker Swarm or Kubernetes, which address challenges of scaling, load balancing, and ensuring high availability across microservices. These platforms provide the tools to manage container lifecycles, automate deployment processes, and maintain the desired state of applications. Networking, another pivotal aspect, involves setting up Docker networks, which ensure that containers can communicate securely and effectively, underpinning the microservices architecture with a reliable communication fabric. In sum, the transition to microservices with Docker encapsulates a journey towards a more modular, resilient, and scalable application infrastructure, emphasizing the importance of detailed setup, orchestration, deployment strategies, and secure networking to leverage the full potential of microservices architecture."} DOCUMENT2 = {
"title": "Utilizing TensorFlow for Deep Learning Projects",
"content": "TensorFlow, a robust library for deep learning, enables the development and training of sophisticated models. The process begins with setting up an environment optimized with GPU support to expedite model training. Developers can construct models using TensorFlow's Sequential API for linear layers or the Functional API for more intricate structures. The training phase is managed through methods like `model.fit()`, while `model.evaluate()` and `model.predict()` are essential for assessment and predictions. Additionally, TensorFlow integrates with TensorBoard, a tool for visualizing model architecture, monitoring training metrics, and analyzing computational bottlenecks, enhancing the model development and evaluation process with detailed insights and diagnostics. This comprehensive approach streamlines the journey from model conception to deployment, emphasizing efficiency and scalability in model training and evaluation."} |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | DOCUMENT3 = {
"title": "Securing IoT Devices Against Cyber Threats",
"content": "The widespread adoption of IoT devices underscores the urgency for stringent security practices to preempt the multifaceted threats they face. Initially, device hardening is essential, involving the modification of default settings, deactivation of non-essential services, and strict application of access controls to minimize vulnerabilities. Ensuring the integrity of firmware through secure boot mechanisms and cryptographic validations is critical to thwart unauthorized firmware modifications. Data encryption, both for data at rest and in transit, using protocols like TLS, is paramount for securing sensitive information. Moreover, network segmentation is a strategic security layer, effectively isolating IoT devices into distinct network zones to mitigate the impact of attacks and enhance the detection of anomalies. This multi-layered approach to IoT security is indispensable for maintaining the integrity, confidentiality, and availability of devices and their data amidst an evolving cyber threat landscape, necessitating continuous innovation and adaptation of security measures."} documents = [DOCUMENT1, DOCUMENT2, DOCUMENT3]
In this step, we’re crafting the foundation of our knowledge base by defining three distinct documents. Each document encapsulates a specific topic, rich in detail and technical depth. The first document dives into the realm of microservices with Docker, outlining the architectural considerations and practical steps involved in implementation. The second document shifts focus to TensorFlow, offering insights into leveraging this powerful library for deep learning projects. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | The third document addresses the critical issue of securing IoT devices against cyber threats, highlighting strategies for bolstering security. Collectively, these documents are prepared to serve as the contextual backbone for our AI’s understanding and response generation, covering a diverse range of subjects from technology infrastructure to cybersecurity. Creating a DataFrame from Documents
df = pd. DataFrame(documents)
df.columns = ['Title', 'Content']
print(df)
Following the document definitions, we transition into structuring this information using a pandas DataFrame. This operation transforms our collection of documents into a structured table, making the data more accessible and easier to handle. As shown in Table 1, each row in the DataFrame represents a document, with columns designated for the document’s title and its content. TitleContent0Implementing Microservices with DockerExpanding on the details of implementing micro…1Utilizing TensorFlow for Deep Learning ProjectsTensorFlow, a robust library for deep learning…2Securing IoT Devices Against Cyber ThreatsThe widespread adoption of IoT devices undersc…Table 1: DataFrame Output: Titles and Contents of Technical Documents on Docker, TensorFlow, and IoT Security (source: by the author). Embedding Documents
# Get the embeddings of each text and add to an embeddings column in the dataframe
def embed_fn(title, text):
return genai.embed_content(model=embedding_model,
content=text,
task_type="retrieval_document",
title=title)["embedding"]
df['Embeddings'] = df.apply(lambda row: embed_fn(row['Title'], row['Content']), axis=1)
print(df)
We create a function called embed_fn that will use the embedding model to generate a vector representation (embedding) of each document’s content. In the embed_fn function, genai.embed_content is called with several parameters that instruct how the document should be processed:
model=embedding_model: This specifies which embedding model to use. We’ve chosen a model designed for creating embeddings, which can effectively map textual content into a high-dimensional space.content=text: The actual text from the document that we want to embed. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | This is where the content of our document is fed into the model.task_type="retrieval_document": This parameter tells the embedding model that our goal is to create embeddings suitable for document retrieval tasks. It optimizes the embedding process to capture features that are important for distinguishing between different documents and understanding their content at a deeper level.title=title: Including the title provides additional context to the embedding model, which can enhance the quality and relevance of the generated embedding by incorporating the document’s main theme or subject matter.["embedding"]: After the embedding is created, this part extracts the embedding vector from the model’s response. This vector is a dense numerical representation of the document. Next, we apply the embed_fn function to each row in our DataFrame. The df.apply method iterates over each row, passing the title and content of each document to our embedding function. The result, which is the embedding vector for each document, is then stored in a new column in our DataFrame called 'Embeddings'. Table 2 provides a detailed view of embeddings generated for the titles and contents of three different technical subjects. TitleContentEmbeddings0Implementing Microservices with DockerExpanding on the details of implementing micro…[0.016841425, -0.03105049, -0.003789942, 0.004…1Utilizing TensorFlow for Deep Learning ProjectsTensorFlow, a robust library for deep learning…[0.0114478255, -0.06682157, -0.013862198, 0.02…2Securing IoT Devices Against Cyber ThreatsThe widespread adoption of IoT devices undersc…[0.036434762, -0.029461706, -0.0027963985, -0…. Table 2: Illustrates the numerical embeddings generated for three distinct technical subjects, showcasing how text data is converted into numerical form for advanced processing and analysis (source: by the author). Displaying Embedding Length
print(len(df['Embeddings'][0]))
To gain insight into the nature of these embeddings, we examine the length of the vector created for the first document. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | This operation reveals the dimensionality of our embeddings, which is crucial for understanding the amount of information each vector holds. The length, or size, of the embedding vector (in this case, 768) indicates the richness of the representation. Each dimension contributes to capturing different facets of the document’s content, from general themes to specific details. This numerical depth allows our AI models to discern and utilize the underlying patterns and meanings within the text. 768
Query Embedding
query = "How can I implement microservices using Docker?" request = genai.embed_content(model=embedding_model,
content=query,
task_type="retrieval_query")
print(request)
In this step, we take a query — essentially a question or a topic of interest from the user — and transform it into an embedding using the same model that was used for document embeddings. However, the task type specified here is retrieval_query, indicating that the model should optimize the embedding for query purposes, allowing for an efficient search or matching against a set of document embeddings. The code segment invoking genai.embed_content illustrates the process of creating an embedding for the query. This code snippet is crucial for understanding how embeddings are produced, though the variable request itself is not directly used in subsequent parts of our tutorial. Instead, its purpose is to illustrate what the output of the genai.embed_content function looks like when applied to a query. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | The printed request output shows the embedding of the query as an array of floating-point numbers. Each number represents a feature in the high-dimensional space where both queries and documents reside. This numerical representation captures the essence of the query in a way that is compatible with the embeddings of the documents, enabling a direct comparison to find the most relevant information. {'embedding': [0.027905477, -0.044570703, 0.008394925, -0.011313404, 0.038450878, -0.004593339, -0.006018273, 0.0022217534, -0.005376673, 0.048733775, ..., -0.014501087, 0.012398757, 0.043249663, 0.026574535, 0.00038662733, -0.032806426, 0.038384434]}
Finding the Most Relevant Passage
def find_best_passage(query, dataframe):
"""
Compute the distances between the query and each document in the dataframe
using the dot product. """
query_embedding = genai.embed_content(model=embedding_model,
content=query,
task_type="retrieval_query")
dot_products = np.dot(np.stack(dataframe['Embeddings']), query_embedding["embedding"])
idx = np.argmax(dot_products)
return dataframe.iloc[idx]['Content'] # Return text from index with max value
This function is a practical application of vector space modeling in natural language processing. By computing the dot product between the query embedding and each document embedding, we measure the similarity between the query and documents. The dot product gives us a scalar value that reflects how aligned the vectors are; a higher value indicates greater similarity. The function uses np.dot to calculate these dot products in bulk for efficiency. It then identifies the index (idx) of the highest dot product, which corresponds to the most relevant document for the query. By retrieving the content at this index, we obtain the passage that best answers the user’s query. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | passage = find_best_passage(query, df)
print(passage)
Finally, we call the function with the query and our DataFrame of document embeddings. The selected passage, printed here, is the part of the document that the model determined to be most relevant to the query “How can I implement microservices using Docker?” This output demonstrates the model’s ability to sift through detailed, technical documents and identify the segment most applicable to the user’s interest, showcasing a powerful application of embedding and retrieval techniques in AI-driven content search and analysis. This approach illustrates how AI can bridge the gap between vast amounts of textual information and specific user inquiries, offering precise and relevant answers drawn from a comprehensive understanding of the embedded content. Expanding on the details of implementing microservices with Docker requires a deeper dive into the intricacies of the architecture and the role Docker plays in it. Microservices architecture is about breaking down a monolithic application into smaller, independently deployable services, each running its own process and communicating through lightweight mechanisms. This architectural style not only enhances scalability and flexibility but also allows for independent development and deployment, significantly reducing downtime and improving productivity. Docker emerges as a vital tool in this landscape, providing a standardized unit of software packaging, which encapsulates everything a microservice needs to run. This encapsulation includes the application itself, along with its dependencies, environment variables, and configuration files, housed within a container. Dockerfile creation is the first technical step, where each service’s environment is precisely defined, detailing the base i...
Crafting a Prompt for the Generative Model
def make_prompt(query, relevant_passage):
escaped = relevant_passage.replace("'", "").replace('"', "").replace("\n", " ")
prompt = textwrap.dedent("""You are a helpful and informative bot that answers questions using text from the reference passage included below. \ |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. \ However, you are talking to a non-technical audience, so be sure to break down complicated concepts and \
strike a friendly and converstional tone. \ If the passage is irrelevant to the answer, you may ignore it. QUESTION: '{query}'
PASSAGE: '{relevant_passage}'
ANSWER:
""").format(query=query, relevant_passage=escaped)
return prompt
The above Python code block is where we prepare the groundwork for the AI to understand and respond to a user’s query. The function make_prompt is designed to craft a detailed instruction set for the generative model, Gemini Pro, guiding it on how to process a given query alongside a relevant passage from our document embeddings. The passage is cleaned of any problematic characters ('"', "'", "\n") to ensure smooth processing. The resulting prompt is structured to encourage the AI to generate outputs that are not only accurate but also accessible to a non-technical audience. This approach underscores the adaptability of Gemini Pro to tailor its outputs based on the context provided, highlighting the model’s ability to navigate between technical detail and generative clarity. query = "How to implement microservices with Docker?" prompt = make_prompt(query, passage)
print(prompt)
Next, we apply our previously defined function make_prompt to a specific query and a passage chosen based on its relevance to the query’s subject. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | The process exemplifies how to dynamically create prompts that instruct the AI on what the query is and what contextual information it should consider in its response. The prompt is designed to emulate a generative exchange, where the AI is informed of the user’s question and given a relevant passage to ground its response in factual and detailed content. You are a helpful and informative bot that answers questions using text from the reference passage included below. Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. However, you are talking to a non-technical audience, so be sure to break down complicated concepts and strike a friendly and converstional tone. If the passage is irrelevant to the answer, you may ignore it. QUESTION: 'How can I implement microservices using Docker?' PASSAGE: 'Expanding on the details of implementing microservices with Docker requires a deeper dive into the intricacies of the architecture and the role Docker plays in it. Microservices architecture is about breaking down a monolithic application into smaller, independently deployable services, each running its own process and communicating through lightweight mechanisms. This architectural style not only enhances scalability and flexibility but also allows for independent development and deployment, significantly reducing downtime and improving productivity. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Docker emerges as a vital tool in this landscape, providing a standardized unit of software packaging, which encapsulates everything a microservice needs to run. This encapsulation includes the application itself, along with its dependencies, environment variables, and configuration files, housed within a container. Dockerfile creation is the first technical step, where each service’s environment is precisely defined, detailing the base image, dependencies, and commands necessary for setting up the microservice. Following this, service orchestration becomes crucial, employing `docker-compose.yml` to manage multi-container applications efficiently, facilitating the definition of services, networking, and volumes within a Dockerized environment. Deployment strategies evolve with the use of orchestration platforms like Docker Swarm or Kubernetes, which address challenges of scaling, load balancing, and ensuring high availability across microservices. These platforms provide the tools to manage container lifecycles, automate deployment processes, and maintain the desired state of applications. Networking, another pivotal aspect, involves setting up Docker networks, which ensure that containers can communicate securely and effectively, underpinning the microservices architecture with a reliable communication fabric. In sum, the transition to microservices with Docker encapsulates a journey towards a more modular, resilient, and scalable application infrastructure, emphasizing the importance of detailed setup, orchestration, deployment strategies, and secure networking to leverage the full potential of microservices architecture.' ANSWER:
Generating a Response
answer = model_generation.generate_content(prompt)
print(answer.text)
In this code snippet, we’re seeing the practical application of Gemini Pro’s generative capabilities, where the model model_generation is tasked with producing text based on a specific prompt that has been formulated in the previous steps. The method generate_content(prompt) takes the prompt — crafted to include both a direct query and relevant background information — and feeds it into the generative model. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | This model then processes the prompt, leveraging its trained AI to synthesize the information provided and generate a coherent, contextually informed response. To implement microservices using Docker, start by defining each service's environment in a Dockerfile. Then, orchestrate the services using tools like `docker-compose.yml` or Kubernetes. Finally, set up Docker networks to ensure secure communication among containers. This approach enhances scalability, flexibility, independent development, and deployment, reducing downtime and boosting productivity. The above response showcases the generative model’s knack for distilling information from a given passage into a concise guide on using Docker for microservices. By advising on key steps, from environment setup in Dockerfiles to securing communication with Docker networks, it reflects the model’s ability to provide practical, expert-like advice. This highlights Gemini Pro’s effectiveness in producing relevant, accurate content grounded in the context provided, underscoring its value in generating insightful outputs. Testing Gemini Pro with an Irrelevant Passage
In this experiment, we’re challenging Gemini Pro with a query about implementing microservices with Docker, paired with an unrelated passage praising AI’s revolutionary impact. This test is aimed at understanding the model’s generation strategy when faced with a mismatch between the query’s intent and the provided context. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | It’s a crucial insight into the model’s ability to discern relevance and make intelligent decisions in crafting outputs, reflecting its potential for accurate and meaningful engagement in various real-world scenarios. prompt = make_prompt(query, "AI is the biggest revolution in human mankind!") print(prompt)
An unrelated passage is given as an experiment to see how the model would handle content that doesn’t match the query. Here, we craft a new prompt for the generative model by combining the user’s query about implementing microservices with Docker with an irrelevant passage proclaiming “AI is the biggest revolution in human mankind!” This juxtaposition sets the stage to observe how the model navigates the disparity between the query’s intent and the provided context, offering insight into its ability to discern relevance in its outputs. You are a helpful and informative bot that answers questions using text from the reference passage included below. Be sure to respond in a complete sentence, being comprehensive, including all relevant background information. However, you are talking to a non-technical audience, so be sure to break down complicated concepts and strike a friendly and converstional tone. If the passage is irrelevant to the answer, you may ignore it. QUESTION: 'How can I implement microservices using Docker?' |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | PASSAGE: 'AI is the biggest revolution in human mankind!' ANSWER:
The printed prompt showcases the structure given to the AI model: a direct question about microservices and Docker juxtaposed with a passage that is not directly relevant. This setup is crucial for testing the model’s response mechanism when faced with mismatched or irrelevant information. answer = model_generation.generate_content(prompt)
print(answer.text)
The above line instructs the previously initialized Gemini Pro generative model to process the crafted prompt and generate a response. Given the prompt’s structure, this step is pivotal in assessing the model’s content discernment capabilities and its strategy for handling contextually irrelevant information. Sorry, I cannot answer your question as the reference passage provided does not have any information on how to implement microservices with Docker. The model’s response to the experiment is remarkably insightful. Despite being provided with a passage unrelated to the query, the model effectively recognizes the lack of relevance and explicitly communicates its inability to provide a meaningful answer based on the given context. This outcome highlights Gemini Pro’s sophisticated ability to assess the relevance of the provided information before attempting to generate a response, illustrating an intelligent approach to content generation that avoids the pitfalls of mechanical regurgitation of irrelevant data. Reflecting on Our Document Embedding Journey with Gemini Pro
As we wrap up this chapter of our exploration, our hands-on journey with document embedding and Gemini Pro has unfolded as a story of discovery and collaboration. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Each code block we’ve crafted together has not merely been about executing tasks; it’s been about weaving a rich narrative that extends the capabilities of generative AI. In this journey, document embeddings have acted as the conduit through which raw data is transformed into Gemini Pro’s nuanced outputs. Our exploration went beyond the mechanics of initiating models and embedding documents. We delved into the heart of AI communication, where Gemini Pro took our guided inputs and spun them into outputs that were both insightful and contextually aware. One of the most compelling aspects of our journey was witnessing Gemini Pro’s intelligent handling of scenarios where the provided query and the contextual passage were starkly mismatched. In these moments, Gemini Pro demonstrated not just a simple repetition of information but an intelligent discernment that often goes unnoticed in AI responses. It showcased an ability to sift through irrelevant data, emphasizing the importance of relevance and precision in the dialogue between humans and machines. Our collaborative effort has highlighted the sophistication Gemini Pro brings to AI responses, underscoring the blend of technical prowess and human intuition that enriches AI-generated text. This experience has not only showcased Gemini Pro’s capabilities and potential but also illuminated the challenges and learning opportunities inherent in fine-tuning AI outputs. As we move forward, let us appreciate the strides we’ve made together. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | Our exploration is a testament to the creative and iterative process of shaping technology to enhance human-machine generation. The road ahead is filled with potential, beckoning us toward a future where generative AI reaches new heights of empathy and understanding, capable of engaging in text generation that truly resonates. What's next? We recommend PyImageSearch University. Course information:
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https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | And that’s exactly what I do. My mission is to change education and how complex Artificial Intelligence topics are taught. If you're serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today. Here you’ll learn how to successfully and confidently apply computer vision to your work, research, and projects. Join me in computer vision mastery. Inside PyImageSearch University you'll find:
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Summary
In this 6th part of the Gemini Pro series, we delve into the intricate world of document embedding within generative AI, illustrating how Gemini Pro can be enhanced through retrieval-augmented generation. The blog post introduces document embedding concepts and their pivotal role in enriching AI responses. |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | As we progress, a step-by-step guide outlines the setup of Gemini Pro for both document embedding and response generation, from installing necessary libraries and configuring API keys to selecting appropriate models for our tasks. Through practical code implementations, we demonstrate how to integrate document embedding into Gemini Pro’s workflow, preparing the environment and embedding documents to transform textual content into numerical representations. This process sets the foundation for a more nuanced and contextually aware AI model. We further explore the generation of outputs based on these embeddings, showcasing the model’s ability to provide informative and relevant answers. A key highlight is our experimentation with irrelevant passages, testing Gemini Pro’s discernment in handling content that does not match the query. This segment underscores the model’s intelligence in recognizing and responding to mismatches, emphasizing its potential to deliver precise and contextually appropriate responses. In summarizing our journey, the blog reflects on the insights gained from integrating document embedding with Gemini Pro. It emphasizes the enhanced capabilities of generative AI when augmented with contextual understanding, providing a glimpse into future advancements in AI generation. This exploration not only broadens our understanding of Gemini Pro’s potential but also paves the way for more sophisticated and context-aware AI applications in the realm of generative interfaces. Citation Information
Sharma, A. “Integrating Document Embedding in Gemini Pro: An Approach to Retrieval-Augmented Generation,” PyImageSearch, P. Chugh, A. R. Gosthipaty, S. Huot, K. Kidriavsteva, and R. Raha, eds., |
https://pyimagesearch.com/2024/04/22/integrating-document-embedding-in-gemini-pro-an-approach-to-retrieval-augmented-generation/ | 2024, https://pyimg.co/6ad0h
@incollection{Sharma_2024_Integrating-Document-Embedding-Gemini-Pro,
author = {Aditya Sharma},
title = {Integrating Document Embedding in Gemini Pro: An Approach to Retrieval-Augmented Generation},
booktitle = {PyImageSearch},
editor = {Puneet Chugh and Aritra Roy Gosthipaty and Susan Huot and Kseniia Kidriavsteva and Ritwik Raha},
year = {2024},
url = {https://pyimg.co/6ad0h},
}
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Home » Blog » Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning
Table of Contents
Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning
Configuring Your Development Environment
Need Help Configuring Your Development Environment? Project Structure
Creating Adversarial Examples
Robustness Toward Adversarial Examples
Summary
Citation Information
Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning
In this tutorial, you will learn about adversarial examples and how they affect the reliability of neural network-based computer vision systems. We will discuss the relationship between the robustness and reliability of deep learning models and understand how engineered noise samples, when added to input images, can change model predictions. Furthermore, we will use Keras and TensorFlow to develop our adversarial examples and see how they change our model’s predictions even though they visibly look the same as the original inputs. Specifically, we will discuss the following in detail:
The relationship between robustness and reliability of deep learning-based models when deployed for various real-world applicationsAdversarial examples and their effects on neural network predictionsDeveloping our adversarial examples using Keras and TensorFlowLearn how we can make our models robust to adversarial examples and enhance their reliability
This lesson is the 1st of a 4-part series on Adversarial Learning:
Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning (this tutorial)Adversarial Learning with Keras and TensorFlow (Part 2): Implementing the Neural Structured Learning (NSL) Framework and Building a Data PipelineAdversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL)Adversarial Learning with Keras and TensorFlow (Part 4): Enhancing Adversarial Defense and Comparing Models Trained With and Without Neural Structured Learning (NSL)
In this 1st part of the tutorial series, we will develop a holistic understanding of adversarial examples, how they affect our network predictions, and how we can protect our models from such engineered examples. In the 2nd part of this tutorial series, we will start building our adversarial attacks and defenses using the TensorFlow NSL (Neural Structured Learning) framework, which makes it very easy to implement such adversarial applications. We will understand the dataset and the data pipeline for our application and discuss the salient features of the NSL framework in detail. Next, in the 3rd part of this tutorial series, we will discuss two types of adversarial attacks used to engineer adversarial examples. Furthermore, we build our model architecture and related modules using Keras and TensorFlow. Finally, in the 4th part of the tutorial series, we will look at our application’s training and inference pipeline and implement these routines using the Keras and TensorFlow libraries. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | To learn how to create adversarial examples, just keep reading. Looking for the source code to this post? Jump Right To The Downloads Section
Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning
Recently, neural network-based systems have been used extensively for diverse applications due to their amazing ability to learn or approximate underlying functions from data directly. We have already seen the awesome capabilities of deep neural network-based computer vision models in image classification, object detection, image generation, and various other applications through different blogs and tutorials on PyImageSearch. However, given their applicability in diverse domains and tasks, it is important to ask if these networks and their predictions are reliable. One way to understand and evaluate the reliability of these models is to quantify their robustness. A more robust model will be less sensitive to changes, have stable predictions, and be more reliable under diverse scenarios. Let us discuss what we exactly mean by robustness and how it relates to the reliability of a model. The robustness of a model, in simplest terms, can be understood as the change in its predictions given a change in its inputs. There are different ways to look at robustness. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | For example, given a model trained to recognize cat and dog images from the real world, can it recognize sketches or cartoons of cats and dogs at test time? OR given a model trained to recognize cat and dog images, can it recognize images of cats and dogs perturbed with a small imperceptible (i.e., cannot be noticed with the naked eye) amount of noise (imagine sprinkling a few grains of salt, i.e., noise over the image). The first example refers to the case of visible domain shift, that is, the input images of the training data (i.e., cat and dog images from the real world) and test data (i.e., sketches or cartoons of cats and dogs) visibly look very different in appearance or in other words have come from very different distributions. Hence, it is expected that given a network trained on one distribution, it may not be robust enough to this domain shift and might give incorrect predictions on the other distribution (i.e., sketches or cartoons). The second example refers to the case where an imperceptible change is made to the images (say, adding a small amount of noise, as in this case). Both training and test distributions look the same to a human. Thus, we might expect that the network will be robust to the small added noise, and there will be no change in its predictions. Let us understand this with an example. Figure 1 shows two images where one image is taken from the real world, and the other image has a small amount of imperceptible noise added. Can you tell after looking at them which image contains the noise? |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | No, right !!!! Figure 1: Original image of a panda (left) and corresponding adversarial example (right) (source: image by the author). If we pass the first image (i.e., original image) through the model, it predicts panda as the correct class, and if we do the same for the second image, it predicts badger as the correct class. Notice that even though the images look the same to the naked eye, the prediction of the model changes. We can interpret this fact as follows: our model is not robust to this imperceptible change or noise we added to our image. However, this raises a serious concern about the reliability of our model. For example, given the scenario where our model is being used for security purposes, its task is to correctly recognize a person’s face and let them into an office if the person is an employee. When a face image of a non-employee is input, our model is trained to identify the face and predict that the person is not an employee and not grant access to the office building. However, what if we add a small amount of engineered noise (not visible to the naked eye) to the face image, which makes the network change its prediction and jeopardizes the office’s security? Is this system reliable? |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Such an example with imperceptible engineered noise added to it such that it changes the neural network’s prediction is commonly referred to as an adversarial example. Note that it is highly important to make our neural network-based systems robust to these adversarial examples so that the model does not drastically change its prediction when such engineered inputs that look the same as normal inputs to a human are added to the database. Configuring Your Development Environment
To follow this guide, you need to have the TensorFlow and OpenCV libraries installed on your system. Luckily, both TensorFlow and OpenCV are pip-installable:
$ pip install tensorflow
$ pip install opencv-contrib-python
If you need help configuring your development environment for OpenCV, we highly recommend that you read our pip install OpenCV guide — it will have you up and running in minutes. Need Help Configuring Your Development Environment? Need help configuring your dev environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University — you’ll be up and running with this tutorial in minutes. All that said, are you:
Short on time?Learning on your employer’s administratively locked system?Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?Ready to run the code immediately on your Windows, macOS, or Linux system? Then join PyImageSearch University today! |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required. And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Project Structure
We first need to review our project directory structure. Start by accessing this tutorial’s “Downloads” section to retrieve the source code and example images. From there, take a look at the directory structure:
├── demo.py
├── inference.py
├── output
├── pyimagesearch
│ ├── __init__.py
│ ├── callbacks.py
│ ├── config.py
│ ├── data.py
│ ├── model.py
│ ├── robust.py
│ └── visualization.py
└── train.py
This consists of the demo.py file, which gives a small overview of the various facets of the adversarial learning paradigm and generates adversarial examples. The inference.py file implements the test-time routine for our adversarial learning application, and we will discuss this in detail in the 4th part of this series. The output folder saves our output results and visualizations, as we will see in the later parts of this series. Furthermore, the pyimagesearch folder contains the following:
The implementations for our training callbacks (i.e., callbacks.py)The parameter configurations (i.e., config.py)The data pipeline (i.e., data.py)The model architecture (i.e., model.py)
Additionally, the robust.py file implements the code to check robustness to different adversarial attacks, and the visualization.py file allows us to plot and visualize the results from our application. Finally, the train.py file implements the code to train our adversarial learning application. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | In this tutorial, we will discuss the demo.py file in detail. Creating Adversarial Examples
In the previous section, we looked at an adversarial example. We discussed how it can affect and change our system’s predictions even for images that visually look similar and render them unreliable. Let us now understand how we can engineer such an adversarial example that can fool our network and change its predictions. From the previous section, we realize that any adversarial example must have two properties:
It should look visibly the same as the original exampleIt should ‘fool’ our model and change its prediction. Let’s discuss this one by one. First, we will add a constant noise to our image and visualize when it starts looking different from our original input. Figure 2 shows our image set with added noise. When the noise level is within ±10, we do not notice a significant difference between the original image (i.e., noise=0) case. When the noise is further increased, we noticed that our image gets brighter (as positive values are added) and darker (as negative noise values are added). |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Figure 2: Original image of a panda with different levels of constant noise (source: image by the author). This helps us conclude that noise within the range of about ±10 does not cause perceptible (or visible) changes to our image. Let us now look at the second point we discussed above. In order to change a model’s prediction, we have to discuss the output probabilities and the loss function. Consider a classification model f pre-trained on a dataset using loss L, which can correctly classify a panda image. The loss takes as input the predicted output probabilities from the model p_model and the ground truth (one-hot) label p_gt. Here, p_model is the softmax output of our model f(x) (i.e., softmax(f(x))):
loss = L(p_model,p_gt)
loss = L(softmax(f(x)),p_gt)
Since this model is already trained to classification on the ImageNet dataset and minimize the loss for image-correct label pairs, this implies that for an input image of a panda, the output softmax(f(x)) and p_gt align the most and minimize the loss. Now, we want to add noise to our input image x such that it does not visibly change x but changes the model’s prediction. In terms of loss, this can be thought of as changing input x to x+noise such that the loss L(p_model,p_gt) is no longer minimum for the panda class. Thus, we want to maximize the following loss expression for our changed input x+noise:
loss = L(softmax(f(x+noise)),p_gt), where noise ∈ (-10, 10)
Note that the noise interval is kept from -10 to 10 as we verified above that this is the acceptable amount of noise we can add to avoid any perceptible or visible change to the image. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | The equation above allows us to engineer an example x+noise such that it is visibly similar to the original panda image and is not correctly classified as panda by the model (i.e., model predictions change). In the above equation, our job is to find the correct engineered noise tensor, which is in the range (-10, 10), and change the model’s prediction from panda to another class. We can use SGD (stochastic gradient descent) to optimize the above equation, keeping x and the weights of model f fixed and optimizing for the right noise tensor that will change the model’s prediction to another class. Let us take an example and code this process in TensorFlow. Let us consider the case of image classification and use ResNet50 pre-trained on ImageNet. The model is trained with cross-entropy loss as it performs image classification. ### import necessary packages
import tensorflow as tf
import cv2
import PIL
import os
import numpy as np
import matplotlib.pyplot as plt
#### define configs
IMG_PATH = "/content/panda.jpeg"
SAVE_PATH = "/content/panda_adversarial.jpeg"
IMG_SIZE = (224,224)
CLASS_INDEX = 388
NUM_CLASSES = 1000
LEARNING_RATE = 1e1
EPSILON = 5
ITERATIONS = 40
#### define function to undo preprocessing
def get_prediction(input_image, labelGT):
pred_model = model.predict(input_image)
prediction = tf.keras.applications.resnet50.decode_predictions(pred_model)
print('Loss for Panda Class=', loss_fn(pred_model, labelGT).numpy())
print('Class Names and Probabilities ',prediction)
def visualize(im_list):
image = PIL.Image.fromarray(im_list[0], 'RGB')
noise = PIL.Image.fromarray(im_list[1], 'RGB')
imageAdversarial = PIL.Image.fromarray(im_list[2], 'RGB')
plt.subplot(131)
plt.imshow(image)
plt.axis('off')
plt.subplot(132)
plt.imshow(noise)
plt.axis('off')
plt.subplot(133)
plt.imshow(imageAdversarial)
plt.axis('off')
plt.savefig(SAVE_PATH)
#### define function to undo preprocessing
def deprocess_img(image_norm):
x = np.squeeze(image_norm, 0)
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
x = x[:, :, ::-1]
x = np.clip(x, 0, 255).astype('uint8')
return x
We start by importing the necessary packages such as tensorflow (Line 2), cv2 (Line 3), and PIL (Line 4) for image processing functionality, the os module (Line 5), numpy (Line 6), and matplotlib for plotting visualizations (Line 7). Next, we define the parameters that we will use for this tutorial. Lines 10 and 11 include a path to the input image (i.e., IMG_PATH) and a path to save the final adversarial example visualization (i.e., SAVE_PATH). Furthermore, we define the input image and class-related configurations. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | On Lines 13-15, this includes the dimensions of the input image (i.e., IMG_SIZE), the class index for the panda class (i.e., CLASS_INDEX), and the total number of classes in the ImageNet dataset (i.e., NUM_CLASSES). Furthermore, on Lines 17-19, we also define the model training-related parameters like learning rate (i.e., LEARNING_RATE), the magnitude of the bound on our noise (i.e., EPSILON), and the number of iterations (i.e., ITERATIONS). Now, we define the get_prediction function, which outputs the predicted class and corresponding probabilities from our model. It takes as arguments the input image and the ground-truth one-hot label of our class under consideration (Line 22). On Line 24, use the model.predict() function to get the prediction from our pre-trained model (i.e., pred_model) and decode the prediction to get the corresponding predicted class names using the decode_predictions() function (Line 25). Finally, we print the value of the loss for the input image and the predictions (Lines 27 and 28). Now that we have defined our get_prediction function, let us go ahead and implement our visualization routine. The visualize function (Lines 30-48) takes as input the image list (i.e., im_list) with the input image array, noise array, and adversarial image array. The elements in the im_list are then converted to an RGB PIL image using the fromarray() function, as shown on Lines 32-34. Next, we use matplotlib to create a subplot with 1 row and 3 columns and plot the input image (Lines 36-38), the noise image (Lines 40-42), and the adversarial image (Lines 44-46). |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Finally, we save our visualization using the savefig function at the SAVE_PATH location (Line 48). Finally, we implement the deprocess_img function, which takes as input a normalized image and unnormalized it (Line 51). On Line 53, we first take the normalized image and remove the batch dimension using the np.squeeze function. Then, we add the channel mean to each channel (Lines 55-57) and reverse the channels to get the image into RGB channel format (Line 59). Finally, we clip the image pixel values to be in the range (0, 255) (Line 61) and return the final image (Line 63). Now that we have defined our parameters and the helper functions, it is time to define our model and load our input image. #### load resnet model
model = tf.keras.applications.resnet50.ResNet50(
include_top=True,
weights='imagenet',
classes=1000,
classifier_activation='softmax'
)
#### classification loss
loss_fn = tf.keras.losses. CategoricalCrossentropy()
#### load image
img = cv2.imread(IMG_PATH)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img,(IMG_SIZE))
#### preprocess image
image_processed = tf.keras.applications.resnet50.preprocess_input(img)
image = np.expand_dims(image_processed,0)
#### predictions with image
pred_gt = CLASS_INDEX
one_hot_gt = tf.one_hot([pred_gt], NUM_CLASSES)
get_prediction(image, one_hot_gt)
#1/1 [==============================] - 2s 2s/step
#Loss for Panda Class= 0.7695639
#Class Names and Probabilities [[('n02510455', 'giant_panda', #0.9522547), ('n02447366', 'badger', 0.027984638), ('n02509815', #'lesser_panda', 0.010850465), ('n02132136', 'brown_bear', #0.0009186132), ('n02493509', 'titi', 0.00030894685)]]
We load the pre-trained imagenet ResNet50 model from keras, as shown on Lines 66-71. Note that we keep the final linear layers (i.e., include_top=True), load the imagined trained weights (i.e., weights='imagenet'), define the total number of imagined classes (i.e., 1000), and final activation (i.e., softmax). On Line 74, we define the loss function, which is CategoricalCrossentropy(). |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | We are now ready to load our input image and make predictions with our pretrained model. On Lines 77-79, we first use the cv2.imread() function and load our original panda image from the IMG_PATH. Then, we convert our image from BGR to RGB format and resize it to the desired IMG_SIZE using the cv2.resize function. The panda class is the 388th index class in the imagenet labels list. We create a corresponding one-hot vector (1000 classes) and store it as pred_gt, as shown on Lines 86 and 87. Next, we use the model.predict() function to get the prediction from our ResNet model (i.e., pred_model) and decode the prediction to get the corresponding predicted class names using the decode_predictions() function as shown. Finally, we print the loss value for the panda image and the predictions. Notice that the model predicted the ‘Giant-Panda’ class with a probability of 0.95 and a loss value of 0.76. This implies that our model is very confident that the image belongs to the ‘Giant-Panda’ class. #### creating adversarial example
noise = tf. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Variable(tf.zeros_like(image), trainable=True)
opt = tf.keras.optimizers. SGD(learning_rate=LEARNING_RATE)
for t in range(ITERATIONS):
with tf. GradientTape() as tape:
adversarial_img = image+ noise
pred_model = model(adversarial_img)
loss = -loss_fn(pred_model, one_hot_gt)
gradients = tape.gradient(loss, noise)
opt.apply_gradients([(gradients, noise)])
noise.assign(tf.clip_by_value(noise, -EPSILON, EPSILON))
We are now ready to create our adversarial example. We start by defining the noise tensor, which is filled with zeros and has the same dimension as our input image. Recall that we want to use SGD to optimize and find this noise tensor, which is why we wrap it in tf. Variable() and set the trainable=True flag (Line 93). Next, we define our SGD-based optimizer with the learning rate (i.e., LEARNING_RATE) (Line 95). Now, we start our iterative optimization, and for each iteration, we get our adversarial_img by adding noise to our original image. Then we pass the adversarial_img through our pre-trained model to get predictions (i.e., pred_model). Note that in this process, the weights of the model stay frozen, as we discussed earlier. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Finally, we compute the loss between the prediction (i.e., pred_model) and the ground truth one-hot vector for panda (i.e., pred_gt). Note that since we want to maximize this loss, we take the negative of this as the loss to optimize (since maximizing a loss is the same as minimizing its negative value). This computation is done with tf. GradientTape() since we want tf to monitor gradients (Line 99). Next, we compute the gradient of the loss w.r.t. noise (i.e., gradients) and use the apply_gradients function to take one SGD step to update noise (Lines 106 and 107). To ensure our noise is always within the range (-EPSILON, EPSILON), we clamp its value at each iteration, as shown on Line 109. Now that we have the optimized noise, let us see how the model predictions change when this noise is added to the original image. #### predictions with adversarial example
noise = tf.clip_by_value(noise, -EPSILON, EPSILON)
adversarial_img = image+noise
get_prediction(adversarial_img, one_hot_gt)
#### visualize
noise = np.squeeze(noise.numpy())
image = deprocess_img(image)
image_adversarial = deprocess_img(adversarial_img.numpy())
im_list = [image, noise, image_adversarial]
visualize(im_list)
#Loss for Panda Class= 14.488977
#Class Names and Probabilities [[('n02447366', 'badger', #0.87211645), ('n02510455', 'giant_panda', 0.10107378), ('n02509815', 'lesser_panda', 0.0050036293), ('n02445715', 'skunk', 0.0024942914), ('n02056570', 'king_penguin', 0.0009352705)]]
Next, we create the adversarial image to see how our model’s predictions change. To ensure our noise is always within the range (-EPSILON, EPSILON), we clamp its value (Line 112) and then create our adversarial example by adding the engineered noise to our image (Line 113). |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | We use our get_prediction function to get predictions from our model on our adversarial image (Line 115). Notice that now our model predicts the input image to be a ‘badger’ with a probability of 0.87, and the loss for the ‘Giant-Panda’ class has increased from 0.76 earlier to 14.48 now. Now that we have seen that our adversarial example changes the model’s prediction to another class, let us check if it visually looks the same as our original example. We remove the batch dimension from our noise tensor and convert it to a numpy array, as shown on Line 118. Then we use the deprocess_img function to undo the preprocessing that we applied to our original input image and adversarial example (Lines 119 and 120) to get the unnormalized image and adversarial example (i.e., image, image_adversarial). Next, we create a list that contains the original image, engineered noise, and our adversarial image (Line 122) and pass it to our visualize() function (Line 123). Figure 3 shows the output visualization of the output image, engineered noise, and adversarial image. Figure 3: Original image of a panda (left), engineered noise (middle), and corresponding adversarial example (right) (source: image by the author). Robustness Toward Adversarial Examples
In the previous sections of this tutorial, we discussed how adversarial examples can change the prediction of our model and render our systems unreliable and prone to attacks. Naturally, the first question that arises is how we can tackle this problem, make our systems robust to such engineered examples, and enhance their reliability in scenarios where such examples might be input to our network. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Let us go back to the case we discussed at the beginning of this tutorial, where our model had to tackle a domain-related distribution shift since it was trained on original images of cats and dogs. We wanted to test it on sketches and cartoons of cats and dogs at inference. How can we tackle this simple problem and enhance our model so that it not only correctly predicts categories on original images but also on sketches and cartoons of cats and dogs? The simplest solution that comes to mind is to train and fine-tune our model using sketch and cartoon images of cats and dogs. Adversarial examples are also very similar and just differ in distribution from the original images. Thus, to make our models robust to adversarial examples, we can simply fine-tune our models on these examples. Tuning our models allows us to teach our system that the adversarial example of a panda should also be given the same label as the original image of a panda, which in turn makes our model robust to such engineered examples and stops it from drastically changing predictions for such engineered examples. Let us use TensorFlow and Keras to fine-tune our pre-trained ResNet50 model on our one adversarial example of a panda that we created. ### fine-tune on adversarial example
opt_fineTune = tf.keras.optimizers. SGD(learning_rate=1e-4)
model.compile(opt_fineTune,loss_fn)
model.fit(adversarial_img, one_hot_gt, epochs=50)
#### predictions with adversarial example after adversarial training
get_prediction(adversarial_img, one_hot_gt)
get_prediction(np.expand_dims(image_processed,0), one_hot_gt)
First, on Line 126, we define the SGD optimizer with a low learning rate of 1e-4 since we only want to fine-tune the mode on our adversarial example. |
https://pyimagesearch.com/2024/01/08/adversarial-learning-with-keras-and-tensorflow-part-1-overview-of-adversarial-learning/ | Next, on Line 127, we compile the model using the model.compile() function, which takes as input the optimizer (i.e., opt_fineTune) and the cross-entropy loss function. Finally, on Line 128, we call model.fit on our adversarial example and the corresponding one-hot vector for panda. Once our model is fine-tuned, we use the get_prediction function on our adversarial image (i.e., adversarial_img) and our original image, as shown on Line 131. You will notice that the loss for our adversarial example has now reduced, and our model predicts the correct ‘Giant-Panda’ class with a probability of 0.52 for the adversarial example. Additionally, our model still classifies the original panda example correctly with a high probability of 0.96. What's next? We recommend PyImageSearch University. Course information:
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Summary
In this tutorial, we learned about the relationship between robustness and reliability of deep learning models. We developed an understanding of how adversarial examples can change our models’ predictions and affect their reliability by making them prone to attacks. Specifically, we discussed adversarial examples and how they can be created using Keras and TensorFlow. Furthermore, we discussed ways to make our models robust to such attacks and fine-tuned them on adversarial examples to make them robust and reliable. Citation Information
Chandhok, S. “Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning,” PyImageSearch, P. Chugh, A. R. Gosthipaty, S. Huot, K. Kidriavsteva, and R. Raha, eds., 2024, https://pyimg.co/h35l2
@incollection{Chandhok_2024_ALwKTFpt1,
author = {Shivam Chandhok},
title = {Adversarial Learning with Keras and TensorFlow (Part 1): Overview of Adversarial Learning},
booktitle = {PyImageSearch},
editor = {Puneet Chugh and Aritra Roy Gosthipaty and Susan Huot and Kseniia Kidriavsteva and Ritwik Raha},
year = {2024},
url = {https://pyimg.co/h35l2},
}
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Home » Blog » Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue
Table of Contents
Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue
Introduction to Chat with Gemini Pro
Recap of Previous Lessons
Leveraging Conversational AI with Gemini Pro for Coding
Exploring Gemini Pro as a Conversational AI
What to Expect: Enhancing Code Generation with Gemini Pro’s AI
Setting Up Gemini Pro for Chat
Exploring Chat Capabilities with Gemini Pro
Preparing Your Development Environment for Gemini Pro
Step 1: Installing the Google Generative AI Library
Step 2: Importing Essential Python Packages
Step 3: Securely Configuring Your API Key
Listing the Available Generative Models
Loading the Gemini Pro Model
Enhancing Code Presentation with Markdown
Start a Chat Session with Gemini Pro
Retrieving the Chat Session History
Multi-Turn Chat: Generating Code and Unit Tests
Enhancing Conversations with Contextual Chat History
Conversational Debugging with Gemini Pro: Refining Image Classification Code
Unveiling the Limits: Advanced Error Resolution in Conversational Debugging
Accessing Full Dialogue and Code
Summary
Citation Information
Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue
In this tutorial, you will learn how to chat with the Gemini Pro generative model through the Google AI Python SDK (software development kit), focusing on creating image classification code using PyTorch. A key aspect of this tutorial is engaging with Gemini Pro as a conversational feedback tool, allowing the model to identify and correct errors in the generated code, and evaluating its effectiveness in this process. This lesson is the 4th in a 6-part series on Gemini Pro:
Introduction to Gemini Pro VisionImage Processing with Gemini Pro Image Classification with Gemini Pro Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue (this tutorial)Lesson 5Lesson 6
To learn how to use the Google AI Python SDK for conversational interactions with Gemini Pro as a feedback tool for generating and refining image classification code in PyTorch, just keep reading. Introduction to Chat with Gemini Pro
Welcome to Chat with Gemini Pro, the fourth lesson in our comprehensive series dedicated to unraveling the capabilities and innovations of the Gemini Pro generative model, part of the Google AI Python SDK. In this engaging tutorial, we will delve deeper into the interactive realm of AI by learning how to communicate with Gemini Pro effectively. Our focus will be on the creation of image classification code using PyTorch, leveraging Gemini Pro not just as a tool for code generation but as an intelligent conversational partner capable of providing feedback and identifying and rectifying errors in the generated code. This hands-on approach aims to evaluate Gemini Pro’s effectiveness as a dynamic learning and development assistant. Recap of Previous Lessons
In our previous journey, Image Classification with Gemini Pro, we delved into how Gemini Pro excels in generating Python code for image classification using the PyTorch framework, and positioning it against formidable counterparts like ChatGPT-3.5 and Bard. We navigated through the challenges of compatibility and error handling in code generation, revealing areas where Gemini Pro excelled and where it faced hurdles. Our exploration offered a glimpse into the potential of AI in simplifying and enhancing the development process in the realm of image processing. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Leveraging Conversational AI with Gemini Pro for Coding
In transitioning from our in-depth look at image classification in the previous tutorial, this chapter turns our attention to an innovative aspect of working with Gemini Pro: utilizing its chat functionality. In this unique tutorial, we’re not just focusing on further code generation but delving into how Gemini Pro can act as an interactive conversational partner. This partnership enables us to refine image classification code through dynamic feedback, error identification, and correction processes, leveraging the Google AI Python SDK and the powerful PyTorch framework. Exploring Gemini Pro as a Conversational AI
Having explored the capabilities of Gemini Pro with the Google AI Python SDK for generating both image classification and image processing code in our recent tutorials, we’re now poised to challenge its limits further. This phase of our journey puts Gemini Pro to the test, examining its potential to function as a conversational AI. It is akin to the roles ChatGPT and Bard have carved out within the conversational AI landscape, but with a twist specific to code generation. The burning question is: Can Gemini Pro, equipped with its advanced capabilities, engage in meaningful dialogue to not only generate code for image processing and classification tasks but also critically assess and correct any errors within that generated code through conversational interactions? What to Expect: Enhancing Code Generation with Gemini Pro’s AI
Join us as we embark on this intriguing exploration. Our mission is to assess Gemini Pro’s proficiency as a conversational partner in the realm of AI-driven code generation. Whether you have been following our series from the beginning or are just diving into the world of AI, this tutorial is crafted to provide you with a comprehensive understanding of how conversational AI can be leveraged to enhance the development process. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | By engaging Gemini Pro in a dialogue aimed at refining and debugging generated code, we strive to uncover new dimensions of its capabilities. This exploration is not just about testing the waters; it’s about pushing the boundaries of what conversational AI can achieve in the specific context of image classification and processing code generation. Stay tuned for the insights and breakthroughs that lie ahead in this and subsequent chapters of our series. Setting Up Gemini Pro for Chat
Continuing from our earlier setup in the tutorial, we will keep utilizing the Google AI Python SDK, which provides access to a range of models, Gemini Pro included. To acquire your API key, head over to Google MakerSuite and log in using your Google account. After logging in, you will be directed to Google AI Studio, where you can create your API key by following the instructions provided. This key is crucial for accessing the Gemini Pro model and other SDK resources programmatically. Here, you’ll find an option to generate your API key, as illustrated in Figure 1. Figure 1: Snapshot of Google AI Studio demonstrating API key generation (source: image by the author). After generating your API key, ensure you copy and store it securely. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | For those utilizing Google Colab, you have the option to safeguard environment variables, file paths, or keys as private, restricting visibility to only yourself and selected notebooks. This key is essential for your interactions with the Gemini Pro model, particularly when creating image processing code. Secure storage of your key guarantees uninterrupted access to the capabilities offered by Gemini Pro. Exploring Chat Capabilities with Gemini Pro
To illustrate the power of conversational AI in the context of coding, let’s start by exploring the basics of initiating a chat with Gemini Pro. This process involves setting up the necessary environment and sending a simple text prompt to the model, which, in turn, responds in a manner akin to a human assistant. This initial step is pivotal, establishing a foundation for more intricate dialogues that unveil Gemini Pro’s true capabilities — its adeptness at understanding, analyzing, and correcting code through sustained interaction. Such engagement with Gemini Pro enables developers to move beyond conventional code generation, utilizing the model’s insights to potentially refine their image classification projects, aiming for improved precision and efficiency. Preparing Your Development Environment for Gemini Pro
Step 1: Installing the Google Generative AI Library
We begin with the installation of the google-generativeai library via pip, enabling interaction with Google’s generative models, particularly Gemini Pro, in Python, as demonstrated below:
! pip install -q -U google-generativeai
The google-generativeai library is installed. Step 2: Importing Essential Python Packages
import textwrap
import google.generativeai as genai
import google.ai.generativelanguage as glm
from IPython.display import Markdown
from google.colab import userdata
This code snippet starts by importing four crucial Python libraries. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | The textwrap library is utilized for its capabilities in handling and formatting text, making it invaluable for organizing output. The google.generativeai library, referred to as genai, serves as the primary interface for accessing a suite of AI functionalities, with a particular focus on the Gemini Pro model. The google.ai.generativelanguage library, or glm, plays a crucial role alongside the google.generativeai library in our code. It introduces the Content and Part methods, essential for organizing chat history into roles and messages, thus facilitating a seamless and dynamic information exchange. This structure not only preserves the conversation’s context but also enriches it with each exchange, ensuring that the model’s responses remain relevant and context-sensitive. This significantly enhances user experience by producing conversations that are both natural and coherent. Ultimately, glm extends the google.generativeai library’s functionality, providing specialized tools for managing complex language-based interactions, from conversational AI to text generation and analysis. The Markdown function from IPython.display is included to improve the presentation of outputs directly in the Colab notebook, allowing for richer text formatting. Additionally, from google.colab import userdata is integrated to facilitate the use of user data within the notebook, enhancing personalized interaction with the models. Step 3: Securely Configuring Your API Key
# Used to securely store your API key
# Or use `os.getenv('GOOGLE_API_KEY')` to fetch an environment variable. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | GOOGLE_API_KEY=userdata.get("GEMINI_API_KEY")
genai.configure(api_key=GOOGLE_API_KEY)
This snippet utilizes the userdata module from the google.colab library to securely retrieve the "GEMINI_API_KEY", subsequently assigning it to GOOGLE_API_KEY. As an alternative, one might opt to use os.getenv('GOOGLE_API_KEY') for acquiring the API key from an environment variable. Following this, genai.configure(api_key=GOOGLE_API_KEY) is executed to configure the GenAI library with the specified API key, thus ensuring authorized access to its features. Employing this method within Google Colab notebooks is advantageous for the secure handling of API keys. Listing the Available Generative Models
for m in genai.list_models():
if "generateContent" in m.supported_generation_methods:
print(m.name)
To get the list of available generative models, we iterate through the list of models available in the genai library, checking for models that support the generateContent method. We do this by calling genai.list_models(), which retrieves a collection of model objects. Each model object contains various properties, including supported_generation_methods, a list indicating the types of content generation the model supports. For every model m in the list returned by genai.list_models(), we check if "generateContent" is one of the supported methods listed in m.supported_generation_methods. If it is, the model supports content generation capabilities, and we print the name of the model (m.name). models/gemini-1.0-pro
models/gemini-1.0-pro-001
models/gemini-1.0-pro-latest
models/gemini-1.0-pro-vision-latest
models/gemini-pro
models/gemini-pro-vision
In our series of tutorials, we’ve previously introduced and explored the capabilities of Gemini Pro and Gemini Pro Vision, the flagship models within the Google Generative AI framework. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | At the time of our last publication, these models represented the cutting edge of what was available for developers and researchers looking to leverage Google’s AI for their projects. However, recent updates have significantly expanded the Gemini model lineup, introducing new variants that enhance the scope and specificity of AI applications. Among these additions are four notable models: Gemini 1.0 Pro, Gemini 1.0 Pro 001, Gemini 1.0 Pro latest, and Gemini 1.0 Pro Vision latest. Each of these models brings something unique to the table, further diversifying the tools available for AI-driven content generation. Particularly of interest is the Gemini-1.0-Pro-001 model. Google describes this model as the premier choice for fine-tuning, highlighting its stability and adaptability for applications such as classification, summarization, and document search. This model’s introduction suggests an emphasis on providing developers with options for customizing AI behavior more closely to their specific needs. If you need more information on this, refer to the Model Tuning Guide. On the other hand, the Gemini-1.0-Pro-latest and Gemini-1.0-Pro-Vision-latest models signify ongoing improvements and updates to the architecture and capabilities of the Gemini Pro series. The designation “latest” implies that these models incorporate the most recent advancements and optimizations, potentially offering superior performance or features compared to their predecessors. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | These new models, especially those supporting the "generateContent" method, represent exciting advancements in the Gemini suite. They not only offer a wider range of options for developers and researchers but also signify Google’s commitment to continuous improvement and expansion of their AI capabilities. Amidst the exciting updates to the Gemini model lineup, with new models enhancing AI applications, our journey through AI-driven content generation continues to evolve. Notably, models like Gemini-1.0-Pro-001 offer specialized opportunities for fine-tuning, emphasizing Google’s push towards customizable AI solutions. Despite these advancements, for today’s lesson, we’ll stick with the familiar Gemini Pro model from our previous tutorials. This approach allows us to build on established knowledge and focus on enhancing our skills in conversational AI and code generation within a consistent framework. Loading the Gemini Pro Model
model = genai. GenerativeModel("gemini-pro")
Next, we instantiate the GenerativeModel class from the genai library, specifically selecting the "gemini-pro" model for initialization. By doing so, we allocate the capabilities of the Gemini Pro model to the model variable, thereby facilitating its use in diverse AI-centric tasks, such as text generation and data analysis. This initialization is key to accessing and employing the broad functionalities of Gemini Pro within our code. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | In this instance, we adhere to the GenerativeModel‘s default configurations by not defining any optional parameters like generation_config and safety_settings. This decision streamlines our setup process, making it straightforward to engage with the model’s inherent configurations for our applications. Enhancing Code Presentation with Markdown
def to_markdown(text):
text = text.replace("•", " *")
return Markdown(textwrap.indent(text, "> ", predicate=lambda _: True))
Next, we have the to_markdown helper function, which transforms a string into Markdown format, ideal for Jupyter notebooks. It starts by converting bullet points into Markdown’s asterisk syntax, followed by indenting each line with a blockquote symbol using textwrap.indent, applying this uniformly across all lines. The final output is a Markdown object, well-suited for display in Markdown-compatible environments. This enhances the text’s presentation, making it more suitable for environments like Jupyter notebooks that support Markdown rendering. Start a Chat Session with Gemini Pro
chat = model.start_chat()
response = chat.send_message(
"""As a culinary expert and food critic who utilizes AI to discover new flavor combinations,
what is an innovative dish combining traditional Italian and Japanese cuisine you would recommend? Please describe its key ingredients and preparation method. """
)
to_markdown(response.text)
Here, we initiate a chat with Gemini Pro, where the user, posing as a culinary expert and food critic interested in AI for discovering new flavor combinations, asks for an innovative dish that blends traditional Italian and Japanese cuisine. This prompt sets the stage for Gemini Pro to generate a creative and detailed response. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Dish Name: Umami Truffle Carbonara
Key Ingredients:
Spaghetti or linguine pasta
Truffle oil
Parmigiano-Reggiano cheese
Pancetta or guanciale
Japanese soy sauce
Mirin
Sake
Egg yolks
Dashi (optional)
Preparation Method:
Prepare the Pasta: Cook the pasta in salted boiling water according to the package instructions. Reserve a cup of the pasta cooking liquid. Fry the Pancetta: In a large skillet, fry the pancetta or guanciale until crispy. Remove the pancetta and set aside. Make the Truffle Carbonara Sauce: In a separate bowl, whisk together the egg yolks, grated Parmigiano-Reggiano cheese, truffle oil, and reserved pasta cooking liquid. Combine the Pasta and Sauce: Drain the pasta and immediately add it to the pancetta skillet. Pour the truffle carbonara sauce over the pasta and toss well. Add Japanese Flavors: Drizzle with soy sauce, mirin, and sake. Toss to incorporate. Garnish: Serve hot, garnished with additional grated Parmigiano-Reggiano cheese and truffle oil. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | For an optional touch, add a few drops of dashi to enhance the umami flavor. Tasting Notes:
This innovative dish combines the rich and creamy flavors of Italian carbonara sauce with the savory and umami notes of Japanese cuisine. The truffle oil adds an aromatic and earthy element, while the soy sauce, mirin, and sake balance out the richness with a touch of sweetness and acidity. The dashi adds an umami boost, enhancing the overall depth of flavor. Gemini Pro responds with a detailed description of “Umami Truffle Carbonara,” a dish that creatively merges Italian and Japanese elements. The response includes key ingredients and a step-by-step preparation method, showcasing Gemini Pro’s ability to generate comprehensive and innovative culinary recommendations. Retrieving the Chat Session History
print(chat.history)
This code block retrieves and prints the chat history, illustrating the conversational exchange between the user and Gemini Pro. It highlights the chat session’s multi-turn capability, where the model remembers and builds upon previous interactions. [parts {
text: "As a culinary expert and food critic who utilizes AI to discover new flavor combinations,\nwhat is an innovative dish combining traditional Italian and Japanese cuisine you would recommend? Please describe its key ingredients and preparation method.\n"
}
role: "user"
, parts {
text: "**Dish Name:** Umami Truffle Carbonara\n\n**Key Ingredients:**\n\n* Spaghetti or linguine pasta\n* Truffle oil\n* Parmigiano-Reggiano cheese\n* Pancetta or guanciale\n* Japanese soy sauce\n* Mirin\n* Sake\n* Egg yolks\n* Dashi (optional)\n\n**Preparation Method:**\n\n1. ** |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Prepare the Pasta:** Cook the pasta in salted boiling water according to the package instructions. Reserve a cup of the pasta cooking liquid.\n2. ** Fry the Pancetta:** In a large skillet, fry the pancetta or guanciale until crispy. Remove the pancetta and set aside.\n3. ** Make the Truffle Carbonara Sauce:** In a separate bowl, whisk together the egg yolks, grated Parmigiano-Reggiano cheese, truffle oil, and reserved pasta cooking liquid.\n4. ** Combine the Pasta and Sauce:** Drain the pasta and immediately add it to the pancetta skillet. Pour the truffle carbonara sauce over the pasta and toss well.\n5. ** Add Japanese Flavors:** Drizzle with soy sauce, mirin, and sake. Toss to incorporate.\n6. ** Garnish:** Serve hot, garnished with additional grated Parmigiano-Reggiano cheese and truffle oil. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | For an optional touch, add a few drops of dashi to enhance the umami flavor.\n\n**Tasting Notes:**\n\nThis innovative dish combines the rich and creamy flavors of Italian carbonara sauce with the savory and umami notes of Japanese cuisine. The truffle oil adds an aromatic and earthy element, while the soy sauce, mirin, and sake balance out the richness with a touch of sweetness and acidity. The dashi adds an umami boost, enhancing the overall depth of flavor." }
role: "model"
]
The above output displays the chat history, capturing the user’s query and Gemini Pro’s response. This demonstrates how chat history provides a coherent and continuous conversation flow, essential for multi-turn dialogues. Multi-Turn Chat: Generating Code and Unit Tests
code_chat = model.start_chat()
response = code_chat.send_message(
"Write a function in Python that calculates the Fibonacci sequence up to a given number n."
"The function should return a list of the sequence." )
to_markdown(response.text)
In this block, a new chat session is initiated specifically for generating Python code. The user or we request Gemini Pro to write a function that calculates the Fibonacci sequence up to a given number. This showcases the model’s ability to switch contexts and generate technical content. def fibonacci(n):
"""Calculates the Fibonacci sequence up to a given number n.
Args:
n: The number up to which the Fibonacci sequence should be calculated. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Returns:
A list of the Fibonacci sequence up to n.
"""
# Initialize the Fibonacci sequence with the first two numbers. fib_sequence = [0, 1]
# Iterate over the numbers from 2 to n.
for i in range(2, n):
# Calculate the next number in the Fibonacci sequence. next_number = fib_sequence[i - 1] + fib_sequence[i - 2]
# Add the next number to the Fibonacci sequence. fib_sequence.append(next_number)
# Return the Fibonacci sequence. return fib_sequence
Gemini Pro generates a Python function for calculating the Fibonacci sequence, demonstrating its capability to understand and respond to coding-related requests. The detailed code includes comments, further illustrating the model’s ability to produce clear and functional code snippets. response = code_chat.send_message("Could you write a unit test for the generated fibonacci function?") to_markdown(response.text)
Following the function generation, we ask Gemini Pro to write a unit test for the Fibonacci function. This request not only progresses the conversation toward software quality assurance but also serves as a key test of the chat model’s multi-turn capability. By asking for unit tests after the initial function creation, we effectively evaluate Gemini Pro’s ability to maintain context and coherence over a series of exchanges. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | This demonstrates a crucial step in software development, ensuring the code functions as intended, while simultaneously testing the model’s capacity to engage in a complex, multi-step dialogue that reflects a real-world development workflow. import unittest
class FibonacciTest(unittest. TestCase):
def test_fibonacci(self):
self.assertEqual(fibonacci(0), [0])
self.assertEqual(fibonacci(1), [0, 1])
self.assertEqual(fibonacci(2), [0, 1, 1])
self.assertEqual(fibonacci(3), [0, 1, 1, 2])
self.assertEqual(fibonacci(4), [0, 1, 1, 2, 3])
self.assertEqual(fibonacci(5), [0, 1, 1, 2, 3, 5])
self.assertEqual(fibonacci(6), [0, 1, 1, 2, 3, 5, 8])
self.assertEqual(fibonacci(7), [0, 1, 1, 2, 3, 5, 8, 13])
self.assertEqual(fibonacci(8), [0, 1, 1, 2, 3, 5, 8, 13, 21])
self.assertEqual(fibonacci(9), [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
self.assertEqual(fibonacci(10), [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55])
The model responds with a Python unit test class for the Fibonacci function. This includes multiple test cases, confirming the model’s proficiency in not only generating initial code but also in contributing to software quality assurance through unit testing. These sections together showcase Gemini Pro’s versatile capabilities in handling both creative and technical requests within a continuous, multi-turn chat session. The ability to maintain context through chat history enables a seamless transition between diverse topics, from culinary innovation to software development. Enhancing Conversations with Contextual Chat History
This section dives into the implementation of chat history in a conversational AI context, utilizing Python and the capabilities of a generative language model. By incorporating a predefined chat history, we simulate a richer, context-aware conversation between a user and the AI model. This technique enhances the model’s ability to provide relevant and informed responses based on the ongoing dialogue. chat_history = model.start_chat(
history=[
glm. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Content(
role="user",
parts=[
glm. Part(text=
"""
My name is Alex. You are my travel guide. I'm passionate about exploring ancient civilizations and their landmarks. What destinations do you recommend? """
)
],
),
glm. Content(role="model", parts=[glm. Part(text="I recommend visiting the Pyramids of Giza in Egypt and Machu Picchu in Peru for a start.")]), glm. Content(role="user", parts=[glm. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Part(text="Tell me something interesting about Machu Picchu.")]), glm. Content(role="model", parts=[glm. Part(text="Machu Picchu is often referred to as the 'Lost City of the Incas' and is a UNESCO World Heritage Site.")]), ]
)
In the above Python code block, we initialize a chat session with historical context. This is achieved by creating instances of glm. Content for both the user and the model, detailing an exchange about exploring ancient civilizations. The user, named Alex, asks for travel recommendations, and the model suggests visiting the Pyramids of Giza and Machu Picchu, followed by a request for interesting facts about Machu Picchu. This sets up a scenario where the model acts as a travel guide, offering personalized advice based on Alex’s interests. Incorporating chat history into a session, as illustrated in the provided code snippet, preloads the conversation with essential context, setting the stage for the interaction. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | This inclusion of alternating messages between the user and model establishes a conversational backdrop, enabling the model to produce responses that are informed and relevant. This approach not only primes the model with the necessary context for a seamless dialogue flow but also mimics a memory function, allowing the model to reference past exchanges, user preferences, and the evolving nature of the conversation. The use of the argument history captures the essence of this feature by creating a narrative of prior interactions, giving the model a semblance of continuity and awareness of the conversation’s trajectory. This simulated memory aids in crafting responses that are not just relevant but also coherent with the dialogue’s established themes, enhancing the natural flow of the chat as though it has been progressing over time, rather than initiating anew with each message. Therefore, the history argument is crucial for enriching the conversation, guiding the model’s understanding and responses based on the cumulative context of past exchanges. response = chat_history.send_message("Are there any specific festivals or events related to ancient civilizations happening soon?") to_markdown(response.text)
Following the established history, we send a new message inquiring about specific festivals or events related to ancient civilizations happening soon. This demonstrates how the model can continue the conversation by accessing and utilizing the provided chat history to generate a coherent and contextually appropriate response. Yes, the Inti Raymi festival in Peru is a popular festival that takes place in June and celebrates the winter solstice and the Inca culture. The model’s response reveals that the Inti Raymi festival, which honors the winter solstice and Inca culture in Peru, takes place in June. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | This demonstrates the model’s capacity to access and deliver precise information pertinent to the user’s request, given that the festival represents ancient civilization, leveraging the established conversational context. response = chat_history.send_message("Can you tell me the specific date when the Inti Raymi Festival in Peru is celebrated??") to_markdown(response.text)
In a subsequent Python block, a more precise question is posed about the specific date of the Inti Raymi Festival. This illustrates refining the conversation’s focus and seeking more detailed information following the initial response. The Inti Raymi Festival in Peru is celebrated on June 24th each year. The model clarifies that the Inti Raymi Festival is celebrated on June 24th each year, providing a direct answer to the refined question. This demonstrates the model’s capability to understand and respond to follow-up inquiries for additional details, further enriching the conversational experience. Through these blocks, we exemplify a dynamic and interactive dialogue with the AI model, highlighting the use of chat history to create a more engaging and informative exchange tailored to the user’s interests and queries. Conversational Debugging with Gemini Pro: Refining Image Classification Code
In this section, we continue our journey with Gemini Pro, delving deeper into its capabilities by testing it on a task we’ve previously tackled: generating image classification code. However, this time, we leverage Gemini Pro’s chat feedback mechanism to engage in a dialogue, pointing out inaccuracies or errors and requesting corrections. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | This interactive process not only showcases the model’s ability to participate in conversational debugging but also tests its capacity to understand and rectify coding errors based on feedback. model = genai. GenerativeModel('gemini-pro')
chat = model.start_chat(history=[])
Here, we initialize a chat session with Gemini Pro, setting the stage for our conversational debugging exercise. This initial step prepares the model for the interactive session, where we aim to generate and refine the image classification code. response = chat.send_message("Write a multiclass image classification code in pytorch framework using a public dataset, I would be training and testing the image classification code in Google colab")
to_markdown(response.text)
We prompt Gemini Pro to generate PyTorch code for multiclass image classification, specifying the use of a public dataset and Google Colab as the environment for training and testing. This sets a clear task for the model, aiming to generate usable code for a specific framework and dataset. # Import necessary libraries
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
# Define the device to use for training and testing
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the public image dataset
train_dataset = torchvision.datasets. ImageFolder(root='path/to/train_directory', transform=torchvision.transforms. ToTensor())
test_dataset = torchvision.datasets. ImageFolder(root='path/to/test_directory', transform=torchvision.transforms. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | ToTensor())
# Create data loaders for training and testing
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# Define the image classification model architecture
class ImageClassifier(nn. Module):
def __init__(self):
super(ImageClassifier, self).__init__()
self.conv1 = nn. Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.pool1 = nn. MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn. Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.pool2 = nn. MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn. Linear(128 * 7 * 7, 1024)
self.fc2 = nn. Linear(1024, 3) # Adjust this to match the number of classes in your dataset
def forward(self, x):
x = self.conv1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = x.view(-1, 128 * 7 * 7)
x = self.fc1(x)
x = self.fc2(x)
return x
# Initialize the model
model = ImageClassifier().to(device)
# Define the loss function and optimizer
criterion = nn. CrossEntropyLoss()
optimizer = optim. SGD(model.parameters(), lr=0.01, momentum=0.9)
# Train the model
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# Get the inputs
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# Zero the parameter gradients
optimizer.zero_grad()
# Forward pass
outputs = model(inputs)
# Compute the loss
loss = criterion(outputs, labels)
# Backward pass
loss.backward()
# Update the parameters
optimizer.step()
# Print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
# Test the model
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
To train and test the image classification code in Google Colab:
Create a new Colab notebook. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Import the necessary libraries. Load the public image dataset. Create data loaders for training and testing. Define the image classification model architecture. Initialize the model. Define the loss function and optimizer. Train the model. Test the model. You can find the public image dataset and the necessary libraries in the resources section of the Colab notebook. The response from the model is a comprehensive PyTorch script tailored for image classification, covering crucial phases from library importation and model architecture definition to the outlining of training and testing routines. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Despite its thoroughness, certain issues merit attention for refinement: the output layer’s number of classes is fixed, indicating a lack of adaptability for datasets varying in class count; activation functions are notably absent, stunting the network’s learning capability; and execution of the provided code reveals runtime and value errors, possibly hinting at deeper architectural mismatches. These elements could significantly enhance the code’s versatility and operational smoothness across diverse datasets. response = chat.send_message("Can you use a public dataset like cifar-10 or mnist?", stream=True)
Following the initial code generation, we inquire about adapting the code for well-known public datasets like CIFAR-10 (Canadian Institute For Advanced Research) or MNIST (Modified National Institute of Standards and Technology). This question tests the model’s ability to modify the generated code to accommodate different datasets, showcasing its adaptability. Yes, you can use a public dataset like CIFAR-10 or MN
IST in the provided code. Here's how you can do it:
**For CIFAR-10:**
1. Import the `torchvision
.datasets` module. 2. Load the CIFAR-10 dataset using the following code:
```python
train_dataset = torchvision.datasets. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | CIFAR10(root='path/to/cifar10_train', train=True, download=True, transform=torchvision. transforms. ToTensor())
test_dataset = torchvision.datasets. CIFAR10(root='path/to/cifar10_test', train=False, download=True, transform=torchvision.transforms. ToTensor())
```
3. Create data loaders for training and testing as usual. **For MNIST:**
1. Import the `torchvision.datasets` module. 2. Load the MNIST dataset using the following code:
```python
train_dataset = torchvision.datasets. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | MNIST(root='path/to/mnist_train', train=True, download=True,
transform=torchvision.transforms. ToTensor())
test_dataset = torchvision.datasets. MNIST(root='path/to/mnist_test', train=False, download=True, transform=torchvision.transforms. ToTensor())
```
3. Create data loaders for training and testing as usual. Once you have loaded the dataset, you can follow the rest of the code as provided earlier. You may need to adjust the number of classes in the model's fully connected layer to match the number of classes in the dataset you are using. For example, for CIFAR-10, you would change the number of classes to 10, and for MNIST, you would change it to 10 as well. Here is the modified code for the model initialization:
```python
class ImageClassifier(nn. Module):
def __init__(self):
super(ImageClassifier, self).__init__()
self.conv1 = nn. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.pool1 = nn. MaxPool2d(kernel_size=2, stride=2)
self.conv2
= nn. Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.pool2 = nn. MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn. Linear(128 * 7 * 7, 1024)
self.fc2 = nn. Linear(1024, 10) # Adjust this to match the number of classes in your dataset
def forward(self, x):
x = self.conv1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = x.view(-1, 128 * 7 * 7)
x = self.fc1(x)
x = self.fc2(x)
return x
```
With these changes, you can use the code to train and test the image classification model on either the CIFAR-10 or MNIST dataset. The model confirms the feasibility of using CIFAR-10 or MNIST and guides the loading of these datasets. This demonstrates Gemini Pro’s responsiveness to feedback and its capacity to offer practical solutions for refining the code based on user requirements. response = chat.send_message("Okay, how about pass a root as ./data to cifar10?", stream=True)
for chunk in response:
print(chunk.text)
We further refine our request by specifying a data path for the CIFAR-10 dataset. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | This tests the model’s ability to incorporate specific user inputs into the code, enhancing its relevance and usability. To pass a root of `./data` to CIFAR-10 in
the provided code, you can use the following code:
```python
train_dataset = torchvision.datasets. CIFAR10(root='./data
', train=True, download=True, transform=torchvision.transforms. ToTensor())
test_dataset = torchvision.datasets. CIFAR10(root='./data', train=False, download=True, transform=torchvision.transforms. ToTensor())
```
This will download the CIFAR-
10 dataset to the `./data` directory and then load it into the training and testing datasets. Here is the complete modified code:
```python
# Import necessary libraries
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
# Define the device to use for training and testing
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the CIFAR-10 dataset
train_dataset = torchvision.datasets. CIFAR10(root='./data',
train=True, download=True, transform=torchvision.transforms. ToTensor())
test_dataset = torchvision.datasets. CIFAR10(root='./data', train=False, download=True, transform=torchvision.transforms. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | ToTensor())
# Create data loaders for training and testing
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# Define the image classification model architecture
class ImageClassifier(nn. Module):
def __init__(self):
super(ImageClassifier, self).__init__()
self.conv1 = nn. Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.pool1 = nn. MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn. Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.pool2 = nn. MaxPool2d(kernel_size=2, stride=
2)
self.fc1 = nn. Linear(128 * 7 * 7, 1024)
self.fc2 = nn. Linear(1024, 10) # Adjust this to match the number of classes in your dataset
def forward(self, x):
x = self.conv1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = x.view(-1, 128 * 7 * 7)
x = self.fc1(x)
x = self.fc2(x)
return x
# Initialize the model
model = ImageClassifier().to(device)
# Define the loss function and optimizer
criterion = nn. CrossEntropyLoss()
optimizer = optim. SGD(model.parameters(), lr=0.01, momentum=0.9)
# Train the model
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# Get the inputs
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# Zero the parameter gradients
optimizer.zero_grad()
# Forward pass
outputs = model(inputs)
# Compute the loss
loss = criterion(outputs, labels)
# Backward pass
loss.backward()
# Update the parameters
optimizer.step()
# Print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
# Test the model
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = model
(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
Now, when you run this code, it will download the CIFAR-10 dataset to the `./data` directory and then train and test the image classification model on the dataset. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Gemini Pro successfully adjusts the code to use the specified ./data path for downloading and loading the CIFAR-10 dataset. This adjustment showcases the model’s flexibility and its capacity to tailor the code based on detailed user specifications. response = chat.send_message("the imageclassifier class does not use any activation function which is essential for adding non-linearity", stream=True)
for chunk in response:
print(chunk.text)
In this step, we highlight a critical issue in the generated code: the absence of activation functions in the ImageClassifier model, which are crucial for learning complex patterns. This feedback is aimed at improving the model’s performance by introducing non-linearity. You're right, the `ImageClassifier` class in the provided code does
not use any activation function. Activation functions are essential for adding non-linearity to the model, which is necessary for it to learn complex patterns in the data
. To add non-linearity to the model, you can use activation functions such as ReLU, LeakyReLU, or Sigmoid. Here is the modified code with a ReLU activation function added after each convolutional layer and the first fully connected layer:
```python
class ImageClassifier(nn. Module
):
def __init__(self):
super(ImageClassifier, self).__init__()
self.conv1 = nn. Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.relu1 = nn. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | ReLU()
self.pool1 = nn. MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn. Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.relu
2 = nn. ReLU()
self.pool2 = nn. MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn. Linear(128 * 7 * 7, 1024)
self.relu3 = nn. ReLU()
self.fc2 = nn. Linear(1024, 10) # Adjust this to match the number of classes in your dataset
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
x = x.view(-1, 128 * 7 * 7)
x = self.fc1(x)
x = self.relu3(x)
x = self.fc2(x)
return x
```
With this change, the model will now use the ReLU activation function after each convolutional layer and the
first fully connected layer. This will add non-linearity to the model and help it learn more complex patterns in the data. Remember to also import the necessary activation function at the beginning of the code:
```python
import torch.nn.functional as F
```
And use `F.relu()` for the activation function in the model:
```python
self.relu1 = F.relu()
self.relu2 = F.relu()
self.relu3 = F.relu()
```
Now, when you train the model, it should perform better due to the addition of the activation function. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Acknowledging the feedback, Gemini Pro revises the model to include ReLU activation functions at appropriate layers. This correction not only addresses the identified error but also exemplifies the model’s ability to understand and implement specific coding recommendations, thereby improving the overall code quality. Unveiling the Limits: Advanced Error Resolution in Conversational Debugging
Through this interactive debugging session, we’ve engaged Gemini Pro in a multi-turn chat that generates code, identifies potential issues, and refines the output based on conversational feedback. This process not only reinforces the model’s utility as a coding assistant but also highlights its potential in conversational AI applications where understanding, adapting to, and acting on user feedback is critical. These discussions we’ve detailed represent just a fraction of the extensive dialogue we engaged in with Gemini Pro. Following our conversation about integrating activation functions, we encountered further challenges, such as runtime and value errors during the model’s training phase. Specifically, we faced a value error indicating a mismatch between the expected input batch size and the target batch size, a critical issue that arose during the loss computation process. This suggested a deeper problem, possibly rooted in the architecture’s design or the data preprocessing steps. We promptly reported these issues back to Gemini Pro, describing the errors encountered during code execution and seeking guidance on resolution. Although Gemini Pro attempted to address these concerns, the dialogue unfolded over three to four additional exchanges where it became evident that resolving these deep-level errors was a complex task. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Despite the model’s adeptness at adjusting simpler aspects of the code (e.g., adding missing activation functions or specifying data paths), it struggled to rectify the more intricate issues that emerged during the computational processes, particularly those affecting the loss calculation. Ultimately, while Gemini Pro proved to be an invaluable resource for identifying and correcting certain errors through conversational feedback, it faced limitations when addressing more complex problems that required a nuanced understanding of the neural network architecture and its interaction with the data. This experience highlighted the potential and boundaries of using conversational AI for debugging and refining machine learning code, underscoring the importance of human oversight in navigating the intricacies of model development and error resolution. Accessing Full Dialogue and Code
For those keen to explore the entirety of our dialogue with Gemini Pro, including the attempts to navigate through complex debugging scenarios, the full exchange is documented in our accompanying Colab notebook. This resource, integral to our tutorial, is available for direct access and download, offering a comprehensive view of conversational debugging in action and serving as a practical guide through the nuanced interactions between AI-driven coding assistance and the multifaceted challenges of machine learning model development. What's next? We recommend PyImageSearch University. Course information:
84 total classes • 114+ hours of on-demand code walkthrough videos • Last updated: February 2024
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I strongly believe that if you had the right teacher you could master computer vision and deep learning. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Or requires a degree in computer science? That’s not the case. All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. And that’s exactly what I do. My mission is to change education and how complex Artificial Intelligence topics are taught. If you're serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today. Here you’ll learn how to successfully and confidently apply computer vision to your work, research, and projects. Join me in computer vision mastery. Inside PyImageSearch University you'll find:
✓ 84 courses on essential computer vision, deep learning, and OpenCV topics
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✓ 114+ hours of on-demand video
✓ Brand new courses released regularly, ensuring you can keep up with state-of-the-art techniques
✓ Pre-configured Jupyter Notebooks in Google Colab
✓ Run all code examples in your web browser — works on Windows, macOS, and Linux (no dev environment configuration required!) ✓ Access to centralized code repos for all 536+ tutorials on PyImageSearch
✓ Easy one-click downloads for code, datasets, pre-trained models, etc. |
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Summary
In this Part 4 of our Gemini Pro series, we embark on an explorative journey into the conversational AI capabilities of Gemini Pro, particularly focusing on its utility in the realm of coding and debugging. The tutorial begins by introducing readers to the initial setup and configuration of Gemini Pro for chat-based interactions, setting the stage for a deep dive into its chat functionalities. Through a structured walkthrough, we explore various aspects of interacting with Gemini Pro, including setting up the development environment, listing available models, and enhancing code presentation with Markdown. A significant portion of the tutorial is dedicated to engaging Gemini Pro in a multi-turn chat, demonstrating the model’s ability to generate code snippets and unit tests. This process not only highlights the practical applications of Gemini Pro but also puts its conversational debugging prowess to the test. We encounter a series of challenges, from basic fixes like adding missing activation functions to more complex issues related to runtime and value errors during code execution. These encounters serve as real-world examples of Gemini Pro’s capabilities and limitations in addressing deep-level errors within the code. The tutorial culminates in a detailed examination of conversational debugging with Gemini Pro, showcasing how it facilitates the refinement of image classification code through dialogue. Despite its successes in certain areas, Gemini Pro’s struggles with more intricate debugging tasks reveal critical insights into the limitations of current AI technologies in understanding and rectifying complex coding errors without human intervention. |
https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | This exploration not only provides a comprehensive overview of Gemini Pro’s chat and coding functionalities but also sheds light on the potential and boundaries of using conversational AI for code generation and debugging. It underscores the necessity for ongoing human oversight in the AI-assisted coding process, particularly when navigating the nuanced challenges of software development. Citation Information
Sharma, A. “Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue,” PyImageSearch, P. Chugh, A. R. Gosthipaty, S. Huot, K. Kidriavsteva, and R. Raha, eds., 2024, https://pyimg.co/onf1q
@incollection{Sharma_2024_ConversingGeminiPro,
author = {Aditya Sharma},
title = {Conversing with Gemini Pro: Crafting and Debugging PyTorch Code Through AI Dialogue},
booktitle = {PyImageSearch},
editor = {Puneet Chugh and Aritra Roy Gosthipaty and Susan Huot and Kseniia Kidriavsteva and Ritwik Raha},
year = {2024},
url = {https://pyimg.co/onf1q},
}
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https://pyimagesearch.com/2024/04/08/conversing-with-gemini-pro-crafting-and-debugging-pytorch-code-through-ai-dialogue/ | Equip yourself with the ability to train a potent computer vision model in a mere afternoon. With a few images, you can import data from any source via API, annotate images using our superior cloud-hosted tool, kickstart model training with a single click, and deploy the model via a hosted API endpoint. Tailor your process by opting for a code-centric approach, leveraging our intuitive, cloud-based UI, or combining both to fit your unique needs. Embark on your journey today with absolutely no credit card required. Step into the future with Roboflow. Join Roboflow Now
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https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | Click here to download the source code to this pos
Table of Contents
Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 2
Configuring Your Development Environment
Having Problems Configuring Your Development Environment? Project Structure
Introduction to NeRF
Input Data Pipeline
Utility and Images
Generate Rays
Sample Points
NeRF Multi-Layer Perceptron
Volume Rendering
Photometric Loss
Breather
Enhancing NeRF
Positional Encoding
Hierarchical Sampling
Credits
Summary
Citation Information
The uniqueness of NeRF is proved by the number of doors it opens up in the field of computer graphics and deep learning. These range from medical imaging, 3D scene reconstruction, animation industry, relighting a scene to depth estimation. In our previous week’s tutorial, we familiarize ourselves with the prerequisites of NeRF. We have also explored the dataset that will be used. Now, it is best to remind ourselves of the initial problem statement. What if there was a way to capture the entire 3D scene just from a sparse set of 2D pictures? In this tutorial, we will focus on the algorithm that NeRF takes to capture the 3D scene from the sparse set of images. This lesson is part 2 of a 3-part series on Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras:
Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 1 (last week’s tutorial)Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 2 (this week’s tutorial)Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 3 (next week’s tutorial)
To learn about Neural Radiance Fields or NeRF, just keep reading. Looking for the source code to this post? |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | Jump Right To The Downloads Section
Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 2
In this tutorial, we dive straight into the concepts of NeRF. We have divided this tutorial into the following sections:
Introduction to NeRF: overview of NeRFInput Data Pipeline: the tf.data input data pipelineUtility and images: building the tf.data pipeline for imagesGenerate rays: building the tf.data pipeline for raysSample points: sampling points from the raysNeRF Multi-Layer Perceptron: the NeRF Multi-Layer Perceptron (MLP) architectureVolume Rendering: understanding the volume rendering processPhotometric Loss: understanding the loss used in NeRFEnhancing NeRF: techniques to enhance NeRFPositional encoding: understanding positional encodingHierarchical sampling: understanding hierarchical sampling
By the end of this tutorial, we will be able to understand the concepts proposed in NeRF. Configuring Your Development Environment
To follow this guide, you need to have the TensorFlow library installed on your system. Luckily, TensorFlow is pip-installable:
$ pip install tensorflow
Having Problems Configuring Your Development Environment? Figure 1: Having trouble configuring your dev environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University — you’ll be up and running with this tutorial in a matter of minutes. All that said, are you:
Short on time?Learning on your employer’s administratively locked system?Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?Ready to run the code right now on your Windows, macOS, or Linux system? Then join PyImageSearch University today! Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides that are pre-configured to run on Google Colab’s ecosystem right in your web browser! |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | No installation required. And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Project Structure
We first need to review our project directory structure. Start by accessing the “Downloads” section of this tutorial to retrieve the source code and example images. Let’s take a look at the directory structure:
$ tree --dirsfirst
. ├── dataset
│ ├── test
│ │ ├── r_0_depth_0000.png
│ │ ├── r_0_normal_0000.png
│ │ ├── r_0.png
│ │ ├── ..
│ │ └── ..
│ ├── train
│ │ ├── r_0.png
│ │ ├── r_10.png
│ │ ├── ..
│ │ └── ..
│ ├── val
│ │ ├── r_0.png
│ │ ├── r_10.png
│ │ ├── ..
│ │ └── ..
│ ├── transforms_test.json
│ ├── transforms_train.json
│ └── transforms_val.json
├── pyimagesearch
│ ├── config.py
│ ├── data.py
│ ├── encoder.py
│ ├── __init__.py
│ ├── nerf.py
│ ├── nerf_trainer.py
│ ├── train_monitor.py
│ └── utils.py
├── inference.py
└── train.py
The parent directory has two python scripts and two folders. The dataset folder contains three subfolders: train, test, and val for the train, test, and validation images. The pyimagesearch folder contains all of the python scripts we will be using for training. Finally, we have the two driver scripts: train.py and inference.py. We will be looking at training and inference in next week’s tutorial. |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | Note: In the interest of time, we have divided the implementation of NeRF into two parts. This blog introduces the concepts, while next week’s blogs will cover the train and inference scripts. Introduction to NeRF
Let’s talk about the premise of the paper. You have images of a particular scene from a few specific viewpoints. Now you want to generate an image of the scene from an entirely new view. This problem falls under novel image synthesis, as shown in Figure 2. Figure 2: Novel view generation. The immediate solution to novel view synthesis that comes to our mind is to use a Generative Adversarial Network (GAN) on the training dataset. With GANs, we are constraining ourselves to the 2D space of images. Mildenhall et al. ( |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | 2020), on the other hand, ask a simple question. Why not capture the entire 3D scenery from the images itself? Let’s take a moment and try to absorb this idea. We are now looking at a transformed problem statement. From novel view synthesis, we have transited to 3D scene capture from a sparse set of 2D images. This new problem statement will also serve as a solution to the novel view synthesis problem. How difficult is it to generate a novel view if we have the 3D scenery at our hands? Note that, NeRF is not the first to tackle this problem. Its predecessors have used various methods, including Convolutional Neural Networks (CNN) and gradient-based mesh optimization. However, according to the paper, these methods could not scale to better resolution due to higher space and time complexity. |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | NeRF aims at optimizing an underlying continuous volumetric scene function. Do not worry if you don’t get all of these terms at first glance. The rest of the blog is dedicated to breaking each of these topics down in the finest details and explaining them one by one. We begin with a sparse set of images and their corresponding camera metadata (orientation and position). Next, we want to achieve a 3D representation of the entire scene, as shown in Figure 3. Figure 3: Images of the 3D scene (source: adapted from Mildenhall et al., 2020, p. 5, fig. 2). The steps for NeRF can be visualized in the following figures:
Generate Rays: In this step, we march rays through each pixel of the image. The rays (Ray A and Ray B) are the red lines (Figure 4) that intersect the image and traverse through the 3D box (scene). |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | Figure 4: Rays passing through the image and traversing the 3D scene (source: adapted from Mildenhall et al., 2020, p. 5, fig. 2). Sample points: In this step we sample points on the rays as shown in Figure 5. We must note that these points are located on the rays, making them 3D points inside the box. Figure 5: Sampling points from the rays (source: adapted from Mildenhall et al., 2020, p. 5, fig. 2). Each point has a unique position and a direction component linked as shown (Figure 6). The direction of each point is the same as the direction of the ray. |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | Figure 6: Spatial location and the direction of each point (source: adapted from Mildenhall et al., 2020, p. 5, fig. 2). Deep Learning: We pass these points into an MLP (Figure 7) and predict the color and density corresponding to that point. Figure 7: Sending the point into an MLP (source: adapted from Mildenhall et al., 2020, p. 5, fig. 2). Volume Rendering: Let’s consider a single ray (Ray A here) and send all the sample points to the MLP to get the corresponding color and density, as shown in Figure 8. After we have the color and density of each point, we can apply classical volume rendering (defined in a later section) to predict the color of the image pixel (pixel P here) through which the ray passes. Figure 8: Volume rendering process (source: adapted from Mildenhall et al., |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | 2020, p. 5, fig. 2). Photometric Loss: The difference between the predicted color of the pixel (shown in Figure 9) and the actual color of the pixel makes the photometric loss. This eventually allows us to perform backpropagation on the MLP and minimize the loss. Figure 9: Computing the photometric loss (source: adapted from Mildenhall et al., 2020, p. 5, fig. 2). Input Data Pipeline
At this point, we have a bird’s eye view of NeRF. However, before describing the algorithm further, we need first to define an input data pipeline. We know from the previous week’s tutorial that our dataset contains images and the corresponding camera orientations. |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | So now, we need to build a data pipeline that produces images and the corresponding rays. In this section, we will build this data pipeline step by step using the tf.data API. tf.data ensures an efficient way to build and use the dataset. If you want a primer on tf.data, you can refer to this tutorial. The entire data pipeline is written in the pyimagesearch/data.py file. So, let’s open the file and start digging! Utility and Images
# import the necessary packages
from tensorflow.io import read_file
from tensorflow.image import decode_jpeg
from tensorflow.image import convert_image_dtype
from tensorflow.image import resize
from tensorflow import reshape
import tensorflow as tf
import json
We begin with importing the necessary packages on Lines 2-8
tensorflow to build the data pipelinejson for reading and working with json data
def read_json(jsonPath):
# open the json file
with open(jsonPath, "r") as fp:
# read the json data
data = json.load(fp)
# return the data
return data
On Lines 10-17, we define the read_json function. This function takes the path to the json file (jsonPath) and returns the parsed data. We open the json file with the open function on Line 12. Then, with the file pointer in hand, we read the contents and parse it with the json.load function on Line 14. |
https://pyimagesearch.com/2021/11/17/computer-graphics-and-deep-learning-with-nerf-using-tensorflow-and-keras-part-2/ | Finally, Line 17 returns the parsed json data. def get_image_c2w(jsonData, datasetPath):
# define a list to store the image paths
imagePaths = []
# define a list to store the camera2world matrices
c2ws = []
# iterate over each frame of the data
for frame in jsonData["frames"]:
# grab the image file name
imagePath = frame["file_path"]
imagePath = imagePath.replace(".", datasetPath)
imagePaths.append(f"{imagePath}.png")
# grab the camera2world matrix
c2ws.append(frame["transform_matrix"])
# return the image file names and the camera2world matrices
return (imagePaths, c2ws)
On Lines 19-37, we define the get_image_c2w function. This function takes the parsed json data (jsonData) and the path to the dataset (datasetPath) and returns the path to the images (imagePaths) and its corresponding camera-to-world (c2ws) matrices. On Lines 21-24, we define two empty lists: imagePaths and c2ws. On Lines 27-34, we iterate over the parsed json data and add the image paths and camera-to-world matrices to the empty lists. After iterating over the entire data, we return both lists (Line 37). Working with tf.data. Dataset instances, we will need a way to transform our dataset while feeding it to the model. To efficiently do this, we use the map functionality. |
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