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You have likely already had the opportunity to interact with generative artificial intelligence (AI) tools (such as virtual assistants and chatbot applications) and noticed that you dont always get the answer you are looking for, and that achieving it may not be straightforward. Large language models (LLMs), the models behind the generative AI revolution, receive instructions on what to do, how to do it, and a set of expectations for their response by means of a natural language text called a prompt. The way prompts are crafted greatly impacts the results generated by the LLM. Poorly written prompts will often lead to hallucinations, sub-optimal results, and overall poor quality of the generated response, whereas good-quality prompts will steer the output of the LLM to the output we want.

In this post, we show how to build efficient prompts for your applications. We use the simplicity of Amazon Bedrock playgrounds and the state-of-the-art Anthropics Claude 3 family of models to demonstrate how you can build efficient prompts by applying simple techniques.

Prompt engineering is the process of carefully designing the prompts or instructions given to generative AI models to produce the desired outputs. Prompts act as guides that provide context and set expectations for the AI. With well-engineered prompts, developers can take advantage of LLMs to generate high-quality, relevant outputs. For instance, we use the following prompt to generate an image with the Amazon Titan Image Generation model:

An illustration of a person talking to a robot. The person looks visibly confused because he can not instruct the robot to do what he wants.

We get the following generated image.

Lets look at another example. All the examples in this post are run using Claude 3 Haiku in an Amazon Bedrock playground. Although the prompts can be run using any LLM, we discuss best practices for the Claude 3 family of models. In order to get access to the Claude 3 Haiku LLM on Amazon Bedrock, refer to Model access.

We use the following prompt:

Claude 3 Haikus response:

The request prompt is actually very ambiguous. 10 + 10 may have several valid answers; in this case, Claude 3 Haiku, using its internal knowledge, determined that 10 + 10 is 20. Lets change the prompt to get a different answer for the same question:

Claude 3 Haikus response:

The response changed accordingly by specifying that 10 + 10 is an addition. Additionally, although we didnt request it, the model also provided the result of the operation. Lets see how, through a very simple prompting technique, we can obtain an even more succinct result:

Claude 3 Haiku response:

Well-designed prompts can improve user experience by making AI responses more coherent, accurate, and useful, thereby making generative AI applications more efficient and effective.

The Claude 3 family is a set of LLMs developed by Anthropic. These models are built upon the latest advancements in natural language processing (NLP) and machine learning (ML), allowing them to understand and generate human-like text with remarkable fluency and coherence. The family is comprised of three models: Haiku, Sonnet, and Opus.

Haiku is the fastest and most cost-effective model on the market. It is a fast, compact model for near-instant responsiveness. For the vast majority of workloads, Sonnet is two times faster than Claude 2 and Claude 2.1, with higher levels of intelligence, and it strikes the ideal balance between intelligence and speedqualities especially critical for enterprise use cases. Opus is the most advanced, capable, state-of-the-art foundation model (FM) with deep reasoning, advanced math, and coding abilities, with top-level performance on highly complex tasks.

Among the key features of the models family are:

To learn more about the Claude 3 family, see Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together, Anthropics Claude 3 Sonnet foundation model is now available in Amazon Bedrock, and Anthropics Claude 3 Haiku model is now available on Amazon Bedrock.

As prompts become more complex, its important to identify its various parts. In this section, we present the components that make up a prompt and the recommended order in which they should appear:

The following is an example of a prompt that incorporates all the aforementioned elements:

In the following sections, we dive deep into Claude 3 best practices for prompt engineering.

For prompts that deal only with text, follow this set of best practices to achieve better results:

The Claude 3 family offers vision capabilities that can process images and return text outputs. Its capable of analyzing and understanding charts, graphs, technical diagrams, reports, and other visual assets. The following are best practices when working with images with Claude 3:

Consider the following example, which is an extraction of the picture a fine gathering (Author: Ian Kirck, https://en.m.wikipedia.org/wiki/File:A_fine_gathering_(8591897243).jpg).

We ask Claude 3 to count how many birds are in the image:

Claude 3 Haikus response:

In this example, we asked Claude to take some time to think and put its reasoning in an XML tag and the final answer in another. Also, we gave Claude time to think and clear instructions to pay attention to details, which helped Claude to provide the correct response.

Lets see an example with the following image:

In this case, the image itself is the prompt: Claude 3 Haikus response:

Lets look at the following example:

Prompt:

Claude 3 Haikus response:

Lets see an example. We pass to Claude the following map chart in image format (source: https://ourworldindata.org/co2-and-greenhouse-gas-emissions), then we ask about Japans greenhouse gas emissions.

Prompt:

Claude 3 Haikus response:

Lets see an example of narration with the following image (source: Sustainable Development Goals Report 2023, https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf):

Prompt:

Claude 3 Haikus response:

In this example, we were careful to control the content of the narration. We made sure Claude didnt mention any extra information or discuss anything it wasnt completely confident about. We also made sure Claude covered all the key details and numbers presented in the slide. This is very important because the information from the narration in text format needs to be precise and accurate in order to be used to respond to questions.

Information extraction is the process of automating the retrieval of specific information related to a specific topic from a collection of texts or documents. LLMs can extract information regarding attributes given a context and a schema. The kinds of documents that can be better analyzed with LLMs are resumes, legal contracts, leases, newspaper articles, and other documents with unstructured text.

The following prompt instructs Claude 3 Haiku to extract information from short text like posts on social media, although it can be used for much longer pieces of text like legal documents or manuals. In the following example, we use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

Retrieval Augmented Generation (RAG) is an approach in natural language generation that combines the strengths of information retrieval and language generation models. In RAG, a retrieval system first finds relevant passages or documents from a large corpus based on the input context or query. Then, a language generation model uses the retrieved information as additional context to generate fluent and coherent text. This approach aims to produce high-quality and informative text by using both the knowledge from the retrieval corpus and the language generation capabilities of deep learning models. To learn more about RAG, see What is RAG? and Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart.

The following prompt instructs Claude 3 Haiku to answer questions about a specific topic and use a context from the retrieved information. We use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

In this post, we explored best prompting practices and demonstrated how to apply them with the Claude 3 family of models. The Claude 3 family of models are the latest and most capable LLMs available from Anthropic.

We encourage you to try out your own prompts using Amazon Bedrock playgrounds on the Amazon Bedrock console, and try out the official Anthropic Claude 3 Prompt Engineering Workshop to learn more advanced techniques. You can send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Refer to the following to learn more about the Anthropic Claude 3 family:

David Laredo is a Prototyping Architect at AWS, where he helps customers discover the art of the possible through disruptive technologies and rapid prototyping techniques. He is passionate about AI/ML and generative AI, for which he writes blog posts and participates in public speaking sessions all over LATAM. He currently leads the AI/ML experts community in LATAM.

Claudia Cortes is a Partner Solutions Architect at AWS, focused on serving Latin American Partners. She is passionate about helping partners understand the transformative potential of innovative technologies like AI/ML and generative AI, and loves to help partners achieve practical use cases. She is responsible for programs such as AWS Latam Black Belt, which aims to empower partners in the Region by equipping them with the necessary knowledge and resources.

Simn Crdova is a Senior Solutions Architect at AWS, focused on bridging the gap between AWS services and customer needs. Driven by an insatiable curiosity and passion for generative AI and AI/ML, he tirelessly explores ways to leverage these cutting-edge technologies to enhance solutions offered to customers.

Gabriel Velazquez is a Sr Generative AI Solutions Architect at AWS, he currently focuses on supporting Anthropic on go-to-market strategy. Prior to working in AI, Gabriel built deep expertise in the telecom industry where he supported the launch of Canadas first 4G wireless network. He now combines his expertise in connecting a nation with knowledge of generative AI to help customers innovate and scale.

Originally posted here:
Prompt engineering techniques and best practices: Learn by doing with Anthropic's Claude 3 on Amazon Bedrock ... - AWS Blog

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Amazon Q Businessis a generative AI-powered assistant that can answer questions, provide summaries, generate content, and extract insights directly from the content in digital as well as scanned PDF documents in your enterprise data sources without needing to extract the text first.

Customers across industries such as finance, insurance, healthcare life sciences, and more need to derive insights from various document types, such as receipts, healthcare plans, or tax statements, which are frequently in scanned PDF format. These document types often have a semi-structured or unstructured format, which requires processing to extract text before indexing with Amazon Q Business.

The launch of scanned PDF document support with Amazon Q Business can help you seamlessly process a variety of multi-modal document types through the AWS Management Console and APIs, across all supported Amazon Q Business AWS Regions. You can ingest documents, including scanned PDFs, from your data sources using supported connectors, index them, and then use the documents to answer questions, provide summaries, and generate content securely and accurately from your enterprise systems. This feature eliminates the development effort required to extract text from scanned PDF documents outside of Amazon Q Business, and improves the document processing pipeline for building your generative artificial intelligence (AI) assistant with Amazon Q Business.

In this post, we show how to asynchronously index and run real-time queries with scanned PDF documents using Amazon Q Business.

You can use Amazon Q Business for scanned PDF documents from the console, AWS SDKs, or AWS Command Line Interface (AWS CLI).

Amazon Q Business provides a versatile suite of data connectors that can integrate with a wide range of enterprise data sources, empowering you to develop generative AI solutions with minimal setup and configuration. To learn more, visit Amazon Q Business, now generally available, helps boost workforce productivity with generative AI.

After your Amazon Q Business application is ready to use, you can directly upload the scanned PDFs into an Amazon Q Business index using either the console or the APIs. Amazon Q Business offers multiple data source connectors that can integrate and synchronize data from multiple data repositories into single index. For this post, we demonstrate two scenarios to use documents: one with the direct document upload option, and another using the Amazon Simple Storage Service (Amazon S3) connector. If you need to ingest documents from other data sources, refer to Supported connectors for details on connecting additional data sources.

In this post, we use three scanned PDF documents as examples: an invoice, a health plan summary, and an employment verification form, along with some text documents.

The first step is to index these documents. Complete the following steps to index documents using the direct upload feature of Amazon Q Business. For this example, we upload the scanned PDFs.

You can monitor the uploaded files on the Data sources tab. The Upload status changes from Received to Processing to Indexed or Updated, as which point the file has been successfully indexed into the Amazon Q Business data store. The following screenshot shows the successfully indexed PDFs.

The following steps demonstrate how to integrate and synchronize documents using an Amazon S3 connector with Amazon Q Business. For this example, we index the text documents.

When the sync job is complete, your data source is ready to use. The following screenshot shows all five documents (scanned and digital PDFs, and text files) are successfully indexed.

The following screenshot shows a comprehensive view of the two data sources: the directly uploaded documents and the documents ingested through the Amazon S3 connector.

Now lets run some queries with Amazon Q Business on our data sources.

Your documents might be dense, unstructured, scanned PDF document types. Amazon Q Business can identify and extract the most salient information-dense text from it. In this example, we use the multi-page health plan summary PDF we indexed earlier. The following screenshot shows an example page.

This is an example of a health plan summary document.

In the Amazon Q Business web UI, we ask What is the annual total out-of-pocket maximum, mentioned in the health plan summary?

Amazon Q Business searches the indexed document, retrieves the relevant information, and generates an answer while citing the source for its information. The following screenshot shows the sample output.

Documents might also contain structured data elements in tabular format. Amazon Q Business can automatically identify, extract, and linearize structured data from scanned PDFs to accurately resolve any user queries. In the following example, we use the invoice PDF we indexed earlier. The following screenshot shows an example.

This is an example of an invoice.

In the Amazon Q Business web UI, we ask How much were the headphones charged in the invoice?

Amazon Q Business searches the indexed document and retrieves the answer with reference to the source document. The following screenshot shows that Amazon Q Business is able to extract bill information from the invoice.

Your documents might also contain semi-structured data elements in a form, such as key-value pairs. Amazon Q Business can accurately satisfy queries related to these data elements by extracting specific fields or attributes that are meaningful for the queries. In this example, we use the employment verification PDF. The following screenshot shows an example.

This is an example of an employment verification form.

In the Amazon Q Business web UI, we ask What is the applicants date of employment in the employment verification form? Amazon Q Business searches the indexed employment verification document and retrieves the answer with reference to the source document.

In this section, we show you how to use the AWS CLI to ingest structured and unstructured documents stored in an S3 bucket into an Amazon Q Business index. You can quickly retrieve detailed information about your documents, including their statuses and any errors occurred during indexing. If youre an existing Amazon Q Business user and have indexed documents in various formats, such as scanned PDFs and other supported types, and you now want to reindex the scanned documents, complete the following steps:

"errorMessage": "Document cannot be indexed since it contains no text to index and search on. Document must contain some text."

If youre a new user and havent indexed any documents, you can skip this step.

The following is an example of using the ListDocuments API to filter documents with a specific status and their error messages:

The following screenshot shows the AWS CLI output with a list of failed documents with error messages.

Now you batch-process the documents. Amazon Q Business supports adding one or more documents to an Amazon Q Business index.

The following screenshot shows the AWS CLI output. You should see failed documents as an empty list.

The following screenshot shows that the documents are indexed in the data source.

If you created a new Amazon Q Business application and dont plan to use it further, unsubscribe and remove assigned users from the application and delete it so that your AWS account doesnt accumulate costs. Moreover, if you dont need to use the indexed data sources further, refer to Managing Amazon Q Business data sources for instructions to delete your indexed data sources.

This post demonstrated the support for scanned PDF document types with Amazon Q Business. We highlighted the steps to sync, index, and query supported document typesnow including scanned PDF documentsusing generative AI with Amazon Q Business. We also showed examples of queries on structured, unstructured, or semi-structured multi-modal scanned documents using the Amazon Q Business web UI and AWS CLI.

To learn more about this feature, refer toSupported document formats in Amazon Q Business. Give it a try on theAmazon Q Business consoletoday! For more information, visitAmazon Q Businessand theAmazon Q Business User Guide. You can send feedback toAWS re:Post for Amazon Qor through your usual AWS support contacts.

Sonali Sahu is leading the Generative AI Specialist Solutions Architecture team in AWS. She is an author, thought leader, and passionate technologist. Her core area of focus is AI and ML, and she frequently speaks at AI and ML conferences and meetups around the world. She has both breadth and depth of experience in technology and the technology industry, with industry expertise in healthcare, the financial sector, and insurance.

Chinmayee Rane is a Generative AI Specialist Solutions Architect at AWS. She is passionate about applied mathematics and machine learning. She focuses on designing intelligent document processing and generative AI solutions for AWS customers. Outside of work, she enjoys salsa and bachata dancing.

Himesh Kumar is a seasoned Senior Software Engineer, currently working at Amazon Q Business in AWS. He is passionate about building distributed systems in the generative AI/ML space. His expertise extends to develop scalable and efficient systems, ensuring high availability, performance, and reliability. Beyond the technical skills, he is dedicated to continuous learning and staying at the forefront of technological advancements in AI and machine learning.

Qing Wei is a Senior Software Developer for Amazon Q Business team in AWS, and passionate about building modern applications using AWS technologies. He loves community-driven learning and sharing of technology especially for machine learning hosting and inference related topics. His main focus right now is on building serverless and event-driven architectures for RAG data ingestion.

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Improve productivity when processing scanned PDFs using Amazon Q Business | Amazon Web Services - AWS Blog

Generation of a dataset of SANS virtual experiments at KWS-1

A code template of the KWS-1 SANS instrument at FRM-II, Garching, was written in McStas (see Supplementary Information for the example code). The instrument description consisted of the following components, set consecutively: a neutron source describing the FRM-II spectrum, a velocity selector, guides that propagate the neutrons to minimize losses, a set of slits to define the divergence of the beam, a sample (one of the recently developed sasmodels component described in the McStas 3.4 documentation), a beamstop, and finally a Position Sensitive Detector (PSD) of size (144times 256) pixels. The sample was changed systematically between 46 SAS models (see Supplementary Information for a complete list of the models considered and their documentation), and for each model, different samples were produced by varying the parameters of the model. The set of 46 SAS models considered presented both isotropic and anisotropic scattering amplitudes. In the anisotropic models, the scattering amplitude is defined to have a dependency on the angle between the incident beam and the orientation of the scattering objects (or structures), which is determined by the model parameters. Consequently, in non-oriented particles with analytical anisotropic models, the resulting scattering pattern can result isotropic. Whenever possible, samples were considered in the dilute regime to avoid structure factor contributions and only observe those arising from the form factor. In models with crystalline structure or with correlations between scatterers where an analytical expression for the scattering amplitude was found, the complete scattering amplitude was considered. In all cases, the analytical expressions were obtained from the small angle scattering models documentation of SasView20 (see Supplementary Information). The instrument template in the Supplementary Information shows how it was also possible to change the instrument configuration when a sample was fixed. The set of parameters that describe the instrument configuration in a given simulation are referred as instrument parameters, and those that define the sample description as sample parameters.

In the case of instrument parameters, a discrete set of 36 instrument configurations were allowed to be selected. This was chosen by the instrument scientist, taking into account the most frequent instrument configurations: two possible values of wavelength (4.5 or 6 ), three possibilities for the distance settings, paired in collimation length - sample to detector distance (8m-1m, 8m-8m, and 20m-20m), three options for the slit configuration (1 cm slit aperture in both directions and a 2 cm wide Hellma Cell; 1.2 cm slit aperture in both directions and a 2cm wide Helma Cell; and 7mm on the horizontal aperture and 1 cm on the vertical aperture with a 1 cm wide Helma Cell), and finally two possible sample holders of different thickness (1mm and 2mm). One of the advantages of MC simulations over analytical approaches to obtain the 2D scattering pattern is that by defining the instrument parameters in the simulation, such as size of apertures for collimation, the sample to detector distance, the size of the detector, the dimensions of the pixels, and so on, the smearing of the data due to instrumental resolution is automatically considered. Therefore, no extra convolution must be performed once the data is collected.

In the case of sample parameters, most parameters describing samples were continuous, and an added difficulty was that the number of parameters per model was not the same nor similar for all models (see Fig. 5).

Distribution of models as a function of the number of parameters, showing the wide range of complexities contemplated in the models set used in this work.There are few models that have more than 15 parameters to set.

There were some models with only two parameters (easy to sample) and several models with more than 15 parameters (hard to sample). Most of the models had around 12 parameters. For p parameters with (n_i) possible choices for parameter i, the number of possible combinations (N) can be calculated as

$$begin{aligned} N = prod _{i=1}^p n_i, end{aligned}$$

(1)

which turns out to be (N=n^p) if (n_i=n) for all (i=1,dots ,p). With only (n=2) possibilities per parameter and (p=15), we rapidly get (N=32768) possible combinations for the complex model, whereas only (N=4) possible combinations for the very simple models. The large complexity of some model descriptions did not allow simulating all possible scenarios without generating a dataset with a large imbalance between classes. Therefore we opted to sample the defined hyper-parameter space strategically by using latin-hypercube sampling21. Briefly explained, this sampling method generates hypercubes in a given high dimensional hyper-parameter space. Then it selects randomly one of these hypercubes, and randomly samples the variables only inside the chosen hypercube. On a later iteration, it selects a new hypercube and repeats the sampling procedure.

Another advantage of MC simulations is that one can perform Monte Carlo integration estimates, which allow to include polydispersity and orientational distributions of scattering objects in a simple and direct manner. On each neutron interaction, the orientation and the polydisperse parameters of the scattering object are randomly chosen from defined probability distributions. For simplicity, distance and dimension parameters (r_i) of the models were allowed to be polydisperse by sampling them from gaussian distributions (taking care of selecting only positive values). The value (r_i) selected on each MC simulation defined the mean value of the gaussian distribution and an extra parameter (Delta r_i) for each (r_i) was included in the MC simulation to define the corresponding variance. The standard deviation of the gaussian distribution on different simulations was allowed to vary between 0 (monodisperse) and (r_i/2) (very polydisperse). In the case of angle parameters that determine the orientation of the scattering object, these were defined by sampling uniformly inside an interval centered at the parameter value (theta _i) and with limits defined by another extra parameter (Delta theta _i). For example, in a cylinder form factor model for the scattering object, both the radius and the length of the cylinders can be polydisperse, and the two angles defining the orientation of the principal axis with respect to the incident beam are allowed to vary uniformly within the simulation defined range. This gives a total of 8 parameters to include polidyspersity and orientational distributions on a single simulation. For more information on how this was implemented in the MC simulation we refer the reader to the documentation of each model that is provided in the Supplementary Information.

We opted for sampling 100 points for each sample model in the models hyper-parameter space due to time-constraints from the simulation side, and to constraints in the database size from the machine learning side. To define the sampling space, we defined upper ((u_b)) and lower ((l_b)) bounds for each sample parameter in each SasView model description. Then we took the default value of the parameter ((p_{0})) given in the SasView documentation as the center point of the sampling region, allowing for sampling in the interval (left[ max (-3 p_{0},l_b),min (3 p_{0},u_b)right]). All sampled parameters were continuous, except the absorption coefficient, which was restricted to have only two possible values (0% or 10%).

The expected dataset size was 331.200 by taking the 46 sample models, 2 absorption coefficients, 100 sample parameters per model, and 36 possible instrument settings. The 46 sample models were chosen so as to be representative, and also to avoid those sample models of high computational cost. Given that some configurations were non optimal, the total dataset was cleaned from zero images (no neutrons arrived in the given virtual experiment) and low statistic images. This was executed by calculating the quantile 0.02 of the standard deviations of the images, and removing them from the database. Also, the quantile 0.99 of the maximum value of the pixels of an image was calculated, and all images with max values higher were removed (for example, images in which simulations failed with saturating pixels). A remaining total of 259.328 virtual experiments defined the final dataset for machine learning purposes, and is the dataset published open access14. For an insight into what the database looks like we show a random selection of one image per model in the dataset in Fig. 6. It is possible to see that there is some variance between models, but also some unfavorable configurations (inadequate instrument paramaters for a given sample) which add noise and difficulties for the classification task. This figure also illustrates that certain anisotropic SAS models can result in isotropic scattering patterns when the scattering objects are completely unoriented (i.e., exhibiting a broad orientational distribution) or oriented in a particular direction with respect to the beam. In such cases, the anisotropy of the scattering pattern due to the form factor cannot be observed. Consequently, from the perspective of machine learning, the observation of an anisotropic scattering pattern directly excludes all isotropic models, whereas the observation of an isotropic scattering pattern does not allow for the direct inference that the model was isotropic.

An insight of the variability present amongst models in random images selected from the dataset. Isotropic (red title) and anisotropic (blue title) images can be found, as well as images with high and poor counting statistics.

Given that we have a dataset of roughly 260.000 virtual experiments, comprising of a set of 46 SANS models measured under different experimental conditions, we can attempt to train supervised machine learning algorithms to predict the SAS model of a sample given the SANS scattering pattern data measured by the PSD at KWS-1. We are taking advantage here of the fact that we know the ground truth of the SAS model used to generate the data by Monte Carlo simulation. The data from a PSD can be seen as an image of one channel, therefore we can use all recent developments in methods for image classification.

It is known by the SANS community that the intensity profile as a function of the scattering vector (q) is normally plotted in logarithmic scale, to be able to see the small features at increasing values of q. In this sense, it is useful for the classification task to perform a logarithmic transformation on the measured data to increase the contribution to the images variance of the features at large q. Since the logarithm is defined only for values larger than 0, and is positive only for values larger than 1, we first add a constant offset of +1 to all pixels and check that there are no negative values in the image. Then we apply the logarithm function to the intensity count in all pixels, emphasizing large q features as can be seen in Fig. 6. Then, we normalized all the images in the dataset to their maximum value in order to take them to values between 0 and 1 as to be independent of the counting statistics of the measurement. The transformed data are then fed to the neural network. Mathematically speaking, the transformation reads

$$begin{aligned} x_{i,j} = frac{log (x_{i,j}+1.0)}{MaxLog}, end{aligned}$$

(2)

for the intensity of pixel (x_{i,j}) in row i and column j, where MaxLog is the maximum of the image after applying the logarithmic transformation. All images were resized to (180times 180) pixels, since the networks used in this work are designed for square input images. The value 180 is a compromise between 144 and 256, in which we believe the loss in information by interpolation and sampling respectively is minimal. We decided to train Convolutional Neural Networks (CNNs) for the task of classification using Pytorch22, by transfering the learning on three architectures (ResNet-5023, DenseNet24, and Inception V325). In all cases, the corresponding PyTorch default weights were used as starting point and all weights were allowed to be modified. Then, we generated an ensemble method, that averaged the last layer weights of all three CNNs and predicted based on the averaged weight. In all cases, we modified the first layer to accept the generated one-channel images of our SANS database in HDF format. We preferred HDF format to keep floating point precision in each pixels intensity count. Also the final fully-connected layer was modified to match the 46 classes, and a soft-max layer was used to obtain values between 0 and 1, to get some notion of probability of classification.

The dataset was split into training, testing, and validation sets in proportions 0.70, 0.20, and 0.10 respectively. For the minimzation problem in multilabel classification, the Cross Entropy loss is a natural selection as the loss function. This function coincides with the multinomial logistic loss and belongs to a set of loss functions that are called comp-sum losses (loss functions obtained by composition of a concave function, such as logarithm in the case of the logistic loss, with a sum of functions of differences of score, such as the negative exponential)15. In our case, we can write the Cross Entropy loss function as

$$begin{aligned} l(x_n,y_n) = -log left( frac{exp (alpha _{y_n}(x_n))}{sum _{c=1}^{C}exp {(alpha _{c}(x_n))}}right) , end{aligned}$$

(3)

where (x_n) is the input, (y_n) is the target label, (alpha _i(x)) is the i-th output value of the last layer when x is the input, and C is the number of classes. In the extreme case where only the correct weight (alpha _{y_n}(x_n)) is equal to 1, the rest are equal to 0, then the quotient is equal to 1, and the logarithm makes the loss function equal to 0. If (alpha _{y_n}(x_n)<1), then the quotient will be between 0 and 1, the logarithm will make it negative, and the -1 pre-factor will transform it to a positive value. Any accepted minimization step of this function forces the weight of the correct label to increase in absolute value.

Finally, for the training phase, Mini-batches were used with a batch size of 64 images during training, and all CNNs were trained during 30 epochs. The Adaptive Moment Estimation (Adam)26 algorithm was used for the minimzation of the loss function, with a learning rate of (eta =1times 10^{-5}). For the testing phase, a batch size of 500 images was used, and for the validation phase, batches of 1000 images were used to increase the support of the estimated final quantities.

The data was obtained from an already completed study that has been published separetly19. It was collected from a sample consisting of a 60(mu)m thick brain slice from a reeler mouse after death. In the cited paper19, they declare that the animal procedures were approved by the institutional animal welfare committee at the Research Centre Jlich GmbH, Germany, and were in accordance with European Union guidelines for the use and care of laboratory animals. For the interest of this work, we only refer to the data for validation of the presented algorithm and we did not sacrifice nor handle any animal lives. The contrast was obtained by deuterated formalin. The irradiation area was of 1 mm(times)1mm. The authors observed an anisotropic Porod scattering ((q<0.04)(^{-1})) that is connected to the preferred orientation of whole nerve fibres, also called axon. They also report a correlation ring ((q=0.083)(^{-1})) that arises from the myelin sheaths, a multilayer of lipid bilayers with the myelin basic protein as a spacer.

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