Search Results for "layer" - Page 4
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Showing 127 open source projects for "layer"
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Ecco
Explain, analyze, and visualize NLP language models
Ecco is an interpretability tool for transformers that helps visualize and analyze how language models generate text, making model behavior more transparent. -
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CAM
Class Activation Mapping
This repository implements Class Activation Mapping (CAM), a technique to expose the implicit attention of convolutional neural networks by generating heatmaps that highlight the most discriminative image regions influencing a network’s class prediction. The method involves modifying a CNN model slightly (e.g., using global average pooling before the final layer) to produce a weighted combination of feature maps as the class activation map. Integration with existing CNNs (with light modifications). Sample scripts/examples using standard architectures. The repo provides example code and instructions for applying CAM to existing CNN architectures. Visualization of discriminative regions per class. -
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Keras TCN
Keras Temporal Convolutional Network
...Parallelism (convolutional layers), flexible receptive field size (possible to specify how far the model can see), stable gradients (backpropagation through time, vanishing gradients). The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the maximum number of steps back in time from current sample at time T, that a filter from (block, layer, stack, TCN) can hit (effective history) + 1. The receptive field of the TCN can be calculated. Once keras-tcn is installed as a package, you can take a glimpse of what is possible to do with TCNs. ... -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
...And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the models’ pre-trained weights, append a new classifier layer on top of it, and retrain the network. This is called transfer learning, and is one of the most used techniques in CV. Aside from a few tricks when performing fine-tuning (if the case), it has been shown (many times) that if training for a new task, models initialized with pre-trained weights tend to learn faster and be more accurate then training from scratch using random initialization. -
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TensorLayer
Deep learning and reinforcement learning library for scientists
...This project can also be found at OpenI and Gitee. 3.0.0 has been pre-released, the current version supports TensorFlow, MindSpore and PaddlePaddle (partial) as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. In the future, it will support TensorFlow, MindSpore, PaddlePaddle, PyTorch and other backends. TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive examples. -
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Lambda Networks
Implementation of LambdaNetworks, a new approach to image recognition
Implementation of λ Networks, a new approach to image recognition that reaches SOTA on ImageNet. The new method utilizes λ layer, which captures interactions by transforming contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Shinel94 has added a Keras implementation! It won't be officially supported in this repository, so either copy / paste the code under ./lambda_networks/tfkeras.py or make sure to install tensorflow and keras before running the provided commands. -
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Tweety is a collection of various Java libraries that implement approaches to different areas of artificial intelligence. In particular, it provides a general interface layer for doing research and working with different knowledge representation formalisms such as classical logics, conditional logics, probabilistic logics, and argumentation. Furthermore, Tweety contains libraries for dealing with agents, multi-agent systems, and dialog systems for agents, as well as belief revision, preference reasoning, preference aggregation, and action languages. ...
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CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
...Let’s break down a CNN into its basic building blocks. A tensor can be thought of as an n-dimensional matrix. In the CNN above, tensors will be 3-dimensional with the exception of the output layer. A neuron can be thought of as a function that takes in multiple inputs and yields a single output. The outputs of neurons are represented above as the red → blue activation maps. -
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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NLP Best Practices
Natural Language Processing Best Practices & Examples
In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive business adoption of artificial intelligence (AI) solutions. In the last few years, researchers have been applying newer deep learning methods to NLP. Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora. This repository contains examples and... -
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FID score for PyTorch
Compute FID scores with PyTorch
...However, due to differences in the image interpolation implementation and library backends, FID results still differ slightly from the original implementation. In difference to the official implementation, you can choose to use a different feature layer of the Inception network instead of the default pool3 layer. -
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CakeChat
CakeChat: Emotional Generative Dialog System
...Embedding layer may be either fixed or fine-tuned along with other weights of the network. -
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Multidimensional Neural Network
Multidimensional Neural Network
In Fully Connected Backpropagation Neural Networks, with many layers and many neurons in layers there is problem known as Gradient Vanishing Problem. Solution to lower its magnitude is to use Not Fully Connected Neural Network, when that is the case than with which neurons from previous layer neuron is connected has to be considered. The simplest solution would be to use Cartesian Coordinate System, and treat layers as one dimensional lines or two dimensional rectangles or three, four, five ... dimensional cuboids. In that model each neuron in layer is connected to neurons in its surrounding in previous layer. -
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization... -
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DaNNet
Deep Artificial Neural Network framework using Armadillo
DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network. -
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Oryx
Lambda architecture on Apache Spark, Apache Kafka for real-time
...This includes implementations of key interface classes which implement the batch, speed, and serving logic. Applications package and deploy their implementations with each instance of the layer binaries. Each of these is a runnable Java .jar which starts all necessary services. -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual... -
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Simultra
Multiagent simulator of road traffic in Qt/C++ and OpenStreetMap.
Simultra is an open-source, hybrid road traffic simulator designed to handle large roadmaps in real-time. It combines microscopic and mesoscopic simulations into one multiagent hybrid simulator. The large-scale maps are modelled mesoscopically in real-time, and the complex traffic interactions benefit from detailed agent-based microscopic simulations. To resolve the concurrency issues within the maps representation and the meso-micro transitions, Simultra combines an event-based mesoscopic... -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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Semantic Assistants
Natural Language Processing (NLP) for the Masses
Semantic Assistants support users in content retrieval, analysis, and development, by offering context-sensitive NLP services directly integrated in standard desktop clients, like a word processor, and web information systems, like a wiki. -
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Seq2seq Chatbot for Keras
This repository contains a new generative model of chatbot
...The canonical seq2seq model became popular in neural machine translation, a task that has different prior probability distributions for the words belonging to the input and output sequences since the input and output utterances are written in different languages. The architecture presented here assumes the same prior distributions for input and output words. Therefore, it shares an embedding layer (Glove pre-trained word embedding) between the encoding and decoding processes through the adoption of a new model. -
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Grenade
Deep Learning in Haskell
...To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. The shapes of the gradients are appropriate for each layer and may be trivial for layers like Relu which have no learnable parameters. -
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Netvlad
NetVLAD: CNN architecture for weakly supervised place recognition
NetVLAD is a deep learning-based image descriptor framework developed by Relja Arandjelović for place recognition and image retrieval. It extends standard CNNs with a trainable VLAD (Vector of Locally Aggregated Descriptors) layer to create compact, robust global descriptors from image features. This implementation includes training code and pretrained models using the Pittsburgh and Tokyo datasets. -
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WebDjVuTextEd
Edit the OCR text layer of DjVu documents in a web browser
WebDjVuTextEd allows to edit the text layer of OCR'ed DjVu documents in a web browser. You can modify the structure (paragraphs, lines, words...) create, delete, edit text nodes, modify their container box by mouse, and run a spellchecker. The program does not directly read the DjVu files, it requires exported XML text data and images. When using without a webserver, you can open and save local files, but cannot take advantages of auto-save and spell checking. -
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PartWriter
A project designed to write sheet music.
The project is composed of two layers. The first layer is an artificial intelligence engine, partwriter, that writes music based on some heuristic. The second layer is the layer that defines the heuristic. The definition of this heuristic defines the type of music that partwriter will create. Currently (December 2012), the deepest layer is more or less complete. The author is now writing second layers of interest, namely species counterpoint heuristics according to Fux's Gradus Ad Parnassum.
