Showing 36 open source projects for "layer"

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  • 1
    CakeChat

    CakeChat

    CakeChat: Emotional Generative Dialog System

    ...Embedding layer may be either fixed or fine-tuned along with other weights of the network.
    Downloads: 0 This Week
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  • 2
    TenorSpace.js

    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...
    Downloads: 1 This Week
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  • 3
    Oryx

    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.
    Downloads: 0 This Week
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  • 4
    Skater

    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...
    Downloads: 0 This Week
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  • 5
    Image classification models for Keras

    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'...
    Downloads: 0 This Week
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  • 6
    Grenade

    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.
    Downloads: 0 This Week
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  • 7

    NeuroSolutions: Formula Generator

    Utility converts the weights file of a MLP Breadboard into a formula

    The NeuroSolutions: Formula Generator utility converts the weights file of a default MLP breadboard (1-hidden layer with a TanhAxon in the hidden layer and either a TanhAxon or BiasAxon in the output layer) into a usable formula that can be copied and pasted into your own programs to compute the output of the trained neural network.
    Downloads: 0 This Week
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  • 8
    ConvNetJS

    ConvNetJS

    Deep learning in Javascript to train convolutional neural networks

    ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and...
    Downloads: 0 This Week
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  • 9
    C-IL2P

    C-IL2P

    Original C-IL2P

    This is a C++ implementation of the original C-IL2P system, invented by Artur D'Avila Garcez and Gerson Zaverucha. C-IL2P is a neural-symbolic learning system which uses a propositional logic program to create a three-layer recursive neural network and uses back-propagation to learn from examples.
    Downloads: 0 This Week
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  • 10

    Black Hole Cortex

    Sphere surface layers of visual cortex approach maximum info density

    ...We can think of Wikipedia as a layer above our brains, a global SphereSurface of large surface area (a cortex layered on billions of minds) and small (10000 most important pages) density.
    Downloads: 0 This Week
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  • 11
    Nen

    Nen

    neural network implementation in java

    3-layer neural network for regression and classification with sigmoid activation function and command line interface similar to LibSVM. Quick Start: "java -jar nen.jar"
    Downloads: 0 This Week
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