Deep Learning Package based on TensorFlow

Related tags

Deep Learningpythonmachine-learningdeep-learningtensorflowdeep-learning-librarytensorflow2
Overview

White-Box-Layer is a Python module for deep learning built on top of TensorFlow and is distributed under the MIT license.

The project was started in May 2021 by YeongHyeon Park.
This project does not limit for participation.
Contribute now!

Installation

Dependencies

whiteboxlayer requires:

  • Numpy: 1.18.5
  • Scipy: 1.4.1
  • TensorFlow: 2.3.0

User installation

You can install the white-box-layer via simple command as below.

$ pip install whiteboxlayer

Development

We welcome new contributors of all experience levels. The white-box-layer community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.

Example

Example for Convolutional Neural Network

An example of constructing a convolutional neural network is covered. The relevant source code is additionally provided following links.

Define TensorFlow based module

class Neuralnet(tf.Module):

    def __init__(self, **kwargs):
        super(Neuralnet, self).__init__()

        self.who_am_i = kwargs['who_am_i']
        self.dim_h = kwargs['dim_h']
        self.dim_w = kwargs['dim_w']
        self.dim_c = kwargs['dim_c']
        self.num_class = kwargs['num_class']
        self.filters = kwargs['filters']

        self.layer = wbl.Layers()

        self.forward = tf.function(self.__call__)

    @tf.function
    def __call__(self, x, verbose=False):

        logit = self.__nn(x=x, name=self.who_am_i, verbose=verbose)
        y_hat = tf.nn.softmax(logit, name="y_hat")

        return logit, y_hat

    def __nn(self, x, name='neuralnet', verbose=True):

        for idx, _ in enumerate(self.filters[:-1]):
            if(idx == 0): continue
            x = self.layer.conv2d(x=x, stride=1, \
                filter_size=[3, 3, self.filters[idx-1], self.filters[idx]], \
                activation='relu', name='%s-%dconv' %(name, idx), verbose=verbose)
            x = self.layer.maxpool(x=x, ksize=2, strides=2, \
                name='%s-%dmp' %(name, idx), verbose=verbose)

        x = tf.reshape(x, shape=[x.shape[0], -1], name="flat")
        x = self.layer.fully_connected(x=x, c_out=self.filters[-1], \
                activation='relu', name="%s-clf0" %(name), verbose=verbose)
        x = self.layer.fully_connected(x=x, c_out=self.num_class, \
                activation=None, name="%s-clf1" %(name), verbose=verbose)

        return x

Initializing module

model = Neuralnet(\
    who_am_i="CNN", \
    dim_h=28, dim_w=28, dim_c=1, \
    num_class=10, \
    filters=[1, 32, 64, 128])

dummy = tf.zeros((1, model.dim_h, model.dim_w, model.dim_c), dtype=tf.float32)
model.forward(x=dummy, verbose=True)

Results

Conv (CNN-1conv) (1, 28, 28, 1) -> (1, 28, 28, 32)
MaxPool (CNN-1mp) (1, 28, 28, 32) > (1, 14, 14, 32)
Conv (CNN-2conv) (1, 14, 14, 32) -> (1, 14, 14, 64)
MaxPool (CNN-2mp) (1, 14, 14, 64) > (1, 7, 7, 64)
FC (CNN-clf0) (1, 3136) -> (1, 128)
FC (CNN-clf1) (1, 128) -> (1, 10)
Conv (CNN-1conv) (1, 28, 28, 1) -> (1, 28, 28, 32)
MaxPool (CNN-1mp) (1, 28, 28, 32) > (1, 14, 14, 32)
Conv (CNN-2conv) (1, 14, 14, 32) -> (1, 14, 14, 64)
MaxPool (CNN-2mp) (1, 14, 14, 64) > (1, 7, 7, 64)
FC (CNN-clf0) (1, 3136) -> (1, 128)
FC (CNN-clf1) (1, 128) -> (1, 10)
You might also like...
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting (RVM) English | 中文 Official repository for the paper Robust High-Resolution Video Matting with Temporal Guidance. RVM is specific

flow-dev 2 Aug 21, 2022
Deep learning library featuring a higher-level API for TensorFlow.
Deep learning library featuring a higher-level API for TensorFlow.

TFLearn: Deep learning library featuring a higher-level API for TensorFlow. TFlearn is a modular and transparent deep learning library built on top of

TFLearn 9.6k Jan 2, 2023
Deep learning library featuring a higher-level API for TensorFlow.
Deep learning library featuring a higher-level API for TensorFlow.

TFLearn: Deep learning library featuring a higher-level API for TensorFlow. TFlearn is a modular and transparent deep learning library built on top of

TFLearn 9.5k Feb 12, 2021
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

This video in better quality. einops Flexible and powerful tensor operations for readable and reliable code. Supports numpy, pytorch, tensorflow, and

Alex Rogozhnikov 6.2k Jan 1, 2023
Deep learning with dynamic computation graphs in TensorFlow
Deep learning with dynamic computation graphs in TensorFlow

TensorFlow Fold TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation graph

null 1.8k Dec 28, 2022
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 3, 2023
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes

Gradient Institute 127 Dec 12, 2022
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

MMdnn MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model manage

Microsoft 5.7k Jan 9, 2023
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Daniel Bourke 3.4k Jan 7, 2023
Owner
YeongHyeon Park
YeongHyeon Park
GitHub Repository
Collection of Docker images for ML/DL and video processing projects

Collection of Docker images for ML/DL and video processing projects. Overview of images Three types of images differ by tag postfix: base: Python with

OSAI 87 Nov 22, 2022
Encode and decode text application

Text Encoder and Decoder Encode and decode text in many ways using this application! Encode in: ASCII85 Base85 Base64 Base32 Base16 Url MD5 Hash SHA-1

Alice 1 Feb 12, 2022
PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021)

mlp-mixer-pytorch PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021) Usage import torch from mlp_mixer

isaac 27 Jul 09, 2022
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Face Detect MQTT Face or Pose detector that emits MQTT events when a face or human body is detected and not detected. I built this as an alternative t

Jacob Morris 38 Oct 21, 2022
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

Hui Wu 19 Oct 21, 2022
Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.

Graph Notebook: easily query and visualize graphs The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Us

Amazon Web Services 501 Dec 28, 2022
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)

ASGNet The code is for the paper "Adaptive Prototype Learning and Allocation for Few-Shot Segmentation" (accepted to CVPR 2021) [arxiv] Overview data/

Gen Li 91 Dec 23, 2022
A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.

Realistic galaxy simulation via score-based generative models Official code for 'Realistic galaxy simulation via score-based generative models'. We us

Michael Smith 32 Dec 20, 2022
New AidForBlind - Various Libraries used like OpenCV and other mentioned in Requirements.txt

AidForBlind Recommended PyCharm IDE Various Libraries used like OpenCV and other

Aalhad Chandewar 1 Jan 13, 2022
Code for "Learning to Segment Rigid Motions from Two Frames".

rigidmask Code for "Learning to Segment Rigid Motions from Two Frames". ** This is a partial release with inference and evaluation code.

Gengshan Yang 157 Nov 21, 2022
Cookiecutter PyTorch Lightning

Cookiecutter PyTorch Lightning Instructions # install cookiecutter pip install cookiecutter

Mazen 8 Nov 06, 2022
This repo includes the supplementary of our paper "CEMENT: Incomplete Multi-View Weak-Label Learning with Long-Tailed Labels"

Supplementary Materials for CEMENT: Incomplete Multi-View Weak-Label Learning with Long-Tailed Labels This repository includes all supplementary mater

Zhiwei Li 0 Jan 05, 2022
PyTorch implementation of the TTC algorithm

Trust-the-Critics This repository is a PyTorch implementation of the TTC algorithm and the WGAN misalignment experiments presented in Trust the Critic

0 Nov 29, 2021
MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios

MetaTTE: a Meta-Learning Based Travel Time Estimation Model for Multi-city Scenarios This is the official TensorFlow implementation of MetaTTE in the

morningstarwang 4 Dec 14, 2022
Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF)

Graph Convolutional Gated Recurrent Neural Network (GCGRNN) Improved from Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF

Lei Lin 21 Dec 18, 2022
SMD-Nets: Stereo Mixture Density Networks

SMD-Nets: Stereo Mixture Density Networks This repository contains a Pytorch implementation of "SMD-Nets: Stereo Mixture Density Networks" (CVPR 2021)

Fabio Tosi 115 Dec 26, 2022
Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"

GINC small-scale in-context learning dataset GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context

P-Lambda 29 Dec 19, 2022
DCSL - Generalizable Crowd Counting via Diverse Context Style Learning

DCSL Generalizable Crowd Counting via Diverse Context Style Learning Requirement

3 Jun 13, 2022
GrailQA: Strongly Generalizable Question Answering

GrailQA is a new large-scale, high-quality KBQA dataset with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It ca

OSU DKI Lab 76 Dec 21, 2022
Permeability Prediction Via Multi Scale 3D CNN

Permeability-Prediction-Via-Multi-Scale-3D-CNN Data: The raw CT rock cores are obtained from the Imperial Colloge portal. The CT rock cores are sub-sa

Mohamed Elmorsy 2 Jul 06, 2022