Author: Deep Learner

PyTorch

Torch Containers simplified in PyTorch

This repository aims to help former Torchies more seamlessly transition to the “Containerless” world of PyTorch by providing a list of PyTorch implementations of Torch Table Layers. Note: As a result of full integration with autograd, PyTorch requires networks to be defined in the following manner: Define all layers to be used in the __init__ method of your network Combine them however you want in the forward method of your network (avoiding in place Tensor ops) And that’s all there […]

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Telegram

Crypto Telegram Bot With Python

Optional Add-on Coinbase Pro Portfolio Tracker https://github.com/whittlem/coinbaseprotracker An all-in-one view of all your Coinbase Pro portfolios. Highly recommended if running multiple bots and keeping track of their progress. Prerequisites When running in containers: a working docker/podman installation Python 3.9.x installed — https://installpython3.com (must be Python 3.9 or greater) % python3 –version Python 3.9.1 Python 3 PIP installed — https://pip.pypa.io/en/stable/installing % python3 -m pip –version pip 21.0.1 from /usr/local/lib/python3.9/site-packages/pip (python 3.9) Installation Manual % git clone https://github.com/whittlem/pycryptobot % cd pycryptobot % […]

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Django

Sane and flexible OpenAPI 3 schema generation for Django REST framework

Sane and flexible OpenAPI 3.0 schema generation for Django REST framework. This project has 3 goals: Extract as much schema information from DRF as possible. Provide flexibility to make the schema usable in the real world (not only toy examples). Generate a schema that works well with the most popular client generators. The code is a heavily modified fork of the DRF OpenAPI generator, which is/was lacking all of the below listed features. Features Serializers modelled as components. (arbitrary nesting […]

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Django

Country-specific Django helpers, to use in Django Rest Framework

Country-specific serializers fields, to Django Rest Framework Documentation (soon) The full documentation is at https://django-rest-localflavor.readthedocs.org. Quickstart Install django-rest-localflavor: pip install django-rest-localflavor Then use it in a project: INSTALLED_APPS = ( # … ‘rest_framework’, ‘rest_localflavor’, ) GitHub https://github.com/gilsondev/django-rest-localflavor    

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Json

JSON:API support for Django REST framework

Overview JSON:API support for Django REST framework By default, Django REST framework will produce a response like: { “count”: 20, “next”: “http://example.com/api/1.0/identities/?page=3”, “previous”: “http://example.com/api/1.0/identities/?page=1”, “results”: [{ “id”: 3, “username”: “john”, “full_name”: “John Coltrane” }] } However, for an identity model in JSON:API format the response should look like the following: { “links”: { “prev”: “http://example.com/api/1.0/identities”, “self”: “http://example.com/api/1.0/identities?page=2”, “next”: “http://example.com/api/1.0/identities?page=3”, }, “data”: [{ “type”: “identities”, “id”: “3”, “attributes”: { “username”: “john”, “full-name”: “John Coltrane” } }], “meta”: { “pagination”: { “count”: […]

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Deep Learning

Deep learning with dynamic computation graphs in TensorFlow

TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation graph depends on the structure of the input data. For example, this model implements TreeLSTMs for sentiment analysis on parse trees of arbitrary shape/size/depth. Fold implements dynamic batching. Batches of arbitrarily shaped computation graphs are transformed to produce a static computation graph. This graph has the same structure regardless of what input it receives, and can be executed efficiently by TensorFlow. […]

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tensorflow

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usage import tfdeploy as td import numpy as np model = td.Model(“/path/to/model.pkl”) inp, outp = model.get(“input”, “output”) batch = np.random.rand(10000, 784) result = outp.eval({inp: batch}) Installation and dependencies    

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Maps

Makes viewing feature maps a breeze with python

Convolutional Neural Networks Are Beautiful We all take our eyes for granted, we glance at an object for an instant and our brains can identify with ease. However distorted the information may be, we do a pretty good job at it. Low light, obscured vision, poor eyesight… There are a myriad of situations where conditions are poor but still we manage to understand what an object is. Context helps, but we humans were created with sight in mind. Computers have […]

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Toolkit

A web application which stands as a toolkit for climate change law assessment

A web application which stands as a toolkit for climate change law assessment. Prerequisites Installing the application Get the source code: git clone https://github.com/eaudeweb/lcc-toolkit cd lcc-toolkit Customize the environment files: cp docker/postgres.env.example docker/postgres.env cp docker/web.env.example docker/web.env cp docker/init.sql.example docker/init.sql Depending on the installation mode, create the docker-compose.override.yml file: cp docker-compose.override.[prod|dev].yml docker-compose.override.yml Start the application stack: docker-compose up -d docker-compose logs Attach to the web service: docker-compose run web Create a superuser (for Ansible see https://gist.github.com/elleryq/9c70e08b1b2cecc636d6) python manage.py createsuperuser That’s it. […]

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