| # Importing the necessary libraries to execute the "Random Walk Method" | |
| import networkx as nx | |
| import random | |
| import matplotlib.pyplot as plt | |
| import operator | |
| #select random graph using gnp_random_graph() function of networkx | |
| Graph = nx.gnp_random_graph(10, 0.5, directed=True) | |
| nx.draw(Graph, with_labels=True, node_color='green') #draw the network graph | |
| plt.figure(figsize=(15,10)) |
https://github.com/tensorflow/tensorflow
https://github.com/keras-team/keras
https://github.com/deepmind/sonnet - TensorFlow-based neural network library.
https://github.com/tensorflow/mesh - Mesh TensorFlow: Model Parallelism Made Easier
| import math | |
| import torch | |
| from transformers import BertTokenizer, BertModel, BertForMaskedLM, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer | |
| # Load pre-trained model (weights) | |
| model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt') | |
| model.eval() | |
| # Load pre-trained model tokenizer (vocabulary) | |
| tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt') |
Modern deep transfer learning approaches have mainly focused on learning \emph{generic} feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large- scale unlabeled data and transferring the graphs to downstream tasks. Our
| # shmarkov.py, simple and concise markov chain text generation | |
| # Copyright (C) 2018 Allison Parrish | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy of | |
| # this software and associated documentation files (the “Software”), to deal in | |
| # the Software without restriction, including without limitation the rights to | |
| # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies | |
| # of the Software, and to permit persons to whom the Software is furnished to do | |
| # so, subject to the following conditions: |
This is a list of films I plan to watch this year, hoping to have movie nights over at my place. Figured I'd put the list somewhere shareable so I can seek contributions easily. The checkboxes are meant to indicate if they have been watched as part of the event, some entries are being added as already watched however because although we don't plan to watch them, they definitely should count as an anime movie classic.
| ''' | |
| From text | |
| # + The sklearn.feature_extraction.text submodule gathers utilities to build feature vectors from text documents. | |
| # > **feature_extraction.text.CountVectorizer([…])** Convert a collection of text documents to a matrix of token counts | |
| # > **feature_extraction.text.HashingVectorizer([…])** Convert a collection of text documents to a matrix of token occurrences | |
| # > **feature_extraction.text.TfidfTransformer([…])** Transform a count matrix to a normalized tf or tf-idf representation | |
| # > **feature_extraction.text.TfidfVectorizer([…])** Convert a collection of raw documents to a matrix of TF-IDF features. | |
| ''' |
| #!/usr/bin/env python | |
| """ | |
| mocking requests calls | |
| """ | |
| import mock | |
| import unittest | |
| import requests | |
| from requests.exceptions import HTTPError |