- Introduces a new global log-bilinear regression model which combines the benefits of both global matrix factorization and local context window methods.
- Decompose large matrices into low-rank approximations.
| import tensorflow as tf | |
| import numpy as np | |
| corpus_raw = 'He is the king . The king is royal . She is the royal queen ' | |
| # convert to lower case | |
| corpus_raw = corpus_raw.lower() | |
| words = [] | |
| for word in corpus_raw.split(): |
| #! /usr/bin/env python | |
| """ | |
| Author: Jeremy M. Stober | |
| Program: SOFTMAX.PY | |
| Date: Wednesday, February 29 2012 | |
| Description: Simple softmax function. | |
| """ | |
| import numpy as np | |
| npa = np.array |
| # | |
| # mnist_cnn_bn.py date. 5/21/2016 | |
| # date. 6/2/2017 check TF 1.1 compatibility | |
| # | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os |
| from __future__ import print_function | |
| import requests | |
| import json | |
| import cv2 | |
| addr = 'http://localhost:5000' | |
| test_url = addr + '/api/test' | |
| # prepare headers for http request | |
| content_type = 'image/jpeg' |
| from math import sqrt | |
| def put_kernels_on_grid (kernel, pad = 1): | |
| '''Visualize conv. filters as an image (mostly for the 1st layer). | |
| Arranges filters into a grid, with some paddings between adjacent filters. | |
| Args: | |
| kernel: tensor of shape [Y, X, NumChannels, NumKernels] | |
| pad: number of black pixels around each filter (between them) |
| #!/bin/bash | |
| mkdir -p ~/.ssh | |
| # generate new personal ed25519 ssh keys | |
| ssh-keygen -o -a 100 -t ed25519 -f ~/.ssh/id_ed25519 -C "rob thijssen <rthijssen@gmail.com>" | |
| ssh-keygen -o -a 100 -t ed25519 -f ~/.ssh/id_robtn -C "rob thijssen <rob@rob.tn>" | |
| # generate new host cert authority (host_ca) ed25519 ssh key | |
| # used for signing host keys and creating host certs |
Z‑Image Turbo is a bit different from “classic” Stable Diffusion, so a lot of old prompting habits don’t quite apply. I’ll walk through how to prompt it deeply and safely, with special focus on controlling content (nudity, stereotypes, unwanted artifacts) even though the model does not support traditional negative prompts at all. ([Hugging Face][1])
Key facts that matter for prompting: